TOP-21-011

TOP-21-011

# Measurement of the cross section of top quark-antiquark pair production in
association with a boson in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

The production of a top quark-antiquark pair in association with a boson
($\ttbar\PW$) is measured in proton-proton collisions at a center-of-mass
energy of 13. The analyzed data was recorded by the CMS experiment at the CERN
LHC and corresponds to an integrated luminosity of $138\fbinv$. Events with
two or three leptons (electrons and muons) and additional jets are selected.
In events with two leptons, a multiclass neural network is used to distinguish
between the signal and background processes. Events with three leptons are
categorized based on the number of jets and of jets originating from quark
hadronization, and the lepton charges. The inclusive $\ttbar\PW$ production
cross section in the full phase space is measured to be $868\pm 40\stat\pm
51\syst\unit{fb}$. The $\ttbar\PWp$ and $\ttbar\PWm$ cross sections are also
measured as $553\pm 30\stat\pm 30\syst$ and $343\pm 26\stat\pm
25\syst\unit{fb}$, respectively, and the corresponding ratio of the two cross
sections is found to be $1.61\pm 0.15\stat\,^{+0.07}_{-0.05}\syst$. The
measured cross sections are larger than but consistent with the standard model
predictions within two standard deviations, and represent the most precise
measurement of these cross sections to date.

##  0.1 Introduction

The production of a top quark-antiquark pair () in association with a vector
boson (and bosons and photons, referred to collectively as ) in proton-proton
($\Pp\Pp$) collisions provides an experimental probe that is sensitive to the
electroweak (EW) couplings of the top quark. These $\PQt\PAQt\PV$ processes
can receive sizable contributions from phenomena beyond the standard model
(SM) of particle physics [1, 2, 3]. The production process of in association
with a boson ($\ttbar\PW$) is different from the $\PQt\PAQt\PV$ processes with
neutral bosons ($\ttbar\PZ$ and $\ttbar\PGg$) since a boson cannot be radiated
from the final-state top quarks. The production proceeds at leading order (LO)
in quantum chromodynamics (QCD) and EW couplings from quark-antiquark initial
states. At next-to-LO (NLO), additional contributions arise from (anti)quark-
gluon initial states. These dominant production modes are illustrated with
representative Feynman diagrams in Fig. 1.

Figure 1: Representative Feynman diagrams of $\ttbar\PW$ production at LO
(upper row) and NLO (lower row).

The gluon-gluon initial states are suppressed in $\ttbar\PW$ production,
leading to a sizable difference between production rates for the $\ttbar\PWp$
and $\ttbar\PWm$ processes because of differences in the parton distribution
functions (PDFs) of the valence and sea quarks in the proton. Specifically,
for the main LO production channels $\PQu\PAQd\to\ttbar\PWp$ and
$\PQd\PAQu\to\ttbar\PWm$, the $\PQu\PAQd$ initial state has about twice the
abundance of the $\PQd\PAQu$ initial state and typically a larger longitudinal
quark momentum, resulting in a noticeable charge asymmetry [4]. Compared to
the other $\PQt\PAQt\PV$ processes, the $\ttbar\PW$ process receives large
corrections from the EW production diagrams at NLO, representing an important
challenge in the calculation of the uncertainties for this process [5].
Therefore, precise cross section measurements are crucial inputs to further
improve the calculations.

At the CERN LHC, $\ttbar\PW$ production has been measured at $\sqrt{s}=7$ [6],
8 [7, 8], and 13 [9, 10, 11]. In recent measurements of production in
association with a Higgs boson ($\ttbar\PH$) [12] and the production of four
top quarks ($\ttbar\ttbar$) [13, 14], a tension was observed between the
observed and predicted yields for the $\ttbar\PW$ process, which represents
one of the dominant backgrounds in these results. It has been shown that the
observed difference can largely be attributed to missing NLO corrections in
theoretical calculations for this process [15, 16, 17, 18, 19, 20, 21, 22].

This paper presents a measurement of the inclusive $\ttbar\PW$ production
cross section in the full phase space, based on an integrated luminosity of
$138\fbinv$ of $\Pp\Pp$ collision data collected with the CMS detector at
$\sqrt{s}=13\TeV$ between 2016 and 2018. Two decay channels of the $\ttbar\PW$
system are analyzed, with either two same-sign or three charged leptons (any
combination of electrons and muons, denoted as ). The dominant background
contributions arise from the presence of “nonprompt” leptons, which are
leptons originating from hadron decays, as well as jets or hadrons that are
misidentified as leptons. Suppression of these background events relies on
multivariate analysis (MVA) techniques, which are optimized to distinguish
between prompt leptons from the decay of bosons and nonprompt leptons.
Additional background contributions in final states with electrons arise from
the misreconstruction of the electron charge and from the production of
electrons in photon conversions. The dominant background contributions with
prompt leptons are $\ttbar\PZ$ and $\ttbar\PH$ production, and the associated
production of a and boson ($\PW\PZ$). The $\ttbar\PW$ cross section is
extracted from a profile likelihood fit to the data.

This analysis supersedes the CMS measurement presented in Ref. [10], which
used data recorded in 2016, corresponding to an integrated luminosity of 35.9.
In addition to the larger data set, the sensitivity is improved through the
use of new machine-learning-based lepton and hadron identification techniques.
A more rigorous event categorization and improved background prediction
techniques are also employed.

The paper is organized as follows: Section 0.2 gives a description of the CMS
detector. In Section 0.3, the data set and simulated samples are described. In
Section 0.4, the event reconstruction is defined. The event selection and
analysis strategy are discussed in Section 0.5, followed by a description of
the estimation of background processes in Section 0.6. The main sources of
experimental and theoretical systematic uncertainties are presented in Section
0.7. Finally, the results and a summary are given in Sections 0.8 and 0.9,
respectively. Tabulated results are provided in the HEPData record for this
analysis [23].

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. 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, can be found in Ref. [24].

Events of interest are selected using a two-tiered trigger system. The first
level (L1), 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 [25]. 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 [26].

##  0.3 Data and simulated samples

The $\Pp\Pp$ collision data used for this measurement have been recorded with
a set of triggers that require the presence of one, two, or three
reconstructed electrons or muons. The trigger thresholds depend on the data-
taking year and on the number and type of leptons required by the trigger. The
overall trigger efficiency after the analysis selection is above 80%.
Simulated event samples are used to evaluate the signal selection efficiency,
validate the background estimates with nonprompt leptons, predict the
background contributions with prompt leptons, and train MVA classifiers. Three
separate sets of simulated event samples are used, corresponding to the
conditions of the three data-taking years.

For the simulation of the $\ttbar\PW$ signal process at NLO accuracy, terms of
the order $\alpS\alpha^{3}$ and $\alpS^{3}\alpha$ are relevant [2, 5, 27, 19],
where and $\alpha$ are the strong coupling and fine-structure constants,
respectively. Events are simulated with the v2.6.0 [28] program, taking LO and
NLO terms of order $\alpha^{3}$ and $\alpS\alpha^{3}$ (“EWK terms”), as well
as $\alpS^{2}\alpha$ and $\alpS^{3}\alpha$ (“QCD terms”) into account. The
theoretical $\ttbar\PW$ cross section is $592\,^{+155}_{-97}\thy\unit{fb}$,
which is obtained at NLO in QCD and EW effects, and includes QCD corrections
at next-to-next-to-leading-logarithmic (NNLL) accuracy [16]. The uncertainty
in the prediction accounts for matrix-element (ME) scale variations and the
PDF choice. We use a cross section value of 49.1$\unit{fb}$, as evaluated with
the generator, to normalize the event sample simulated for the EWK terms in
the ME calculation, and the remainder of the full NLO+NNLL cross section
prediction for the event sample simulated for the QCD terms.

The program is also used to simulate background events at NLO accuracy for the
production of or a single top quark in association with a vector boson
($\ttbar\HepParticle{\Pell}{}{+}\HepParticle{\Pell}{}{-}$ with
$m(\Pell\Pell)>1\GeV$, referred to as “$\ttbar\PZ/\PGg^{\ast}$”, $\ttbar\PGg$,
$\PQt\PZ\PQq$, and $\PQt\PGg$), as well as of $\ttbar\ttbar$, Drell–Yan (DY),
$\PV\PV\PV$, $\PW\PZ$, and $\PV\PH$ production. Background processes for
single top quark production in association with a Higgs boson ($\PQt\PH\PQq$
and $\PQt\PH\PW$) and for $\ttbar\PV\PV$ production are simulated with at LO
accuracy. The v2 [29, 30, 31] program is used to simulate events for the
production of two ($\PW\PW$) or two bosons ($\PZ\PZ$), , and $\ttbar\PH$ at
NLO precision. The background from SM events composed uniquely of jets
produced through the strong interaction, referred to as QCD multijet events,
is simulated at LO with v8.226 (v8.230) [32] in 2016 (2017–2018), and is used
for the validation of background predictions associated with processes
involving nonprompt leptons.

All samples are processed with the parton shower (PS) simulation. In the event
simulation using , the FxFx [33] (MLM [34]) merging scheme is used for NLO
(LO) samples, in order to avoid possible double counting between the ME and PS
calculations. For the $\ttbar\PW$ signal sample, a merging scale of 42is used
to define the limit at which the PS effects are matched to the ME calculation,
and the values of the renormalization and factorization scales,
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$, are set dynamically to correspond
to the characteristic scale of the hard process, depending on the kinematic
properties of the event after the FxFx merging prescription as described in
Ref. [33]. The propagation of simulated particles through the CMS detector and
modeling of the detector response is performed using the toolkit [35]. The
simulated samples include inelastic $\Pp\Pp$ interactions in the same or
nearby bunch crossing (pileup), assuming a value of 69.2mb for the total
inelastic $\Pp\Pp$ cross section [36, 37].

In the simulation used for the 2017–2018 data, the NNPDF3.1 [38] sets are used
for the PDFs, and the underlying event is modeled with the CP5 tuning
parameters [39, 40]. The 2016 samples for most processes are simulated using
the same PDF sets and PS tune. For some background processes, however, the
2016 samples are modeled using the NNPDF3.0 [41] PDF sets, and with the
CUETP8M1 [42] or CUETP8M2T4 [43] tunes.

##  0.4 Event reconstruction

The particle-flow (PF) algorithm [44] 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 track momentum at the main interaction vertex, the
corresponding ECAL cluster energy, and the energy sum of all bremsstrahlung
photons attached to the track. The primary interaction vertex is taken to be
the vertex corresponding to the hardest scattering in the event, evaluated
using tracking information alone, as described in Section 9.4.1 of Ref. [45].
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.

Electrons are identified with an MVA discriminant [46] and are required to
have transverse momentum $\pt>10\GeV$ and $\abs{\eta}<2.5$. Electrons that are
reconstructed within the transition region between the barrel and the endcap
sections of the detector within $1.479<\abs{\eta}<1.566$, as well as those
associated with tracks with more than one missing hit in the tracking system,
are excluded from the analysis.

Muons are identified as tracks in the central tracker consistent with either a
track or several hits in the muon system, and associated with calorimeter
deposits compatible with the muon hypothesis. Reconstructed muons must have
$\pt>10\GeV$ and $\abs{\eta}<2.4$, and fulfill selection criteria on the
geometrical matching of the tracks reconstructed in the silicon tracker and
the muon system, and on the quality of the global fit [47].

The “isolation” of a lepton is defined as the sum of the of all reconstructed
PF particles inside a cone of
$\DR=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}<R_{\text{max}}$
(where $\Delta\eta$ and $\Delta\phi$ are the $\eta$ and the azimuthal angle
differences, respectively) around the direction of the lepton, excluding the
lepton itself. We use a variable cone size that depends on the lepton ,
defined as $R_{\text{max}}=10/\\!\min[\max(\pt,\,50),\,200]$ (where is given
in ). The relative isolation, , the isolation divided by the lepton , is
required to be less than 0.4. Any electron is furthermore required to be
separated from any muon by $\DR>0.05$.

The impact parameter of a lepton track with respect to the primary interaction
vertex, with components in the transverse ($d_{xy}$) and longitudinal
($d_{z}$) planes relative to the direction of colliding proton beams, must
satisfy $\abs{d_{xy}}<0.05\cm$ and $\abs{d_{z}}<0.1\cm$, respectively. The
impact parameter significance of the lepton track, defined as the absolute
value of the ratio between the impact parameter value and its uncertainty,
must be less than 8.

Two additional sets of lepton selection criteria, referred to as “loose” and
“tight” in the following, are used to suppress background events associated
with the presence of nonprompt leptons. The tight definition includes a
requirement on a dedicated lepton MVA discriminant optimized for the selection
of leptons in top quark events, which has been used in Refs. [48, 49] and is
based on the earlier developments in Refs. [50, 51]. The prompt-lepton
selection efficiency of this discriminant is evaluated in simulated event
samples to be about 95% for leptons with $\pt>25\GeV$, and 50–70% for leptons
with $10<\pt<25\GeV$, depending on $\abs{\eta}$. The loose definition includes
all tight leptons and additionally leptons that fail the lepton MVA
requirement but instead pass loose requirements on two properties of the
reconstructed jet closest to the lepton, which are also input variables to the
lepton MVA. Additionally, loose and tight electrons must not be associated
with a secondary vertex that is consistent with a photon conversion [46], must
have a charge measurement consistent among the three independent measurements
described in Ref. [52], and fulfill additional criteria based on the shape of
the electromagnetic shower and the consistency between the track and the
shower [46].

In each event, jets are clustered from the reconstructed PF particles with the
anti-algorithm [53, 54] using a distance parameter of 0.4. The jet momentum is
determined as the vector sum of the momenta of all particles in the jet, and
is found from simulation to be, on average, within 5–10% of the true momentum
over the entire spectrum and detector acceptance. Additional tracks and
calorimetric energy depositions can come from pileup, 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 [55]. Jet energy corrections are derived from
simulation studies so that the average measured energy of jets becomes
identical to that of particle-level jets. In situ measurements of the momentum
balance in dijet, photon+jet, +jet, and QCD multijet events are used to
determine any residual differences between the jet energy scale in data and
simulation, and appropriate corrections are applied [56]. Additional selection
criteria are applied to each jet to remove jets potentially dominated by
instrumental effects or reconstruction failures [55]. Jets with $\pt>25\GeV$
and $\abs{\eta}<2.4$ are considered in the analysis, and those including a
loose lepton among their PF constituents inside a cone of $\DR<0.4$ are
removed.

The DeepJet algorithm [57, 58, 59] is used to identify jets arising from the
hadronization of hadrons (-tagged jets). The “loose” and “medium” tagging
selection requirements in this analysis correspond to selection efficiencies
of about 90 and 85%, respectively, for quark jets with $\pt>30\GeV$ as
estimated in simulated events. For quark jets (light quark and gluon jets),
misidentification rates of 50 and 15% (10 and 1%) are obtained.

The missing transverse momentum vector is computed as the negative vector sum
of the of all reconstructed PF particles in an event, and its magnitude is
denoted as [60]. The value of is modified to account for corrections to the
energy scale of the reconstructed jets in the event.

##  0.5 Event selection and analysis strategy

The measurement targets the $\ttbar\PW$ signal process in which the boson
decays to a lepton (electron or muon) and a neutrino, and the final state of
the system includes either lepton+jets or dileptons. We select events
containing two or three leptons. The dilepton and trilepton channels are
analyzed separately, and combined for the final cross section measurement. To
ensure that orthogonal event samples are used for the analyses of the two
channels, only events with exactly two (three) loose leptons are considered in
the dilepton (trilepton) channel. The signal regions of each channel are then
defined by requiring that all of the loose leptons also pass the tight
selection, which have further requirements discussed in the remainder of this
section. Events with at least one loose-not-tight lepton are used in each
channel for the estimation of the nonprompt-lepton backgrounds, as discussed
in Section 0.6.

Same-sign dilepton channel: Events in the dilepton channel are required to
have exactly two tight leptons and no additional loose leptons. We require
that the two leptons have the same sign electric charge, which significantly
reduces background contributions from SM processes with two opposite-sign
prompt leptons (, and DY production). The highest (leading) lepton is required
to have $\pt>30\GeV$ if it is an electron, or $\pt>25\GeV$ if it is a muon.
The second-highest (subleading) lepton is required to have $\pt>20\GeV$,
independent of flavor. To reduce background contributions from low-mass
resonances and photon conversions, the dilepton invariant mass $m(\Pell\Pell)$
is required to be larger than 30, and the separation between the leptons must
be $\DR>0.4$. Events with two electrons and $\abs{m(\Pe\Pe)-m_{\PZ}}<15\GeV$
are removed, where $m_{\PZ}$ is the world-average boson mass [61]. The events
are also required to have $\ptmiss>30\GeV$ and at least two jets, out of which
at least two pass the loose tagging selection or at least one passes the
medium tagging selection, which reduces background contributions from DY and
$\PZ\PZ$ production.

A multiclass neural network (NN) classifier is used to distinguish between
events from the signal $\ttbar\PW$ process and those from three classes of
background processes: $\ttbar\PZ/\PGg^{\ast}$ and $\ttbar\PH$ as a combined
category, $\ttbar\PGg$, and backgrounds with nonprompt leptons. The classifier
is trained with the simulated $\ttbar\PW$, $\ttbar\PZ/\PGg^{\ast}$,
$\ttbar\PH$, and $\ttbar\PGg$ samples, and with simulated events as a source
of nonprompt-lepton background events.

The NN has two fully connected hidden layers with 128 and 64 rectified linear
units [62], respectively, and an output layer with four nodes corresponding to
the four categories defined above. The training is performed using the
TensorFlow package [63] with the keras interface [64] and the adam optimizer
[65]. The training phase aims at reducing the cross-entropy loss function
[66], and potential overtraining is mitigated using dropout [67].

The value of each output node can be interpreted as the probability for a
given event to belong to that corresponding category. While we only use the
distribution of the $\ttbar\PW$ output node in the fit to extract the
$\ttbar\PW$ cross section, the inclusion of the three separate background
classes with significantly different kinematic properties in a multiclass NN
leads to a better discriminating power between signal and background events
compared to a binary classification approach, even though only a single output
node is used in the fit. The final NN discriminant that is used in the fit is
obtained from a monotonic transformation of the $\ttbar\PW$ output node value,
which is designed to give an approximately linearly decreasing distribution of
the total expected background.

The input variables include several kinematic observables associated with the
reconstructed leptons and jets, such as , $\phi$, , and invariant masses
calculated for several combinations of leptons and jets. Further input
variables are the jet and -tagged jet multiplicities, the tagging discriminant
values of the selected jets, information about the flavor and charge of the
leptons, and . In Fig. 2, six of the input variables that provide the best
discrimination between the four classes of events are shown. Overall, a
reasonably good agreement between data and background predictions is observed,
with some disagreement in the tails of the distributions. These discrepancies
are reduced once the results from the fit are incorporated, leading to an
improved agreement between data and prediction. We have validated that the
agreement between data and simulation for all the input variables is
reasonable. Additional validations have been performed in the control regions
defined in Section 0.6.

Figure 2: Comparison of the number of observed (points) and predicted (colored
histograms) events in the same-sign dilepton signal region. The distributions
of the leading (upper left) and subleading (upper right) lepton , the leading
jet (middle left), the number of jets (middle right), the number of loose
-tagged jets (lower left), and (lower right) are displayed. The predictions
are shown before the fit to data (“Prefit”). The vertical bars on the points
represent the statistical uncertainties in the data, and the hatched bands the
systematic uncertainty in the predictions. The last bins include the overflow
contributions. In the lower panels, the ratio of the event yields in data to
the overall sum of the predictions is presented.

Trilepton channel: Events in the trilepton channel are required to have
exactly three tight leptons, with $\pt>25\GeV$ for the leading lepton and
$\pt>15\GeV$ for the other two leptons. It is required that
$m(\Pell\Pell)>12\GeV$ for each pair of leptons to remove background
contributions from low-mass resonances, and
$\abs{m(\Pell\Pell)-m_{\PZ}}>10\GeV$ for each pair of leptons of same flavor
and opposite sign to remove DY contributions. The sum of charges of the three
leptons is required to be $\pm 1$, and events must contain at least two jets,
out of which at least one passes the medium tagging selection.

The analysis strategy in the trilepton channel relies on an event
categorization based on the number of jets and medium -tagged jets, and on the
charges of the selected leptons. The number of observed and predicted events
in all twelve categories used in the fit is shown in Fig. 3. The invariant
mass of the three leptons, $m(3\Pell)$, was identified as the observable with
the best discriminating power between signal and background contributions, and
is used across all event categories as the discriminant variable.

Figure 3: Distribution of the number of medium -tagged jets per event from
data (points) and prediction (colored histograms) for events in the trilepton
signal region with different numbers of jets (j). Events with total trilepton
charge $-1$ and $+1$ are shown in the left and right halves of the plot,
respectively. The predictions are shown before the fit to data (“Prefit”). The
vertical bars on the points represent the statistical uncertainties in the
data, and the hatched bands the systematic uncertainty in the predictions. The
lower panel shows the ratio of the event yields in data to the overall sum of
the predictions.

##  0.6 Background estimation

The nonprompt-lepton background contributes to both the same-sign dilepton and
trilepton channels. Its contribution is estimated from control samples in data
using a tight-to-loose ratio method [50, 68, 12]. The lepton misidentification
rate is defined as the probability $f$ for a loose lepton to also satisfy the
tight selection. It is parameterized as a function of the lepton and
$\abs{\eta}$, and measured in a data sample enriched in QCD multijet events
(“multijet sample”). The nonprompt-lepton background contribution in the
signal channels is then estimated by applying the measured misidentification
rate as a weight to events that pass the event selection but have at least one
“loose-not-tight” lepton, , a lepton that passes the loose but fails the tight
selection (“loose-lepton sample”). The weights $w$ are defined as

$w=(-1)^{n+1}\prod_{i=1}^{n}\frac{f_{i}}{1-f_{i}},$ (1)

where the product extends over all electrons and muons that pass the loose but
fail the tight selection, and $n$ refers to the total number of such leptons.
The factor of $(-1)^{n+1}$ ensures that contributions from events with
different numbers of nonprompt leptons are properly taken into account [68].
In both the multijet and loose-lepton sample, the prompt-lepton contributions
are estimated from simulated event samples and subtracted.

Events in the multijet sample are required to have exactly one loose lepton,
at least one jet that is separated from the lepton by $\DR>0.7$, and
$\ptmiss<20\GeV$ to reduce contributions from prompt $\PW\text{+jets}$ events.
The numbers of multijet events with a loose-not-tight and a tight lepton are
individually determined for each lepton and $\abs{\eta}$ bin from maximum
likelihood fits to the transverse mass variable

$\mT^{\text{fixed}}=\sqrt{2\pt^{\text{fixed}}\,\ptmiss\,[1-\cos(\Delta\phi)]},$
(2)

where $\pt^{\text{fixed}}=35\GeV$ is a constant, and $\Delta\phi$ is the
azimuthal angle between the lepton momentum and . This $\mT^{\text{fixed}}$
definition exploits the fact that the reconstructed in QCD multijet events is
mainly due to resolution effects and is typically small, resulting in a
falling distribution of $\mT^{\text{fixed}}$. In contrast, the
$\mT^{\text{fixed}}$ distribution for $\PW\text{+jets}$ events, which is the
main source of prompt-lepton backgrounds in the QCD multijet selection, has a
broad maximum around 80. Since we use $\pt^{\text{fixed}}$ instead of the
reconstructed lepton , the value of $\mT^{\text{fixed}}$ does not depend on
the latter, allowing for a good separation of QCD multijet and
$\PW\text{+jets}$ events over the full range [12]. In each and $\abs{\eta}$
bin, the lepton misidentification rate is then measured as
$f_{i}=N_{\text{pass}}/(N_{\text{pass}}+N_{\text{fail}})$, where
$N_{\text{pass}}$ and $N_{\text{fail}}$ are the numbers of events where the
lepton is loose-not-tight or tight, respectively, as extracted from the fit
and after subtracting the fitted contributions from prompt leptons.

The nonprompt-lepton background prediction with the tight-to-loose ratio
method is validated using same-sign dilepton events from data satisfying the
full event selection but with a $\ptmiss<30\GeV$ requirement, making this
validation region orthogonal to the signal region. The comparison of the
distributions from data and prediction of four NN input variables in the
validation region, shown in Fig. 4, shows good agreement. This is also true
for the other NN input variables, demonstrating that the prediction provides a
good description of the kinematic variables used in the NN training for the
nonprompt-lepton background. This validation region is used to assign an
additional systematic uncertainty in the nonprompt-lepton background
prediction, as described in Section 0.7.

Figure 4: Comparison of the number of observed (points) and predicted (colored
histograms) events in the validation region for nonprompt leptons. The
distributions of the number of jets (upper left) and loose -tagged jets (upper
right), and of the of the leading jet (lower left) and of the leading lepton
(lower right) are displayed. The predictions are shown before the fit to data
(“Prefit”). The vertical bars on the points represent the statistical
uncertainties in the data, and the hatched bands the systematic uncertainty in
the predictions. The last bins include the overflow contributions. In the
lower panels, the ratio of the event yields in data to the overall sum of the
predictions is presented.

Background contributions arising from the mismeasured electron charge
(indicated as “Charge misID” in the figures) are relevant in the same-sign
dilepton channel. The mismeasurement of the electron charge is typically the
result of the emission of a hard bremsstrahlung photon that undergoes
asymmetric conversion. The reconstructed lepton is then typically the electron
or positron that receives most of the energy of the converted photon, and thus
has an equal probability to have either the same or opposite charge compared
to the original electron or positron [46]. The charge misidentification rate
is defined as the probability that the reconstructed charge of an electron
differs from its true value. It is parameterized as a function of the lepton
and $\abs{\eta}$, and is measured in simulated samples of DY and production
using tight electrons. The predicted number of background events is estimated
from data by applying the misidentification rates as weights to events that
pass the dilepton event selection but have two opposite-sign electrons. The
charge misidentification rate for muons was found to be negligible.

Background processes with one or more reconstructed electrons due to the
conversion of a photon are estimated from simulated event samples. In these
events, an electron or a positron typically carries most of the energy of the
converted photon, while the other lepton is of low energy and not
reconstructed. The largest source of events with electrons from photon
conversions in the signal regions is from $\ttbar\PGg$ production.

Background contributions with prompt leptons are estimated from simulated
event samples, and are normalized to the cross sections predicted from SM
calculations. The main contributions arise from $\ttbar\PH$,
$\ttbar\PZ/\PGg^{\ast}$, and diboson ($\PW\PZ$ and $\PZ\PZ$) production.
Smaller contributions, including $\PV\PV\PV$ and rare top quark
($\ttbar\PV\PV$, $\ttbar\ttbar$, $\PQt\PH\PQq$, and $\PQt\PZ\PQq$) production,
are grouped together and are indicated as “Other” in the figures.

For the trilepton channel, the predictions from simulated event samples for
the dominant processes with a boson ($\PW\PZ$, $\PZ\PZ$, and $\ttbar\PZ$) are
validated using dedicated data control regions. Events are selected for a
$\PW\PZ$ and $\ttbar\PZ$ production control region by applying the full event
selection except for the requirement on same-flavor opposite-sign lepton
pairs, where we require that there be at least one pair with
$\abs{m(\Pell\Pell)-m_{\PZ}}<10\GeV$. This ensures that the control region is
orthogonal to the trilepton signal region. The events are grouped according to
the number of jets and medium -tagged jets, and the distributions of these
quantities from data and prediction are compared in Fig. 5. The control region
is further split into four categories based on the flavors of the selected
leptons, which are included in the fit for the extraction of the $\ttbar\PW$
cross section.

Figure 5: The distributions of the number of jets in events from the control
region enriched in $\PW\PZ$ and $\ttbar\PZ$ production from data (points) and
prediction (colored histograms). The events are divided into three groups
depending on the number of medium -tagged jets in the event. The predictions
are shown before the fit to data (“Prefit”). The vertical bars on the points
represent the statistical uncertainties in the data, and the hatched bands the
systematic uncertainty in the predictions. In the lower panels, the ratio of
the event yields in data to the overall sum of the predictions is presented.

A control region enriched in $\PZ\PZ$ and $\ttbar\PZ$ events is defined by an
event selection that requires exactly four tight leptons. Ordering the leptons
in decreasing order of momentum, they are required to have $\pt>25$, 15, 15,
and 10, respectively. Additionally, the presence of at least one boson
candidate, defined as a same-flavor opposite-sign lepton pair with
$\abs{m(\Pell\Pell)-m_{\PZ}}<10\GeV$, is required. The remaining events are
then categorized into four groups: (1) events with two boson candidates and
any number of jets; (2) events with no second boson candidate and no jets; (3)
events with no second boson candidate and exactly one medium -tagged jet; and
(4) events with no second boson candidate and at least two medium -tagged
jets. Events in categories (3) and (4) may have any number of additional jets
that are not medium tagged. Events not passing any of these criteria are
removed from the control region. The distributions of the number of jets and
medium -tagged jets for this control region are shown in Fig. 6 from data and
prediction. The four groups are also included in the fit for the extraction of
the $\ttbar\PW$ cross section, in order to constrain the normalization of the
$\PZ\PZ$ and $\ttbar\PZ$ backgrounds.

Figure 6: Distribution of the number of jets j and medium -tagged jets in
events from the control region enriched in $\PZ\PZ$ and $\ttbar\PZ$ events for
data (points) and prediction (colored histograms). Events in the first bin
have two boson candidates (2) and events in the other bins have exactly one
boson candidate (1). The predictions are shown before the fit to data
(“Prefit”). The small contribution from background processes with nonprompt
leptons is estimated from simulated event samples. The vertical bars on the
points represent the statistical uncertainties in the data, and the hatched
bands the systematic uncertainty in the predictions. In the lower panels, the
ratio of the event yields in data to the overall sum of the predictions is
presented.

##  0.7 Systematic uncertainties

Multiple sources of systematic uncertainty affect the signal and background
event yields and the distributions of the discriminating observables used for
the signal extraction. These sources can be divided into two categories.
Sources that affect only the yield of the signal or background processes are
considered as rate uncertainties. Sources that also modify the shape of the
fitted distributions are referred to as shape uncertainties. The main sources
of experimental and theoretical systematic uncertainties are described below.

Integrated luminosity: The integrated luminosities for the 2016, 2017, and
2018 data-taking years have individual uncertainties between 1.2 and 2.5% [69,
70, 71], while the overall uncertainty for the 2016–2018 period is 1.6%. The
uncertainty in the integrated luminosity is a rate uncertainty and affects
both the normalization of the background contributions from simulated event
samples, as well as the extraction of the measured cross section from the
final estimate of the number of signal events.

Pileup reweighting: The distribution of the number of additional $\Pp\Pp$
interactions per event in simulation is matched to data by reweighting the
profile of the true number of interactions to the one inferred from the
instantaneous luminosity profile in data. A systematic uncertainty is
estimated by varying the assumed minimum-bias cross section of 69.2mb by
$\pm$4.6% [36, 37]. This variation is propagated throughout the analysis,
yielding a shape uncertainty that is considered fully correlated among the
data-taking years.

Trigger efficiency: The efficiency of the trigger selection is measured in
data and simulated events using independent trigger paths based on hadronic
activity or signatures. Per-event weights that depend on the lepton are
applied to the simulated samples in order to correct for discrepancies. The
impact on the final discriminants due to the trigger efficiency uncertainty is
estimated by varying these scale factors within their uncertainties, which are
determined to be 2%. This shape uncertainty is treated as uncorrelated among
data-taking years, as well as between the dilepton and trilepton channels. A
separate uncertainty is determined for each lepton flavor category in the
dilepton channel.

L1 inefficiency: During the 2016 and 2017 data-taking periods, a gradual shift
in the timing of the inputs of the ECAL L1 trigger in the region at
$\abs{\eta}>2$ caused a specific trigger inefficiency. For events containing
an electron (a jet) with larger than $\approx$50($\approx$100), in the region
$2.5<\abs{\eta}<3.0$ the efficiency loss is $\approx$10–20%, depending on ,
$\eta$, and time. Correction factors were computed from data and applied to
the acceptance evaluated by simulation, resulting in a shape uncertainty that
is correlated between 2016 and 2017. As expected in analyses that do not rely
on forward objects, the effect on the results is small (below 1%).

Lepton efficiency: The efficiency of the tight-lepton selection is measured in
data and simulation using the “tag-and-probe” method in
$\PZ\to\HepParticle{\Pell}{}{+}\HepParticle{\Pell}{}{-}$ events [47, 46]. Per-
lepton corrections are derived separately for each lepton flavor, and a shape
uncertainty is obtained and found to be at most a few percent in each bin.
This is treated as correlated between data-taking years, and uncorrelated
between the lepton flavors.

Jet energy scale and resolution: The variations of the jet momenta due to the
uncertainty in the jet energy scale and resolution are composed of 21
uncertainty sources which account for different detector regions and the year-
to-year correlations [56]. The uncertainties in the resolution and response
are accounted for by varying the jet energy scale in simulation within their
respective uncertainties and recomputing after each variation. The
uncertainties are not relevant for the trilepton channel, where is not used.
This resulting shape uncertainty is considered to be partially correlated
between data-taking years.

tagging efficiency: To match the tagging efficiency in simulation to data, the
full DeepJet discriminant shape is corrected with scale factors measured with
the “iterative fit” method [72, 57]. The uncertainty in the tagging efficiency
scale factors is considered to be fully correlated between and quark jets, and
uncorrelated for other quark flavors. It is applied as a function of and
$\abs{\eta}$ of the jets, and propagated as shape uncertainty to the fitted
distributions. These sources are considered as partially correlated between
data-taking years.

Background normalization: Several rate uncertainties are considered for the
normalization of the background processes. While the initial normalizations of
the $\ttbar\PZ$, $\PW\PZ$, and $\PZ\PZ$ background contributions are taken
from SM predictions, the inclusion of the control regions described in Section
0.6 provides strong constraints on these values. The $\PW\PZ$ and $\PZ\PZ$
events with a large number of -tagged jets are either due to misidentified
light-flavor jets, or to the inclusion of a phase space region that is not
well described in simulation. This has been studied in Ref. [68], and we apply
an additional 40 (10)% uncertainty to the predicted number of events with two
or more (fewer than two) -tagged jets, following those results. An additional
uncertainty of 30% is applied to $\PW\PZ$ and $\PZ\PZ$ events with at least
three selected jets in order to account for mismodeling effects observed in
the corresponding control region described in Section 0.6. The normalization
of the $\ttbar\PH$ background is constrained within a 20% uncertainty [12].
The normalization of the $\ttbar\PGg$ process includes an uncertainty of 8%
[73]. To account for a possible mismodeling of photon conversions in the
simulation, an additional 30% uncertainty is applied to the predicted number
of events, following the studies performed in Ref. [74]. The normalizations of
the $\PQt\PH\PQq$ and $\PQt\PH\PW$ backgrounds are each constrained within a
50% uncertainty [75]. The $\PQt\PZ\PQq$ normalization uncertainty amounts to
10%, following the latest measurement done by CMS [48]. A 50% uncertainty is
applied to the $\ttbar\PV\PV$ normalization, which reflects the current
experimental precision [76]. A 50% uncertainty is estimated for the triboson
production, as the main contribution to this background comes from the
$\PW\PW\PW$ production, studied in Ref. [77]. For other background processes
with smaller production cross sections, the yields are constrained within 50%.
Even if several of these processes have been measured to a higher precision,
the assigned uncertainty conservatively takes into account the extrapolation
of these predictions to the kinematic region important for this analysis,
which contains events with high jet and -tagged jet multiplicities.

Nonprompt-lepton background: The misidentification rates used to estimate the
nonprompt-lepton background are affected by a statistical uncertainty
associated with the multijet sample, as well as inaccuracies in the modeling
of the contamination due to prompt leptons in this sample. For each lepton
flavor, shape uncertainties are added to the prediction of the background
processes with nonprompt leptons. These account for the overall normalization,
as well as for the and $\eta$ dependence of the statistical uncertainty and
the observed mismodeling effects. These are taken as correlated among the
data-taking years. An additional systematic uncertainty of 20% is applied to
the nonprompt-background estimate that covers the differences between the
prediction and the observation in the validation region for nonprompt leptons.
This systematic uncertainty is taken as correlated among the data-taking years
and the considered final states.

Charge misidentification background: We compare data and simulated events in a
selection with exactly two tight electrons of same sign and
$\abs{m(\Pe\Pe)-m_{\PZ}}<15\GeV$. Based on this comparison, we assign a rate
uncertainty of 20% to the charge misidentification background. Since this
background is negligible for the trilepton channel, this uncertainty is not
included there.

Modeling uncertainties: Uncertainties in the modeling of the signal and
background processes may impact the acceptance and selection efficiencies, the
shape of the fitted distributions, and the predicted cross sections used for
the normalization of the simulated event samples. All modeling uncertainties
are considered as correlated among data-taking years.

Uncertainties in the simulated event yields due to missing higher-order
diagrams in the ME calculations are evaluated by varying the values of
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$. We vary the scales by a factor of
two up and down, separately for each scale as well as simultaneously by the
same factor, and evaluate the envelope of the six resulting variations to
determine the uncertainty in each bin. The resulting shape uncertainty is
evaluated for all backgrounds that are estimated from simulation. For the
signal process, the effect from these variations on the predicted cross
section is not included as a systematic uncertainty. We find that the effect
of an additional uncertainty in the relative weight of the signal samples
corresponding to the EWK and QCD terms is negligible.

For the signal process, the effect of the energy scale in initial- and final-
state radiation (ISR and FSR) in the PS simulation is taken into account as a
shape uncertainty. The effect of the color-reconnection model is evaluated
from a comparison of simulated $\ttbar\PW$ events simulated with alternative
models [78], and an uncertainty of 1% is assigned to cover the observed
differences. Comparisons to higher-order generators, which have been performed
in Refs. [20, 79] and could in principle result in signal modeling deviations
not covered by the ME and PS uncertainties described here, have not been
considered. The effect on the shape of the signal due to the PDF uncertainty
is also taken into account as a shape uncertainty, following the procedure
described in Ref. [80]. Using the Hessian NNPDF3.1 set [38], the difference
with respect to the central value is computed for each of the 100 PDF
variations, and these variations are added in quadrature. Then, the variation
is taken into account, adding the variation in quadrature.

##  0.8 Results

A binned profile likelihood fit is performed on data using the predicted
distributions for the signal and background processes [81, 82] in order to
extract the production rate of the $\ttbar\PW$ process. The fit is based on a
likelihood function $L$ built from the Poisson probabilities to obtain the
observed yields given the predicted signal and background estimates, and
includes terms for the systematic uncertainties and their correlations. In the
dilepton signal region, the NN output score in eight categories defined by
lepton flavor and charge is used. In the trilepton signal region, the
$m(3\Pell)$ distributions are fitted using twelve event categories defined by
the jet and medium -tagged jet multiplicities, as well as the charges of the
leptons, as defined in Section 0.5. The fit further includes the event yields
in the control region enriched in $\PW\PZ$ and $\ttbar\PZ$ events (in 48 bins
grouped by the number of jets and -tagged jets, and lepton flavors), and in
the control region enriched in $\PW\PZ$ and $\ttbar\PZ$ events (in four bins
grouped by the number of jets, -tagged jets, and candidates), as defined in
Section 0.6. All of the event categories are further split into the three
data-taking years.

Rate uncertainties are represented in the likelihood fit by a log-normal
probability density function, and shape uncertainties are assumed to follow a
Gaussian function. Statistical uncertainties in the yields per bin are taken
into account through a single nuisance parameter for all processes [83, 84].

A comparison between the total number of observed and predicted events
obtained before the fit in the dilepton and trilepton channels is shown in
Table 0.8. Good agreement between data and the total signal plus background
prediction within the systematic uncertainties is observed for each channel.

Number of predicted and observed events in the dilepton and trilepton signal
regions before the fit to the data. The uncertainties in the predicted number
of events include both the statistical and systematic components. The
uncertainties in the total number of predicted background and background plus
signal events in each channel are also given. The symbol “” indicates that the
corresponding background does not apply.  Process
$\HepParticle{\Pell}{}{+}\HepParticle{\Pell}{}{+}$
$\HepParticle{\Pell}{}{-}\HepParticle{\Pell}{}{-}$
$\HepParticle{\Pell}{}{\pm}\HepParticle{\Pell}{}{\mp}\HepParticle{\Pell}{}{+}$
$\HepParticle{\Pell}{}{\pm}\HepParticle{\Pell}{}{\mp}\HepParticle{\Pell}{}{-}$
$\ttbar\PW$ $677\pm 21$ $355\pm 12$ $119.4\pm 9.2$ $65.3\pm 5.4$ Nonprompt
$2490\pm 600$ $2360\pm 570$ $325\pm 75$ $298\pm 71$ Charge misID $520\pm 110$
$520\pm 111$ $\ttbar\PH$ $167\pm 34$ $169\pm 34$ $56\pm 12$ $57\pm 12$
$\ttbar\PZ/\PGg^{\ast}$ $335\pm 26$ $333\pm 26$ $145\pm 13$ $147\pm 13$
Diboson $382\pm 88$ $285\pm 65$ $46.8\pm 9.1$ $38.0\pm 7.5$ Other $178\pm 34$
$126\pm 27$ $43.4\pm 8.2$ $33.5\pm 7.4$ Conversions $177\pm 54$ $192\pm 59$
$22.9\pm 7.1$ $24.0\pm 7.4$ Total background $4250\pm 620$ $4000\pm 590$
$639\pm 80$ $600\pm 76$ Total prediction $4920\pm 620$ $4350\pm 590$ $758\pm
81$ $663\pm 76$ Data 5143 4486 834 744

The observed and predicted NN discriminant distributions are shown in Fig. 7
for events with positive (left) and negative (right) charged leptons in the
dilepton signal region, before (prefit, upper) and after (postfit, lower) the
fit to the data. Since the lepton charges are input variables in the NN
training, the NN is able to separately optimize the separation of signal and
background for events with positive and negative lepton charges. As a result,
the shapes of the NN discriminant distributions are different for the two
classes of events. Figures 8 and 9 show the $m(3\Pell)$ distributions for
events in the trilepton signal region with total trilepton charge $+1$ and
$-1$, respectively. The prefit (postfit) distributions are shown in the upper
(lower) panels, with events having 2 (left) and 3 (right) jets, of which
exactly 2 are medium -tagged jets, in both figures. The $p$-value quantifying
the agreement between the data and the prediction in the postfit distributions
of the trilepton signal region has a value of 0.027.

Figure 7: Comparison of the number of observed (points) and predicted (colored
histograms) events in the same-sign dilepton signal region. The distributions
of the NN output score are displayed in final states with two leptons of
positive (left) and negative (right) charges. The predictions are shown
“prefit” as in Fig. 2 (upper row), and with the values of the normalizations
and nuisance parameters obtained in the fit to the data applied (“postfit”,
lower row). The vertical bars on the points represent the statistical
uncertainties in the data, and the hatched bands the systematic uncertainty in
the predictions. In the lower panels, the ratio of the event yields in data to
the overall sum of the predictions is presented.

Figure 8: Comparison of the number of observed (points) and predicted (colored
histograms) events in the trilepton signal region with positive sum of lepton
charges. The distributions of the three-lepton invariant mass are displayed in
final states with two (left) and three (right) jets, of which exactly two pass
the medium tagging requirements. The predictions are shown “prefit” as in Fig.
3 (upper row), and with the values of the normalizations and nuisance
parameters obtained in the fit to the data applied (“postfit”, lower row). The
vertical bars on the points represent the statistical uncertainties in the
data, the horizontal bars the bin widths, and the hatched bands the systematic
uncertainty in the predictions. The last bins include the overflow
contributions. In the lower panels, the ratio of the event yields in data to
the overall sum of the predictions is presented.

Figure 9: Comparison of the number of observed (points) and predicted (colored histograms) events in the trilepton signal region with negative sum of lepton charges. The distributions of the three-lepton invariant mass are displayed in final states with two (left) and three (right) jets, of which exactly two pass the medium tagging requirements. The predictions are shown “prefit” as in Fig. 3 (upper row), and with the values of the normalizations and nuisance parameters obtained in the fit to the data applied (“postfit”, lower row). The vertical bars on the points represent the statistical uncertainties in the data, the horizontal bars the bin widths, and the hatched bands the systematic uncertainty in the predictions. The last bins include the overflow contributions. In the lower panels, the ratio of the event yields in data to the overall sum of the predictions is presented.  Table 1: Sources of systematic uncertainty in the predicted signal and background event yields with their impact on the measured $\ttbar\PW$ production cross section, estimated after the fit to the data. Only systematic uncertainty sources with values greater than 0.1% are included in the table. The production cross sections of the $\ttbar\PW$, $\PW\PZ$, $\PZ\PZ$, and $\ttbar\PZ$ processes are simultaneously constrained in the fit. The second-to-last row refers to the statistical uncertainty in the simulated event samples.  Source | Uncertainty [%]
---|---
Experimental uncertainties |
| Integrated luminosity | 1.9
|  tagging efficiency | 1.6
| Trigger efficiency | 1.2
| Pileup reweighting | 1.0
| L1 inefficiency | 0.7
| Jet energy scale | 0.6
| Jet energy resolution | 0.4
| Lepton selection efficiency | 0.4
Background uncertainties |
|  $\ttbar\PH$ normalization | 2.6
| Charge misidentification | 1.6
| Nonprompt leptons | 1.3
|  $\PV\PV\PV$ normalization | 1.2
|  $\ttbar\PV\PV$ normalization | 1.2
| Conversions normalization | 0.7
|  $\ttbar\PGg$ normalization | 0.6
|  $\PZ\PZ$ normalization | 0.6
| Other normalizations | 0.5
|  $\ttbar\PZ$ normalization | 0.3
|  $\PW\PZ$ normalization | 0.2
|  $\PQt\PZ\PQq$ normalization | 0.2
|  $\PQt\PH\PQq$ normalization | 0.2
Modeling uncertainties |
|  $\ttbar\PW$ scale | 1.8
|  $\ttbar\PW$ color reconnection | 1.0
| ISR & FSR scale for $\ttbar\PW$ | 0.8
|  $\ttbar\PGg$ scale | 0.4
|  $\PV\PV\PV$ scale | 0.3
|  $\ttbar\PH$ scale | 0.2
| Conversions | 0.2
Simulation statistical uncertainty | 1.8
Total systematic uncertainty | 5.8

The sources and estimates of the various systematic uncertainties relevant to
this measurement are given in Table 1. The reduction in the uncertainties
between the prefit and postfit values is clearly evident in Figs. 7–9. This is
due to the categorization of the signal regions and the inclusion of the
various control regions in the fit, both of which help to constrain some of
the uncertainty sources. The dominant experimental uncertainties include those
in the integrated luminosity measurement, the background estimation of
electron charge misidentification, and the jet identification efficiency. The
normalization uncertainties in the predictions of the $\ttbar\PH$,
$\PV\PV\PV$, and $\ttbar\PV\PV$ processes also represent substantial
contributions to the total uncertainty in the measurement. Given the
similarities of the decay products of these rare processes with those of the
$\ttbar\PW$ signal process, they make a sizable contribution to the total
uncertainty despite their low number of excepted events. Additional important
sources arise from the modeling uncertainties associated with the simulation
of $\ttbar\PW$ events and from the statistical uncertainty in the predicted
number of signal and background events.

Compared to the previous CMS measurement of $\ttbar\PW$ production [10], which
was performed with a subset of the data analyzed here, we achieve a
significant improvement in the precision of the cross section measurement,
with a reduction of the statistical (systematic) uncertainty by a factor of
three (more than two). This is mainly attributed to the larger data sample, as
well as to an optimized analysis strategy and improved estimates of the
dominant background contributions using data control samples. A higher
selection efficiency of $\ttbar\PW$ events and a stronger background
suppression of the nonprompt-lepton background contribution is achieved by the
lepton selection with the dedicated lepton MVA discriminant. An additional
source of improvement in the obtained signal-to-background ratio is associated
with the enhanced tagging performance following the installation of the new
pixel detector in 2017 and a more performant tagging algorithm [57, 58, 59].

The measured $\ttbar\PW$ inclusive cross section is shown in Table 0.8 with
its complete statistical and systematic uncertainties. A comparison of the
measured $\sigma_{\ttbar\PW}$ values obtained from this analysis using
different final states separately is shown in Fig. 10. In addition, two SM
predictions are shown both in Table 0.8 and Fig. 10. In comparison both with
the SM NLO + NNLL calculation [16] and the SM prediction using NLO plus an
improved FxFx merging procedure [21], the measured cross section is larger but
consistent within two standard deviations. The constraints on $\ttbar\PW$
production obtained in the $\ttbar\PH$ and $\ttbar\ttbar$ studies in Refs.
[12, 13] are consistent with our measured cross section within one standard
deviation, and confirm the underprediction of the $\ttbar\PW$ cross section by
the SM predictions.

Summary of measured and predicted production cross sections of $\ttbar\PW$,
$\ttbar\PWp$, and $\ttbar\PWm$ production, as well as of the
$\sigma_{\ttbar\PWp}/\sigma_{\ttbar\PWm}$ ratio. The SM predictions quoted at
NLO + NNLL accuracy are taken from Refs. [16, 15]. The SM predictions quoted
at NLO accuracy and including corrections from an improved FxFx merging
procedure (NLO + FxFx) have been provided by the authors of Ref. [21]. The
theoretical uncertainties include scale variations and PDF uncertainties.
Observable Measurement SM prediction NLO + NNLL NLO + FxFx
$\sigma_{\ttbar\PW}$ $868\pm 40\stat\pm 51\syst\unit{fb}$
$592\,^{+155}_{-97}\thy\unit{fb}$ $722\,^{+71}_{-78}\thy\unit{fb}$
$\sigma_{\ttbar\PWp}$ $553\pm 30\stat\pm 30\syst\unit{fb}$
$384\,^{+53}_{-33}\thy\unit{fb}$ $475\,^{+46}_{-52}\thy\unit{fb}$
$\sigma_{\ttbar\PWm}$ $343\pm 26\stat\pm 25\syst\unit{fb}$
$198\,^{+26}_{-17}\thy\unit{fb}$ $247\,^{+24}_{-27}\thy\unit{fb}$
$\sigma_{\ttbar\PWp}/\sigma_{\ttbar\PWm}$ $1.61\pm
0.15\stat\,^{+0.07}_{-0.05}\syst$ $1.94\,^{+0.37}_{-0.24}\thy$
$1.92\,^{+0.27}_{-0.29}\thy$

Figure 10: The measured $\ttbar\PW$ production cross section for the
individual dilepton ($\Pe\Pe$, $\Pe\PGm$, $\PGm\PGm$, and combined) channels
and the trilepton channel, as well as their combination. The inner black bars
show the statistical uncertainty, and the outer green bars give the total
uncertainty. The predictions from two SM calculations from Refs. [16, 21] are
shown by the black and red vertical lines, with the associated bands
corresponding to the total uncertainty.

The separate $\ttbar\PWp$ and $\ttbar\PWm$ cross section measurements are of
special interest because of the enhancement in $\Pp\Pp$ collisions of the
former with respect to the latter due to valence quark effects, and therefore
provide important information on the PDF of the proton. The cross sections are
measured in a simultaneous fit to data, where the events in categories with
positive (negative) sum of lepton charges are used to constrain the
$\ttbar\PWp$ ($\ttbar\PWm$) cross section. The resulting cross sections and
corresponding uncertainties are given in Table 0.8 and shown in Fig. 11. The
correlation between the two measured cross sections originates from the use of
common nuisance parameters and the same control regions in the fit. The sum of
the two measured cross sections is consistent with the measured inclusive
$\ttbar\PW$ cross section within the statistical uncertainties. Both cross
sections are larger than but consistent with the SM NLO + NNLL calculations
from Ref. [15] and the SM NLO predictions using an improved FxFx merging
procedure provided by the authors of Ref. [21]. These predictions and their
uncertainties are shown in Table 0.8.

Figure 11: The measurement of the $\ttbar\PWp$ vs. $\ttbar\PWm$ cross sections
(black cross), along with the 68 (green dashed) and 95% (blue solid)
intervals. The SM prediction provided by the authors of Ref. [21] is shown by
the red dot, with the horizontal and vertical bars corresponding to the total
uncertainties.

The measurement of the ratio between the cross sections of the two processes,
$\sigma_{\ttbar\PWp}/\sigma_{\ttbar\PWm}$, is performed by modifying the fit
procedure to directly measure the ratio. Figure 12 shows the scan of the
negative log-likelihood [82], with the accompanying best fit value and 68 and
95% confidence level () intervals for the ratio. The ratio measurement has a
reduced relative systematic uncertainty compared to the individual cross
sections because of partial correlations between some of the uncertainty
sources that cancel in the ratio. The measured ratio is smaller than the
ratios calculated from the theory predictions [15, 21], all of which are shown
in Table 0.8. In this study, we have propagated the uncertainties in the SM
prediction of the $\ttbar\PWp$ and $\ttbar\PWm$ cross sections to the
uncertainty in the ratio assuming no correlation, which results in an
overestimation of the latter uncertainty.

Figure 12: The scan of the negative log-likelihood used in the measurement of
the cross section ratio $\sigma_{\ttbar\PWp}/\sigma_{\ttbar\PWm}$. The best
fit value is indicated, with the inner green and outer blue bands displaying
the 68 and 95% intervals, respectively. An SM prediction and its uncertainty,
found from the cross section values provided by the authors of Ref. [21], are
shown by the vertical red line and hatched red band.

##  0.9 Summary

The cross section of the associated production of a boson with a top quark-
antiquark pair ($\ttbar\PW$) in proton-proton collisions at $\sqrt{s}=13\TeV$
is measured using data collected with the CMS detector in 2016–2018,
corresponding to an integrated luminosity of $138\fbinv$. The measurement is
performed in events with two same-sign or three charged leptons (electrons and
muons) and additional jets. Events are categorized according to the charges
and flavors of the leptons, and the number of jets and jets from the
hadronization of quarks. In the dilepton channel, a multiclass neural network
is used to improve the separation of signal from background processes. In the
trilepton channel, the invariant mass of the three leptons is used as the
discriminating variable. Background contributions with leptons originating
from hadron decays, jets or hadrons misidentified as leptons, and with
misreconstructed charge are estimated from control samples in data. All other
background processes are estimated from simulated event samples, and control
samples in data are used to constrain the dominant contributions.

The inclusive $\ttbar\PW$ production cross section in the full phase space is
measured to be $868\pm 40\stat\pm 51\syst\unit{fb}$. The $\ttbar\PWp$ and
$\ttbar\PWm$ cross sections are also determined as $553\pm 30\stat\pm 30\syst$
and $343\pm 26\stat\pm 25\syst\unit{fb}$, respectively. The measured ratio
between these production cross sections is $1.61\pm
0.15\stat\,^{+0.07}_{-0.05}\syst$. The measured cross sections are larger than
but consistent with standard model predictions within two standard deviations,
while the $\sigma_{\ttbar\PWp}/\sigma_{\ttbar\PWm}$ ratio is smaller than but
consistent with the predicted value. These results are all consistent with
earlier measurements and are the most precise determinations published to
date.

###### 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 and BNSF (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); GSRI
(Greece); NKFIH (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); MES and NSC
(Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR
(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and
NSTDA (Thailand); TUBITAK and TENMAK (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, 758316, 765710, 824093, 884104, and COST
Action CA16108 (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
Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288
(Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s
Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under
project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New
National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K
124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460,
K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council
of Science and Industrial Research, India; the Latvian Council of Science; the
Ministry of Education and Science, project no. 2022/WK/14, and the National
Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
(Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018
(Portugal); the National Priorities Research Program by Qatar National
Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”,
and the Programa Estatal de Fomento de la Investigación Científica y Técnica
de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa
del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd
Century Project Advancement Project, and the National Science, Research and
Innovation Fund via the Program Management Unit for Human Resources &
Institutional Development, Research and Innovation, grant B05F650021
(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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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Tumasyan10009-0000-0684-6742

Institut für Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341,
J.W. Andrejkovic, T. Bergauer0000-0002-5786-0293, S.
Chatterjee0000-0003-2660-0349, K. Damanakis0000-0001-5389-2872, M.
Dragicevic0000-0003-1967-6783, A. Escalante Del Valle0000-0002-9702-6359, P.S.
Hussain0000-0002-4825-5278, M. Jeitler20000-0002-5141-9560, N.
Krammer0000-0002-0548-0985, L. Lechner0000-0002-3065-1141, D.
Liko0000-0002-3380-473X, I. Mikulec0000-0003-0385-2746, P. Paulitsch, F.M.
Pitters, J. Schieck20000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784, D.
Schwarz0000-0002-3821-7331, S. Templ0000-0003-3137-5692, W.
Waltenberger0000-0002-6215-7228, C.-E. Wulz20000-0001-9226-5812

Universiteit Antwerpen, Antwerpen, Belgium M.R. Darwish30000-0003-2894-2377,
T. Janssen0000-0002-3998-4081, T. Kello4, H. Rejeb Sfar, P. Van
Mechelen0000-0002-8731-9051

Vrije Universiteit Brussel, Brussel, Belgium E.S. Bols0000-0002-8564-8732, J.
D’Hondt0000-0002-9598-6241, A. De Moor0000-0001-5964-1935, M.
Delcourt0000-0001-8206-1787, 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 B.
Clerbaux0000-0001-8547-8211, G. De Lentdecker0000-0001-5124-7693, L.
Favart0000-0003-1645-7454, D. Hohov0000-0002-4760-1597, J.
Jaramillo0000-0003-3885-6608, K. Lee0000-0003-0808-4184, M.
Mahdavikhorrami0000-0002-8265-3595, I. Makarenko0000-0002-8553-4508, A.
Malara0000-0001-8645-9282, S. Paredes0000-0001-8487-9603, L.
Pétré0009-0000-7979-5771, 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 D. Dobur0000-0003-0012-4866, J.
Knolle0000-0002-4781-5704, L. Lambrecht0000-0001-9108-1560, G. Mestdach, M.
Niedziela0000-0001-5745-2567, C. Rendón, C. Roskas0000-0002-6469-959X, A.
Samalan, K. Skovpen0000-0002-1160-0621, M. Tytgat0000-0002-3990-2074, N. Van
Den Bossche0000-0003-2973-4991, B. Vermassen, L. Wezenbeek0000-0001-6952-891X

Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke0000-0003-0252-3609, G. Bruno0000-0001-8857-8197, 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.
Jaffel0000-0001-7419-4248, Sa. Jain0000-0001-5078-3689, V. Lemaitre, K.
Mondal0000-0001-5967-1245, J. Prisciandaro, A. Taliercio0000-0002-5119-6280,
T.T. Tran0000-0003-3060-350X, 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, E. Coelho0000-0001-6114-9907, C.
Hensel0000-0001-8874-7624, 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 Filho0000-0003-3904-0571, H. Brandao
Malbouisson0000-0002-1326-318X, W. Carvalho0000-0003-0738-6615, J.
Chinellato5, E.M. Da Costa0000-0002-5016-6434, G.G. Da
Silveira60000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680, V. Dos
Santos Sousa0000-0002-4681-9340, S. Fonseca De Souza0000-0001-7830-0837, J.
Martins70000-0002-2120-2782, C. Mora Herrera0000-0003-3915-3170, K. Mota
Amarilo0000-0003-1707-3348, L. Mundim0000-0001-9964-7805, H.
Nogima0000-0001-7705-1066, A. Santoro0000-0002-0568-665X, S.M. Silva Do
Amaral0000-0002-0209-9687, A. Sznajder0000-0001-6998-1108, M.
Thiel0000-0001-7139-7963, F. Torres Da Silva De Araujo80000-0002-4785-3057, A.
Vilela Pereira0000-0003-3177-4626

Universidade Estadual Paulista, Universidade Federal do ABC, São Paulo, Brazil
C.A. Bernardes60000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
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. Aleksandrov0000-0001-6934-2541, G.
Antchev0000-0003-3210-5037, R. Hadjiiska0000-0003-1824-1737, P.
Iaydjiev0000-0001-6330-0607, M. Misheva0000-0003-4854-5301, M. Rodozov, M.
Shopova0000-0001-6664-2493, G. Sultanov0000-0002-8030-3866

University of Sofia, Sofia, Bulgaria A. Dimitrov0000-0003-2899-701X, T.
Ivanov0000-0003-0489-9191, L. Litov0000-0002-8511-6883, B.
Pavlov0000-0003-3635-0646, P. Petkov0000-0002-0420-9480, A.
Petrov0009-0003-8899-1514, E. Shumka0000-0002-0104-2574

Instituto De Alta Investigación, Universidad de Tarapacá, Casilla 7 D, Arica,
Chile S. Thakur0000-0002-1647-0360

Beihang University, Beijing, China T. Cheng0000-0003-2954-9315, T.
Javaid90009-0007-2757-4054, M. Mittal0000-0002-6833-8521, L.
Yuan0000-0002-6719-5397

Department of Physics, Tsinghua University, Beijing, China M.
Ahmad0000-0001-9933-995X, G. Bauer10, Z. Hu0000-0001-8209-4343, S.
Lezki0000-0002-6909-774X, K. Yi10,110000-0002-2459-1824

Institute of High Energy Physics, Beijing, China G.M.
Chen90000-0002-2629-5420, H.S. Chen90000-0001-8672-8227, M.
Chen90000-0003-0489-9669, F. Iemmi0000-0001-5911-4051, C.H. Jiang, A.
Kapoor0000-0002-1844-1504, H. Kou0000-0003-4927-243X, H.
Liao0000-0002-0124-6999, Z.-A. Liu120000-0002-2896-1386, V.
Milosevic0000-0002-1173-0696, F. Monti0000-0001-5846-3655, R.
Sharma0000-0003-1181-1426, J. Tao0000-0003-2006-3490, J. Thomas-
Wilsker0000-0003-1293-4153, J. Wang0000-0002-3103-1083, H.
Zhang0000-0001-8843-5209, J. Zhao0000-0001-8365-7726

State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos0000-0002-8953-1232, Y. An0000-0003-1299-1879, Y.
Ban0000-0002-1912-0374, C. Chen, A. Levin0000-0001-9565-4186, C.
Li0000-0002-6339-8154, Q. Li0000-0002-8290-0517, X. Lyu, Y. Mao, S.J.
Qian0000-0002-0630-481X, X. Sun0000-0003-4409-4574, D.
Wang0000-0002-9013-1199, J. Xiao0000-0002-7860-3958, H. Yang

Sun Yat-Sen University, Guangzhou, China M. Lu0000-0002-6999-3931, 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.
Gao40000-0001-7205-2318, D. Leggat, H. Okawa0000-0002-2548-6567, Y.
Zhang0000-0002-4554-2554

Zhejiang University, Hangzhou, Zhejiang, China Z. Lin0000-0003-1812-3474, C.
Lu0000-0002-7421-0313, M. Xiao0000-0001-9628-9336

Universidad de Los Andes, Bogota, Colombia C. Avila0000-0002-5610-2693, D.A.
Barbosa Trujillo, A. Cabrera0000-0002-0486-6296, C. Florez0000-0002-3222-0249,
J. Fraga0000-0002-5137-8543

Universidad de Antioquia, Medellin, Colombia J. Mejia
Guisao0000-0002-1153-816X, F. Ramirez0000-0002-7178-0484, M.
Rodriguez0000-0002-9480-213X, J.D. Ruiz Alvarez0000-0002-3306-0363

University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia D. Giljanovic0009-0005-6792-6881, 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.
Kovac0000-0002-2391-4599, T. Sculac0000-0002-9578-4105

Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic0000-0001-5847-0062,
B.K. Chitroda0000-0002-0220-8441, D. Ferencek0000-0001-9116-1202, D.
Majumder0000-0002-7578-0027, M. Roguljic0000-0001-5311-3007, A.
Starodumov130000-0001-9570-9255, T. Susa0000-0001-7430-2552

University of Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K.
Christoforou0000-0003-2205-1100, M. Kolosova0000-0002-5838-2158, S.
Konstantinou0000-0003-0408-7636, J. Mousa0000-0002-2978-2718, C. Nicolaou, F.
Ptochos0000-0002-3432-3452, P.A. Razis0000-0002-4855-0162, H. Rykaczewski, H.
Saka0000-0001-7616-2573

Charles University, Prague, Czech Republic M. Finger130000-0002-7828-9970, M.
Finger Jr.130000-0003-3155-2484, A. Kveton0000-0001-8197-1914

Escuela Politecnica Nacional, Quito, Ecuador E. Ayala0000-0002-0363-9198

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 H.
Abdalla140000-0002-4177-7209, Y. Assran15,16

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy0000-0003-4681-0079, M.A. Mahmoud0000-0001-8692-5458

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia S.
Bhowmik0000-0003-1260-973X, R.K. Dewanjee0000-0001-6645-6244, K.
Ehataht0000-0002-2387-4777, M. Kadastik, T. Lange0000-0001-6242-7331, S.
Nandan0000-0002-9380-8919, C. Nielsen0000-0002-3532-8132, J.
Pata0000-0002-5191-5759, M. Raidal0000-0001-7040-9491, L.
Tani0000-0002-6552-7255, C. Veelken0000-0002-3364-916X

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.
Bharthuar0000-0001-5871-9622, E. Brücken0000-0001-6066-8756, F.
Garcia0000-0002-4023-7964, J. Havukainen0000-0003-2898-6900, M.S.
Kim0000-0003-0392-8691, R. Kinnunen, T. Lampén0000-0002-8398-4249, K. Lassila-
Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén0009-0002-4847-8882, M. Lotti, L. Martikainen0000-0003-1609-3515, M.
Myllymäki0000-0003-0510-3810, J. Ott0000-0001-9337-5722, M.m.
Rantanen0000-0002-6764-0016, H. Siikonen0000-0003-2039-5874, E.
Tuominen0000-0002-7073-7767, J. Tuominiemi0000-0003-0386-8633

Lappeenranta-Lahti University of Technology, Lappeenranta, Finland P.
Luukka0000-0003-2340-4641, H. Petrow0000-0002-1133-5485, T. Tuuva

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France C.
Amendola0000-0002-4359-836X, M. Besancon0000-0003-3278-3671, F.
Couderc0000-0003-2040-4099, M. Dejardin0009-0008-2784-615X, D. Denegri, J.L.
Faure, F. Ferri0000-0002-9860-101X, S. Ganjour0000-0003-3090-9744, P.
Gras0000-0002-3932-5967, G. Hamel de Monchenault0000-0002-3872-3592, P.
Jarry0000-0002-1343-8189, V. Lohezic0009-0008-7976-851X, J.
Malcles0000-0002-5388-5565, J. Rander, A. Rosowsky0000-0001-7803-6650, M.Ö.
Sahin0000-0001-6402-4050, A. Savoy-Navarro170000-0002-9481-5168, P.
Simkina0000-0002-9813-372X, M. Titov0000-0002-1119-6614

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut
Polytechnique de Paris, Palaiseau, France C. Baldenegro
Barrera0000-0002-6033-8885, F. Beaudette0000-0002-1194-8556, A. Buchot
Perraguin0000-0002-8597-647X, P. Busson0000-0001-6027-4511, A.
Cappati0000-0003-4386-0564, C. Charlot0000-0002-4087-8155, F.
Damas0000-0001-6793-4359, O. Davignon0000-0001-8710-992X, B.
Diab0000-0002-6669-1698, G. Falmagne0000-0002-6762-3937, B.A. Fontana Santos
Alves0000-0001-9752-0624, S. Ghosh0009-0006-5692-5688, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi0009-0008-2093-8131, B.
Harikrishnan0000-0003-0174-4020, G. Liu0000-0001-7002-0937, J.
Motta0000-0003-0985-913X, M. Nguyen0000-0001-7305-7102, C.
Ochando0000-0002-3836-1173, L. Portales0000-0002-9860-9185, R.
Salerno0000-0003-3735-2707, U. Sarkar0000-0002-9892-4601, J.B.
Sauvan0000-0001-5187-3571, Y. Sirois0000-0001-5381-4807, A.
Tarabini0000-0001-7098-5317, E. Vernazza0000-0003-4957-2782, A.
Zabi0000-0002-7214-0673, A. Zghiche0000-0002-1178-1450

Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram180000-0001-7476-0158, J. Andrea0000-0002-8298-7560, D.
Apparu0009-0004-1837-0496, D. Bloch0000-0002-4535-5273, G.
Bourgatte0009-0005-7044-8104, J.-M. Brom0000-0003-0249-3622, E.C.
Chabert0000-0003-2797-7690, C. Collard0000-0002-5230-8387, D. Darej, U.
Goerlach0000-0001-8955-1666, C. Grimault, A.-C. Le Bihan0000-0002-8545-0187,
P. Van Hove0000-0002-2431-3381

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S.
Beauceron0000-0002-8036-9267, C. Bernet0000-0002-9923-8734, B.
Blancon0000-0001-9022-1509, G. Boudoul0009-0002-9897-8439, A. Carle, N.
Chanon0000-0002-2939-5646, J. Choi0000-0002-6024-0992, D.
Contardo0000-0001-6768-7466, P. Depasse0000-0001-7556-2743, C.
Dozen190000-0002-4301-634X, H. El Mamouni, J. Fay0000-0001-5790-1780, S.
Gascon0000-0002-7204-1624, M. Gouzevitch0000-0002-5524-880X, G.
Grenier0000-0002-1976-5877, B. Ille0000-0002-8679-3878, I.B. Laktineh, M.
Lethuillier0000-0001-6185-2045, L. Mirabito, S. Perries, L.
Torterotot0000-0002-5349-9242, M. Vander Donckt0000-0002-9253-8611, P.
Verdier0000-0003-3090-2948, S. Viret

Georgian Technical University, Tbilisi, Georgia I.
Bagaturia200000-0001-8646-4372, I. Lomidze0009-0002-3901-2765, Z.
Tsamalaidze130000-0001-5377-3558

RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany V.
Botta0000-0003-1661-9513, L. Feld0000-0001-9813-8646, K.
Klein0000-0002-1546-7880, M. Lipinski0000-0002-6839-0063, D.
Meuser0000-0002-2722-7526, A. Pauls0000-0002-8117-5376, N.
Röwert0000-0002-4745-5470, M. Teroerde0000-0002-5892-1377

RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany S.
Diekmann0009-0004-8867-0881, A. Dodonova0000-0002-5115-8487, N.
Eich0000-0001-9494-4317, D. Eliseev0000-0001-5844-8156, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, D.
Fasanella0000-0002-2926-2691, B. Fischer0000-0002-3900-3482, T.
Hebbeker0000-0002-9736-266X, K. Hoepfner0000-0002-2008-8148, F.
Ivone0000-0002-2388-5548, M.y. Lee0000-0002-4430-1695, L. Mastrolorenzo, M.
Merschmeyer0000-0003-2081-7141, A. Meyer0000-0001-9598-6623, S.
Mondal0000-0003-0153-7590, S. Mukherjee0000-0001-6341-9982, D.
Noll0000-0002-0176-2360, A. Novak0000-0002-0389-5896, F. Nowotny, A.
Pozdnyakov0000-0003-3478-9081, Y. Rath, W. Redjeb0000-0001-9794-8292, H.
Reithler0000-0003-4409-702X, A. Schmidt0000-0003-2711-8984, S.C. Schuler, A.
Sharma0000-0002-5295-1460, L. Vigilante, S. Wiedenbeck0000-0002-4692-9304, S.
Zaleski

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany C.
Dziwok0000-0001-9806-0244, G. Flügge0000-0003-3681-9272, W. Haj
Ahmad210000-0003-1491-0446, O. Hlushchenko, T. Kress0000-0002-2702-8201, A.
Nowack0000-0002-3522-5926, O. Pooth0000-0001-6445-6160, A.
Stahl0000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A.
Zotz0000-0002-1320-1712

Deutsches Elektronen-Synchrotron, Hamburg, Germany H. Aarup
Petersen0009-0005-6482-7466, M. Aldaya Martin0000-0003-1533-0945, P. Asmuss,
S. Baxter0009-0008-4191-6716, M. Bayatmakou0009-0002-9905-0667, O.
Behnke0000-0002-4238-0991, A. Bermúdez Martínez0000-0001-8822-4727, S.
Bhattacharya0000-0002-3197-0048, A.A. Bin Anuar0000-0002-2988-9830, F.
Blekman220000-0002-7366-7098, K. Borras230000-0003-1111-249X, D.
Brunner0000-0001-9518-0435, A. Campbell0000-0003-4439-5748, A.
Cardini0000-0003-1803-0999, C. Cheng, F. Colombina0009-0008-7130-100X, S.
Consuegra Rodríguez0000-0002-1383-1837, G. Correia Silva0000-0001-6232-3591,
M. De Silva0000-0002-5804-6226, L. Didukh0000-0003-4900-5227, G. Eckerlin, D.
Eckstein0000-0002-7366-6562, L.I. Estevez Banos0000-0001-6195-3102, O.
Filatov0000-0001-9850-6170, E. Gallo220000-0001-7200-5175, A.
Geiser0000-0003-0355-102X, A. Giraldi0000-0003-4423-2631, G. Greau, A.
Grohsjean0000-0003-0748-8494, V. Guglielmi0000-0003-3240-7393, M.
Guthoff0000-0002-3974-589X, A. Jafari240000-0001-7327-1870, N.Z.
Jomhari0000-0001-9127-7408, B. Kaech0000-0002-1194-2306, 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, M. Komm0000-0002-7669-4294, D.
Krücker0000-0003-1610-8844, W. Lange, D. Leyva Pernia0009-0009-8755-3698, K.
Lipka0000-0002-8427-3748, W. Lohmann250000-0002-8705-0857, R.
Mankel0000-0003-2375-1563, I.-A. Melzer-Pellmann0000-0001-7707-919X, M.
Mendizabal Morentin0000-0002-6506-5177, J. Metwally, A.B.
Meyer0000-0001-8532-2356, G. Milella0000-0002-2047-951X, M.
Mormile0000-0003-0456-7250, A. Mussgiller0000-0002-8331-8166, A.
Nürnberg0000-0002-7876-3134, Y. Otarid, D. Pérez Adán0000-0003-3416-0726, A.
Raspereza0000-0003-2167-498X, B. Ribeiro Lopes0000-0003-0823-447X, J.
Rübenach, A. Saggio0000-0002-7385-3317, A. Saibel0000-0002-9932-7622, M.
Savitskyi0000-0002-9952-9267, M. Scham26,230000-0001-9494-2151, V. Scheurer,
S. Schnake230000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger220000-0001-6699-6662, M. Shchedrolosiev0000-0003-3510-2093,
R.E. Sosa Ricardo0000-0002-2240-6699, D. Stafford, N.
Tonon${}^{\textrm{\textdagger}}$0000-0003-4301-2688, M. Van De
Klundert0000-0001-8596-2812, F. Vazzoler0000-0001-8111-9318, A. Ventura
Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, D.
Walter0000-0001-8584-9705, Q. Wang0000-0003-1014-8677, Y.
Wen0000-0002-8724-9604, K. Wichmann, L. Wiens230000-0002-4423-4461, C.
Wissing0000-0002-5090-8004, S. Wuchterl0000-0001-9955-9258, Y.
Yang0009-0009-3430-0558, A. Zimermmane Castro Santos0000-0001-9302-3102

University of Hamburg, Hamburg, Germany A. Albrecht0000-0001-6004-6180, S.
Albrecht0000-0002-5960-6803, M. Antonello0000-0001-9094-482X, S.
Bein0000-0001-9387-7407, L. Benato0000-0001-5135-7489, M.
Bonanomi0000-0003-3629-6264, P. Connor0000-0003-2500-1061, K. De
Leo0000-0002-8908-409X, M. Eich, K. El Morabit0000-0001-5886-220X, F. Feindt,
A. Fröhlich, C. Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, M.
Hajheidari, J. Haller0000-0001-9347-7657, A. Hinzmann0000-0002-2633-4696, H.R.
Jabusch0000-0003-2444-1014, G. Kasieczka0000-0003-3457-2755, P. Keicher, R.
Klanner0000-0002-7004-9227, W. Korcari0000-0001-8017-5502, T.
Kramer0000-0002-7004-0214, V. Kutzner0000-0003-1985-3807, F.
Labe0000-0002-1870-9443, J. Lange0000-0001-7513-6330, A.
Lobanov0000-0002-5376-0877, T. Loesche0000-0003-2360-351X, C.
Matthies0000-0001-7379-4540, A. Mehta0000-0002-0433-4484, L.
Moureaux0000-0002-2310-9266, M. Mrowietz, A. Nigamova0000-0002-8522-8500, Y.
Nissan, A. Paasch0000-0002-2208-5178, K.J. Pena Rodriguez0000-0002-2877-9744,
M. Rieger0000-0003-0797-2606, O. Rieger, D. Savoiu0000-0001-6794-7475, P.
Schleper0000-0001-5628-6827, M. Schröder0000-0001-8058-9828, J.
Schwandt0000-0002-0052-597X, M. Sommerhalder0000-0001-5746-7371, H.
Stadie0000-0002-0513-8119, G. Steinbrück0000-0002-8355-2761, A. Tews, M.
Wolf0000-0003-3002-2430

Karlsruher Institut fuer Technologie, Karlsruhe, Germany J.
Bechtel0000-0001-5245-7318, S. Brommer0000-0001-8988-2035, M. Burkart, E.
Butz0000-0002-2403-5801, R. Caspart0000-0002-5502-9412, T.
Chwalek0000-0002-8009-3723, A. Dierlamm0000-0001-7804-9902, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, J.O. Gosewisch,
A. Gottmann0000-0001-6696-349X, F. Hartmann270000-0001-8989-8387, M.
Horzela0000-0002-3190-7962, U. Husemann0000-0002-6198-8388, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, S.
Maier0000-0001-9828-9778, S. Mitra0000-0002-3060-2278, Th.
Müller0000-0003-4337-0098, M. Neukum, M. Oh0000-0003-2618-9203, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, J. Rauser, M.
Schnepf, D. Seith, I. Shvetsov0000-0002-7069-9019, H.J.
Simonis0000-0002-7467-2980, N. Trevisani0000-0002-5223-9342, R.
Ulrich0000-0002-2535-402X, J. van der Linden0000-0002-7174-781X, R.F. Von
Cube0000-0002-6237-5209, M. Wassmer0000-0002-0408-2811, S.
Wieland0000-0003-3887-5358, R. Wolf0000-0001-9456-383X, S.
Wozniewski0000-0001-8563-0412, S. Wunsch, X. Zuo0000-0002-0029-493X

Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia
Paraskevi, Greece G. Anagnostou, P. Assiouras0000-0002-5152-9006, G.
Daskalakis0000-0001-6070-7698, A. Kyriakis, A. Stakia0000-0001-6277-7171

National and Kapodistrian University of Athens, Athens, Greece M.
Diamantopoulou, D. Karasavvas, P. Kontaxakis0000-0002-4860-5979, A.
Manousakis-Katsikakis0000-0002-0530-1182, A. Panagiotou, I.
Papavergou0000-0002-7992-2686, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K.
Vellidis0000-0001-5680-8357, I. Zisopoulos0000-0001-5212-4353

National Technical University of Athens, Athens, Greece G.
Bakas0000-0003-0287-1937, T. Chatzistavrou, K. Kousouris0000-0002-6360-0869,
I. Papakrivopoulos0000-0002-8440-0487, G. Tsipolitis, A. Zacharopoulou

University of Ioánnina, Ioánnina, Greece K. Adamidis, I. Bestintzanos, I.
Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios0009-0003-7233-0738, C.
Kamtsikis, P. Katsoulis, P. Kokkas0009-0009-3752-6253, P.G. Kosmoglou
Kioseoglou0000-0002-7440-4396, N. Manthos0000-0003-3247-8909, 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. Csanád0000-0002-3154-6925, K.
Farkas0000-0003-1740-6974, M.M.A. Gadallah280000-0002-8305-6661, S.
Lökös290000-0002-4447-4836, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, A.J.
Rádl300000-0001-8810-0388, O. Surányi0000-0002-4684-495X, G.I.
Veres0000-0002-5440-4356

Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók310000-0002-4440-2701, G. Bencze, C. Hajdu0000-0002-7193-800X, D.
Horvath32,330000-0003-0091-477X, F. Sikler0000-0001-9608-3901, V.
Veszpremi0000-0001-9783-0315

Institute of Nuclear Research ATOMKI, Debrecen, Hungary N.
Beni0000-0002-3185-7889, S. Czellar, J. Karancsi310000-0003-0802-7665, J.
Molnar, Z. Szillasi, D. Teyssier0000-0002-5259-7983

Institute of Physics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari340000-0003-0498-4265

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo300000-0002-9110-9663, F. Nemes300000-0002-1451-6484, T.
Novak0000-0001-6253-4356

Panjab University, Chandigarh, India J. Babbar0000-0002-4080-4156, S.
Bansal0000-0003-1992-0336, S.B. Beri, V. Bhatnagar0000-0002-8392-9610, G.
Chaudhary0000-0003-0168-3336, S. Chauhan0000-0001-6974-4129, N.
Dhingra350000-0002-7200-6204, R. Gupta, A. Kaur0000-0002-1640-9180, A.
Kaur0000-0003-3609-4777, H. Kaur0000-0002-8659-7092, M.
Kaur0000-0002-3440-2767, S. Kumar0000-0001-9212-9108, P.
Kumari0000-0002-6623-8586, M. Meena0000-0003-4536-3967, K.
Sandeep0000-0002-3220-3668, T. Sheokand, J.B. Singh360000-0001-9029-2462, A.
Singla0000-0003-2550-139X, A. K. Virdi0000-0002-0866-8932

University of Delhi, Delhi, India A. Ahmed0000-0002-4500-8853, A.
Bhardwaj0000-0002-7544-3258, B.C. Choudhary0000-0001-5029-1887, M. Gola, A.
Kumar0000-0003-3407-4094, M. Naimuddin0000-0003-4542-386X, P.
Priyanka0000-0002-0933-685X, K. Ranjan0000-0002-5540-3750, S.
Saumya0000-0001-7842-9518, A. Shah0000-0002-6157-2016

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S.
Baradia0000-0001-9860-7262, S. Barman370000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, D. Bhowmik, S. Dutta0000-0001-9650-8121, S.
Dutta, B. Gomber380000-0002-4446-0258, M. Maity37, P.
Palit0000-0002-1948-029X, G. Saha0000-0002-6125-1941, B.
Sahu0000-0002-8073-5140, S. Sarkar

Indian Institute of Technology Madras, Madras, India P.K.
Behera0000-0002-1527-2266, S.C. Behera0000-0002-0798-2727, P.
Kalbhor0000-0002-5892-3743, J.R. Komaragiri390000-0002-9344-6655, D.
Kumar390000-0002-6636-5331, A. Muhammad0000-0002-7535-7149, L.
Panwar390000-0003-2461-4907, R. Pradhan0000-0001-7000-6510, P.R.
Pujahari0000-0002-0994-7212, A. Sharma0000-0002-0688-923X, A.K.
Sikdar0000-0002-5437-5217, P.C. Tiwari390000-0002-3667-3843, S.
Verma0000-0003-1163-6955

Bhabha Atomic Research Centre, Mumbai, India K. Naskar400000-0003-0638-4378

Tata Institute of Fundamental Research-A, Mumbai, India T. Aziz, I.
Das0000-0002-5437-2067, S. Dugad, M. Kumar0000-0003-0312-057X, G.B.
Mohanty0000-0001-6850-7666, P. Suryadevara

Tata Institute of Fundamental Research-B, Mumbai, India S.
Banerjee0000-0002-7953-4683, R. Chudasama0009-0007-8848-6146, M.
Guchait0009-0004-0928-7922, S. Karmakar0000-0001-9715-5663, S.
Kumar0000-0002-2405-915X, G. Majumder0000-0002-3815-5222, K.
Mazumdar0000-0003-3136-1653, S. Mukherjee0000-0003-3122-0594, A.
Thachayath0000-0001-6545-0350

National Institute of Science Education and Research, An OCC of Homi Bhabha
National Institute, Bhubaneswar, Odisha, India S.
Bahinipati410000-0002-3744-5332, C. Kar0000-0002-6407-6974, P.
Mal0000-0002-0870-8420, T. Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu420000-0003-4671-815X, A. Nayak420000-0002-7716-4981, P.
Saha0000-0002-7013-8094, S.K. Swain, D. Vats420009-0007-8224-4664

Indian Institute of Science Education and Research (IISER), Pune, India A.
Alpana0000-0003-3294-2345, S. Dube0000-0002-5145-3777, B.
Kansal0000-0002-6604-1011, A. Laha0000-0001-9440-7028, S.
Pandey0000-0003-0440-6019, A. Rastogi0000-0003-1245-6710, S.
Sharma0000-0001-6886-0726

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi43,440000-0001-5741-3357, E. Khazaie440000-0001-9810-7743, M.
Zeinali450000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S.
Chenarani460000-0002-1425-076X, 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 Baria, Università di Barib, Politecnico di Baric, Bari, Italy
M. Abbresciaa,b0000-0001-8727-7544, R. Alya,b0000-0001-6808-1335, C.
Arutaa,b0000-0001-9524-3264, 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,b0000-0003-3719-8041, W.
Elmetenaweea,b0000-0001-7069-0252, F. Erricoa,b0000-0001-8199-370X, L.
Fiorea0000-0002-9470-1320, G. Iasellia,c0000-0003-2546-5341, M.
Incea,b0000-0001-6907-0195, G. Maggia,c0000-0001-5391-7689, M.
Maggia0000-0002-8431-3922, I. Margjekaa,b0000-0002-3198-3025, V.
Mastrapasquaa,b0000-0002-9082-5924, S. Mya,b0000-0002-9938-2680, S.
Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b0000-0003-3279-6114, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, R.
Radognaa0000-0002-1094-5038, D. Ramosa0000-0002-7165-1017, A.
Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, Ü.
Sözbilira0000-0001-6833-3758, A. Stamerraa0000-0003-1434-1968, R.
Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631

INFN Sezione di Bolognaa, Università di Bolognab, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia0000-0001-8679-4443, 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, 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,b0000-0002-6013-8293, S. Lo
Meoa,470000-0003-3249-9208, L. Lunertia,b0000-0002-8932-0283, 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 Cataniaa, Università di Cataniab, Catania, Italy S.
Costaa,b,480000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,480000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153

INFN Sezione di Firenzea, Università di Firenzeb, Firenze, Italy G.
Barbaglia0000-0002-1738-8676, G. Bardellia,b0000-0002-4662-3305, B.
Camaiania,b0000-0002-6396-622X, A. Cassesea0000-0003-3010-4516, R.
Ceccarellia,b0000-0003-3232-9380, 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,b0000-0001-7297-2624, M.
Meschinia0000-0002-9161-3990, S. Paolettia0000-0003-3592-9509, R.
Seiditaa,b0000-0002-3533-6191, 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, S.
Meola270000-0002-8233-7277, D. Piccolo0000-0001-5404-543X

INFN Sezione di Genovaa, Università di Genovab, Genova, Italy M.
Bozzoa,b0000-0002-1715-0457, P. Chatagnona0000-0002-4705-9582, F.
Ferroa0000-0002-7663-0805, R. Mulargiaa0000-0003-2437-013X, E.
Robuttia0000-0001-9038-4500, S. Tosia,b0000-0002-7275-9193

INFN Sezione di Milano-Bicoccaa, Università di Milano-Bicoccab, Milano, Italy
A. Benagliaa0000-0003-1124-8450, G. Boldrinia0000-0001-5490-605X, F.
Brivioa,b0000-0001-9523-6451, F. Cetorellia,b0000-0002-3061-1553, 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.
Malbertia0000-0001-6794-8419, 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. Pinolinia, S. Ragazzia,b0000-0001-8219-2074,
N. Redaellia0000-0002-0098-2716, T. Tabarelli de Fatisa,b0000-0001-6262-4685,
D. Zuoloa,b0000-0003-3072-1020

INFN Sezione di Napolia, Università di Napoli ’Federico II’b, Napoli, Italy;
Università della Basilicatac, Potenza, Italy; Università G. Marconid, 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,490000-0001-6471-5492, P. Paoluccia,270000-0002-8773-4781, B.
Rossia0000-0002-0807-8772, C. Sciaccaa,b0000-0002-8412-4072

INFN Sezione di Padovaa, Università di Padovab, Padova, Italy; Università di
Trentoc, Trento, Italy P. Azzia0000-0002-3129-828X, N.
Bacchettaa,500000-0002-2205-5737, 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, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, G. Grossoa, L. Layera,51, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, A.T.
Meneguzzoa,b0000-0002-5861-8140, F. Montecassianoa0000-0001-8180-9378, M.
Passaseoa0000-0002-7930-4124, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b0000-0003-3466-7500, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, H. Yarara,b, P. Zottoa,b0000-0003-3953-5996, A.
Zucchettaa,b0000-0003-0380-1172

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,520000-0002-0820-0483, C. Aimèa,b0000-0003-0449-4717, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b0000-0002-1162-2505, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b0000-0001-9647-9420, 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 Perugiaa, Università di Perugiab, Perugia, Italy P.
Asenova,530000-0003-2379-9903, G.M. Bileia0000-0002-4159-9123, D.
Ciangottinia,b0000-0002-0843-4108, L. Fanòa,b0000-0002-9007-629X, M.
Magherinia,b0000-0003-4108-3925, G. Mantovania,b, V.
Mariania,b0000-0001-7108-8116, M. Menichellia0000-0002-9004-735X, F.
Moscatellia,530000-0002-7676-3106, 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 Pisaa, Università di Pisab, Scuola Normale Superiore di Pisac,
Pisa, Italy; Università di Sienad, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, V.
Bertacchia,c0000-0001-9971-1176, R. Bhattacharyaa0000-0002-7575-8639, L.
Bianchinia,b0000-0002-6598-6865, T. Boccalia0000-0002-9930-9299, E.
Bossinia,b0000-0002-2303-2588, D. Bruschinia,c0000-0001-7248-2967, 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, G.
Mandorlia,c0000-0002-5183-9020, D. Matos Figueiredoa0000-0003-2514-6930, A.
Messineoa,b0000-0001-7551-5613, M. Musicha,b0000-0001-7938-5684, F.
Pallaa0000-0002-6361-438X, S. Paroliaa,b0000-0002-9566-2490, G. Ramirez-
Sancheza,c0000-0001-7804-5514, A. Rizzia,b0000-0002-4543-2718, G.
Rolandia,c0000-0002-0635-274X, S. Roy Chowdhurya0000-0001-5742-5593, T.
Sarkara0000-0003-0582-4167, A. Scribanoa0000-0002-4338-6332, 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 Romaa, Sapienza Università di Romab, Roma, Italy P.
Barriaa0000-0002-3924-7380, M. Campanaa,b0000-0001-5425-723X, 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. Pandolfia0000-0001-8713-3874, R.
Paramattia,b0000-0002-0080-9550, C. Quarantaa,b0000-0002-0042-6891, S.
Rahatloua,b0000-0001-9794-3360, C. Rovellia0000-0003-2173-7530, F.
Santanastasioa,b0000-0003-2505-8359, L. Soffia0000-0003-2532-9876, R.
Tramontanoa,b0000-0001-5979-5299

INFN Sezione di Torinoa, Università di Torinob, Torino, Italy; Università del
Piemonte Orientalec, 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, 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, M. Grippoa,b0000-0003-0770-269X, B.
Kiania,b0000-0002-1202-7652, F. Leggera0000-0003-1400-0709, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, A.
Meccaa,b0000-0003-2209-2527, 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,b0000-0001-6768-1056, D. Trocinoa0000-0002-2830-5872, G.
Umoreta,b0000-0002-6674-7874, A. Vagnerinia,b0000-0001-8730-5031

INFN Sezione di Triestea, Università di Triesteb, 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,b0000-0002-2253-819X

Kyungpook National University, Daegu, Korea S. Dogra0000-0002-0812-0758, C.
Huh0000-0002-8513-2824, B. Kim0000-0002-9539-6815, D.H.
Kim0000-0002-9023-6847, G.N. Kim0000-0002-3482-9082, J. Kim, J.
Lee0000-0002-5351-7201, S.W. Lee0000-0002-1028-3468, C.S.
Moon0000-0001-8229-7829, Y.D. Oh0000-0002-7219-9931, S.I.
Pak0000-0002-1447-3533, M.S. Ryu0000-0002-1855-180X, S.
Sekmen0000-0003-1726-5681, Y.C. Yang0000-0003-1009-4621

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 E. Asilar0000-0001-5680-599X, T.J.
Kim0000-0001-8336-2434, J. Park0000-0002-4683-6669

Korea University, Seoul, Korea S. Choi0000-0001-6225-9876, S. Han, B.
Hong0000-0002-2259-9929, K. Lee, K.S. Lee0000-0002-3680-7039, J. Lim, J. Park,
S.K. Park, J. Yoo0000-0003-0463-3043

Kyung Hee University, Department of Physics, Seoul, Korea J.
Goh0000-0002-1129-2083

Sejong University, Seoul, Korea H. S. Kim0000-0002-6543-9191, Y. Kim, S. Lee

Seoul National University, Seoul, Korea J. Almond, J.H. Bhyun, J.
Choi0000-0002-2483-5104, S. Jeon0000-0003-1208-6940, J.
Kim0000-0001-9876-6642, J.S. Kim, S. Ko0000-0003-4377-9969, H.
Kwon0009-0002-5165-5018, H. Lee0000-0002-1138-3700, S. Lee, B.H.
Oh0000-0002-9539-7789, S.B. Oh0000-0003-0710-4956, H. Seo0000-0002-3932-0605,
U.K. Yang, I. Yoon0000-0002-3491-8026

University of Seoul, Seoul, Korea W. Jang0000-0002-1571-9072, D.Y. Kang, Y.
Kang0000-0001-6079-3434, D. Kim0000-0002-8336-9182, S. Kim0000-0002-8015-7379,
B. Ko, J.S.H. Lee0000-0002-2153-1519, Y. Lee0000-0001-5572-5947, J.A. Merlin,
I.C. Park0000-0003-4510-6776, Y. Roh, D. Song, I.J. Watson0000-0003-2141-3413,
S. Yang0000-0001-6905-6553

Yonsei University, Department of Physics, Seoul, Korea S.
Ha0000-0003-2538-1551, H.D. Yoo0000-0002-3892-3500

Sungkyunkwan University, Suwon, Korea M. Choi0000-0002-4811-626X, M.R.
Kim0000-0002-2289-2527, H. Lee, Y. Lee0000-0002-4000-5901, Y.
Lee0000-0001-6954-9964, I. Yu0000-0003-1567-5548

College of Engineering and Technology, American University of the Middle East
(AUM), Dasman, Kuwait T. Beyrouthy, Y. Maghrbi0000-0002-4960-7458

Riga Technical University, Riga, Latvia K. Dreimanis0000-0003-0972-5641, M.
Seidel0000-0003-3550-6151, V. Veckalns0000-0003-3676-9711

Vilnius University, Vilnius, Lithuania M. Ambrozas0000-0003-2449-0158, 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, S.Y.
Hoh540000-0003-3233-5123, I. Yusuff540000-0003-2786-0732, 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 Coello0000-0002-3761-911X, J.A.
Murillo Quijada0000-0003-4933-2092, A. Sehrawat0000-0002-6816-7814, L.
Valencia Palomo0000-0002-8736-440X

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala0000-0002-8294-8692, H. Castilla-Valdez0009-0005-9590-9958, I.
Heredia-De La Cruz550000-0002-8133-6467, R. Lopez-
Fernandez0000-0002-2389-4831, C.A. Mondragon Herrera, D.A. Perez
Navarro0000-0001-9280-4150, A. Sánchez Hernández0000-0001-9548-0358

Universidad Iberoamericana, Mexico City, Mexico C. Oropeza
Barrera0000-0001-9724-0016, F. Vazquez Valencia0000-0001-6379-3982

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I.
Pedraza0000-0002-2669-4659, H.A. Salazar Ibarguen0000-0003-4556-7302, C. Uribe
Estrada0000-0002-2425-7340

University of Montenegro, Podgorica, Montenegro I. Bubanja, J.
Mijuskovic560009-0009-1589-9980, N. Raicevic0000-0002-2386-2290

National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A.
Ahmad0000-0002-4770-1897, M.I. Asghar, A. Awais0000-0003-3563-257X, M.I.M.
Awan, M. Gul0000-0002-5704-1896, H.R. Hoorani0000-0002-0088-5043, W.A.
Khan0000-0003-0488-0941, 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.
Grzanka0000-0002-3599-854X, M. Malawski0000-0001-6005-0243

National Centre for Nuclear Research, Swierk, Poland H.
Bialkowska0000-0002-5956-6258, M. Bluj0000-0003-1229-1442, B.
Boimska0000-0002-4200-1541, M. Górski0000-0003-2146-187X, M.
Kazana0000-0002-7821-3036, M. Szleper0000-0002-1697-004X, P.
Zalewski0000-0003-4429-2888

Institute of Experimental Physics, Faculty of Physics, University of Warsaw,
Warsaw, Poland K. Bunkowski0000-0001-6371-9336, K. Doroba0000-0002-7818-2364,
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. Araujo0000-0002-8152-3756, P. Bargassa0000-0001-8612-3332, D.
Bastos0000-0002-7032-2481, 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.
Niknejad0000-0003-3276-9482, M. Pisano0000-0002-0264-7217, J.
Seixas0000-0002-7531-0842, J. Varela0000-0003-2613-3146

VINCA Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia
P. Adzic570000-0002-5862-7397, 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 Fernández0000-0003-1525-4620, M. Barrio
Luna, Cristina F. Bedoya0000-0001-8057-9152, C.A. Carrillo
Montoya0000-0002-6245-6535, M. Cepeda0000-0002-6076-4083, M.
Cerrada0000-0003-0112-1691, N. Colino0000-0002-3656-0259, B. De La
Cruz0000-0001-9057-5614, A. Delgado Peris0000-0002-8511-7958, D. Fernández Del
Val0000-0003-2346-1590, J.P. Fernández Ramos0000-0002-0122-313X, J.
Flix0000-0003-2688-8047, M.C. Fouz0000-0003-2950-976X, O. Gonzalez
Lopez0000-0002-4532-6464, S. Goy Lopez0000-0001-6508-5090, J.M.
Hernandez0000-0001-6436-7547, M.I. Josa0000-0002-4985-6964, J. León
Holgado0000-0002-4156-6460, D. Moran0000-0002-1941-9333, C. Perez
Dengra0000-0003-2821-4249, A. Pérez-Calero Yzquierdo0000-0003-3036-7965, J.
Puerta Pelayo0000-0001-7390-1457, I. Redondo0000-0003-3737-4121, D.D. Redondo
Ferrero0000-0002-3463-0559, L. Romero, S. Sánchez Navas0000-0001-6129-9059, J.
Sastre0000-0002-1654-2846, L. Urda Gómez0000-0002-7865-5010, J. Vazquez
Escobar0000-0002-7533-2283, C. Willmott

Universidad Autónoma de Madrid, Madrid, Spain J.F. de
Trocóniz0000-0002-0798-9806

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, J. Fernandez
Menendez0000-0002-5213-3708, S. Folgueras0000-0001-7191-1125, I. Gonzalez
Caballero0000-0002-8087-3199, J.R. González Fernández0000-0002-4825-8188, E.
Palencia Cortezon0000-0001-8264-0287, C. Ramón Álvarez0000-0003-1175-0002, V.
Rodríguez Bouza0000-0002-7225-7310, A. Soto Rodríguez0000-0002-2993-8663, A.