SMP-21-002

SMP-21-002

# Measurement of the Drell–Yan forward-backward asymmetry at high dilepton
masses in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A measurement of the forward-backward asymmetry of pairs of oppositely charged
leptons (dimuons and dielectrons) produced by the Drell–Yan process in proton-
proton collisions is presented. The data sample corresponds to an integrated
luminosity of 138collected with the CMS detector at the LHC at a center-of-
mass energy of 13. The asymmetry is measured as a function of lepton pair mass
for masses larger than 170and compared with standard model predictions. An
inclusive measurement across both channels and the full mass range yields an
asymmetry of $0.612\pm 0.005\stat\pm 0.007\syst$. As a test of lepton flavor
universality, the difference between the dimuon and dielectron asymmetries is
measured as well. No statistically significant deviations from standard model
predictions are observed. The measurements are used to set limits on the
presence of additional gauge bosons. For a boson in the sequential standard
model the observed (expected) 95% confidence level lower limit on the mass is
4.4(3.7 ).

##  0.1 Introduction

Drell–Yan (DY) production of pairs of same-flavor, oppositely charged leptons
($\Pell^{+}\Pell^{-}$) in proton-proton ($\Pp\Pp$) collisions occurs via the
$s$-channel exchange of $\PZ/\PGg^{*}$ bosons. At leading order (LO) in
quantum chromodynamics (QCD), the $\PZ/\PGg^{*}$ bosons are produced via
quark-antiquark () annihilation. The presence of both vector and axial
couplings of gauge bosons to leptons results in a forward-backward asymmetry,
, in the angular distribution of the final-state lepton with respect to the
initial-state quark. Deviations from the standard model (SM) predictions of
can result from the presence of an additional neutral gauge boson () [1, 2, 3,
4, 5, 6, 7], quark-lepton compositeness [8], large extra dimensions [9],
vector-like fermions [10], certain dark matter candidates [11], or leptoquarks
[12]. Hints of a violation of lepton flavor universality in several
measurements recently reported by the LHCb Collaboration [13, 14, 15] have
sparked interest in models for physics beyond the SM that could explain these
effects. Many of these models include heavy neutral gauge bosons or
leptoquarks with non-flavor-universal couplings [16, 17], which could produce
signatures in high-mass dilepton distributions [18]. As compared to
measurements of differential cross sections at high dilepton masses,
measurements of have a reduced dependence on systematic uncertainties related
to the reconstruction and identification of high-momentum leptons. Hence,
precision measurements of can provide stringent tests of the electroweak
sector of the SM and of lepton flavor universality. The main results of this
paper are measurements of as a function of dilepton mass for both muons and
electrons in the high-mass region ($>$170), which is particularly sensitive to
potential contributions from new physics.

The asymmetry is defined in terms of the angle $\theta$ between the negatively
charged final-state lepton and the initial-state quark (meant here in contrast
to the antiquark) in the $\Pell^{+}\Pell^{-}$ center-of-mass frame,

$A_{\mathrm{FB}}=\frac{\sigma_{\mathrm{F}}-\sigma_{\mathrm{B}}}{\sigma_{\mathrm{F}}+\sigma_{\mathrm{B}}},$
(1)

where $\sigma_{\mathrm{F}}$ ($\sigma_{\mathrm{B}}$) is the total cross section
for forward (backward) events, defined by $\cos\theta>0$ ($<$0). The sign of
$\theta$ is defined so that $\cos\theta=1$ events are those in which the
negatively charged final-state lepton is traveling in the same direction as
the incident quark. The value can also be directly related to the parton-level
differential cross section. The latter can be written as [19, 20]:

$\dd{\sigma}{\cos\theta}\propto\frac{3}{8}\biggl{[}1+\cos^{2}\theta+\frac{A_{0}}{2}\left(1-3\cos^{2}\theta\right)+A_{4}\cos\theta\biggr{]},$
(2)

where $A_{0}$ and $A_{4}$ are the standard dimensionless constants
parameterizing the angular distribution of the DY process [19, 20]. The terms
that are functions of $\cos\theta$ with even parity do not contribute to ,
leading to the relation:

$\frac{3}{8}A_{4}=A_{\mathrm{FB}}.$ (3)

This form of the angular distribution is general for any $s$-channel processes
mediated by a spin-1 boson and is thus applicable in the case of interference
from additional heavy vector bosons. The angular coefficients $A_{0}$ and
$A_{4}$ vary as functions of the mass ($m$), transverse momentum (), and
rapidity ($y$) of the dilepton system. As the dilepton transverse momentum
approaches zero, $A_{0}$ vanishes. It is nonzero for finite $\PZ/\PGg^{*}$
caused by processes in which $\PZ/\PGg^{*}$ bosons are produced in association
with additional jets. Thus, measurements of $A_{0}$ probe higher-order
corrections in perturbative QCD. Additional results of this paper are the
measurements of $A_{0}$ as a function of the dilepton mass in the same high-
mass region ($>$170).

Electroweak interference between the photon and the boson leads to negative
values of with large absolute value for masses below the pole ($m<80\GeV$),
and leads to large and positive values of above the pole ($m>110\GeV$). Near
the boson mass peak, reflects pure exchange and is close to zero because of
the small value of the charged-lepton vector coupling to bosons. In the high-
mass region, above the boson peak, the SM value of is approximately constant
with a value of $\approx$0.6. The $A_{0}$ coefficient is expected to be
$\approx$0.06 at 170 and should decrease at higher masses.

For the case of a new heavy , off-shell interference can produce deviations at
masses significantly lower than the mass. The deviation from the SM is
insensitive to the width of the [6, 7]. This measurement thus offers a
complementary approach to previous searches for new physics in the dilepton
channel that have searched for a peak in the invariant mass distribution
caused by the resonant production of a new particle [21, 22]. Additionally,
because the constraining power of this technique is based on interference
rather than direct production, its sensitivity to higher mass scales is not
limited by the center-of-mass energy and will continuously improve with the
increased statistical precision with the addition of future LHC data.

When the dilepton system has nonzero , the exact directions of the incident
partons are unknown since they are no longer collinear with the proton beams.
To minimize the impact of this effect on the asymmetry measurement, the
Collins–Soper rest frame [23] is used. In this frame, $\theta^{*}$ is defined
as the angle between the negatively charged lepton and the axis that bisects
the angle between the incident parton directions. Approximating the leptons as
massless, $\cos\theta^{*}$ can be computed from lab frame variables as:

$\cos\theta^{*}=\frac{2(P_{1}^{+}P_{2}^{-}-P_{1}^{-}P_{2}^{+})}{m\sqrt{m^{2}+\pt^{2}}},$
(4)

with

$P^{\pm}_{i}=\frac{1}{\sqrt{2}}(E_{i}\pm p_{z,i}),$

where $E_{i}$ is the energy and $p_{z,i}$ the longitudinal momentum of the
lepton ($i=1$) and antilepton ($i=2$). The sign of $\cos\theta^{*}$ is defined
with respect to the direction of the incident quark, which is unknown for
collisions in a $\Pp\Pp$ collider. Typically, the quarks in the collision will
carry a larger momentum fraction than the antiquarks, since only the quarks
are valence partons of protons. Thus, usually the lepton pair will have
longitudinal momentum along the direction of the incident quark. Therefore,
instead of using the initial quark direction, one can define the positive axis
to be the longitudinal direction of the lepton pair. We denote the angular
variable defined in this way as:

$\cos\theta_{\mathrm{R}}=\frac{p_{z}}{\abs{p_{z}}}\cos\theta^{*}.$ (5)

where $p_{z}$ is the longitudinal momentum of the dilepton system. The
presence of events where the quark direction does not match the lepton pair
direction dilutes the asymmetry observed in $\cos\theta_{\mathrm{R}}$ as
compared with the underlying asymmetry in $\cos\theta^{*}$. The fraction of
events for which the quark direction is the same as the longitudinal momentum
of the lepton pair increases with the absolute value of the dilepton rapidity.
At an invariant mass of 500, this fraction is $\approx$60% at $\abs{y}=0.2$
and $\approx$95% at $\abs{y}=2$.

The asymmetry, , was previously measured by the CMS Collaboration at
$\sqrt{s}=7\TeV$ [24] and 8 [25] and by the ATLAS Collaboration at
$\sqrt{s}=7\TeV$ [26]. Around the boson peak, measurements of have been used
by CMS and ATLAS to extract the effective weak mixing angle
$\mathrm{sin}^{2}\theta^{\ell}_{\mathrm{eff}}$ [27, 26]. The results presented
in this paper at $\sqrt{s}=13\TeV$ takes a new approach to measuring . Rather
than counting forward and backward events, is extracted by fitting a set of
templates constructed to represent the different terms in Eq. (2) to the
measured angular distribution. These templates are constructed from Monte
Carlo (MC) simulations, which include the dilution effect. The measured by the
template fitting method corrects for the dilution effect and thus determines
the asymmetry in $\cos\theta^{*}$, whereas the measured by the traditional
counting method does not correct for the dilution and determines the asymmetry
in $\cos\theta_{\mathrm{R}}$. Additionally, the template-based measurement of
optimally combines the information from the full fiducial dilepton rapidity
range into a single measurement, rather than being reported differentially in
rapidity as is done in the counting method. This means that the values from
this measurement are not directly comparable with the previous CMS results.
The previous ATLAS publication utilized the counting method but also included
additional results unfolding the dilution effect, that are comparable with
this measurement. This template-based measurement is a maximum-likelihood
estimate of and leads to a $\approx$20% smaller uncertainty than a simple
counting-based measurement [28]. However it relies more heavily on parton
distribution functions, and cannot be as easily reinterpreted using new models
as a counting-based measurement of the diluted .

##  0.2 Analysis strategy

The observed distribution of the reconstructed scattering angle,
$\cos\theta_{\mathrm{R}}$, abbreviated as $c_{\mathrm{R}}$, can be expressed
as a convolution of the $\cos\theta^{*}$, abbreviated $c_{*}$, distribution
defined in Eq. (2),

$f(c_{\mathrm{R}})=C\int\rd
c_{*}R(c_{\mathrm{R}};c_{*})\varepsilon(c_{*})\dd{\sigma}{c_{*}},$ (6)

where $C$ is a normalization constant, $R$ is a “reconstruction function” that
incorporates detector resolution and the dilution effect, and $\varepsilon$ is
an efficiency function. It is important to point out that convolutions are
linear operations, which implies that the resulting reconstructed distribution
can be expressed as a sum of convolutions of each of the terms in Eq. (2).

This linearity allows the fitting function to be represented by a set of
parameter-independent templates corresponding to the different terms in Eq.
(2). By fitting the observed data distribution to combinations of these signal
templates and additional background templates, one can extract and $A_{0}$.
The DY signal templates, representing the reconstructed angular distribution
corresponding to each of the terms in the true angular distribution of Eq.
(2), are constructed by reweighting MC simulated events. The details of the
signal template construction are discussed in Section 0.7.

The templates for the dominant backgrounds are extracted from MC simulation
and validated in a control region of $\Pe\Pgm$ events. Templates for
additional backgrounds that are not well modeled by MC are constructed based
on control samples in data.

Because the form of Eq. (2) is general for any $s$-channel spin-1 process, the
measured values of can be used to set limits on the existence of heavy bosons.

To search for signs of lepton flavor universality violation, the difference
between and $A_{0}$ in the muon and electron channels can be measured
directly. The parameters of interest in these measurements are
$\Delta\AFB=A_{\mathrm{FB},\PGm\PGm}-A_{\mathrm{FB,ee}}$ and $\Delta
A_{0}=\mathrm{A}_{0,\PGm\PGm}-\mathrm{A}_{0,\Pe\Pe}$. The direct measurement
of $\Delta\AFB$ is less sensitive to systematic uncertainties common to the
muon and electron channels than the measurement of in the individual channels,
and thus has reduced systematic uncertainty.

### Fiducial corrections and measurement interpretation

In this analysis, and $A_{0}$ are measured as functions of the dilepton mass,
but and $A_{0}$ also depend on the dilepton and rapidity. A single $A_{0}$
parameter and a single parameter are measured in each mass bin, representing
effective values, integrated over and rapidity. However, because the
acceptance of dilepton events in the CMS detector is not independent of the
dilepton $\pt$ and rapidity, the effective $A_{0}$ or of recorded events is
not the same as the effective value in the full phase space. The effect was
studied in MC simulations and it was found that differences between the
fiducial and full phase space values of $A_{0}$ were insignificant, but the
differences in could be as large as 2% in the lower mass bins. Using these MC
simulations, a correction factor was derived to convert the fiducial directly
measured in the data to that of the full phase space. This correction factor
is then applied to the result of the template fit so that the final reported
value is a measurement of the in the full phase space. Uncertainties in the MC
simulation are propagated through the evaluation of this correction factor,
and are treated as a source of systematic uncertainty in the final measurement

This template fitting technique automatically accounts for all other
resolution, dilution, migration, and acceptance effects since they are modeled
in the simulation. Uncertainties in the simulation of these effects can be
accounted for using variations of the corresponding templates, as discussed in
Section 0.8. The final result can thus be interpreted as a measurement of the
partonic in different bins of the reconstructed lepton pair mass. Because the
mass bins used are large and the SM is essentially independent of the dilepton
mass in the high-mass region of our measurement, the measurement is not
unfolded to parton-level mass bins.

##  0.3 The CMS detector and physics objects

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, is reported in Ref. [29].

Events of interest are selected using a two-tiered trigger system. The first
level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed latency of about 4 [30]. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage [31].

The particle-flow algorithm [32] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the primary interaction vertex as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The energy of muons is obtained from the
curvature of the corresponding track. The energy of charged hadrons is
determined from a combination of their momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for the response
function of the calorimeters to hadronic showers. Finally, the energy of
neutral hadrons is obtained from the corresponding corrected ECAL and HCAL
energies. The candidate vertex with the largest value of summed physics-object
$\pt^{2}$ is assigned to be the primary $\Pp\Pp$ interaction vertex. The
physics objects are the jets, clustered using the anti-jet finding algorithm
[33, 34] with the tracks assigned to candidate vertices as inputs.

Muons are measured in the range $\abs{\eta}<2.4$, with detection planes made
using three technologies: drift tubes, cathode strip chambers, and resistive-
plate chambers. The single-muon trigger efficiency exceeds 90% over the full
$\eta$ range, and the efficiency to reconstruct and identify muons is greater
than 96%. Matching muons to tracks measured in the silicon tracker results in
a relative resolution of 1% in the barrel and 3% in the endcaps for muons with
up to 100, and of better than 7% for muons in the barrel with up to 1 [35].

The single-electron trigger efficiency is approximately 80% over the full
$\eta$ range, and the efficiency to reconstruct and identify electrons is
greater than 65% for electrons with $\pt>20\GeV$. The momentum resolution for
electrons with $\pt\approx 45\GeV$ from $\Z\to\Pe\Pe$ decays ranges from
1.7–4.5%. It is generally better in the barrel region than in the endcaps, and
also depends on the bremsstrahlung energy emitted by the electron as it
traverses the material in front of the ECAL [36].

Jets are clustered from the particle-flow candidates in an event using the
anti-jet finding algorithm with a distance parameter of 0.4. Jet momentum is
determined as the vectorial sum of all particle momenta in the jet, and is
found from simulation to be, on average, within 5–10% of the true momentum
over the whole spectrum and detector acceptance. Additional $\Pp\Pp$
interactions within the same or nearby bunch crossings (”pileup”) can
contribute additional tracks and calorimetric energy depositions, increasing
the apparent jet momentum. To mitigate this effect, tracks identified as
originating from pileup vertices are discarded and an offset correction is
applied to correct for remaining contributions [37]. Jet energy corrections
are derived from simulation studies so that the average measured energy of
jets becomes identical to that of particle level jets [38]. In situ
measurements of the momentum balance in dijet, photon+jet, Z+jet, and multijet
events are used to determine any residual differences between the jet energy
scale in data and in simulation, and appropriate corrections are made [38].
The missing transverse momentum vector () is defined as the negative vector
sum of all the particle-flow candidates in an event, and its magnitude is
denoted as [39]. The is modified to account for corrections to the energy
scale of the reconstructed jets in the event.

##  0.4 Data and Monte Carlo samples

The analysis is performed with $\Pp\Pp$ collision data collected with the CMS
detector at the LHC in 2016–2018 at $\sqrt{s}=13\TeV$ . The total integrated
luminosity amounts to 138 [40, 41, 42].

Various MC generators have been used to simulate the DY signal and background
processes. The DY signal samples, $Z/\PGg*\to\Pe\Pe$ and
$Z/\PGg^{*}\to\Pgm\Pgm$, and the background sample $Z/\PGg^{*}\to\PGt\PGt$,
have been generated at next-to-leading order (NLO) with [43] (shortened as
aMC@NLO), using version v2.2.2 (v2.6.0) for samples corresponding to the 2016
(2017–2018) data-taking period. Up to two additional partons are allowed using
the FxFx merging scheme [44]. The samples are interfaced with [45] to simulate
the parton shower, hadronization, and quantum electrodynamics final-state
radiation. The CUETP8M1 [46] (CP5 [47]) tune and version v8.226 (v8.230) are
used for the samples corresponding to the 2016 (2017–2018) data-taking period.
The NLO NNPDF 3.0 parton distribution functions (PDFs) [48, 49] are used for
all three data-taking periods.

Other processes that can give a final state with two oppositely charged same-
flavor leptons are diboson production ($\PW\PW$, $\PW\PZ$, $\PZ\PZ$), photon-
induced dilepton production ($\PGg\PGg\to\Pell\Pell$), top quark pair
production (), and single top quark production in association with a $\PW$
boson ($\PQt\PW$). The and $\PQt\PW$ backgrounds are generated at NLO using
v2.0 [50, 51, 52, 53], and interfaced to with the CUETP8M2T4 [54] (CP5) tune
for the 2016 (2017–2018) data-taking period. Background samples of
$\PZ\PZ\to\Pell\Pell\Pell^{\prime}\Pell^{\prime}$,
$\PZ\PZ\to\Pell\Pell\PGn\PGn$, and $\PW\PW\to\Pell\PGn\Pell\PGn$ are generated
at NLO with interfaced to . Background samples of
$\PW\PZ\to\PQq\PQq\Pell\Pell$ and $\PZ\PZ\to\PQq\PQq\Pell\Pell$ are generated
at NLO with aMC@NLO interfaced to . The $\PW\PW$, $\PW\PZ$, and $\PZ\PZ$
samples corresponding to the 2016 (2017–2018) data-taking period are
interfaced with using the CUETP8M1 (CP5) tune. The $\ttbar$, $\PQt\PW$, and
diboson backgrounds corresponding to the 2016 (2017–2018) data-taking period
use the NNPDF 3.0 (3.1) PDFs. The photon-induced background,
$\PGg\PGg\to\Pell\Pell$, is simulated using the CepGen [55] implementation of
LPAIR [56, 57], interfaced to v6.429 [58], and using the default proton
structure function parameterization of Suri–Yennie [59]. This contribution is
split into three parts because the interaction at each proton vertex can be
elastic or inelastic.

The cross sections of $\PW\PZ$ and $\PZ\PZ$ diboson samples are normalized to
the NLO predictions calculated with MCFM 6.6 [60], whereas the cross sections
of the $\PW\PW$ samples are normalized to the next-to-NLO (NNLO) predictions
[61]. The total cross section of the process is normalized to the prediction
with NNLO accuracy in QCD and next-to-next-to-leading-logarithmic accuracy for
the soft gluon radiation resummation calculated with 2.0 [62].

The detector response for all MC samples is simulated using a detailed
description of the CMS detector based on [63]. The pileup distribution in
simulation is weighted to match the one observed in data.

##  0.5 Event selection

Events are required to have two leptons of the same flavor and opposite
charges. The dimuon and dielectron events are selected by single-muon and
single-electron triggers, respectively. The thresholds of these triggers are
different for the different data-taking years, and the leading muon or
electron in the event is required to have a above the trigger threshold. In
the analysis, the leading muon requirement for the years 2016/2017/2018 is
26/29/26and for electrons it is 29/38/35. The subleading lepton is required to
have $\pt>15\GeV$. All muons are required to be within the acceptance of the
muon system ($\abs{\eta}<2.4$) and all electrons must be within
$\abs{\eta}<2.5$, excluding the barrel-endcap transition region of the ECAL
($1.44<\abs{\eta}<1.57$). Additionally, to remove cosmic-ray–induced events,
the azimuthal angle ($\phi$) between the two muons is required to differ from
$\pi$ by more than 5mrad.

Each reconstructed muon is required to pass identification criteria that are
based on the number of hits observed in the tracker, the response of the muon
detectors, and a set of matching criteria between muon track parameters, as
measured by the inner tracker and muon detectors. To suppress nonprompt muons
coming from heavy-flavor decays, both muons must be isolated from other
particles in a cone of size $\Delta R=0.3$, where $\Delta
R=\sqrt{\smash[b]{(}\Delta\eta)^{2}+(\Delta\phi)^{2}}$ refers to the distance
from the muon to a given track. More details on the muon identification and
reconstruction used in this analysis can be found in Refs. [35, 64].

The reconstructed electron candidates are required to pass identification
criteria that are based on electromagnetic shower shape variables. Electrons
originating from photon conversions are suppressed by requiring that the
candidates have at most one missing inner tracker hit and are not consistent
with being part of a conversion pair. Electrons are also required to be
isolated from other particles within a cone of size $\Delta R=0.3$. The
electron isolation criteria are based on the ratio of the electron to the sum
of energy deposits associated with the photons as well as with the charged and
neutral hadrons reconstructed by the particle-flow algorithm. More details on
the electron reconstruction and identification criteria used in this analysis
are described in Ref. [36].

To suppress backgrounds that contain the decays of top quarks, two additional
requirements are applied. First, events are required to have
$\ptmiss<100\GeV$. Second, it is required that neither of the two highest jets
in the event with $\abs{\eta}<2.4$ are identified as a bottom quark jet
(tagged). Jets originating from decays of bottom quarks are identified using
an algorithm that combines lifetime information from tracks and secondary
vertices [65]. A working point is used that has a 68% efficiency of correctly
identifying a bottom quark jet and a 1% probability of misidentifying a light-
flavor quark or gluon jet as a bottom quark jet.

##  0.6 Backgrounds

At large lepton pair masses, the dominant background comes from fully leptonic
decays of events. There are also backgrounds from $\PQt\PW$ events, diboson
processes and $\PGt\PGt$ leptonic decays. All of these backgrounds are well
modeled in simulation, and the estimated event yields are validated in data in
a control region of $\Pe\Pgm$ events. Multijet and $\PW$+jets events where one
or more jets are incorrectly identified as leptons (denoted as “MisID” events)
are also a source of background. This background is larger for the $\Pe\Pe$
channel than for the $\Pgm\Pgm$ channel. A technique based on control samples
in data is employed to estimate this background. There are also backgrounds
coming from $t$-channel photon-induced dilepton production, which are modeled
with MC simulation as well.

The MisID background is estimated from data using the “misidentification rate”
method. Descriptions of this method are reported in Refs. [66, 67]. The
misidentification rate is defined as the probability of a jet, having been
reconstructed as a lepton candidate, to pass the lepton selection
requirements. This rate is measured in a sample with two leptons coming from a
boson decay and an additional, potentially misidentified, lepton. These two
leptons are required to pass the lepton identification requirements and have
an invariant mass within 7of the boson mass [68]. The presence of the third
lepton candidate is used as a probe to measure the misidentification rate.
These lepton candidates are required to pass a set of identification and
isolation requirements less stringent than the full selection requirements of
the analysis. The MisID background can then be estimated from a sample of data
events with two lepton candidates where at least one of the candidates fails
the full selection requirements. Events from this sample are assigned weights
based on the expected misidentification probability of the failing lepton
candidates. Contamination of this sample by lepton pairs from DY and other
prompt processes is subtracted using MC simulation. These reweighted events
are used to estimate the yield and shape of the MisID background.

This method of background estimation is validated in a control region. This
control region has all the same selection criteria and covers the same
dilepton mass range ($m>170\GeV$) as the signal region except the lepton pairs
are required to have same-sign rather than opposite-sign charges. A large
fraction of the events in this control region stem from misidentified jets. In
the $\Pe\Pe$ channel, there is also a significant contribution to this sample
from opposite-sign DY events where one of the electrons has had its charge
incorrectly assigned. The rate of this misassignment is not modeled well in
simulation, and so a correction to the MC is derived in the mass window of the
peak ($70<m<110\GeV$) and applied. The MisID background estimate as well as MC
estimates of other backgrounds are compared with the observed yield of same-
sign events. A $\cos\theta_{\mathrm{R}}$-dependent correction to the MisID
background estimate is derived using the ratio of the MisID estimate to the
number of observed same-sign events minus other backgrounds. The uncertainty
in this correction is calculated as the quadratic sum of the statistical
uncertainties from the limited sample size, uncertainty in the amount of DY
pairs reconstructed as same-sign pairs, as well as systematic uncertainty
reflecting possible differences in shape between same-sign and opposite-sign
MisID estimates.

Because the $\PGt\PGt$, $\PQt\PW$, , and diboson backgrounds all have decays
to $\Pe\Pgm$ pairs as well, the MC simulation of these processes can be
validated in data. The $\Pe\Pgm$ control region used has all the same
selection criteria as the signal region, except that events are required to
have one muon and one electron rather than a pair of the same flavor. The muon
is required to have ${\pt>26/29/26\GeV}$ for the years 2016/2017/2018 and pass
the muon trigger used in the main analysis, and the electron is required to
have ${\pt>15\GeV}$. There are also MisID events in this $\Pe\Pgm$ control
region coming from QCD multijet and W+jets backgrounds. These are estimated
using the misidentification rate technique previously described. The dilepton
invariant mass and $\cos\theta_{\mathrm{R}}$ distributions of $\Pe\Pgm$ events
are shown in Fig. 1. Good agreement is observed between the simulated and
observed yield of $\Pe\Pgm$ events across the entire mass range.

Figure 1: The invariant mass distribution (left) and $\cos\theta_{\mathrm{R}}$
distribution (right) of $\Pe\Pgm$ events observed in CMS data (black dots with
statistical uncertainties) and expected backgrounds (stacked histograms). The
hatched bands show the systematic uncertainty in the expected yield. The
sources of this uncertainty are discussed in Section 0.8. The lower panels
show the ratio of the data to the expectations. The gray bands represent the
total uncertainty in the predicted yields.

Because diboson events are produced via electroweak processes, they are
expected to have a small forward-backward asymmetry in their lepton pairs;
events are also known to have an asymmetry, but it is too small to be detected
in its lepton pairs alone [69]. The QCD and W+jets backgrounds should have no
asymmetry. Based on MC and control sample estimates, we predict an overall
forward-backward asymmetry of $\approx$0.01 in the sample of $\Pe\Pgm$ events.
Using the definition in Eq. (1), the of the $\Pe\Pgm$ sample is found to be
$0.012\pm 0.003$, consistent with this expectation.

Although the overall normalizations and asymmetries of the MC $\Pe\Pgm$
estimates are observed to be consistent with data, there are discrepancies
between the predicted and observed shapes of the $\cos\theta_{\mathrm{R}}$
distribution in certain ranges of dilepton mass. To address these
discrepancies, a $\cos\theta_{\mathrm{R}}$ correction is derived from the
$\Pe\Pgm$ sample in data, based on the ratio of the observed and predicted
$\cos\theta_{\mathrm{R}}$ distributions. Because the asymmetry is modeled
well, this correction is derived symmetrically in $\cos\theta_{\mathrm{R}}$
using four bins of $\abs{\cos\theta_{\mathrm{R}}}$. To account for the
changing $\cos\theta_{\mathrm{R}}$ shapes as a function of the dilepton mass,
the correction is derived separately in different mass bins. Corrections are
derived in five mass bins (170, 200, 250, 320, 510, 3000) with edges matching
those used in the measurement (defined in Section 0.7), combining the highest
mass bins because of a limited event count. These corrections are applied to
modify the shapes of the templates used to model the corresponding backgrounds
in the signal region. They change the estimated $\cos\theta_{\mathrm{R}}$
shape of these backgrounds and introduce uncertainties in these shapes at the
level of by $\approx 5\pm 4\%$ and $\approx 15\pm 10\%$ in the lowest and
highest mass bins, respectively.

Figure 2 shows a comparison between measured $\Pgm\Pgm$ and $\Pe\Pe$ events
and our estimates in the signal region after all corrections have been
applied. Good agreement is observed between the simulated and observed amount
of $\Pgm\Pgm$ and $\Pe\Pe$ events.

Figure 2:  A comparison of CMS data and expected signal and background
distributions in dilepton invariant mass (upper row),
$\cos\theta_{\mathrm{R}}$ (middle row) and dilepton rapidity (lower row). The
left plot shows the $\Pgm\Pgm$ channel and the right plot the $\Pe\Pe$
channel. The black points with error bars represent the data and their
statistical uncertainties, whereas the combined signal and background
expectation is shown as stacked histograms. The hatched band shows the
systematic uncertainty in the expected signal and background yield. The
sources of this uncertainty are discussed in Section 0.8. The lower panels
show the ratio of the data to the expectation. The gray bands represents the
total uncertainty in the predicted yield.

##  0.7 Template construction

From the MC sample of DY events, templates for each of the three terms in Eq.
(2) need to be constructed. To avoid any of the templates having a negative
yield in any of the bins, two reparameterizations are performed. The angular
distribution is rewritten in a slightly different form than Eq. (2):

$\frac{\rd\sigma}{\rd{}c_{*}}\propto\frac{3}{4(2+\alpha)}\biggl{[}1+c_{*}^{2}+\alpha(1-c_{*}^{2})+\frac{4(2+\alpha)}{3}\;\AFB\;c_{*}\biggr{]}.$
(7)

This form is equivalent to Eq. (2) for $\alpha=2A_{0}/(2-A_{0})$. The term
corresponding to NLO QCD production ($1-c_{*}^{2}$) is now strictly positive.

The three templates, $f_{\mathrm{S}}$, $f_{\alpha}$, and $f_{\mathrm{A}}$, can
now be constructed to represent the $1+c_{*}^{2}$, $1-c_{*}^{2}$, and $c_{*}$
terms in Eq. (7). These templates are constructed by reweighting MC events
using generator-level quantities but are binned in the reconstructed
variables. Note that the generator-level $c_{*}$ and the reconstructed
$c_{\mathrm{R}}$ can have different signs. To minimize the impact of
statistical fluctuations in our MC simulations, each event is used twice in
the template construction. Each event is used once with $+c_{*}$ and once with
$-c_{*}$, and each use is given half weight to keep the normalization
unchanged. The following reweighting factors are used to construct the
$f_{\mathrm{S}}$, $f_{\alpha}$, and $f_{\mathrm{A}}$ templates respectively:

$w_{\mathrm{S}}(\abs{c_{*}})=\frac{1+c_{*}^{2}}{1+c_{*}^{2}+\alpha\left(1-c_{*}^{2}\right)},$
(8)
$w_{\alpha}(\abs{c_{*}})=\frac{1-c_{*}^{2}}{1+c_{*}^{2}+\alpha\left(1-c_{*}^{2}\right)},$
(9)
$w_{\mathrm{A}}(c_{*})=\frac{c_{*}}{1+c_{*}^{2}+\alpha\left(1-c_{*}^{2}\right)}.$
(10)

The $\alpha$ values in the denominator are extracted from fits to generator
level distributions of our MC simulation events.

Then a histogram of CMS data events, $f_{\text{data}}$, could be parameterized
by:

$f_{\text{data}}=\sum_{j}f^{j}_{\text{bkg}}+N(\alpha)\left[\alpha\;f_{\alpha}+f_{\mathrm{S}}+\frac{\AFB}{N(\alpha)}f_{\mathrm{A}}\right],$
(11)

where the normalization factor $N(\alpha)=3/[4(2+\alpha)]$ has been introduced
for convenience and $f^{j}_{\text{bkg}}$ represents templates for the
different backgrounds.

To avoid the negative values of the antisymmetric template ($f_{\mathrm{A}}$),
two positive-valued linear combinations of $f_{\mathrm{A}}$ and
$f_{\mathrm{S}}$, are constructed as:

$f_{\mathrm{S}}+\frac{\AFB}{N(\alpha)}f_{\mathrm{A}}=(1+\frac{\AFB}{N(\alpha)})\frac{f_{\mathrm{S}}+f_{\mathrm{A}}}{2}+(1-\frac{\AFB}{N(\alpha)})\frac{f_{\mathrm{S}}-f_{\mathrm{A}}}{2}\equiv(1+\frac{\AFB}{N(\alpha)})f_{+}+(1-\frac{\AFB}{N(\alpha)})f_{-}.$
(12)

Then CMS data in each mass bin can be fit by the following parameterization:

$f_{\text{data}}=\sum_{j}f^{j}_{\text{bkg}}+N(\alpha)\left[\alpha\;f_{\alpha}+(1+\frac{\AFB}{N(\alpha)})f_{+}+(1-\frac{\AFB}{N(\alpha)})f_{-}\right],$
(13)

where the asymmetry and $\alpha$ are allowed to float.

Separate templates are made for seven different mass bins to facilitate the
extraction of the and $A_{0}$ coefficients as functions of mass. The bin edges
used (in units of ) are: 170, 200, 250, 320, 510, 700, 1000 and infinity,
where the last bin contains all events with dilepton mass greater than 1000.

Within each mass bin, the two-dimensional (2D) templates are binned in
$c_{\mathrm{R}}$ and lepton pair rapidity $\abs{y}$. Because of the acceptance
effects, there are very few events with high rapidity and large
$\abs{c_{\mathrm{R}}}$. Therefore, a different $c_{\mathrm{R}}$ binning is
adopted at high rapidity. There are four bins in $\abs{y}$ with bin edges at:
0.0, 0.6, 1.0, 1.5, and 2.4. For $\abs{y}<1.0$, there are eight uniform
$c_{\mathrm{R}}$ bins from -1.0 to 1.0. For $\abs{y}>1.0$, the pairs of bins
with $\abs{c_{\mathrm{R}}}>0.5$ are merged leaving six $c_{\mathrm{R}}$ bins
in this region. Additionally, there are few events with large rapidity at high
mass so for the highest two mass bins (700–1000 and $>$1000) the two rapidity
bins with $\abs{y}>1.0$ are merged leaving three $\abs{y}$ bins in this
region.

There are several templates for various background processes. Because of their
similar shapes and the small magnitude of the $\PQt\PW$ background, the top
quark related background processes (, $\PQt\PW$) are combined into a single
background template. Diboson backgrounds ($\PW\PW$, $\PZ\PZ$, $\PW\PZ$) are
combined into a single background template as well. The
$\PGg\PGg\to\Pell\Pell$ process is a separate background template and
constitutes 1–4% of the total cross section in the mass range of the
measurement. The MisID estimate is also included as a separate background
template. A $\PZ/\PGg^{*}\to\PGt\PGt$ template is included as well. To
minimize statistical fluctuations, the templates for the top and MisID
backgrounds are symmetrized in $\cos\theta_{\mathrm{R}}$. The templates for
the diboson, $\PZ/\PGg^{*}\to\PGt\PGt$, and $\PGg\PGg\to\Pell\Pell$ processes
are not symmetrized because these processes have perceptible asymmetries.

Differential measurements of and $A_{0}$ are performed by fitting each mass
bin independently, with a set of nuisance parameters uncorrelated with those
in other mass bins. The results of an inclusive measurement in which all mass
bins ($m>170\GeV$) are fit simultaneously is also reported. Within each mass
bin all three years are fit simultaneously as separate categories. Two sets of
fits are performed to extract and $A_{0}$: one in which the and $A_{0}$
parameters in the $\Pe\Pe$ channel and $\Pgm\Pgm$ channels are allowed to
float independently, and one in which they are fit with common and $A_{0}$
parameters. An additional set of fits is performed to extract $\Delta\AFB$ and
$\Delta A_{0}$. In these fits, the and $A_{0}$ parameters in the $\Pe\Pe$
channel are used as a reference and the and $A_{0}$ parameters in the
$\Pgm\Pgm$ channel are constructed in relation to them using the definitions
of $\Delta\AFB$ and $\Delta A_{0}$. The and $A_{0}$ values in the $\Pe\Pe$
channel are allowed to freely float in the measurements of $\Delta\AFB$ and
$\Delta A_{0}$. These measurements of $\Delta\AFB$ and $\Delta A_{0}$ are
performed separately for each mass bin, as well as inclusively across all mass
bins.

##  0.8 Systematic uncertainties

Systematic uncertainties in the normalization and shape of templates arise
from a variety of sources and are defined through nuisance parameters in the
likelihood. For systematic uncertainties that can change the shape of a
template, shifted templates are constructed by varying the source of the
systematic uncertainty up and down within its uncertainty. Shape uncertainties
are incorporated into the likelihood by interpolation between the nominal and
shifted templates, constrained with a Gaussian prior. The interpolation is
calculated with a sixth-order polynomial for shifts smaller than one Gaussian
$\sigma$, and with a linear function for shifts beyond one Gaussian $\sigma$.
Normalization uncertainties are included using log-normal priors.

PDFs: variations in PDFs can change the dilution factor and kinematic
distributions of the MC simulation samples. The 100 NNPDF set replicas are
converted to 60 Hessian eigenvector variations [70]. For each of these 60
variations an “up” template is constructed using the variation’s weight. A
“down” template is then constructed that symmetrizes the deviation from the
nominal template. Each variation is treated as a separate shape uncertainty
yielding 60 nuisance parameters associated with PDFs in the fit.

MisID background estimate: the uncertainty in the normalization of the MisID
background and the uncertainty in the shape of the MisID
$\cos\theta_{\mathrm{R}}$ distribution are considered separately. Their
normalizations are assigned a 50% uncertainty based on closure studies of the
misidentification rate technique performed in simulation. The uncertainties in
the shape corrections come from the statistical uncertainties of the same-sign
validation and systematic uncertainties reflecting the shape differences
between the same-sign and opposite-sign MisID estimates.

Lepton efficiencies: scale factors are derived and applied to the simulated MC
events to account for the differences in the detector performance between CMS
data and the MC simulation. The efficiencies for the trigger, lepton
identification, and lepton isolation are measured as functions of lepton and
$\eta$ using the “tag-and-probe” [35, 36] method for both data and simulation.
For muons, separate uncertainties are included for the muon trigger,
identification, and isolation efficiencies, with the uncertainty in
reconstruction efficiency being negligible. For electrons, isolation is part
of the identification requirements rather than a separate selection, so
separate uncertainties are included for the electron trigger, reconstruction,
and identification/isolation efficiencies. The efficiency uncertainties also
include the effects of timing issues in the ECAL endcaps and muon chambers
that caused inefficiencies in the first-level trigger in the range of 0–3% for
the different data-taking periods. For each of these uncertainties, up and
down templates are constructed from MC simulations using the up and down
values from the uncertainty in the scale factors.

Lepton scale corrections: the muon momentum and electron energy scales can be
affected by detector alignment and imperfect calibration. Such issues are
corrected by additional energy and momentum scale corrections applied to both
simulation and data. A bias in the muon momentum reconstruction can occur
because of the differences in the tracker alignment between CMS data and
simulation, as well as a residual magnetic field mismodeling. The muon
momentum scale corrections are applied using the procedure described in Refs.
[71, 64]. The electron energy deposits, as measured in the ECAL, are subject
to a set of corrections involving information from both the ECAL and tracker
[36]. Separate up and down templates are constructed for the electron
smearing, and muon and electron scales based on the uncertainty of these
corrections.

Cross sections: a systematic uncertainty is attributed to the normalization of
signal and background samples estimated by MC event generators. Based on NNLO
calculations using the v3.1 simulation code [72], a 3% uncertainty is assigned
to the DY cross section. Based on NLO calculations, a 5% uncertainty is
attributed to the backgrounds from top quark decays [62], and a 4% uncertainty
in the diboson backgrounds [73, 61, 74]. A 6% uncertainty is attributed to the
normalization of the $\PGg\PGg\to\ell\ell$ background, and it has been
verified that this value covers the differences of samples generated using
Suri–Yennie structure functions and the LuxQED [75, 76] photon PDFs.

Statistical uncertainties in templates: we treat the uncertainty due to the
finite number of MC events, as well as the finite number of data events used
to build the MisID templates, using a modified “Barlow–Beeston–like” approach
[77], which adds a Gaussian uncertainty reflecting the number of events
accumulated in each template bin. However, template bins at
$\pm\cos\theta_{\mathrm{R}}$ have partially correlated uncertainties because
of events being used multiple times in the construction of signal, MisID
background, and top quark background templates, but not reused in the
$\PGg\PGg\to\Pell\Pell$, $\PGt\PGt$, and diboson background templates. To
account for this correlation, additional constraint terms are added in the
fit. These additional terms constrain the differences in the
Barlow–Beeston–like nuisance parameters in correlated bins with Gaussian
constraints. For each channel and mass bin, the correlation coefficient
between bins at $\pm\cos\theta_{\mathrm{R}}$ is computed and the standard
deviation of the Gaussian function chosen conservatively based on the maximum
amount of uncorrelated uncertainty in each mass bin. Based on the computed
correlations, a variance of $0.1$ is used for the Gaussian constraints on the
template bins in the first four mass bins, and a variance of $0.6$ for the
Gaussian constraints on template bins in the highest three mass bins.

Integrated luminosity: the integrated luminosities of the 2016, 2017, and 2018
data-taking periods are individually known with uncertainties in the 1.2–2.5%
range [40, 41, 42], while the total 2016–2018 integrated luminosity has an
uncertainty of 1.6%, the improvement in precision reflecting the
(uncorrelated) time evolution of some systematic effects.

Renormalization + factorization scale and strong coupling: the renormalization
($\mu_{\mathrm{R}}$) and factorization scales ($\mu_{\mathrm{F}}$), and strong
coupling () used in MC generation can also have an effect on the shape and
normalization of the templates. The $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$
uncertainties are included by reweighting simulated events to match
alternative scenarios where $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ are
scaled up and down by a factor of two, both independently and simultaneously.
The unphysical combinations where $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$
are scaled in opposite directions are not included. Three nuisance parameters,
describing $\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$ and combined
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ scale variations are included in the
fit and are taken to be uncorrelated with respect to each other. Simulated
events are also reweighted to account for variations of and used to construct
up and down templates. A central value of $\alpS=0.118$ and variations of
$\pm$ 0.0015 are used [78].

Background shape corrections: as discussed in Section 0.6, a shape correction
based on $\Pe\Pgm$ data events is applied to MC simulation derived backgrounds
from top quarks and diboson events. The uncertainties in this shape correction
are modeled with four independent nuisance parameters. Each parameter allows
the values of the shape correction in a particular
$\abs{\cos\theta_{\mathrm{R}}}$ bin to vary up and down within its statistical
uncertainty.

DY correction: it is known that the DY dilepton spectrum is difficult to model
at low [79, 80]. Because acceptance is correlated with , mismodeling of the
spectrum can result in mismodeling of the DY contribution. For this reason,
the DY MC spectrum in each mass bin is corrected based on data. For each mass
bin used in the measurement, the normalized DY MC simulation distribution is
compared with the normalized data distribution, less the contributions from
backgrounds. A correction factor is then derived based on the ratio of these
two distributions. When building the templates used in the measurement, events
are reweighted with this additional factor so that the events used to build
the templates match the spectrum of data events. To model the uncertainty in
this correction seven separate nuisance parameters are used, one for each bin.
For each parameter the correction in a particular bin is varied up and down
within its statistical uncertainty to produce up and down templates. The total
uncertainty in these corrections leads to an uncertainty in the signal
templates of roughly 1%.

Pileup: to account for the uncertainty originating from the differences in the
measured and simulated pileup distribution, the total inelastic cross section
is varied by $\pm$ 4.6% [81], and the reweighting factor applied to MC
simulated samples is recomputed. Up and down MC simulation templates are
constructed based on the up and down variations of the reweighting factor.

tag veto efficiency: scale factors dependent on jet flavor, , and $\eta$ are
applied to simulated events to correct for differences in tagging efficiency
and mistag rates between data and simulation [65]. Up and down templates are
constructed based on the uncertainty in these efficiencies.

modeling: the uncertainty in the calibration of the jet energy scale and
resolution affects the modeling of . These effects are estimated by changing
the jet energy in the simulation up and down by one standard deviation. These
up and down variations are used to construct sets of up and down templates for
use in the fitting procedure.

As discussed in Section 0.2, there is also an uncertainty in the final
measurement of based on the extrapolation from the fiducial region to the full
phase space. This uncertainty is added as an additional source of systematic
uncertainty when the correction is applied after the template fit.

The contributions from different sources of systematic uncertainty are
evaluated by redoing the fit while different groups of nuisances are fixed to
their nominal values and taking the quadrature difference between the
resulting uncertainty and the full uncertainty. A comparison of the magnitude
of the different systematic uncertainties on the and $\Delta\AFB$ measurements
is shown in Table 1. The single largest source of systematic uncertainty in
the measurement of are the PDFs, but it is a negligible source of uncertainty
in the measurement of $\Delta\AFB$. The dominant sources of systematic
uncertainty are common to the electron and muon channels and thus their
combination reduces the statistical uncertainty of the measurement but does
not significantly reduce the systematic uncertainty.

Table 1:  A comparison of the magnitude of the different sources of systematic
uncertainty for the measurement of when combining the muon and electron
channels and for the measurement of $\Delta\AFB$. Results for the 170–200mass
bin are shown because that is the mass bin in which the systematic uncertainty
has the largest contribution to the total uncertainty; the results for other
mass bins are similar. Results are also reported as a fraction of the overall
systematic uncertainty for the measurement of and $\Delta\AFB$. Sources are
listed in order of the size of their contribution to the uncertainty in .

Source | Unc. on ($\times 10^{-3}$) |  Frac. of total
sys. unc. on  | Unc. on $\Delta\AFB$ ($\times 10^{-3}$) |  Frac. of total
sys. unc. on $\Delta\AFB$
---|---|---|---|---
PDFs | 8.1 |  47% | 0.8 |  1%
MC and MisID backgrounds stat. unc. | 4.1 |  12% | 6.8 |  42%
\+ $\mu_{\mathrm{F}}$/$\mu_{\mathrm{R}}$ scales | 3.3 |  8% | 3.2 |  9%
DY cross section | 3.0 |  7% | 0.9 |  1%
Pileup | 2.8 |  5% | 3.8 |  13%
Fiducial correction | 2.7 |  5% |  $<$0.1 |  $<$1%
cross section | 2.7 |  5% | 0.1 |  $<$1%
DY correction | 2.1 |  3% | 0.8 |  1%
$\Pe\Pgm$ shape corrections | 1.8 |  2% | 0.4 |  $<$1%
Integrated luminosity | 1.2 |  1% | 1.0 |  1%
Electron identification/isolation | 1.0 |  1% | 2.7 |  6%
Electron MisID normalization | 0.9 |  1% | 4.3 |  17%
Electron MisID shape | 0.8 |  $<$1% | 2.6 |  6%
tagging uncertainty | 0.8 |  $<$1% | 0.3 |  $<$1%
uncertainties | 0.7 |  $<$1% | 0.5 |  $<$1%
Muon identification/isolation | 0.6 |  $<$1% | 1.2 |  1%
Muon MisID shape | 0.5 |  $<$1% | 0.6 |  $<$1%
$\PGg\PGg$ cross section | 0.4 |  $<$1% | 0.6 |  $<$1%
Muon MisID normalization | 0.4 |  $<$1% | 0.1 |  $<$1%
Electron trigger | 0.4 |  $<$1% | 1.2 |  1%
Diboson cross section | 0.2 |  $<$1% | 0.1 |  $<$1%
Electron reconstruction | 0.2 |  $<$1% | 0.7 |  $<$1%
Muon momentum scale | 0.1 |  $<$1% | 0.1 |  $<$1%
Electron momentum scale | 0.1 |  $<$1% | 0.1 |  $<$1%
Muon trigger | 0.1 |  $<$1% | 0.1 |  $<$1%

##  0.9 Results

For all relevant results presented in this section, the best-fit value of the
parameter and an approximate 68% confidence interval are extracted following
the procedure described in Section 3.2 of [82]. The results for the template
fits to data to extract in different mass bins are presented in Table 0.9 and
shown graphically in Fig. 3; the results for $A_{0}$ are presented in Table
0.9 and shown graphically in Fig. 4. The results for the extraction of
$\Delta\AFB$ and $\Delta A_{0}$ in different mass bins and inclusively are
presented in Table 0.9. The results for $\Delta\AFB$ are also shown
graphically in Fig. 5. The resulting exclusion limits are shown in Fig. 6. The
contribution of $\PGg\PGg\to\Pell\Pell$ events as compared to DY events in
each mass bin is shown in Table 0.9. Comparisons of the data and the postfit
predictions are shown in Figs. 7–9.

Results for the measurement of from the maximum likelihood fit to data in
different dilepton mass bins in the different channels as well as an inclusive
measurement across all mass bins. The first and second uncertainties listed
with each measurement are statistical and systematic, respectively. The last
column lists the predictions of and associated uncertainties from aMC@NLO.
Mass () muons electrons combined aMC@NLO Pred. 170–200 $0.610\pm 0.012\pm
0.011$ $0.654\pm 0.015\pm 0.012$ $0.628\pm 0.009\pm 0.011$ $0.612\pm 0.007$
200–250 $0.592\pm 0.012\pm 0.010$ $0.635\pm 0.015\pm 0.011$ $0.608\pm 0.009\pm
0.010$ $0.608\pm 0.006$ 250–320 $0.558\pm 0.014\pm 0.009$ $0.610\pm 0.018\pm
0.010$ $0.578\pm 0.011\pm 0.009$ $0.603\pm 0.007$ 320–510 $0.598\pm 0.014\pm
0.009$ $0.583\pm 0.018\pm 0.009$ $0.592\pm 0.011\pm 0.008$ $0.603\pm 0.005$
510–700 $0.610\pm 0.027\pm 0.008$ $0.624\pm 0.033\pm 0.009$ $0.616\pm 0.021\pm
0.008$ $0.604\pm 0.004$ 700–1000 $0.617\pm 0.042\pm 0.009$ $0.563\pm 0.048\pm
0.008$ $0.594\pm 0.032\pm 0.008$ $0.606\pm 0.002$ $>$1000 $0.595\pm 0.070\pm
0.011$ $0.694\pm 0.076\pm 0.014$ $0.638\pm 0.052\pm 0.011$ $0.610\pm 0.002$
Inclusive, Mass $>$170 $0.602\pm 0.006\pm 0.007$ $0.628\pm 0.008\pm 0.007$
$0.612\pm 0.005\pm 0.007$ $0.608\pm 0.006$

Figure 3: Measurement of the DY forward-backward asymmetry as a function the
dilepton mass compared with the MC predictions. The green line is the
predicted value for from the aMC@NLO simulation and the shaded green region
its uncertainty. The red, blue, and black points and error bars represent the
dimuon, dielectron, and combined measurements, respectively. Error bars on the
measurements include both statistical and systematic components. The bottom
panel shows the ratio between the combined measurement and the aMC@NLO
prediction. In the bottom panel, the vertical error bars represent the
uncertainty in the combined measurement and the shaded green band the
uncertainty in the aMC@NLO prediction.

Results for the measurement of $A_{0}$ from the maximum likelihood fit to data
in different dilepton mass bins in the different channels as well as an
inclusive measurement across all mass bins. The first and second uncertainties
listed with each measurement are statistical and systematic, respectively. The
last columns lists the predictions of $A_{0}$ and associated uncertainties
from aMC@NLO. To help in the interpretation of these results, we also list the
average dilepton $\pt$ of the data events in each mass bin.  Mass () Avg. ()
$A_{0}$ muons $A_{0}$ electrons $A_{0}$ combined aMC@NLO Pred. 170–200 38 $\ \
\ 0.090\pm 0.010\pm 0.023$ $\ \ \ 0.072\pm 0.013\pm 0.032$ $\ \ \ 0.086\pm
0.008\pm 0.022$ $0.06\pm 0.01$ 200–250 43 $\ \ \ 0.041\pm 0.011\pm 0.024$ $\ \
\ 0.069\pm 0.015\pm 0.050$ $\ \ \ 0.045\pm 0.009\pm 0.024$ $0.06\pm 0.01$
250–320 48 $-0.027\pm 0.016\pm 0.029$ $\ \ \ 0.065\pm 0.021\pm 0.044$
$-0.001\pm 0.013\pm 0.027$ $0.06\pm 0.01$ 320–510 55 $-0.009\pm 0.020\pm
0.032$ $\ \ \ 0.037\pm 0.025\pm 0.042$ $\ \ \ 0.007\pm 0.015\pm 0.029$
$0.05\pm 0.01$ 510–700 65 $-0.072\pm 0.045\pm 0.037$ $\ \ \ 0.112\pm 0.059\pm
0.051$ $-0.005\pm 0.036\pm 0.034$ $0.04\pm 0.01$ 700–1000 73 $\ \ \ 0.053\pm
0.081\pm 0.039$ $\ \ \ 0.086\pm 0.099\pm 0.068$ $\ \ \ 0.065\pm 0.063\pm
0.040$ $0.03\pm 0.01$ $>$1000 88 $-0.251\pm 0.140\pm 0.048$ $-0.162\pm
0.163\pm 0.105$ $-0.219\pm 0.107\pm 0.050$ $0.03\pm 0.01$ Inclusive, Mass
$>$170 44 $\ \ \ 0.040\pm 0.006\pm 0.015$ $\ \ \ 0.059\pm 0.008\pm 0.019$ $\ \
\ 0.047\pm 0.005\pm 0.013$ $0.06\pm 0.01$

Figure 4: Measurement of the DY forward-backward asymmetry as a function the
dilepton mass compared with the MC predictions. The green line is the
predicted value for from the aMC@NLO simulation and the shaded green region
its uncertainty. The red, blue, and black points and error bars represent the
dimuon, dielectron, and combined measurements, respectively. Error bars on the
measurements include both statistical and systematic components.

Results for the measurement of $\Delta\AFB$ and $\Delta A_{0}$ between the
muon and electron channels from the maximum likelihood fit to data in
different dilepton mass bins as well as an inclusive measurement across all
mass bins. The first and second uncertainties listed with each measurement are
statistical and systematic, respectively.  Mass () $\Delta$ $\Delta A_{0}$
170–200 $-0.045\pm 0.019\pm 0.009$ $\ \ \ 0.018\pm 0.016\pm 0.032$ 200–250
$-0.042\pm 0.019\pm 0.006$ $-0.027\pm 0.019\pm 0.048$ 250–320 $-0.052\pm
0.023\pm 0.006$ $-0.092\pm 0.026\pm 0.045$ 320–510 $\ \ \ 0.015\pm 0.023\pm
0.008$ $-0.046\pm 0.032\pm 0.045$ 510–700 $-0.013\pm 0.043\pm 0.007$
$-0.184\pm 0.075\pm 0.053$ 700–1000 $\ \ \ 0.055\pm 0.064\pm 0.008$ $-0.034\pm
0.128\pm 0.068$ $>$ 1000 $-0.099\pm 0.104\pm 0.014$ $-0.090\pm 0.214\pm 0.111$
Inclusive, Mass $>$ 170 $-0.026\pm 0.010\pm 0.004$ $-0.018\pm 0.011\pm 0.018$

Figure 5: Measurement of the difference in forward-backward asymmetry between
the dimuon and dielectron channels. The green line is drawn at zero, the
predicted value for $\Delta\AFB$ assuming lepton flavor universality. The
black points and error bars represent the measurements of $\Delta\AFB$ in
different mass bins. The blue line and shaded light blue region represent the
inclusive measurement of $\Delta\AFB$ and corresponding uncertainty. The error
bars on the measurements and the shaded region include both statistical and
systematic components.

The fraction of photon-induced background as compared with the total amount of
DY signal plus photon-induced events
($N_{\PGg\PGg}/(N_{\PGg\PGg}+N_{\mathrm{DY}})$) in different dilepton mass
bins. These numbers are averaged across the different years and channels.

Mass () | 170–200 | 200–250 | 250–320 | 320–510 | 510–700 | 700–1000 |  $>$ 1000
---|---|---|---|---|---|---|---
$\PGg\PGg\to\Pell\Pell$ fraction | 1.8% | 2.1% | 2.5% | 2.8% | 3.3% | 3.7% | 4.1%

In the combined measurement of , no statistically significant deviations from
the SM predictions are observed. A small difference is found between the muon
and electron ’s, with $\Delta\AFB$ found to be consistently below zero in the
lowest three mass bins, as well as in the inclusive measurement. Based on a
likelihood scan, the inclusive measurement $\Delta\AFB$ differs from zero at
the level of 2.4 standard deviations.

Given that the existence of new gauge bosons would change the asymmetry well
below their resonance masses, the measurement of can be used to constrain the
existence of heavy bosons. The constraining power is model dependent, because
the interference between the boson and $\PZ/\PGg^{*}$ depends on the couplings
of the boson. To give an example of how these measurements can be used to
constrain models with bosons, constraints are derived for the sequential
standard model (SSM) [83]. The SSM contains a boson with the same coupling
strength as the SM boson, up to an overall normalization factor in the left-
handed coupling, $\kappa_{\mathrm{L}}$. In order to set 95% confidence level
() upper limits on properties of the boson, a hypothesis test between the SSM
and SM is performed based on a comparison of the predicted values of in each
model and our measurements. The test statistic used is the difference in
$\chi^{2}$ between the two models. Predictions for in the SSM are derived from
MC estimates using aMC@NLO [84]. Only the measurements of in the three highest
mass bins are used for the $\chi^{2}$ calculation. It was checked that the
interference from an off-shell several-SSM produces negligible effects in the
lower mass bins and their inclusion would not improve the limit.

Figure 6 shows the expected and observed 95% exclusion limits on SSM bosons.
For $\kappa_{\mathrm{L}}=1$, which corresponds to a boson with exactly the
same couplings as the SM boson, a with $m_{\PZpr}<4.4\TeV$ is excluded at 95%
, whereas the expected limit was 3.7.

Currently these limits are not as strong as those from direct dilepton
resonance searches [22, 21], which report limits at 4.90and 5.15 by ATLAS and
CMS, respectively. However, the -based approach is sensitive to wide-width
resonances, which may be missed in a search for narrow resonances [7].
Additionally, the sensitivity of the -based approach will continue to improve
with additional data, with the sensitivity to heavy mass scales expected to
scale as the fourth root of the increase in integrated luminosity. This means
limits from the -based approach should surpass the projected sensitivity of
resonance searches [85] during the High-Luminosity upgrade of the LHC [86].

The full tabulated results are provided in HEPData [87].

Figure 6:  Exclusion limits at 95% on the coupling parameter
$\kappa_{\mathrm{L}}$ for a in the sequential standard model as a function of
the mass. The expected (observed) limit is shown by the dashed (solid) line.
The inner and outer shaded areas around the expected limits show the 68%
(green) and 95% (yellow) intervals, respectively. The dashed blue line shows
$\kappa_{L}=1$ which corresponds to a with exactly the same couplings as the
SM boson.

Figure 7: The postfit distributions in the 170–200, 200–250 and 250–320mass
bins are shown in upper, middle and lower rows, respectively. The left column
is the $\Pgm\Pgm$ channel, and the right column the $\Pe\Pe$ channel. The
contribution of the $\PGt\PGt$ background is not visible on the scale of these
plots and has been omitted. The 2D templates follow the
$\cos\theta_{\mathrm{R}}$ and $\abs{y}$ binning defined in Section 0.7 but are
presented here in one dimension, with the dotted lines indicating the
different $\abs{y}$ bins. The black points and error bars represent the data
and their statistical uncertainties. The bottom panel in each figure shows the
ratio between the number of events observed in data and the best fit value.
The gray shaded region in the bottom panel shows the total uncertainty in the
best fit result.

Figure 8: The postfit distributions in the 320–510 and 510–700mass bins are
shown in the upper and lower rows, respectively. The left column is the
$\Pgm\Pgm$ channel, and the right column the $\Pe\Pe$ channel. The
contribution of the $\PGt\PGt$ background is not visible on the scale of these
plots and has been omitted. The 2D templates follow the
$\cos\theta_{\mathrm{R}}$ and $\abs{y}$ binning defined in Section 0.7 but are
presented here in one dimension, with the dotted lines indicating the
different $\abs{y}$ bins. The black points and error bars represent the data
and their statistical uncertainties. The bottom panel in each figure shows the
ratio between the number of events observed in data and the best fit value.
The gray shaded region in the bottom panel shows the total uncertainty in the
best fit result.

Figure 9: The postfit distributions in the 700–1000 and $>$1000mass bins are
shown in the upper and lower rows, respectively. The left plot is the
$\Pgm\Pgm$ channel, and the right plot the $\Pe\Pe$ channel. The contribution
of the $\PGt\PGt$ background is not visible on the scale of these plots and
has been omitted. The 2D templates follow the $\cos\theta_{\mathrm{R}}$ and
$\abs{y}$ binning defined in Section 0.7 but are presented here in one
dimension, with the dotted lines indicating the different $\abs{y}$ bins. The
black points and error bars represent the data and their statistical
uncertainties. The bottom panel in each figure shows the ratio between the
number of events observed in data and the best fit value. The gray shaded
region in the bottom panel shows the total uncertainty in the best fit result.

##  0.10 Summary

The CMS detector at the LHC has been used to measure the Drell–Yan forward-
backward asymmetry () and the angular coefficient $A_{0}$ as functions of
dilepton mass for muon and electron pairs with invariant mass above 170. The
measurement is performed using proton-proton collision data collected in
2016–2018 at $\sqrt{s}=13\TeV$ with an integrated luminosity of 138using a
template fitting approach. The combined dimuon and dielectron measurements
show good agreement with the standard model predictions across the full mass
range. An inclusive measurement across the full mass range yields an of
$0.612\pm 0.005\stat\pm 0.007\syst$ and an $A_{0}$ of $0.047\pm 0.005\stat\pm
0.013\syst$. As a test of lepton flavor universality, the difference between
the dimuon and dielectron $\AFB\mathrm{s}$ is measured and found to agree with
zero to within 2.4 standard deviations. Using the combined measurements,
limits are set on the existence of additional gauge bosons. For a boson in the
canonical sequential standard model the observed (expected) 95% confidence
level lower limit on the mass is 4.4(3.7 ).

###### 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 and other
centers 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, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT
(Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST,
STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (European
Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander
von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds
pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-
Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-
Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science –
EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology
Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports
(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under
Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and
under project number 400140256 - GRK2497; the Lendület (“Momentum”) Program
and the János Bolyai Research Scholarship of the Hungarian Academy of
Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants
123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary);
the Council of Science and Industrial Research, India; the Latvian Council of
Science; the Ministry of Science and Higher Education and the National Science
Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland);
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia);
the Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the
Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Tumasyan Institut für
Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341, J.W.
Andrejkovic, T. Bergauer0000-0002-5786-0293, S. Chatterjee0000-0003-2660-0349,
K. Damanakis, M. Dragicevic0000-0003-1967-6783, A. Escalante Del
Valle0000-0002-9702-6359, R. Frühwirth1, M. Jeitler10000-0002-5141-9560, N.
Krammer, L. Lechner0000-0002-3065-1141, D. Liko, I. Mikulec, P. Paulitsch,
F.M. Pitters, J. Schieck10000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784,
D. Schwarz, S. Templ0000-0003-3137-5692, W. Waltenberger0000-0002-6215-7228,
C.-E. Wulz10000-0001-9226-5812 Institute for Nuclear Problems, Minsk, Belarus
V. Makarenko0000-0002-8406-8605, T. Nechaeva, U. Yevarouskaya Universiteit
Antwerpen, Antwerpen, Belgium M.R. Darwish2, E.A. De Wolf, T.
Janssen0000-0002-3998-4081, T. Kello3, A. Lelek0000-0001-5862-2775, H. Rejeb
Sfar, P. Van Mechelen0000-0002-8731-9051, S. Van Putte, N. Van
Remortel0000-0003-4180-8199 Vrije Universiteit Brussel, Brussel, Belgium E.S.
Bols0000-0002-8564-8732, J. D’Hondt0000-0002-9598-6241, A. De Moor, M.
Delcourt, H. El Faham0000-0001-8894-2390, S. Lowette0000-0003-3984-9987, S.
Moortgat0000-0002-6612-3420, A. Morton0000-0002-9919-3492, D.
Müller0000-0002-1752-4527, A.R. Sahasransu0000-0003-1505-1743, S.
Tavernier0000-0002-6792-9522, W. Van Doninck, D. Vannerom0000-0002-2747-5095
Université Libre de Bruxelles, Bruxelles, Belgium D. Beghin, B.
Bilin0000-0003-1439-7128, B. Clerbaux0000-0001-8547-8211, G. De Lentdecker, L.
Favart0000-0003-1645-7454, A.K. Kalsi0000-0002-6215-0894, K. Lee, M.
Mahdavikhorrami, I. Makarenko0000-0002-8553-4508, L.
Moureaux0000-0002-2310-9266, S. Paredes0000-0001-8487-9603, L. Pétré, A.
Popov0000-0002-1207-0984, N. Postiau, E. Starling0000-0002-4399-7213, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496 Ghent University,
Ghent, Belgium T. Cornelis0000-0001-9502-5363, D. Dobur, J.
Knolle0000-0002-4781-5704, L. Lambrecht, G. Mestdach, M.
Niedziela0000-0001-5745-2567, C. Rendón, C. Roskas, A. Samalan, K.
Skovpen0000-0002-1160-0621, M. Tytgat0000-0002-3990-2074, B. Vermassen, L.
Wezenbeek Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke, A. Bethani0000-0002-8150-7043, G. Bruno, F. Bury0000-0002-3077-2090,
C. Caputo0000-0001-7522-4808, P. David0000-0001-9260-9371, C.
Delaere0000-0001-8707-6021, I.S. Donertas0000-0001-7485-412X, A.
Giammanco0000-0001-9640-8294, K. Jaffel, Sa. Jain0000-0001-5078-3689, V.
Lemaitre, K. Mondal0000-0001-5967-1245, J. Prisciandaro, A. Taliercio, M.
Teklishyn0000-0002-8506-9714, T.T. Tran, P. Vischia0000-0002-7088-8557, S.
Wertz0000-0002-8645-3670 Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil G.A. Alves0000-0002-8369-1446, C. Hensel, A.
Moraes0000-0002-5157-5686, P. Rebello Teles0000-0001-9029-8506 Universidade do
Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, M. Alves Gallo Pereira0000-0003-4296-7028, M.
Barroso Ferreira Filho, H. Brandao Malbouisson, W.
Carvalho0000-0003-0738-6615, J. Chinellato4, E.M. Da Costa0000-0002-5016-6434,
G.G. Da Silveira50000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680,
V. Dos Santos Sousa, S. Fonseca De Souza0000-0001-7830-0837, C. Mora
Herrera0000-0003-3915-3170, K. Mota Amarilo, L. Mundim0000-0001-9964-7805, H.
Nogima, A. Santoro, S.M. Silva Do Amaral0000-0002-0209-9687, A.
Sznajder0000-0001-6998-1108, M. Thiel, F. Torres Da Silva De
Araujo60000-0002-4785-3057, A. Vilela Pereira0000-0003-3177-4626 Universidade
Estadual Paulista (a), Universidade Federal do ABC (b), São Paulo, Brazil C.A.
Bernardes50000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
D.S. Lemos0000-0003-1982-8978, P.G. Mercadante0000-0001-8333-4302, S.F.
Novaes0000-0003-0471-8549, Sandra S. Padula0000-0003-3071-0559 Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria A. Aleksandrov, G. Antchev0000-0003-3210-5037, R. Hadjiiska, P.
Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia,
Sofia, Bulgaria A. Dimitrov, T. Ivanov, L. Litov0000-0002-8511-6883, B.
Pavlov, P. Petkov, A. Petrov Beihang University, Beijing, China T.
Cheng0000-0003-2954-9315, T. Javaid7, M. Mittal, L. Yuan Department of
Physics, Tsinghua University, Beijing, China M. Ahmad0000-0001-9933-995X, G.
Bauer, C. Dozen80000-0002-4301-634X, Z. Hu0000-0001-8209-4343, J.
Martins90000-0002-2120-2782, Y. Wang, K. Yi10,11 Institute of High Energy
Physics, Beijing, China E. Chapon0000-0001-6968-9828, G.M.
Chen70000-0002-2629-5420, H.S. Chen70000-0001-8672-8227, M.
Chen0000-0003-0489-9669, F. Iemmi, A. Kapoor0000-0002-1844-1504, D. Leggat, H.
Liao, Z.-A. Liu70000-0002-2896-1386, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, R. Sharma0000-0003-1181-1426, J.
Tao0000-0003-2006-3490, J. Thomas-Wilsker, J. Wang0000-0002-4963-0877, H.
Zhang0000-0001-8843-5209, J. Zhao0000-0001-8365-7726 State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China A. Agapitos,
Y. An, Y. Ban, C. Chen, A. Levin0000-0001-9565-4186, Q. Li0000-0002-8290-0517,
X. Lyu, Y. Mao, S.J. Qian, D. Wang0000-0002-9013-1199, J. Xiao, H. Yang Sun
Yat-Sen University, Guangzhou, China M. Lu, Z. You0000-0001-8324-3291
Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam
Application (MOE) - Fudan University, Shanghai, China X. Gao3, H.
Okawa0000-0002-2548-6567, Y. Zhang0000-0002-4554-2554 Zhejiang University,
Hangzhou, China, Zhejiang, China Z. Lin0000-0003-1812-3474, M.
Xiao0000-0001-9628-9336 Universidad de Los Andes, Bogota, Colombia C.
Avila0000-0002-5610-2693, A. Cabrera0000-0002-0486-6296, C.
Florez0000-0002-3222-0249, J. Fraga Universidad de Antioquia, Medellin,
Colombia J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez0000-0002-3306-0363
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia D. Giljanovic, N.
Godinovic0000-0002-4674-9450, D. Lelas0000-0002-8269-5760, I.
Puljak0000-0001-7387-3812 University of Split, Faculty of Science, Split,
Croatia Z. Antunovic, M. Kovac, T. Sculac0000-0002-9578-4105 Institute Rudjer
Boskovic, Zagreb, Croatia V. Brigljevic0000-0001-5847-0062, D.
Ferencek0000-0001-9116-1202, D. Majumder0000-0002-7578-0027, M. Roguljic, A.
Starodumov120000-0001-9570-9255, T. Susa0000-0001-7430-2552 University of
Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K. Christoforou, A.
Ioannou, G. Kole0000-0002-3285-1497, M. Kolosova, S. Konstantinou, J.
Mousa0000-0002-2978-2718, C. Nicolaou, F. Ptochos0000-0002-3432-3452, P.A.
Razis, H. Rykaczewski, H. Saka0000-0001-7616-2573 Charles University, Prague,
Czech Republic M. Finger13, M. Finger Jr.130000-0003-3155-2484, A. Kveton
Escuela Politecnica Nacional, Quito, Ecuador E. Ayala Universidad San
Francisco de Quito, Quito, Ecuador E. Carrera Jarrin0000-0002-0857-8507
Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt A.A.
Abdelalim14,150000-0002-2056-7894, Y. Assran16,17 Center for High Energy
Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt M.A.
Mahmoud0000-0001-8692-5458, Y. Mohammed0000-0001-8399-3017 National Institute
of Chemical Physics and Biophysics, Tallinn, Estonia S.
Bhowmik0000-0003-1260-973X, R.K. Dewanjee0000-0001-6645-6244, K. Ehataht, M.
Kadastik, S. Nandan, C. Nielsen, J. Pata, M. Raidal0000-0001-7040-9491, L.
Tani, C. Veelken Department of Physics, University of Helsinki, Helsinki,
Finland P. Eerola0000-0002-3244-0591, H. Kirschenmann0000-0001-7369-2536, K.
Osterberg0000-0003-4807-0414, M. Voutilainen0000-0002-5200-6477 Helsinki
Institute of Physics, Helsinki, Finland S. Bharthuar, E.
Brücken0000-0001-6066-8756, F. Garcia0000-0002-4023-7964, J.
Havukainen0000-0003-2898-6900, M.S. Kim0000-0003-0392-8691, R. Kinnunen, T.
Lampén, K. Lassila-Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén, M. Lotti, L. Martikainen, M. Myllymäki, J. Ott0000-0001-9337-5722, H.
Siikonen, E. Tuominen0000-0002-7073-7767, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland P. Luukka0000-0003-2340-4641,
H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola0000-0002-4359-836X, M. Besancon, F. Couderc0000-0003-2040-4099, M.
Dejardin, D. Denegri, J.L. Faure, F. Ferri0000-0002-9860-101X, S. Ganjour, P.
Gras, G. Hamel de Monchenault0000-0002-3872-3592, P. Jarry, B.
Lenzi0000-0002-1024-4004, E. Locci, J. Malcles, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro18, M. Titov0000-0002-1119-6614, G.B. Yu0000-0001-7435-2963 Laboratoire
Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de
Paris, Palaiseau, France S. Ahuja0000-0003-4368-9285, F.
Beaudette0000-0002-1194-8556, M. Bonanomi0000-0003-3629-6264, A. Buchot
Perraguin, P. Busson, A. Cappati, C. Charlot, O. Davignon, B. Diab, G.
Falmagne0000-0002-6762-3937, S. Ghosh, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi, I. Kucher0000-0001-7561-5040, J.
Motta, M. Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, P.
Paganini0000-0001-9580-683X, J. Rembser, R. Salerno0000-0003-3735-2707, U.
Sarkar0000-0002-9892-4601, J.B. Sauvan0000-0001-5187-3571, Y.
Sirois0000-0001-5381-4807, A. Tarabini, A. Zabi, A. Zghiche0000-0002-1178-1450
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram190000-0001-7476-0158, J. Andrea, D. Apparu, D. Bloch0000-0002-4535-5273,
G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard0000-0002-5230-8387, D.
Darej, J.-C. Fontaine19, U. Goerlach, C. Grimault, A.-C. Le Bihan, E.
Nibigira0000-0001-5821-291X, P. Van Hove0000-0002-2431-3381 Institut de
Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France E.
Asilar0000-0001-5680-599X, S. Beauceron0000-0002-8036-9267, C.
Bernet0000-0002-9923-8734, G. Boudoul, C. Camen, A. Carle, N.
Chanon0000-0002-2939-5646, D. Contardo, P. Depasse0000-0001-7556-2743, H. El
Mamouni, J. Fay, S. Gascon0000-0002-7204-1624, M.
Gouzevitch0000-0002-5524-880X, B. Ille, I.B. Laktineh, H.
Lattaud0000-0002-8402-3263, A. Lesauvage0000-0003-3437-7845, M.
Lethuillier0000-0001-6185-2045, L. Mirabito, S. Perries, K. Shchablo, V.
Sordini0000-0003-0885-824X, L. Torterotot0000-0002-5349-9242, G. Touquet, M.
Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia I.
Lomidze, T. Toriashvili20, Z. Tsamalaidze13 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany V. Botta, L. Feld0000-0001-9813-8646,
K. Klein, M. Lipinski, D. Meuser, A. Pauls, N. Röwert, J. Schulz, M.
Teroerde0000-0002-5892-1377 RWTH Aachen University, III. Physikalisches
Institut A, Aachen, Germany A. Dodonova, D. Eliseev, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B. Fischer, T.
Hebbeker0000-0002-9736-266X, K. Hoepfner, F. Ivone, L. Mastrolorenzo, M.
Merschmeyer0000-0003-2081-7141, A. Meyer0000-0001-9598-6623, G. Mocellin, S.
Mondal, S. Mukherjee0000-0001-6341-9982, D. Noll0000-0002-0176-2360, A. Novak,
A. Pozdnyakov0000-0003-3478-9081, Y. Rath, H. Reithler, A.
Schmidt0000-0003-2711-8984, S.C. Schuler, A. Sharma0000-0002-5295-1460, L.
Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany C. Dziwok, G. Flügge, W. Haj
Ahmad210000-0003-1491-0446, O. Hlushchenko, T. Kress, A.
Nowack0000-0002-3522-5926, O. Pooth, D. Roy0000-0002-8659-7762, A.
Stahl220000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A. Zotz Deutsches
Elektronen-Synchrotron, Hamburg, Germany H. Aarup Petersen, M. Aldaya Martin,
P. Asmuss, S. Baxter, M. Bayatmakou, O. Behnke, A. Bermúdez Martínez, S.
Bhattacharya, A.A. Bin Anuar0000-0002-2988-9830, F.
Blekman0000-0002-7366-7098, K. Borras23, D. Brunner, A.
Campbell0000-0003-4439-5748, A. Cardini0000-0003-1803-0999, C. Cheng, F.
Colombina, S. Consuegra Rodríguez0000-0002-1383-1837, G. Correia Silva, V.
Danilov, M. De Silva, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez
Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E. Gallo24, A.
Geiser, A. Giraldi, A. Grohsjean0000-0003-0748-8494, M. Guthoff, A.
Jafari250000-0001-7327-1870, N.Z. Jomhari0000-0001-9127-7408, H.
Jung0000-0002-2964-9845, A. Kasem230000-0002-6753-7254, M.
Kasemann0000-0002-0429-2448, H. Kaveh0000-0002-3273-5859, C.
Kleinwort0000-0002-9017-9504, R. Kogler0000-0002-5336-4399, D.
Krücker0000-0003-1610-8844, W. Lange, K. Lipka, W. Lohmann26, R. Mankel, I.-A.
Melzer-Pellmann0000-0001-7707-919X, M. Mendizabal Morentin, J. Metwally, A.B.
Meyer0000-0001-8532-2356, M. Meyer0000-0003-2436-8195, J.
Mnich0000-0001-7242-8426, A. Mussgiller, A. Nürnberg, Y. Otarid, D. Pérez
Adán0000-0003-3416-0726, D. Pitzl, A. Raspereza, B. Ribeiro Lopes, J.
Rübenach, A. Saggio0000-0002-7385-3317, A. Saibel0000-0002-9932-7622, M.
Savitskyi0000-0002-9952-9267, M. Scham27, V. Scheurer, S. Schnake, P. Schütze,
C. Schwanenberger240000-0001-6699-6662, M. Shchedrolosiev, R.E. Sosa
Ricardo0000-0002-2240-6699, D. Stafford, N. Tonon0000-0003-4301-2688, M. Van
De Klundert0000-0001-8596-2812, F. Vazzoler0000-0001-8111-9318, R.
Walsh0000-0002-3872-4114, D. Walter, Q. Wang0000-0003-1014-8677, Y.
Wen0000-0002-8724-9604, K. Wichmann, L. Wiens, C. Wissing, S.
Wuchterl0000-0001-9955-9258 University of Hamburg, Hamburg, Germany R.
Aggleton, S. Albrecht0000-0002-5960-6803, S. Bein0000-0001-9387-7407, L.
Benato0000-0001-5135-7489, P. Connor0000-0003-2500-1061, K. De
Leo0000-0002-8908-409X, M. Eich, K. El Morabit, F. Feindt, A. Fröhlich, C.
Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, P. Gunnellini, M.
Hajheidari, J. Haller0000-0001-9347-7657, A. Hinzmann0000-0002-2633-4696, G.
Kasieczka, R. Klanner0000-0002-7004-9227, T. Kramer, V. Kutzner, J.
Lange0000-0001-7513-6330, T. Lange0000-0001-6242-7331, A.
Lobanov0000-0002-5376-0877, A. Malara0000-0001-8645-9282, A.
Mehta0000-0002-0433-4484, A. Nigamova, K.J. Pena Rodriguez, M.
Rieger0000-0003-0797-2606, O. Rieger, P. Schleper, M.
Schröder0000-0001-8058-9828, J. Schwandt0000-0002-0052-597X, J.
Sonneveld0000-0001-8362-4414, H. Stadie, G. Steinbrück, A. Tews, I.
Zoi0000-0002-5738-9446 Karlsruher Institut fuer Technologie, Karlsruhe,
Germany J. Bechtel0000-0001-5245-7318, S. Brommer, M. Burkart, E.
Butz0000-0002-2403-5801, R. Caspart0000-0002-5502-9412, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels, J.O. Gosewisch, A. Gottmann, F.
Hartmann220000-0001-8989-8387, C. Heidecker, U. Husemann0000-0002-6198-8388,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra0000-0002-3060-2278,
Th. Müller, M. Neukum, G. Quast0000-0002-4021-4260, K.
Rabbertz0000-0001-7040-9846, J. Rauser, D. Savoiu0000-0001-6794-7475, M.
Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich0000-0002-2535-402X, J.
Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber0000-0002-3639-2267, S.
Wieland, R. Wolf0000-0001-9456-383X, S. Wozniewski, S. Wunsch Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, A. Kyriakis, D. Loukas, A.
Stakia0000-0001-6277-7171 National and Kapodistrian University of Athens,
Athens, Greece M. Diamantopoulou, D. Karasavvas, P.
Kontaxakis0000-0002-4860-5979, C.K. Koraka, A. Manousakis-Katsikakis, A.
Panagiotou, I. Papavergou, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K. Vellidis,
E. Vourliotis National Technical University of Athens, Athens, Greece G.
Bakas, K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos, G. Tsipolitis, A.
Zacharopoulou University of Ioánnina, Ioánnina, Greece K. Adamidis, I.
Bestintzanos, I. Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios, P.
Katsoulis, P. Kokkas, N. Manthos, I. Papadopoulos0000-0002-9937-3063, J.
Strologas0000-0002-2225-7160 MTA-ELTE Lendület CMS Particle and Nuclear
Physics Group, Eötvös Loránd University, Budapest, Hungary M.
Csanad0000-0002-3154-6925, K. Farkas, M.M.A. Gadallah280000-0002-8305-6661, S.
Lökös290000-0002-4447-4836, P. Major, K. Mandal0000-0002-3966-7182, G.
Pasztor0000-0003-0707-9762, A.J. Rádl, O. Surányi, G.I.
Veres0000-0002-5440-4356 Wigner Research Centre for Physics, Budapest, Hungary
M. Bartók300000-0002-4440-2701, G. Bencze, C. Hajdu0000-0002-7193-800X, D.
Horvath31,320000-0003-0091-477X, F. Sikler0000-0001-9608-3901, V.
Veszpremi0000-0001-9783-0315 Institute of Nuclear Research ATOMKI, Debrecen,
Hungary S. Czellar, D. Fasanella0000-0002-2926-2691, F.
Fienga0000-0001-5978-4952, J. Karancsi300000-0003-0802-7665, J. Molnar, Z.
Szillasi, D. Teyssier Institute of Physics, University of Debrecen, Debrecen,
Hungary P. Raics, Z.L. Trocsanyi330000-0002-2129-1279, B. Ujvari34 Karoly
Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo350000-0002-9110-9663, F. Nemes35, T. Novak National Institute of
Science Education and Research, HBNI, Bhubaneswar, India S.
Bahinipati360000-0002-3744-5332, C. Kar0000-0002-6407-6974, P. Mal, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair Bindhu37, A.
Nayak370000-0002-7716-4981, P. Saha, N. Sur0000-0001-5233-553X, S.K. Swain, D.
Vats37 Panjab University, Chandigarh, India S. Bansal0000-0003-1992-0336, S.B.
Beri, V. Bhatnagar0000-0002-8392-9610, G. Chaudhary0000-0003-0168-3336, S.
Chauhan0000-0001-6974-4129, N. Dhingra380000-0002-7200-6204, R. Gupta, A.
Kaur, H. Kaur, M. Kaur0000-0002-3440-2767, P. Kumari0000-0002-6623-8586, M.
Meena, K. Sandeep0000-0002-3220-3668, J.B. Singh390000-0001-9029-2462, A.K.
Virdi0000-0002-0866-8932 University of Delhi, Delhi, India A. Ahmed, A.
Bhardwaj0000-0002-7544-3258, B.C. Choudhary0000-0001-5029-1887, M. Gola, S.
Keshri0000-0003-3280-2350, A. Kumar0000-0003-3407-4094, M.
Naimuddin0000-0003-4542-386X, P. Priyanka0000-0002-0933-685X, K. Ranjan, A.
Shah0000-0002-6157-2016 Saha Institute of Nuclear Physics, HBNI, Kolkata,
India M. Bharti40, R. Bhattacharya, S. Bhattacharya0000-0002-8110-4957, D.
Bhowmik, S. Dutta, S. Dutta, B. Gomber410000-0002-4446-0258, M. Maity42, P.
Palit0000-0002-1948-029X, P.K. Rout0000-0001-8149-6180, G. Saha, B.
Sahu0000-0002-8073-5140, S. Sarkar, M. Sharan Indian Institute of Technology
Madras, Madras, India P.K. Behera0000-0002-1527-2266, S.C. Behera, P.
Kalbhor0000-0002-5892-3743, J.R. Komaragiri430000-0002-9344-6655, D. Kumar43,
A. Muhammad, L. Panwar430000-0003-2461-4907, R. Pradhan, P.R. Pujahari, A.
Sharma0000-0002-0688-923X, A.K. Sikdar, P.C. Tiwari430000-0002-3667-3843
Bhabha Atomic Research Centre, Mumbai, India K. Naskar44 Tata Institute of
Fundamental Research-A, Mumbai, India T. Aziz, S. Dugad, M. Kumar, G.B.
Mohanty0000-0001-6850-7666 Tata Institute of Fundamental Research-B, Mumbai,
India S. Banerjee0000-0002-7953-4683, R. Chudasama, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee0000-0003-3122-0594 Indian
Institute of Science Education and Research (IISER), Pune, India A. Alpana, S.
Dube0000-0002-5145-3777, B. Kansal, A. Laha, S. Pandey0000-0003-0440-6019, A.
Rastogi0000-0003-1245-6710, S. Sharma0000-0001-6886-0726 Isfahan University of
Technology, Isfahan, Iran H. Bakhshiansohi45,460000-0001-5741-3357, E.
Khazaie46, M. Zeinali47 Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran S. Chenarani48, S.M. Etesami0000-0001-6501-4137, M.
Khakzad0000-0002-2212-5715, M. Mohammadi Najafabadi0000-0001-6131-5987
University College Dublin, Dublin, Ireland M. Grunewald0000-0002-5754-0388
INFN Sezione di Bari a, Bari, Italy, Università di Bari b, Bari, Italy,
Politecnico di Bari c, Bari, Italy M. Abbresciaa,b0000-0001-8727-7544, R.
Alya,b,490000-0001-6808-1335, C. Arutaa,b, A. Colaleoa0000-0002-0711-6319, D.
Creanzaa,c0000-0001-6153-3044, N. De Filippisa,c0000-0002-0625-6811, M. De
Palmaa,b0000-0001-8240-1913, A. Di Florioa,b, A. Di
Pilatoa,b0000-0002-9233-3632, W. Elmetenaweea,b0000-0001-7069-0252, F.
Erricoa,b0000-0001-8199-370X, L. Fiorea0000-0002-9470-1320, A.
Gelmia,b0000-0002-9211-2709, G. Iasellia,c0000-0003-2546-5341, M.
Incea,b0000-0001-6907-0195, S. Lezkia,b0000-0002-6909-774X, G.
Maggia,c0000-0001-5391-7689, M. Maggia0000-0002-8431-3922, I. Margjekaa,b, V.
Mastrapasquaa,b0000-0002-9082-5924, S. Mya,b0000-0002-9938-2680, S.
Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, D. Ramosa,
A. Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, Ü.
Sözbilira, R. Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631
INFN Sezione di Bologna a, Bologna, Italy, Università di Bologna b, Bologna,
Italy G. Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia, L. Brigliadoria, R.
Campaninia,b0000-0002-2744-0597, P. Capiluppia,b0000-0003-4485-1897, A.
Castroa,b0000-0003-2527-0456, F.R. Cavalloa0000-0002-0326-7515, C.
Cioccaa0000-0003-0080-6373, M. Cuffiania,b0000-0003-2510-5039, G.M.
Dallavallea0000-0002-8614-0420, T. Diotalevia,b0000-0003-0780-8785, F.
Fabbria0000-0002-8446-9660, A. Fanfania,b0000-0003-2256-4117, P.
Giacomellia0000-0002-6368-7220, L. Giommia,b0000-0003-3539-4313, C.
Grandia0000-0001-5998-3070, L. Guiduccia,b, S. Lo Meoa,50, L. Lunertia,b, S.
Marcellinia0000-0002-1233-8100, G. Masettia0000-0002-6377-800X, F.L.
Navarriaa,b0000-0001-7961-4889, A. Perrottaa0000-0002-7996-7139, F.
Primaveraa,b0000-0001-6253-8656, A.M. Rossia,b0000-0002-5973-1305, T.
Rovellia,b0000-0002-9746-4842, G.P. Sirolia,b0000-0002-3528-4125 INFN Sezione
di Catania a, Catania, Italy, Università di Catania b, Catania, Italy S.
Albergoa,b,510000-0001-7901-4189, S. Costaa,b,510000-0001-9919-0569, A. Di
Mattiaa0000-0002-9964-015X, R. Potenzaa,b, A.
Tricomia,b,510000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153 INFN Sezione
di Firenze a, Firenze, Italy, Università di Firenze b, Firenze, Italy G.
Barbaglia0000-0002-1738-8676, A. Cassesea0000-0003-3010-4516, R.
Ceccarellia,b, V. Ciullia,b0000-0003-1947-3396, C.
Civininia0000-0002-4952-3799, R. D’Alessandroa,b0000-0001-7997-0306, E.
Focardia,b0000-0002-3763-5267, G. Latinoa,b0000-0002-4098-3502, P.
Lenzia,b0000-0002-6927-8807, M. Lizzoa,b, M. Meschinia0000-0002-9161-3990, S.
Paolettia0000-0003-3592-9509, R. Seiditaa,b, G. Sguazzonia0000-0002-0791-3350,
L. Viliania0000-0002-1909-6343 INFN Laboratori Nazionali di Frascati,
Frascati, Italy L. Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124,
D. Piccolo0000-0001-5404-543X INFN Sezione di Genova a, Genova, Italy,
Università di Genova b, Genova, Italy M. Bozzoa,b0000-0002-1715-0457, F.
Ferroa0000-0002-7663-0805, R. Mulargiaa, E. Robuttia0000-0001-9038-4500, S.
Tosia,b0000-0002-7275-9193 INFN Sezione di Milano-Bicocca a, Milano, Italy,
Università di Milano-Bicocca b, Milano, Italy A. Benagliaa0000-0003-1124-8450,
G. Boldrini0000-0001-5490-605X, F. Brivioa,b, F. Cetorellia,b, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, A.
Ghezzia,b0000-0002-8184-7953, P. Govonia,b0000-0002-0227-1301, L.
Guzzia,b0000-0002-3086-8260, M.T. Lucchinia,b0000-0002-7497-7450, M.
Malbertia, S. Malvezzia0000-0002-0218-4910, A. Massironia0000-0002-0782-0883,
D. Menascea0000-0002-9918-1686, L. Moronia0000-0002-8387-762X, M.
Paganonia,b0000-0003-2461-275X, D. Pedrinia0000-0003-2414-4175, B.S. Pinolini,
S. Ragazzia,b0000-0001-8219-2074, N. Redaellia0000-0002-0098-2716, T.
Tabarelli de Fatisa,b0000-0001-6262-4685, D. Valsecchia,b,22, D.
Zuoloa,b0000-0003-3072-1020 INFN Sezione di Napoli a, Napoli, Italy,
Università di Napoli ’Federico II’ b, Napoli, Italy, Università della
Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy S.
Buontempoa0000-0001-9526-556X, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, A. De Iorioa,b0000-0002-9258-1345, F.
Fabozzia,c0000-0001-9821-4151, A.O.M. Iorioa,b0000-0002-3798-1135, L.
Listaa,b,520000-0001-6471-5492, S. Meolaa,d,220000-0002-8233-7277, P.
Paoluccia,220000-0002-8773-4781, B. Rossia0000-0002-0807-8772, C.
Sciaccaa,b0000-0002-8412-4072 INFN Sezione di Padova a, Padova, Italy,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia0000-0002-3129-828X, N. Bacchettaa0000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, R. Carlina,b0000-0001-7915-1650, P.
Checchiaa0000-0002-8312-1531, T. Dorigoa0000-0002-1659-8727, U.
Dossellia0000-0001-8086-2863, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, G. Grosso, L. Layera,53, E.
Lusiani0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, A.T.
Meneguzzoa,b0000-0002-5861-8140, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, H. Yarara,b, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679 INFN Sezione di Pavia a, Pavia, Italy,
Università di Pavia b, Pavia, Italy C. Aimèa,b, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b, S.P. Rattia,b, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778 INFN Sezione di Perugia a, Perugia, Italy,
Università di Perugia b, Perugia, Italy P. Asenova,540000-0003-2379-9903, G.M.
Bileia0000-0002-4159-9123, D. Ciangottinia,b0000-0002-0843-4108, L.
Fanòa,b0000-0002-9007-629X, M. Magherinib, G. Mantovania,b, V. Mariania,b, M.
Menichellia0000-0002-9004-735X, F. Moscatellia,540000-0002-7676-3106, A.
Piccinellia,b0000-0003-0386-0527, M. Presillaa,b0000-0003-2808-7315, A.
Rossia,b0000-0002-2031-2955, A. Santocchiaa,b0000-0002-9770-2249, D.
Spigaa0000-0002-2991-6384, T. Tedeschia,b0000-0002-7125-2905 INFN Sezione di
Pisa a, Pisa, Italy, Università di Pisa b, Pisa, Italy, Scuola Normale
Superiore di Pisa c, Pisa, Italy, Università di Siena d, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, V.
Bertacchia,c0000-0001-9971-1176, L. Bianchinia0000-0002-6598-6865, T.
Boccalia0000-0002-9930-9299, E. Bossinia,b0000-0002-2303-2588, R.
Castaldia0000-0003-0146-845X, M.A. Cioccia,b0000-0003-0002-5462, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, M.R. Di
Domenicoa,d0000-0002-7138-7017, S. Donatoa0000-0001-7646-4977, A.
Giassia0000-0001-9428-2296, F. Ligabuea,c0000-0002-1549-7107, E.
Mancaa,c0000-0001-8946-655X, G. Mandorlia,c0000-0002-5183-9020, D. Matos
Figueiredo, A. Messineoa,b0000-0001-7551-5613, M. Musicha, F.
Pallaa0000-0002-6361-438X, S. Paroliaa,b, G. Ramirez-Sancheza,c, A.
Rizzia,b0000-0002-4543-2718, G. Rolandia,c0000-0002-0635-274X, S. Roy
Chowdhurya,c, A. Scribanoa, N. Shafieia,b0000-0002-8243-371X, P.
Spagnoloa0000-0001-7962-5203, R. Tenchinia0000-0003-2574-4383, G.
Tonellia,b0000-0003-2606-9156, N. Turinia,d0000-0002-9395-5230, A.
Venturia0000-0002-0249-4142, P.G. Verdinia0000-0002-0042-9507 INFN Sezione di
Roma a, Rome, Italy, Sapienza Università di Roma b, Rome, Italy P.
Barriaa0000-0002-3924-7380, M. Campanaa,b, F. Cavallaria0000-0002-1061-3877,
D. Del Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, E. Longoa,b0000-0001-6238-6787, P.
Meridiania0000-0002-8480-2259, G. Organtinia,b0000-0002-3229-0781, F.
Pandolfia, R. Paramattia,b0000-0002-0080-9550, C. Quarantaa,b, S.
Rahatloua,b0000-0001-9794-3360, C. Rovellia0000-0003-2173-7530, F.
Santanastasioa,b0000-0003-2505-8359, L. Soffia0000-0003-2532-9876, R.
Tramontanoa,b INFN Sezione di Torino a, Torino, Italy, Università di Torino b,
Torino, Italy, Università del Piemonte Orientale c, Novara, Italy N.
Amapanea,b0000-0001-9449-2509, R. Arcidiaconoa,c0000-0001-5904-142X, S.
Argiroa,b0000-0003-2150-3750, M. Arneodoa,c0000-0002-7790-7132, N.
Bartosika0000-0002-7196-2237, R. Bellana,b0000-0002-2539-2376, A.
Belloraa,b0000-0002-2753-5473, J. Berenguer Antequeraa,b0000-0003-3153-0891,
C. Biinoa0000-0002-1397-7246, N. Cartigliaa0000-0002-0548-9189, M.
Costaa,b0000-0003-0156-0790, R. Covarellia,b0000-0003-1216-5235, N.
Demariaa0000-0003-0743-9465, B. Kiania,b0000-0001-6431-5464, F.
Leggera0000-0003-1400-0709, C. Mariottia0000-0002-6864-3294, S.
Masellia0000-0001-9871-7859, E. Migliorea,b0000-0002-2271-5192, E.
Monteila,b0000-0002-2350-213X, M. Montenoa0000-0002-3521-6333, M.M.
Obertinoa,b0000-0002-8781-8192, G. Ortonaa0000-0001-8411-2971, L.
Pachera,b0000-0003-1288-4838, N. Pastronea0000-0001-7291-1979, M.
Pelliccionia0000-0003-4728-6678, M. Ruspaa,c0000-0002-7655-3475, K.
Shchelinaa0000-0003-3742-0693, F. Sivieroa,b0000-0002-4427-4076, V.
Solaa0000-0001-6288-951X, A. Solanoa,b0000-0002-2971-8214, D.
Soldia,b0000-0001-9059-4831, A. Staianoa0000-0003-1803-624X, M. Tornagoa,b, D.
Trocinoa0000-0002-2830-5872, A. Vagnerinia,b INFN Sezione di Trieste a,
Trieste, Italy, Università di Trieste b, Trieste, Italy S.
Belfortea0000-0001-8443-4460, V. Candelisea,b0000-0002-3641-5983, M.
Casarsaa0000-0002-1353-8964, F. Cossuttia0000-0001-5672-214X, A. Da
Rolda,b0000-0003-0342-7977, G. Della Riccaa,b0000-0003-2831-6982, G.
Sorrentinoa,b Kyungpook National University, Daegu, Korea S.
Dogra0000-0002-0812-0758, C. Huh0000-0002-8513-2824, B. Kim, D.H.
Kim0000-0002-9023-6847, G.N. Kim0000-0002-3482-9082, J. Kim, J. Lee, S.W.
Lee0000-0002-1028-3468, C.S. Moon0000-0001-8229-7829, Y.D.
Oh0000-0002-7219-9931, S.I. Pak, S. Sekmen0000-0003-1726-5681, Y.C. Yang
Chonnam National University, Institute for Universe and Elementary Particles,
Kwangju, Korea H. Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187 Hanyang
University, Seoul, Korea B. Francois0000-0002-2190-9059, T.J.
Kim0000-0001-8336-2434, J. Park0000-0002-4683-6669 Korea University, Seoul,
Korea S. Cho, S. Choi0000-0001-6225-9876, B. Hong0000-0002-2259-9929, K. Lee,
K.S. Lee0000-0002-3680-7039, J. Lim, J. Park, S.K. Park, J. Yoo Kyung Hee
University, Department of Physics, Seoul, Republic of Korea, Seoul, Korea J.
Goh0000-0002-1129-2083, A. Gurtu Sejong University, Seoul, Korea H.S.
Kim0000-0002-6543-9191, Y. Kim Seoul National University, Seoul, Korea J.
Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee0000-0002-1138-3700, S. Lee, B.H. Oh, M. Oh0000-0003-2618-9203, S.B. Oh, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026 University of
Seoul, Seoul, Korea W. Jang, D.Y. Kang, Y. Kang, S. Kim, B. Ko, J.S.H.
Lee0000-0002-2153-1519, Y. Lee, J.A. Merlin, I.C. Park, Y. Roh, M.S. Ryu, D.
Song, I.J. Watson0000-0003-2141-3413, S. Yang Yonsei University, Department of
Physics, Seoul, Korea S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea M.
Choi, H. Lee, Y. Lee, I. Yu0000-0003-1567-5548 College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait,
Dasman, Kuwait T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga,
Latvia K. Dreimanis0000-0003-0972-5641, V. Veckalns550000-0003-3676-9711
Vilnius University, Vilnius, Lithuania M. Ambrozas, A. Carvalho Antunes De
Oliveira0000-0003-2340-836X, A. Juodagalvis0000-0002-1501-3328, A.
Rinkevicius0000-0002-7510-255X, G. Tamulaitis0000-0002-2913-9634 National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia N. Bin
Norjoharuddeen0000-0002-8818-7476, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico J.F. Benitez0000-0002-2633-6712, A. Castaneda
Hernandez0000-0003-4766-1546, H.A. Encinas Acosta, L.G. Gallegos Maríñez, M.
León Coello, J.A. Murillo Quijada0000-0003-4933-2092, A. Sehrawat, L. Valencia
Palomo0000-0002-8736-440X Centro de Investigacion y de Estudios Avanzados del
IPN, Mexico City, Mexico G. Ayala, H. Castilla-Valdez, E. De La Cruz-
Burelo0000-0002-7469-6974, I. Heredia-De La Cruz560000-0002-8133-6467, R.
Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, R. Reyes-
Almanza0000-0002-4600-7772, A. Sánchez Hernández0000-0001-9548-0358
Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, C. Oropeza
Barrera0000-0001-9724-0016, F. Vazquez Valencia Benemerita Universidad
Autonoma de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen, C. Uribe
Estrada University of Montenegro, Podgorica, Montenegro J. Mijuskovic57, N.
Raicevic University of Auckland, Auckland, New Zealand D.
Krofcheck0000-0001-5494-7302 University of Canterbury, Christchurch, New
Zealand P.H. Butler0000-0001-9878-2140 National Centre for Physics, Quaid-I-
Azam University, Islamabad, Pakistan A. Ahmad, M.I. Asghar, A. Awais, M.I.M.
Awan, M. Gul0000-0002-5704-1896, H.R. Hoorani, W.A. Khan, M.A. Shah, M.
Shoaib0000-0001-6791-8252, M. Waqas0000-0002-3846-9483 AGH University of
Science and Technology Faculty of Computer Science, Electronics and
Telecommunications, Krakow, Poland V. Avati, L. Grzanka, M. Malawski National
Centre for Nuclear Research, Swierk, Poland H. Bialkowska, M.
Bluj0000-0003-1229-1442, B. Boimska0000-0002-4200-1541, M. Górski, M. Kazana,
M. Szleper0000-0002-1697-004X, P. Zalewski Institute of Experimental Physics,
Faculty of Physics, University of Warsaw, Warsaw, Poland K. Bunkowski, K.
Doroba, A. Kalinowski0000-0002-1280-5493, M. Konecki0000-0001-9482-4841, J.
Krolikowski0000-0002-3055-0236 Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal M. Araujo, P.
Bargassa0000-0001-8612-3332, D. Bastos, A. Boletti0000-0003-3288-7737, P.
Faccioli0000-0003-1849-6692, M. Gallinaro0000-0003-1261-2277, J.
Hollar0000-0002-8664-0134, N. Leonardo0000-0002-9746-4594, T. Niknejad, M.
Pisano, J. Seixas0000-0002-7531-0842, O. Toldaiev0000-0002-8286-8780, J.
Varela0000-0003-2613-3146 Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov0000-0003-3777-6606, V.
Karjavine, V. Korenkov0000-0002-2342-7862, A. Lanev, A. Malakhov, V.
Matveev58,59, V. Palichik, V. Perelygin, M. Savina, V. Shalaev, S. Shmatov, S.
Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B.S. Yuldashev60, A. Zarubin, I.
Zhizhin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia G. Gavrilov0000-0003-3968-0253, V. Golovtcov, Y. Ivanov, V.
Kim610000-0001-7161-2133, E. Kuznetsova62, V. Murzin, V. Oreshkin, I. Smirnov,
D. Sosnov0000-0002-7452-8380, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia Yu. Andreev0000-0002-7397-9665,
A. Dermenev, S. Gninenko0000-0001-6495-7619, N. Golubev, A.
Karneyeu0000-0001-9983-1004, D. Kirpichnikov0000-0002-7177-077X, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov0000-0001-6435-4463, A. Toropin Moscow
Institute of Physics and Technology, Moscow, Russia T. Aushev National
Research Center ’Kurchatov Institute’, Moscow, Russia V. Epshteyn, V.
Gavrilov, N. Lychkovskaya, A. Nikitenko63, V. Popov, A. Stepennov, M. Toms, E.
Vlasov0000-0002-8628-2090, A. Zhokin National Research Nuclear University
’Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia M.
Chadeeva640000-0003-1814-1218, A. Oskin, P. Parygin, E. Popova, V. Rusinov, D.
Selivanova P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M.
Azarkin, I. Dremin0000-0001-7451-247X, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia A. Belyaev, E. Boos0000-0002-0193-5073, V. Bunichev, M.
Dubinin650000-0002-7766-7175, L. Dudko0000-0002-4462-3192, V.
Klyukhin0000-0002-8577-6531, O. Kodolova0000-0003-1342-4251, I.
Lokhtin0000-0002-4457-8678, O. Lukina, S. Obraztsov, S. Petrushanko, V.
Savrin, A. Snigirev0000-0003-2952-6156 Novosibirsk State University (NSU),
Novosibirsk, Russia V. Blinov66, T. Dimova66, L. Kardapoltsev66, A. Kozyrev66,
I. Ovtin66, O. Radchenko66, Y. Skovpen660000-0002-3316-0604 Institute for High
Energy Physics of National Research Centre ‘Kurchatov Institute’, Protvino,
Russia I. Azhgirey0000-0003-0528-341X, I. Bayshev, D. Elumakhov, V. Kachanov,
D. Konstantinov0000-0001-6673-7273, P. Mandrik0000-0001-5197-046X, V. Petrov,
R. Ryutin, S. Slabospitskii0000-0001-8178-2494, A. Sobol, S.
Troshin0000-0001-5493-1773, N. Tyurin, A. Uzunian, A. Volkov National Research
Tomsk Polytechnic University, Tomsk, Russia A. Babaev, V. Okhotnikov Tomsk
State University, Tomsk, Russia V. Borshch, V. Ivanchenko0000-0002-1844-5433,
E. Tcherniaev0000-0002-3685-0635 University of Belgrade: Faculty of Physics
and VINCA Institute of Nuclear Sciences, Belgrade, Serbia P.
Adzic670000-0002-5862-7397, M. Dordevic0000-0002-8407-3236, P.
Milenovic0000-0001-7132-3550, J. Milosevic0000-0001-8486-4604 Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain M. Aguilar-Benitez, J. Alcaraz Maestre0000-0003-0914-7474, A. Álvarez
Fernández, I. Bachiller, M. Barrio Luna, Cristina F.
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Cepeda0000-0002-6076-4083, M. Cerrada, N. Colino0000-0002-3656-0259, B. De La
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Lopez0000-0001-6508-5090, J.M. Hernandez0000-0001-6436-7547, M.I.
Josa0000-0002-4985-6964, J. León Holgado0000-0002-4156-6460, D. Moran, Á.
Navarro Tobar0000-0003-3606-1780, C. Perez Dengra, A. Pérez-Calero
Yzquierdo0000-0003-3036-7965, J. Puerta Pelayo0000-0001-7390-1457, I.
Redondo0000-0003-3737-4121, L. Romero, S. Sánchez Navas, L. Urda
Gómez0000-0002-7865-5010, C. Willmott Universidad Autónoma de Madrid, Madrid,
Spain J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain B.
Alvarez Gonzalez0000-0001-7767-4810, J. Cuevas0000-0001-5080-0821, C.
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González Fernández, E. Palencia Cortezon0000-0001-8264-0287, C. Ramón Álvarez,
V. Rodríguez Bouza0000-0002-7225-7310, A. Soto Rodríguez, A. Trapote, N.
Trevisani0000-0002-5223-9342, C. Vico Villalba Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain J.A.
Brochero Cifuentes0000-0003-2093-7856, I.J. Cabrillo, A.
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Scodellaro0000-0002-4974-8330, I. Vila, J.M. Vizan Garcia0000-0002-6823-8854
University of Colombo, Colombo, Sri Lanka M.K. Jayananda, B. Kailasapathy68,
D.U.J. Sonnadara, D.D.C. Wickramarathna University of Ruhuna, Department of
Physics, Matara, Sri Lanka W.G.D. Dharmaratna0000-0002-6366-837X, K. Liyanage,
N. Perera, N. Wickramage CERN, European Organization for Nuclear Research,
Geneva, Switzerland T.K. Aarrestad0000-0002-7671-243X, D. Abbaneo, J.
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