TOP-22-006

TOP-22-006

# Search for physics beyond the standard model in top quark production with
additional leptons in the context of effective field theory

###### Abstract

A search for new physics in top quark production with additional final-state
leptons is performed using data collected by the CMS experiment in proton-
proton collisions at $\sqrt{s}=13\TeV$ at the LHC during 2016–2018. The data
set corresponds to an integrated luminosity of 138. Using the framework of
effective field theory (EFT), potential new physics effects are parametrized
in terms of $26$ dimension-six EFT operators. The impacts of EFT operators are
incorporated through the event-level reweighting of Monte Carlo simulations,
which allows for detector-level predictions. The events are divided into
several categories based on lepton multiplicity, total lepton charge, jet
multiplicity, and -tagged jet multiplicity. Kinematic variables corresponding
to the transverse momentum () of the leading pair of leptons and/or jets as
well as the of on-shell bosons are used to extract the 95% confidence
intervals of the $26$ Wilson coefficients corresponding to these EFT
operators. No significant deviation with respect to the standard model
prediction is found.

##  0.1 Introduction

Searches for new fundamental particles and interactions are motivated by the
strong evidence for phenomena (such as dark matter [1, 2]) that are not
described by the standard model (SM) of particle physics. However, there is no
a priori reason to assume that particles will be light enough to be produced
on-shell at the CERN LHC. Indirect methods of probing higher energy scales are
thus an important part of searches for new physics at the energy frontier. One
example of this type of approach is effective field theory (EFT), a flexible
framework that comprehensively describes the off-shell effects of new physics
phenomena at a mass scale $\Lambda$. The EFT treats the SM Lagrangian as the
lowest order term in an expansion of a more complete Lagrangian at a mass
scale $\Lambda$ in the form of a series of higher-dimensional operators, which
are built from products of SM fields that respect the SM symmetries. The EFT
Lagrangian is written as

$\mathcal{L}_{\mathrm{EFT}}=\mathcal{L}_{\mathrm{SM}}+\sum\limits_{d,i}\frac{c^{d}_{i}}{\Lambda^{d-4}}\mathcal{O}^{d}_{i},$
(1)

where $\mathcal{L}_{\mathrm{SM}}$ is the SM Lagrangian, $\mathcal{O}^{d}_{i}$
are the EFT operators of dimension $d$, and $c^{d}_{i}$ are the Wilson
coefficients (WCs) which control the strength of the EFT effects. Since each
higher order term in Eq. (1) is suppressed by powers of $\Lambda^{d-4}$, the
smallest dimension operators tend to produce the largest expected deviations
from the SM ($d\leq 4$) processes. We do not consider operators that violate
baryon or lepton number, so all operators of odd dimension are excluded,
making the dimension-six operators the leading new physics contributions [3].
The next contributions would arise from dimension-eight operators, which are
not considered here.

This paper focuses specifically on operators that couple the top quark to
leptons, bosons, and other heavy (top or bottom) quarks. Searching for new
physics in the top quark sector is motivated by the uniquely large mass of the
top quark [4, 5] and the resulting Yukawa coupling to the Higgs field [6] of
roughly unity. The LHC provides a rich environment of top quarks produced with
additional leptons. Furthermore, the leptonic final-state decays of the top
quark provide experimentally clean signatures with relatively low background
contributions. The dominant SM contributions to these signatures arise from
processes in which one or more top quarks are produced in association with a
heavy boson or other top quarks. Referred to as associated top quark
production, these processes include $\ttbar\PH$ [7], $\ttbar\PW$ [8],
$\ttbar\PZ$ [9], $\PQt\PZ\PQq$ [10], $\PQt\PH\PQq$ [7], $\ttbar\ttbar$ [11].
While each of these processes have been studied individually, the analysis
presented in this paper takes a more global approach, using the EFT framework
to probe the potential effects of heavy new physics impacting these associated
top quark processes simultaneously.

The analysis described in this paper builds on the approach developed in Ref.
[12], which studied 16 dimension-six EFT operators with data collected in
2017, corresponding to an integrated luminosity of 41.5. With this approach,
EFT effects are incorporated into the event weights of the simulated samples,
allowing detector-level predictions that account for all relevant interference
effects (not only between new physics and the SM, but also among new physics
operators) and correlations among WCs. This approach has been subsequently
utilized to study $\ttbar\PZ$, $\PQt\PZ\PQq$, and $\PQt\PW\PZ$ [13] and to
study $\ttbar\PH$ and $\ttbar\PZ$ processes in which the Higgs or boson is
boosted [14]; the former simultaneously probes five WCs, while the latter
simultaneously probes eight WCs. Expanding on these previous analyses, we
study the effects of $26$ operators, incorporate additional signatures, and
improve the sensitivity by fitting differential kinematical distributions and
making use of all data collected by the CMS experiment in 2016–2018,
corresponding to a total integrated luminosity of 138. This paper thus
represents the most global detector-level EFT analysis to date.

The sections in this paper are organized as follows. The CMS detector is
introduced in Section 0.2. In Section 0.3, the data and simulated samples are
discussed. The object reconstruction and event selection are covered in
Sections 0.4 and 0.5. Section 0.6 describes the background estimation. The
statistical methods are explained in Section 0.7, and the systematic
uncertainties are detailed in Section 0.8. Section 0.9 presents the results,
which are summarized in Section 0.10. The tabulated results are provided in
the HEPData record for this analysis [15].

##  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 measured in gas-ionization detectors
embedded in the steel flux-return yoke outside the solenoid. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, can be found in Ref. [16].

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 [17]. 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 [18].

##  0.3 Data samples and signal simulation

This analysis uses data from proton-proton collisions at $\sqrt{s}=13\TeV$
collected by the CMS experiment during 2016–2018 with a combination of
single-, double-, and triple-lepton triggers. The minimum lepton transverse
momentum () requirements are chosen such that all events are within the fully
efficient regions of the triggers. The trigger efficiencies, calculated using
independent missing transverse energy triggers, are higher than 95% across the
full spectrum with uncertainties, systematic and statistical, smaller than 2%.

The analysis aims to study dimension-six EFT effects on processes in which top
quarks are produced in association with additional charged leptons. Processes
that lead to the same multilepton final-state signatures but are not impacted
by these EFT operators are backgrounds for this analysis. The expected
background contributions are estimated using a combination of simulated
samples and control samples in data, as described in Section 0.6.

The signal contribution is modeled at leading order (LO) using the event
generator (version 2.6.5) [19, 20, 21] with the dim6top model described in
Ref. [22] to incorporate the EFT effects. Using the Warsaw basis [23] of gauge
invariant dimension-six operators, this model focuses on operators involving
one or more top quarks, providing tree-level modeling of their effects. While
the model described in Ref. [22] allows for the EFT effects to vary
independently for each generation of leptons, we impose the assumption that
the EFT effects impact each lepton generation in the same way. In this
analysis, we aim to include all operators from Ref. [22] that significantly
impact processes in which one or more top quarks are produced in association
with charged leptons; as listed in Table 0.3, this amounts to $26$ operators
in total. The $26$ operators fall into four main categories: operators
involving four heavy quarks (4hq), operators involving two heavy quarks and
two light quarks (2hq2lq), operators involving two heavy quarks and two
leptons (2hq2), and operators involving two heavy quarks and bosons (2hqV).
The leptons in the 2hq2 operators may be either charged or neutral, and the
bosons in the 2hqV operators include both gauge bosons and the Higgs boson.
Figure 1 shows examples of how WCs from each of these categories can impact
associated top processes. The definitions of the operators associated with all
of these WCs are provided in Ref. [22]. In order to allow to properly handle
the emission of gluons from the vertices involving the ${c_{\PQt G}}$ WC
(which impacts interactions involving top quarks, gluons, and the Higgs
boson), an extra factor of the strong coupling $g_{\mathrm{s}}$ is applied to
the ${c_{\PQt G}}$ coefficient, as explained in Ref. [24]. The simulations use
the NNPDF3.1 [25] sets of parton distribution functions (PDFs). Parton
showering and hadronization are performed with 8.240 [26] with the CP5 tune
[27]. The decays of Higgs bosons and top quarks are handled with . Both the
leptonic and hadronic top quark decays can contribute. The top quark mass used
in the simulation is 172.5. The default input scheme in the dim6top model is
used. The $\PQt\Pell\overline{\Pell}\PQq$ and $\PQt\PH\PQq$ samples are
produced using the four-flavor scheme, while the five-flavor scheme is used
for the rest of the samples ($\ttbar\PH$, $\ttbar\Pell\overline{\Pell}$,
$\ttbar\Pell\PGn$, and $\ttbar\ttbar$).

Figure 1: Example Feynman diagrams illustrating Wilson coefficients from each
of the categories listed in Table 0.3. From left to right, the diagrams show
vertices associated with the ${c_{\PQt G}}$, ${c^{(\Pell)}_{\PQt\Pell}}$,
${c^{11}_{\PQQ\PQq}}$, and ${c^{1}_{\PQQ\PQt}}$.

List of Wilson coefficients (WCs) included in this analysis, grouped according
to the categories of WCs defined in Ref. [22]; the abbreviations for the WCs
categories used in this paper are noted parenthetically. The definitions of
the WCs and the definitions of the corresponding operators can be found in
Table 1 of Ref. [22]. An extra factor of the strong coupling is applied to the
${c_{\PQt G}}$ coefficient, as explained in the text.  Operator category
Wilson coefficients Two-heavy (2hqV) ${c_{\PQt\varphi}}$, ${c^{-}_{\varphi
Q}}$, ${c^{3}_{\varphi Q}}$, ${c_{\varphi\PQt}}$, ${c_{\varphi\PQt\PQb}}$,
${c_{\PQt\PW}}$, ${c_{\PQt\PZ}}$, ${c_{\PQb\PW}}$, ${c_{\PQt G}}$ Two-heavy-
two-lepton (2hq2) ${c^{3(\Pell)}_{Q\Pell}}$, ${c^{-(\Pell)}_{Q\Pell}}$,
${c^{(\Pell)}_{Q\Pe}}$, ${c^{(\Pell)}_{\PQt\Pell}}$,
${c^{(\Pell)}_{\PQt\Pe}}$, ${c^{S(\Pell)}_{\PQt}}$, ${c^{T(\Pell)}_{\PQt}}$
Two-heavy-two-light (2hq2lq) ${c^{31}_{\PQQ\PQq}}$, ${c^{38}_{\PQQ\PQq}}$,
${c^{11}_{\PQQ\PQq}}$, ${c^{18}_{\PQQ\PQq}}$, ${c^{1}_{\PQt\PQq}}$,
${c^{8}_{\PQt\PQq}}$ Four-heavy (4hq) ${c^{1}_{\PQQ\PQQ}}$,
${c^{1}_{\PQQ\PQt}}$, ${c^{8}_{\PQQ\PQt}}$, ${c^{1}_{\PQt\PQt}}$

Processes that are significantly impacted by these operators constitute the
signal processes for this analysis: $\ttbar\PH$,
$\ttbar\Pell\overline{\Pell}$, $\ttbar\Pell\PGn$,
$\PQt\Pell\overline{\Pell}\PQq$, $\PQt\PH\PQq$, and $\ttbar\ttbar$. The
$\ttbar\Pell\overline{\Pell}$ and $\PQt\Pell\overline{\Pell}\PQq$ samples
incorporate contributions from on- and off-shell bosons, contributions from
virtual photons, and contributions in which the lepton pair is produced
directly from a 2hq2 EFT vertex. The $\ttbar\Pell\PGn$ process similarly
includes lepton pairs produced from on-shell bosons, as well as those from
nonresonant processes, allowing the effects from 2hq2 operators to be
incorporated in the sample. The processes involving a Higgs boson enter our
signal selection (defined in Section 0.5) when the Higgs boson decays into one
or more leptons. All simulated signal processes are normalized with their
respective cross sections which are given Table 0.3. The cross section
computations may include quantum chromodynamics (QCD) and electroweak (EWK)
corrections.

Theoretical cross sections at next-to-LO (NLO) used for normalization of
simulated signal samples. The uncertainties are broken into normalization
components due to modeling the parton distribution functions (PDFs) and QCD
order. Entries without a value are negligible.  Process Cross section (pb)
Accuracy Ref. $\ttbar\PH$ $0.5071\pm
2.4\%\,\text{(PDF)}\,^{+7.6\%}_{-7.1\%}\,\text{(QCD)}$ NLO (QCD + EWK) [28]
$\ttbar\Pell\overline{\Pell}$ ($m_{\Pell\Pell}>10\GeV$)
$0.281\,^{+12\%}_{-10\%}\,\text{(QCD)}$ NLO (QCD + EWK) [28] $\ttbar\Pell\PGn$
$0.235\,^{+10\%}_{-11\%}\,\text{(QCD)}$ NLO (QCD + EWK) (incl.
$\alpS\alpha^{4}$ [29] terms and multijet merging)
$\PQt\Pell\overline{\Pell}\PQq$ ($m_{\Pell\Pell}>30\GeV$) $0.076\pm
2.7\%\,\text{(PDF)}\pm 2.0\%\,\text{(QCD)}$ NLO QCD [19, 20, 21] $\PQt\PH\PQq$
$0.071\pm 5.1\%\,\text{(PDF)}\,^{+6.5\%}_{-15\%}\,\text{(QCD)}$ NLO QCD [28]
$\ttbar\ttbar$ $0.01337\pm
6.9\%\,\text{(PDF)}\,^{+3.6\%}_{-11\%}\,\text{(QCD)}$ NLO (QCD + EWK) + NLL’
[30]

For each of the six signal processes, we account for diagrams with zero EFT
vertices (, the SM contribution) and diagrams with one EFT vertex (, the new
physics contribution). The amplitude for each process will thus depend
linearly on the WCs, and the cross section will depend quadratically on the
WCs. With $26$ WCs, the dependence of the cross section on the WCs will
therefore be given by a $26$-dimensional quadratic function. Since the weight
of each generated event corresponds to the event’s contribution to the
inclusive cross section, each event weight will also depend quadratically on
the $26$ WCs. For each generated event, we determine the $26$-dimensional
quadratic parametrization using the event reweighting technique [31]. Once we
have obtained the $26$-dimensional quadratic parametrization for each event,
we can find the dependence of any observable bin (, distinct category of
events defined by the properties of the final-state objects) on the WCs by
summing the quadratic parametrizations for each of the events that passes the
selection criteria for the given bin. Since we are thus able to write the
predicted yield of any observable bin as a function of the $26$ WCs, we can
obtain detector-level predictions at any arbitrary point in the
$26$-dimensional EFT space. This is the key enabling concept of this analysis,
and it allows us to rigorously account for all EFT effects across all analysis
bins simultaneously when performing the likelihood fitting with the
statistical framework. This approach was developed in Ref. [12], which
contains a more detailed description of the method of parametrizing the
predicted yields in terms of the WCs.

Similar to Ref. [12], we include an additional final-state parton in the
matrix element generation for the $\ttbar\PX$ processes using the MLM scheme
[21]. The inclusion of the additional parton can improve the modeling at high
jet multiplicities, and can also significantly impact the dependence of the
$\ttbar\PX$ processes on the WCs [24]. For the other processes
($\PQt\Pell\overline{\Pell}\PQq$, $\PQt\PH\PQq$, and $\ttbar\ttbar$), an
additional jet is not included because of technical limitations, and an
additional uncertainty is applied to account for this where relevant, as
described in Section 0.8.

##  0.4 Object reconstruction and identification

The global event reconstruction (also called particle-flow event
reconstruction [32]) aims to reconstruct and identify each individual particle
in an event, with an optimized combination of all subdetector information. In
this process, the identification of the particle type (photon (), electron,
muon, charged hadron, neutral hadron) plays an important role in the
determination of the particle direction and energy. Photons (, coming from
decays or from electron bremsstrahlung) are identified as ECAL energy clusters
not linked to the extrapolation of any charged particle trajectory to the
ECAL. Electrons (, coming from photon conversions in the tracker material or
from hadron semileptonic decays) are identified as a primary charged particle
track and potentially many ECAL energy clusters corresponding to this track
extrapolation to the ECAL and to possible bremsstrahlung photons emitted along
the way through the tracker material. Muons (, from hadron semileptonic
decays) 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. Charged hadrons are identified
as charged particle tracks neither identified as electrons, nor as muons.
Finally, neutral hadrons are identified as HCAL energy clusters not linked to
any charged hadron trajectory, or as a combined ECAL and HCAL energy excess
with respect to the expected charged hadron energy deposit.

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 energy of muons is
obtained from the corresponding track momentum. The energy of charged hadrons
is determined from a combination of the track momentum and the corresponding
ECAL and HCAL energies, corrected for the response function of the
calorimeters to hadronic showers. Finally, the energy of neutral hadrons is
obtained from the corresponding corrected ECAL and HCAL energies.

The electron momentum is estimated by combining the energy measurement in the
ECAL with the momentum measurement in the tracker. The momentum resolution for
electrons with $\pt\approx 45\GeV$ from $\PZ\to\Pe\Pe$ decays ranges from 1.6
to 5%. It is generally better in the barrel region than in the endcaps, and
also depends on the bremsstrahlung energy emitted by the electron as it
traverses the material in front of the ECAL [33, 34]. We require electrons to
have pseudorapidity $\abs{\eta}<2.5$.

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

Reconstructed electrons and muons are required to satisfy selection criteria
aiming to select prompt leptons produced in decays of the or bosons, as well
as those that couple directly to the top quarks in the beyond-SM scenarios we
consider. This lepton selection, fully described in Ref. [7], is performed by
means of a multivariate discriminator [36] that takes as its input the
variables related to the lepton isolation and its impact parameter. In
addition, kinematic information of charged and neutral particles around the
lepton candidate is used by feeding it into the jet reconstruction and tagging
algorithms, described below. The score of the tagging algorithm is used as an
input to the discriminator.

The hadronic jets are clustered using the infrared and collinear safe anti-
algorithm [37, 38] with a distance parameter of 0.4. Jet momentum is
determined as the vectorial sum of all particle momenta in the jet, and is
found from simulation to be, on average, within 5 to 10% of the true momentum
over the whole spectrum and detector acceptance. Additional proton-proton
interactions within the same or nearby bunch crossings (pileup) can contribute
additional tracks and calorimetric energy depositions to the jet momentum. To
mitigate this effect, charged particles identified to be originating from
pileup vertices are discarded and an offset correction is applied to correct
for remaining contributions [39]. Jet energy corrections are derived from
simulation to bring the measured response of jets to that of particle level
jets on average. In situ measurements of the momentum balance in dijet,
$\PGg+\text{jet}$, $\PZ+\text{jet}$, and multijet events are used to account
for any residual differences in the jet energy scale (JES) between data and
simulation [40]. The jet energy resolution (JER) amounts typically to 15–20%
at 30, 10% at 100, and 5% at 1 [40]. Additional selection criteria are applied
to each jet to remove jets potentially dominated by anomalous contributions
from various subdetector components or reconstruction failures.

Jets originating from quark decays (jets) are identified using the algorithm
[41] known as DeepJet [42, 43], which uses a deep neural network to classify
jets with different working points. The analysis uses a medium working point
which correctly identifies jets with an efficiency of about 70%, and a loose
working point with an efficiency of about 85%. The misidentification rate for
gluon or light-flavor quark jets for these two working points is 1.0% and 10%,
respectively.

##  0.5 Event selection and categorization

The analysis targets events in which top quarks are produced in association
with additional charged leptons. The event selection categories are defined
primarily by the multiplicity of charged leptons, jets, and jets. This event
selection scheme aims to isolate subsamples of the broader multilepton data
set into categories containing different admixtures of the contributing signal
processes, resulting in 43 unique categories. The events in each category are
binned according to a kinematical variable, which will be described in Section
0.5.4. While it is not possible to completely isolate any of these individual
processes, the division of the sample into subsamples with different
compositions increases the statistical power to determine which specific
processes might be responsible for any deviation from SM expectations that may
be observed in data.

Figure 2: Summary of the event selection categorization. The details for the
selection requirements are described in Sections 0.5.1–0.5.3.

We require each event to have either two same-sign leptons
($2\Pell\textrm{ss}$), three leptons ($3\Pell$), or four or more leptons
($4\Pell$). The $3\Pell$ event category is further subdivided into events with
and without on-shell boson decays (pair of same-flavor and opposite-sign
leptons with an invariant mass within 10of the boson mass), referred to as the
on-and off-subcategories, respectively. The events in the $3\Pell\textrm{
off-}\PZ$ category and in the $2\Pell\textrm{ss}$ category are subdivided
based on the sum of the charges of the leptons. A schematic summary of the
event selection categorization is shown in Fig. 2. The selected events in all
categories must have at least two jets with $\pt>30\GeV$ and $\abs{\eta}<2.4$.
Events containing a pair of leptons with an invariant mass of less than 12are
rejected to avoid backgrounds from light resonances. We use the DeepJet
algorithm to impose the further requirement of one or more -tagged jets,
depending on the lepton multiplicity. The leptons only enter our event
selections via their $\PGt\to\Pe$ and $\PGt\to\PGm$ decay modes. Table 0.5
provides a summary of the requirements for each event category, which are
detailed in the following sections.

Object requirements for the 43 event selection categories. Requirements
separated by commas indicate a division into subcategories. The kinematical
variable that is used in the event category is also listed. Section 0.5.4
provides further details regarding the kinematical distributions.  Event
category Leptons $m_{\Pell\Pell}$ tags Lepton charge sum Jets Kinematical
variable $2\Pell\textrm{ss }2\PQb$ 2 No requirement 2 $>$0, $<$0 4, 5, 6,
$\geq$7 $\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ $2\Pell\textrm{ss
}3\PQb$ 2 No requirement $\geq$3 $>$0, $<$0 4, 5, 6, $\geq$7
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ $3\Pell\textrm{
off-}\PZ\textrm{ }1\PQb$ 3 $\abs{m_{\PZ}-m_{\Pell\Pell}}>10\GeV$ 1 $>$0, $<$0
2, 3, 4, $\geq$5 $\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$
$3\Pell\textrm{ off-}\PZ\textrm{ }2\PQb$ 3
$\abs{m_{\PZ}-m_{\Pell\Pell}}>10\GeV$ $\geq$2 $>$0, $<$0 2, 3, 4, $\geq$5
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ $3\Pell\textrm{ on-}\PZ$ 1 3
$\abs{m_{\PZ}-m_{\Pell\Pell}}<10\GeV$ 1 No requirement 2, 3, 4, $\geq$5
$\pt(\PZ)$ $3\Pell\textrm{ on-}\PZ$ 2 3 $\abs{m_{\PZ}-m_{\Pell\Pell}}<10\GeV$
$\geq$2 No requirement 2, 3, 4, $\geq$5 $\pt(\PZ)$ or
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ $4\Pell$ $\geq$4 No requirement
$\geq$2 No requirement 2, 3, $\geq$4
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$

###  0.5.1 The $2\Pell\textrm{ss}$ event category

The $2\Pell\textrm{ss}$ event category targets $\ttbar\PH$, $\ttbar\Pell\PGn$,
and $\ttbar\ttbar$ events, where the events contain two leptons of the same
charge which must pass the tight object selection criteria. The higher (lower)
lepton must have $\pt>25$ (15). The charge requirement significantly reduces
$\ttbar+\text{jets}$ background by leveraging the precision of the CMS
detector to reliably reconstruct the electron and muon charges. We require the
uncertainty in the muon to be smaller than 20% and apply electron selection
criteria, described as the “selective algorithm” in Ref. [33], that demand the
consistency among three independent measurements of the electron charge, based
on two different parametrizations of the electron track and the relative
positions of the electron track and its energy deposit in the ECAL. The
$2\Pell\textrm{ss}$ events must have jet multiplicity of $\geq$4 with
$\pt>30\GeV$ and $\abs{\eta}<2.4$. We also split the $2\Pell\textrm{ss}$
events based on total lepton charge because the $\ttbar\PWp$ cross section in
proton-proton collisions is roughly twice that of the $\ttbar\PWm$ cross
section. The $2\Pell\textrm{ss}$ events are further subdivided based on jet
multiplicity, which helps target $\ttbar\ttbar$ events since events with
higher numbers of -tagged jets are enriched in $\ttbar\ttbar$ events.

###  0.5.2 The $3\Pell$ event category

The $3\Pell$ event category targets $\ttbar\Pell\overline{\Pell}$,
$\PQt\Pell\overline{\Pell}\PQq$, $\ttbar\PH$, and $\ttbar\Pell\PGn$ events.
This category requires exactly three leptons to pass the tight selection
criteria. The first, second, and third leptons must be above the threshold of
25, 15, and 10, respectively. In case the third lepton is an electron, the
threshold is $\pt>15\GeV$, which suppresses the contributions from nonprompt
electrons and helps stay above trigger thresholds. We also require at least
two jets with $\pt>30\GeV$. In the $3\Pell$ event category, we separate events
which contain a same-flavor opposite-sign pair of leptons with a mass within
10of $m_{\PZ}$ (91.2) in order to isolate an enhanced contribution from the
on-shell boson decay, primarily from the $\ttbar\PZ$ process. Events that do
not lie within this region are further separated based on whether the sum of
the lepton charges is positive or negative to again exploit the difference in
cross section between $\ttbar\PWp$ and $\ttbar\PWm$. All $3\Pell$ events are
also categorized by the number of jets passing the medium DeepJet working
point: exactly one jet, or $\geq$2 jets. Requiring one jet enhances
$\PQt\Pell\overline{\Pell}\PQq$ events, while requiring $\geq$2 jets helps to
separate $\PQt\Pell\overline{\Pell}\PQq$ and $\ttbar\Pell\overline{\Pell}$
events.

###  0.5.3 The $4\Pell$ event category

The $4\Pell$ event category targets $\ttbar\Pell\overline{\Pell}$ and
$\ttbar\PH$ events, requiring at least four leptons passing the specific
selection criteria. The first through fourth leptons must be above the
threshold of 25, 15, 10, and 10, respectively. If the last two leptons are
electrons, the requirement becomes $\pt>15\GeV$ for both because of the same
reasons described in the $3\Pell$ event category. The events must have at
least two jets with $\pt>30\GeV$ and $\abs{\eta}<2.4$. At least two of these
jets must be jets, where one of them is required to pass the DeepJet medium
working point, while the second is allowed to pass the loose working point.

###  0.5.4 Kinematical variables

The selections described in Sections 0.5.1–0.5.3 result in 43 unique
categories of events. In order to gain additional sensitivity to EFT effects,
the events in each of the 43 categories are binned according to a kinematical
variable.

For most of the event categories, we use the variable
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$. To form this variable, we sum
vectorially the momenta of all possible pairs of objects in the collections of
leptons and jets and select the combination with the largest . The value of
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ is the of that combination.
Thus, this variable may represent the transverse momenta of two leptons, two
jets, or a lepton and a jet. The
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ variable is useful because it
combines sensitivity to a broad range of EFT effects that grow with energy
with access to a combination of EFT operators involving jets and/or leptons.
The $\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ variable thus provides
broadly good sensitivity to most of the WCs included in this analysis,
motivating its use in the majority of the event categories. For most of the
on-shell $3\Pell$ categories, however, we do not use
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$. Instead, we use a variable
that aims to provide sensitivity to the EFT operators involving bosons, as
these operators may modify the kinematical variables of the boson. Denoted as
$\pt(\PZ)$, this variable is defined as the of the same-flavor opposite-sign
lepton pair associated with the boson. The $\pt(\PZ)$ variable is used for all
of the on-shell boson $3\Pell$ event categories, except for the event
categories with two or three jets and two jets. As shown in Fig. 3, events
with ${c^{31}_{\PQQ\PQq}}$ and ${c^{38}_{\PQQ\PQq}}$ vertices can contribute
to these final states, and in these cases the boson is not associated with the
EFT vertex, so using $\pt(\PZ)$ in these event categories decreases
sensitivity to the ${c^{31}_{\PQQ\PQq}}$ and ${c^{38}_{\PQQ\PQq}}$
coefficients. For this reason, we utilize
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ instead of $\pt(\PZ)$ in these
specific on-shell categories, since this more general variable provides good
sensitivity to the ${c^{31}_{\PQQ\PQq}}$ and ${c^{38}_{\PQQ\PQq}}$ WCs.

Figure 3: Example Feynman diagram illustrating how the ${c^{31}_{\PQQ\PQq}}$
and ${c^{38}_{\PQQ\PQq}}$ WCs can impact the processes in $3\Pell$ categories
with two quarks and an on-shell boson. These two WCs are a part of the 2hq2lq
group, but unlike the other WCs in this group, these two WCs are associated
with operators that have vertices involving a top and bottom quark pair, as
pictured in the figure.

In summary, the $\pt(\PZ)$ variable is used for all of the on-shell
categories, except for the two and three jet categories with two -tagged jets;
the $\pt(\PZ)$ variable is thus used in a total of six event categories. In
the remaining 37 event categories, the
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ variable is used. For the
categories that are binned in $\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$,
four bins are used. For the categories that are binned in $\pt(\PZ)$ (which
generally have larger numbers of selected events), five bins are used. This
results in 178 total analysis bins. Binning the 43 analysis categories in
terms of the $\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ and $\pt(\PZ)$
variables provides an improvement in sensitivity of up to a factor of about
two, compared to the case where the 43 analysis categories are not further
subdivided.

##  0.6 Background estimation

In addition to contributions from signal processes, we expect contributions
from other SM processes to the signal regions (SRs) defined above. We
distinguish between reducible and irreducible backgrounds. A background is
considered to be irreducible if all final state leptons are genuine prompt
leptons. The contribution from irreducible backgrounds is dominated by diboson
($\PW\PZ$ and $\PZ\PZ$) production, but we expect a subleading contribution
from triboson and $\PQt\PW\PZ$ production processes. Although the $\PQt\PW\PZ$
process in principle could be impacted by a subset of the WCs studied in this
analysis, the analysis does not have strong sensitivity to the effects (and
the predicted contribution is also small, making up only about 3% of the total
predicted yield); the process is therefore categorized as background. We
estimate the contribution of these processes using simulations with the , [44,
45, 46, 47, 48], and [49] programs. The $\PW\PZ$ production is normalized to
the next-to-NLO in perturbative QCD and NLO in EWK theory [50]. For $\PZ\PZ$
production, the $\qqbar\to\PZ\PZ$ samples are next-to-LO (NLO) in QCD and LO
in electroweak [44, 45]; the $\Pg\Pg\to\PZ\PZ$ samples are LO, and their cross
sections are scaled to match a cross section that is NLO in QCD [51]. The
diboson contributions are modeled with , and . The triboson and $\PQt\PW\PZ$
contributions are modeled with .

We distinguish three types of reducible backgrounds. The leading contribution
is due to processes with nonprompt leptons (, from the decay of hadrons) in
the final state. We estimate the contribution from these events following the
misidentification probability (MP) method described in Ref. [7]. This method
is performed by selecting events passing all the criteria described in Section
0.5 with the exception of those on leptons, which are required to pass a
looser identification criteria, but to fail the full set of requirements
described in Section 0.4. This region, denoted as the application region, is
enriched in events with nonprompt leptons, but resembling the kinematics of
the events in the SR.

We obtain an estimation of the nonprompt-lepton contribution to the SR by
weighting each event in the application region by a weight $w$, defined as

$w=(-1)^{n+1}\prod_{i=1}^{n}\frac{f(\pt,\eta)_{i}}{1-f(\pt,\eta)_{i}},$ (2)

where $n$ is the number of leptons in the event failing the lepton selection
criteria and $f(\pt,\eta)_{i}$ is the MP, defined as the probability for a
nonprompt lepton passing the looser lepton selection to pass the required
selection. This probability is measured in a sample of multijet events
collected by a set of low-lepton triggers and is measured as a function of the
lepton and $\eta$, separately for electrons and muons. The validity of the
method has been checked using samples of simulated and multijet events, as
well as dedicated control regions (CRs), as well as dedicated
$2\Pell\textrm{ss}$ control regions (CRs), which are defined with lower jet
multiplicities than the $2\Pell\textrm{ss}$ SRs in order to ensure
orthogonality with the SR.

An additional contribution to reducible backgrounds in the $2\Pell\textrm{ss}$
SR is due to cases in which the charge of one of the final state leptons is
measured incorrectly. This contribution is dominated by events and is
estimated by selecting events passing the same selection as the
$2\Pell\textrm{ss}$ SR, but inverting the same-sign requirement on the
dilepton system. Events in this region are weighted by the probability of each
of the leptons to have their charge measured with the wrong sign. This
probability is negligible for muons; however the probability for electrons is
larger and we estimate it using simulations. Additionally, we compare the
prediction from the simulation with the observation of a region enriched in
$\PZ\to\Pe\Pe$, where the charge of one of the electrons has been measured
with the incorrect sign. The two agree within the applied uncertainties (30%),
but we correct for the residual differences by a constant scale factor (SF) in
our prediction.

Finally, a small contribution arises from the conversion of photons
interacting with the detector material. This contribution is significantly
suppressed by the electron reconstruction and identification algorithms, since
we require electron tracks to have hits in the innermost layers of the silicon
tracker and we veto electron candidates that are matched to a reconstructed
photon conversion vertex. We estimate this contribution using $\ttbar\PGg$
simulation (similar to Ref. [52]) at LO with .

##  0.7 Statistical methods

A maximum likelihood fit is performed across all of the analysis bins,
treating the number of observed events in each bin as an independent Poisson
measurement [53]. The total yield in each bin is parametrized as a quadratic
function of the $26$ WCs, which are the parameters of interest in the
likelihood fit. The yield in each bin also depends on the effects of the
systematic uncertainties (as listed in Section 0.8), which are treated as
nuisance parameters (NPs) in the likelihood fit. The likelihood has the
functional form

$L=\prod^{N_{\text{bins}}}_{i=1}P(n_{i}|\nu_{i}(c,\theta))\prod^{N_{\mathrm{NP}}}_{j=1}p(\hat{\theta}_{j}|\theta_{j}),$
(3)

where $P(n_{i}|\nu_{i}(c,\theta))$ is the probability of observing $n_{i}$
events in the $i$-th category given by the Poisson distribution, and
$p(\hat{\theta}_{j}|\theta_{j})$ is the prior probability for the $j$-th NP
evaluated at the maximum likelihood estimate $\hat{\theta}_{j}$. As described
in Section 0.3, the number of expected events is parametrized as a quadratic
function of the WCs. This can be expressed as

$\nu_{i}(c,\theta)=\mathrm{SM}_{i}(\theta)+\sum_{j}\frac{c_{j}}{\Lambda^{2}}l_{ij}(\theta)+\sum_{j}\frac{c_{j}^{2}}{\Lambda^{4}}q_{ij}(\theta)+\sum_{j\neq
k}\frac{c_{j}c_{k}}{\Lambda^{4}}m_{ijk}(\theta),$ (4)

where $c$ are the WCs, $\theta$ are the NPs, and $l_{j}$, $q_{j}$, and
$m_{jk}$ respectively are the linear, quadratic, and cross-term structure
constants of the parametrization. In order to find the 1 and 2 standard
deviation ($\sigma$) confidence intervals (CIs) for each WC, a scan is
performed over each WC, profiling the other $25$ WCs and the NPs. The test
statistic is then given by

$\Lambda_{p}(c_{i})=-2\ln{\frac{L(\hat{c_{i}},\hat{\theta})}{L(c_{i},\hat{\hat{\theta}})}},$
(5)

where $\hat{c_{i}}$ and $\hat{\theta}$ are the values which maximize the
likelihood, and $\hat{\hat{\theta}}$ corresponds to all the profiled
parameters which maximize the likelihood at a particular WC value. The test
statistic asymptotically follows a $\chi^{2}$ distribution where the degrees
of freedom correspond to the number of free parameters. The scan is performed
over a discrete set of values for the selected WC; at each WC value, the
likelihood fit finds the corresponding values of the other WCs and NPs which
minimize the negative log-likelihood (NLL) function. The 1 and 2$\sigma$ CIs
are extracted at the points where the values of the test statistic curve are
equal to the values of 1 and 4 respectively. In order to explore the effects
of each WC individually (without interference between WCs), a scan is also
performed where the other $25$ WCs are held to their SM values of zero
(instead of profiled). The 1 and 2$\sigma$ CIs for this scan are extracted in
the same way as the profiled fit. Simultaneous scans are also performed for a
selected subset of WC pairs (denoted as 2D). The 2D scans are performed
similarly to the single WC scans; we step through a discrete set of points for
the pair of scanned WCs, with the other $24$ WCs either profiled or fixed to
their SM values of zero. The 68.3, 95.5, and 99.7% 2D CIs are extracted where
test statistic is equal to 2.30, 6.18, and 11.83, respectively.

##  0.8 Systematic uncertainties

The systematic uncertainties for this analysis are split into two main
categories: uncertainties which only affect the rate of the signal and
background processes, and ones which affect both the rate and shape of the
measured distributions. Shape uncertainties which are specified as fully
correlated across all distributions and data-taking years are expressed as a
single NP per systematic term. The sources of systematic uncertainties
considered are the following: the integrated luminosity, the JES and JER, jet
tagging SFs, the theoretical cross section, the renormalization
($\mu_{\mathrm{R}}$) and factorization ($\mu_{\mathrm{F}}$) scales, the parton
shower, the additional radiation, the electron and muon identification and
isolation, the trigger efficiency, the pileup, the L1 ECAL trigger efficiency
corrections, the misidentified-lepton rate, the charge misreconstruction rate,
and the mismodeling of the jet multiplicity in diboson events A breakdown of
the systematic uncertainties and their average impact on the nominal predicted
yields can be found in Table 0.8.

Integrated luminosity:

The uncertainty in the integrated luminosity is estimated to be 1.6% [54, 55,
56] for the 2016–2018 data set.

Jet energy scale and resolution:

These systematic uncertainties are evaluated by shifting the scale and
resolution applied to the reconstructed jets by $\pm 1\sigma$ in bins of and
$\eta$. The JES uncertainty is correlated across years, and is modeled with a
total of five independent parameters. The JER uncertainty is uncorrelated
across years, and is modeled with a total of four independent parameters. The
impact of the JES and JER uncertainties is on average 1% of the nominal
prediction across all analysis bins.

jet tagging SFs:

The uncertainties resulting from the tagging efficiency and misidentification
rate are assessed by varying, within their uncertainties, the tagging data-to-
simulation SFs. The SFs for the the heavy-flavor (and quark) jets are varied
together, and the SFs for the light-flavor (gluon and , , and quark) jets are
also together, independently from the heavy-flavor SFs. The uncertainties for
both flavor components are split into a component that is correlated across
all data-taking years and components that are uncorrelated across all data-
taking years. The impact of the jet tagging uncertainty is on average 1% of
the nominal prediction across all analysis bins.

Theoretical cross section:

The predicted yields for all signal and background categories are normalized
to their theoretical cross section values, calculated at NLO precision or
greater, with the exception of $\ttbar\PGg$, which is LO. The theoretical
uncertainties on these calculations come from the PDF choice and the choice of
the QCD scales ($\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$). The average
uncertainty across all analysis bins due to the scales is 1–4% of the total
nominal predicted rate, while that from the PDFs is 1%.

Renormalization and factorization scales:

Uncertainties in the matrix element generators due to the $\mu_{\mathrm{R}}$
and $\mu_{\mathrm{F}}$ scales are measured by shifting the $\mu_{\mathrm{R}}$
and $\mu_{\mathrm{F}}$ up (down) by a factor of 2 (0.5) independently, which
allows the shape of the variations to be incorporated coherently across bins.
Since the inclusive cross sections for the simulated samples are normalized to
NLO predictions and the uncertainty on this overall normalization is already
accounted for with the theoretical cross section uncertainty discussed above,
the $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ systematic uncertainties do not
impact the inclusive cross section.111Because the precision of the
$\ttbar\PGg$ cross section is LO, the $\mu_{\mathrm{R}}$ and
$\mu_{\mathrm{F}}$ uncertainties impact both the inclusive cross section and
the kinematic shape for this sample. Rather, these systematic uncertainties
affect the kinematic shapes and, correspondingly, the acceptance. The
uncertainty due to the choice of $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ is
about 3% of the nominal prediction across all analysis bins.

Parton shower:

The uncertainty due to initial- and final-state radiation (ISR and FSR) in the
parton-shower simulation is estimated by varying the scale of each up (down)
by a factor of 2 (0.5) respectively. The uncertainty due to the FSR and ISR
modeling is on average 1–2% of the nominal prediction across all analysis
bins.

Additional radiation:

Because of parton-matching limitations in the generator, additional partons
cannot be included in the single ($\PQt\Pell\overline{\Pell}\PQq$ and
$\PQt\PH\PQq$) LO EFT samples. Instead, the $\PQt\Pell\overline{\Pell}\PQq$ LO
sample is compared to the NLO $\PQt\PZ\PQq$ sample. In each jet bin, any
discrepancy not covered by the systematic uncertainties is ascribed as an
additional radiation uncertainty. Since this is an uncertainty due to issues
in $t$-channel simulations, the same uncertainty is also applied to the
$\PQt\PH\PQq$ sample. The average change in yield due to the additional
radiation is 7% of the total nominal predicted rate.

Electron and muon identification and isolation:

The lepton SFs used to correct the efficiency on simulation to reproduce the
efficiency in data are derived with a “tag-and-probe” method [33, 35, 57]. The
lepton identification, isolation, and tracking efficiency SF uncertainties are
estimated and propagated to the final fitting variable distributions. The
total uncertainty of the resulting SFs is the quadratic sum of the statistical
and systematic uncertainties, for electrons and muons separately. The
uncertainty in the electron (muon) SF results in about 2 (1)% variation of the
nominal prediction across all analysis bins.

Trigger efficiency:

The impact due to the uncertainty in the trigger efficiency is estimated as
well by varying the SFs within their uncertainties separately for each data-
taking year and final state. This uncertainty is treated as uncorrelated
between data-taking years. The uncertainty is on average $\leq$1% of the
nominal prediction across all analysis bins.

Pileup:

Effects due to the uncertainty in the distribution of the number of pileup
interactions are evaluated by varying the total inelastic proton-proton cross
section used to calculate the number of pileup interactions in data by 4.6%
from its nominal value, which corresponds to a 1$\sigma$ variation [58]. The
uncertainty in each analysis category is on average 1% of the nominal
prediction across all analysis bins.

L1 ECAL trigger efficiency:

To model the ECAL L1 trigger efficiency in 2016–2017 [17], a weight with its
uncertainty is applied to the simulation. The uncertainty in the predicted
yields due to the L1 ECAL trigger efficiency is about 1% of the total nominal
predicted rate.

Misidentified-lepton rate:

The misidentification rates used to estimate the nonprompt-lepton background
are affected by a statistical uncertainty associated with the number of events
in the kinematic region used in the measurement as well as by uncertainties
due to the different composition of this measurement region and the SR. Three
sources of systematic uncertainty are considered. The first uncertainty stems
from the statistical uncertainty associated with the multijet measurement
region, while the second accounts for the uncertainty in the subtraction of
the prompt-lepton contribution from the yield in this region. The effect of
these two uncertainties is taken into account as variations of the MP map
overall scale, as well as the dependences on and $\eta$. In addition to the
measurement in the MP, the residual differences between the MP estimation with
multijet and simulated samples is taken as an additional source of systematic
uncertainty. The uncertainty in the misidentified-lepton rate is on average of
30% of the total expected misidentified leptons, and an average of 3% of the
nominal prediction across all analysis bins.

Charge misreconstruction rate:

An uncertainty of 30% is assigned to the yield of the misreconstructed-charge
background to account for the differences observed between the prediction and
data in the charge misidentification CR, as noted in Section 0.6. The
uncertainty in each analysis category is on average 1% of the nominal
prediction across all analysis bins.

Jet mismodeling:

A discrepancy between the simulations and data was observed in some of the
diboson CRs for high jet multiplicities. This is due to a mismodeling in QCD
radiation. An additional uncertainty derived from the difference between the
data and simulation in each jet bin in this CR is added to cover this
discrepancy. The uncertainty is treated as correlated across jet bins. The
uncertainty in the jet mismodeling is on average 7% of the nominal prediction
across all analysis bins.

For the majority of the WCs studied in this analysis, the precision of the
result is dominated by the statistical uncertainty; the systematic
uncertainties represent the dominant contribution for six of the WCs,
including two WCs from the 2hq2lq category (${c^{18}_{\PQQ\PQq}}$ and
${c^{8}_{\PQt\PQq}}$) as well as four WCs from the 2hqV category (${c_{\PQt
G}}$, ${c_{\PQt\varphi}}$, ${c^{-}_{\varphi Q}}$, and ${c_{\varphi\PQt}}$).
For all of the WCs that are dominated by systematic uncertainties, the NLO
cross section uncertainties (to which the LO samples are normalized) represent
the leading sources of uncertainty. While normalization uncertainties
represent the leading systematic uncertainties, this does not imply that the
EFT primarily impacts the normalization. As discussed in Section 0.5.4,
binning the events in each selection category according to a kinematical
distribution significantly improves the sensitivity, confirming that many of
the WCs indeed have strong impacts on the kinematical shapes. Rather, the fact
that the leading systematic uncertainties are normalization uncertainties is a
reflection of the fact that EFT may lead to small deviations with respect to
the SM. A precise modeling of the SM distribution is thus important for
identifying potentially small deviations from the SM, not only in the
normalization but also in shape. Other systematic uncertainties that often
have relatively large impacts for these WCs include various uncertainties
related to the modeling (, ISR, FSR, $\mu_{\mathrm{R}}$, and the diboson jet
mismodeling uncertainty). For the WCs that are dominated by systematic
uncertainties, the uncertainty on the nonprompt-lepton contribution generally
represents the leading experimental systematic uncertainty.

The shape variation due to the PDF uncertainty is measured by reweighting the
spectra using 100 replica sets and variations in . The total uncertainty is
then measured using the recommendation in PDF4LHC [59]. This uncertainty had a
negligible effect on the analysis, so it was not included.

Summary of systematic uncertainties along with the average change in the SM
prediction yields  Systematic uncertainty Average change in the yields
Integrated luminosity 1.6% Jet energy scale and resolution 1% jet tagging
scale factors 1% Theoretical cross section 1–4% (QCD) 1% (PDF) Renormalization
and factorization scales 3% Parton shower 1–2% Additional radiation 7%
Electron and muon identification and isolation 2% (electron) 1% (muon) Trigger
efficiency $\leq$1% Pileup 1% L1 prefiring 1% Misidentified-lepton rate 3%
Charge misreconstruction rate 1% Jet mismodeling 7%

##  0.9 Results

Figure 4: The categories shown in these plots are $2\Pell\textrm{ss }2\PQb$
and $2\Pell\textrm{ss }3\PQb$. The prefit plots for each category are shown on
the left side while the postfit plots are shown on the right side. The
differential distribution in the plots is
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$. The jet subcategories are
arranged from low jet multiplicity to high jet multiplicity from left to right
for each individual plot. For example, in the $2\Pell\textrm{ss }2\PQb$ (+)
plot, the first four bins are the
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ variable for $2\Pell\textrm{ss
}2\PQb$ (+) 4j, the next four bins are for $2\Pell\textrm{ss }2\PQb$ (+) 5j,
The process labeled “Conv.” corresponds to the photon conversion background,
“Misid. leptons” corresponds to misidentified leptons, and “Charge misid.”
corresponds to leptons with a mismeasured charge.

Figure 5: The categories shown in these plots are $3\Pell\textrm{
off-}\PZ\textrm{ }1\PQb$ and $3\Pell\textrm{ off-}\PZ\textrm{ }2\PQb$. The
prefit plots for each category are shown on the left side while the postfit
plots are shown on the right side. The differential distribution in the plots
is $\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$. The jet subcategories are
arranged from low jet multiplicity to high jet multiplicity from left to right
for each individual plot. For example, in the $3\Pell\textrm{ off-}\PZ\textrm{
}1\PQb$ (+) plot, the first four bins are the
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ variable for $3\Pell\textrm{
off-}\PZ\textrm{ }1\PQb$ (+) 2j, the next four bins are for $3\Pell\textrm{
off-}\PZ\textrm{ }1\PQb$ (+) 3j,

Figure 6: The categories shown in these plots are $3\Pell\textrm{
on-}\PZ\textrm{ }1\PQb$, $3\Pell\textrm{ on-}\PZ\textrm{ }2\PQb$, and
$4\Pell$. The prefit plots for each category are shown on the left side while
the postfit plots are shown on the right side. The differential distribution
is $\pt(\PZ)$ in the plots of $3\Pell\textrm{ on-}\PZ\textrm{ }1\PQb$ and
$3\Pell\textrm{ on-}\PZ\textrm{ }2\PQb$ (4j and 5j), and
$\pt(\Pell{\HepParticle{j}{}{}})_{\text{max}}$ in the plots of $3\Pell\textrm{
on-}\PZ\textrm{ }2\PQb$ (2j and 3j) and $4\Pell$. The jet subcategories are
arranged from low jet multiplicity to high jet multiplicity from left to right
for each individual plot. For example, in the $3\Pell\textrm{ on-}\PZ\textrm{
}1\PQb$ plot, the first five bins are the $\pt(\PZ)$ variable for
$3\Pell\textrm{ on-}\PZ\textrm{ }2\PQb$ 2j, the next five bins are for
$3\Pell\textrm{ on-}\PZ\textrm{ }2\PQb$ 3j,

The number of observed events for all 178 bins is shown in Figs. 4, 5, and 6
separated by the different signal categories, with the expectation obtained by
setting all WCs to their SM values of zero (prefit) or simultaneously fitting
the $26$ WCs and the NPs by minimizing the NLL (postfit). To visualize the
relative yields across the categories, Fig. 7 combines the bins of the
kinematic variables, resulting in a plot of jet multiplicity for each
selection category. The hatched regions in the stacked plot and shaded regions
in the ratio plot correspond to the total systematic uncertainty. The result
of the fit is in agreement with the SM hypothesis.

Figure 7: Observed data and expected yields in the prefit (upper) and postfit
(lower) scenarios. All kinematic variables have been combined, resulting in
distributions for the jet multiplicity only. The postfit values are obtained
by simultaneously fitting all $26$ Wilson coefficients (WCs) and the nuisance
parameters (NPs). The lower panel contains the ratios of the observed yields
over the expected. The error bands are computed by propagating the
uncertainties from the WCs and NPs.

The 1 and 2$\sigma$ CIs extracted from the likelihood fits described in
Section 0.7 are shown in Fig. 8. The solid black (dashed red) bars show the
results of the fits in which the other $25$ WCs are profiled (fixed to their
SM values of zero). The CIs for all $26$ WCs are consistent with the SM. The 1
and 2$\sigma$ CIs for each WC are listed in Tables 0.9 and 0.9, respectively.
We note that, as mentioned in Section 0.3, the definition of the operator
associated with ${c_{\PQt G}}$ here includes an explicit factor of the strong
coupling constant, which should be accounted for when comparing to results
extracted based on other conventions.

Figure 8: Summary of CIs extracted from the likelihood fits described in
Section 0.7. The WC 1$\sigma$ (thick line) and 2$\sigma$ (thin line) CIs are
shown for the case where the other WCs are profiled (in solid black), and the
case where the other WCs are fixed to their SM values of zero (in dashed red).
To make the figure more readable, the intervals for ${c_{\PQt\varphi}}$,
${c_{\varphi\PQt}}$, and ${c^{-}_{\varphi Q}}$ were scaled by 0.5, and the
intervals for ${c_{\PQt G}}$, ${c^{1}_{\PQt\PQq}}$, ${c^{11}_{\PQQ\PQq}}$,
${c^{38}_{\PQQ\PQq}}$, and ${c^{31}_{\PQQ\PQq}}$ were scaled by 5.

The 1$\sigma$ uncertainty intervals extracted from the likelihood fits
described in Section 0.7. The intervals are shown for the case where the other
Wilson coefficients (WCs) are profiled, and the case where the other WCs are
fixed to their SM values of zero.  WC/$\Lambda^{2}$ [$\text{TeV}^{-2}$]
1$\sigma$ CI (other WCs profiled) 1$\sigma$ CI (other WCs fixed to SM) WC
category 2hq2 ${c^{T(\Pell)}_{\PQt}}$ [$-0.22$, 0.22] [$-0.26$, 0.26]
${c^{S(\Pell)}_{\PQt}}$ [$-1.52$, 1.53] [$-1.81$, 1.82]
${c^{(\Pell)}_{\PQt\Pe}}$ [$-0.93$, 1.41] [$-1.15$, 1.68]
${c^{(\Pell)}_{\PQt\Pell}}$ [$-0.95$, 1.32] [$-1.29$, 1.47]
${c^{(\Pell)}_{Q\Pe}}$ [$-1.10$, 1.16] [$-1.32$, 1.40]
${c^{-(\Pell)}_{Q\Pell}}$ [$-0.71$, 1.53] [$-1.07$, 1.64]
${c^{3(\Pell)}_{Q\Pell}}$ [$-1.87$, 1.50] [$-1.76$, 1.63] WC category 2hqV
${c_{\varphi\PQt}}$ [$-7.52$, 1.59] [$-2.59$, 1.32] ${c_{\varphi\PQt\PQb}}$
[$-1.69$, 1.70] [$-1.61$, 1.67] ${c^{3}_{\varphi Q}}$ [$-0.09$, 1.35]
[$-0.13$, 1.25] ${c_{\PQb\PW}}$ [$-0.39$, 0.40] [$-0.39$, 0.39] ${c_{\PQt G}}$
[$-0.15$, 0.12] [$-0.08$, 0.15] ${c^{-}_{\varphi Q}}$ [$-4.44$, 1.17]
[$-1.20$, 1.57] ${c_{\PQt\varphi}}$ [$-6.40$, $-0.82$] [$-5.37$, $-0.64$]
${c_{\PQt\PZ}}$ [$-0.40$, 0.32] [$-0.31$, 0.32] ${c_{\PQt\PW}}$ [$-0.31$,
0.22] [$-0.26$, 0.21] WC category 4hq ${c^{1}_{\PQQ\PQt}}$ [$-1.77$, 1.71]
[$-1.86$, $-0.41$]${}\cup{}$[0.19, 1.70] ${c^{8}_{\PQQ\PQt}}$ [$-3.17$, 3.86]
[$-3.39$, $-0.34$]${}\cup{}$[0.86, 3.87] ${c^{1}_{\PQQ\PQQ}}$ [$-1.89$, 2.19]
[$-1.96$, $-0.16$]${}\cup{}$[0.49, 2.25] ${c^{1}_{\PQt\PQt}}$ [$-0.99$, 1.05]
[$-1.00$, $-0.08$]${}\cup{}$[0.21, 1.11] WC category 2hq2lq
${c^{8}_{\PQt\PQq}}$ [$-0.45$, 0.03] [$-0.46$, 0.02] ${c^{18}_{\PQQ\PQq}}$
[$-0.47$, $-0.00$] [$-0.45$, 0.00] ${c^{1}_{\PQt\PQq}}$ [$-0.11$, 0.11]
[$-0.12$, 0.10] ${c^{11}_{\PQQ\PQq}}$ [$-0.10$, 0.10] [$-0.10$, 0.10]
${c^{38}_{\PQQ\PQq}}$ [$-0.09$, 0.08] [$-0.09$, 0.08] ${c^{31}_{\PQQ\PQq}}$
[$-0.04$, 0.03] [$-0.04$, 0.03]

The 2$\sigma$ uncertainty intervals extracted from the likelihood fits
described in Section 0.7. The intervals are shown for the case where the other
Wilson coefficients (WCs) are profiled, and the case where the other WCs are
fixed to their SM values of zero.  WC/$\Lambda^{2}$ [$\text{TeV}^{-2}$]
2$\sigma$ CI (other WCs profiled) 2$\sigma$ CI (other WCs fixed to SM) WC
category 2hq2 ${c^{T(\Pell)}_{\PQt}}$ [$-0.37$, 0.37] [$-0.40$, 0.40]
${c^{S(\Pell)}_{\PQt}}$ [$-2.60$, 2.62] [$-2.80$, 2.80]
${c^{(\Pell)}_{\PQt\Pe}}$ [$-1.78$, 2.21] [$-1.91$, 2.39]
${c^{(\Pell)}_{\PQt\Pell}}$ [$-1.80$, 2.11] [$-2.02$, 2.20]
${c^{(\Pell)}_{Q\Pe}}$ [$-1.91$, 1.96] [$-2.04$, 2.12] WC category 2hqV
${c^{-(\Pell)}_{Q\Pell}}$ [$-1.58$, 2.28] [$-1.80$, 2.33]
${c^{3(\Pell)}_{Q\Pell}}$ [$-2.84$, 2.55] [$-2.69$, 2.58] ${c_{\varphi\PQt}}$
[$-10.52$, 7.87] [$-4.93$, 3.18] ${c_{\varphi\PQt\PQb}}$ [$-3.25$, 3.26]
[$-3.14$, 3.18] ${c^{3}_{\varphi Q}}$ [$-0.84$, 2.00] [$-0.85$, 1.89]
${c_{\PQb\PW}}$ [$-0.76$, 0.76] [$-0.75$, 0.75] ${c_{\PQt G}}$ [$-0.28$, 0.24]
[$-0.22$, 0.25] ${c^{-}_{\varphi Q}}$ [$-6.06$, 8.12] [$-2.68$, 2.94]
${c_{\PQt\varphi}}$ [$-8.85$, 2.75] [$-7.54$, 2.11] ${c_{\PQt\PZ}}$ [$-0.71$,
0.64] [$-0.58$, 0.59] ${c_{\PQt\PW}}$ [$-0.55$, 0.46] [$-0.47$, 0.41] WC
category 4hq ${c^{1}_{\PQQ\PQt}}$ [$-2.34$, 2.27] [$-2.41$, 2.22]
${c^{8}_{\PQQ\PQt}}$ [$-4.37$, 4.97] [$-4.45$, 4.96] ${c^{1}_{\PQQ\PQQ}}$
[$-2.56$, 2.84] [$-2.57$, 2.89] ${c^{1}_{\PQt\PQt}}$ [$-1.33$, 1.38] [$-1.31$,
1.43] WC category 2hq2lq ${c^{8}_{\PQt\PQq}}$ [$-0.68$, 0.25] [$-0.68$, 0.24]
${c^{18}_{\PQQ\PQq}}$ [$-0.68$, 0.22] [$-0.67$, 0.21] ${c^{1}_{\PQt\PQq}}$
[$-0.21$, 0.21] [$-0.22$, 0.20] ${c^{11}_{\PQQ\PQq}}$ [$-0.19$, 0.19]
[$-0.19$, 0.20] ${c^{38}_{\PQQ\PQq}}$ [$-0.17$, 0.16] [$-0.17$, 0.16]
${c^{31}_{\PQQ\PQq}}$ [$-0.08$, 0.07] [$-0.08$, 0.07]

The disjoint 1$\sigma$ intervals that appear in some of the individual scans
(, the scans in which the other $25$ WCs are fixed to zero) shown in Fig. 8
are a result of the quadratic nature of the EFT parametrization. In principle,
this inherent degeneracy would apply for all WCs; however, the degeneracy can
be broken when contributions from multiple processes in multiple bins result
in one of the two minima having significantly better agreement with the
observed data. The individual scans over the 4hq WCs are the only cases with a
double minima that is sufficiently degenerate to lead to disjoint CIs. These
double minima disappear when profiling over the other 25 WCs, since the
interferences with the other WCs can compensate for one another’s effects
within the range between the two minima, resulting in a single long, flat
minimum instead of two disjoint minima. Double minima in the individual scans
(even relatively shallow double minima that do not manifest in disjoint CIs)
can broaden the CI interval and sometimes lead to individual CIs that are
wider than the corresponding profiled CIs, as described in Ref. [12]. For many
of the WCs, the CIs obtained from the profiled and individual scans are
similar; for some of the WCs, this results from the fact that there are not
significant correlations between the given WC and the other WCs. However, in
other cases there are non-trivial correlations among several of the WCs, and
this will be explored further in Section 0.9.1.

Performing the likelihood fit over all $26$ WCs simultaneously, this work
supersedes Ref. [12] as the most global detector-level EFT analysis to date.
Not only does this work incorporate 10 additional WCs that were not studied in
Ref. [12], but it also obtains significantly improved constraints on the WCs.
For the WCs that are common between the analyses, the 2$\sigma$ profiled CIs
generally improve by factors of approximately 2 to 6, depending on the WC. The
differential approach leveraged by this analysis provides the majority of the
improvement, though the larger data set also helps to increase the
sensitivity.

###  0.9.1 Two-dimensional correlations among WCs

To explore correlations among the WCs in the $26$-dimensional fit, this
section presents 2D scans for several pairs of WCs with nonnegligible
correlations. These pairs include several from the 2hqV category of WCs (shown
in Figs. 9–10) and several from the 4hq category of WCs (shown in Fig. 11). In
most cases, the 2D scans in which the other 24 WCs are profiled are very
similar to the 2D scans in which the other WCs are fixed to zero, indicating
that while the correlations between the given pair of WCs are important, the
correlations with the other $24$ are less significant. For example, Fig. 9
shows a 2D scan over the ${c_{\PQt\PZ}}$ and ${c_{\PQt\PW}}$ WCs with the
other 24 WCs fixed to their SM values of zero (on the left) or profiled (on
the right); while a strong correlation between ${c_{\PQt\PZ}}$ and
${c_{\PQt\PW}}$ is evident, the other 24 WCs are not significantly correlated
with either of these two WCs. Fig. 10 shows correlations between other 2hqV
WCs. Two disjoint contours of the 2$\sigma$ CI are visible in the 2D scan over
${c_{\PQt\varphi}}$ and ${c_{\PQt G}}$ in the right-hand side of this figure;
the confidence intervals shown in Fig. 8 do not have two disjoint intervals
because the minimum is not deep enough to cross the 2$\sigma$ threshold in the
one-dimensional scan. Pairs of WCs from the 4hq category are shown in Fig. 11.
Near the SM, where their interference terms compensate for one another as
discussed above, these four WCs have significant correlations with each other.
However, farther from the SM at the 1 and 2$\sigma$ limits, the quadratic
terms dominate over the linear terms, so the effects of all WCs monotonically
increase the yields; for this reason, the WCs can no longer compensate for
each other, so they become uncorrelated in the fit. This is why the contour
plots in Fig. 11 show minimal correlations between the pairs of WCs.

Figure 9: The observed 68.3, 95.5, and 99.7% confidence level contours of a 2D
scan for ${c_{\PQt\PW}}$ and ${c_{\PQt\PZ}}$ with the other WCs fixed to their
SM values (left), and profiled (right). Diamond markers show the SM
prediction.

Figure 10: The observed 68.3, 95.5, and 99.7% confidence level contours of a
2D scan with the other WCs profiled, for ${c^{-}_{\varphi Q}}$ and
${c_{\varphi\PQt}}$ (left), and for ${c_{\PQt G}}$ and ${c_{\PQt\varphi}}$
(right). Diamond markers show the SM prediction.

Figure 11: The observed 68.3, 95.5, and 99.7% confidence level contours of a
2D scan with the other WCs profiled, for ${c^{1}_{\PQQ\PQt}}$ and
${c^{8}_{\PQQ\PQt}}$ (upper left), for ${c^{1}_{\PQQ\PQQ}}$ and
${c^{8}_{\PQQ\PQt}}$ (upper right), for ${c^{1}_{\PQQ\PQt}}$ and
${c^{1}_{\PQt\PQt}}$ (lower left), and for ${c^{1}_{\PQQ\PQQ}}$ and
${c^{1}_{\PQQ\PQt}}$ (lower right). Diamond markers show the SM prediction.

###  0.9.2 Interpretation of sensitivity

In this section, we discuss the sensitivity to the WCs (which is quantified by
the CIs), focusing on the likelihood fits with other WCs profiled. While all
178 analysis bins contribute to the sensitivity to the $26$ WCs, the relative
contribution of each bin varies by WC. Organizing the WCs based primarily on
the interactions they modify and the processes they most strongly impact, the
WCs may be classified into seven main groups, summarized in Table 0.9.2. The
WCs that belong to each grouping are listed in the center column of Table
0.9.2, while the right-hand column notes the category (or categories) of
analysis bins that generally provide the leading sensitivity to the WCs in the
given group. It should be emphasized that the categories of bins listed in the
right-hand column of Table 0.9.2 represent a simplified picture of the
interpretation of the sensitivity: while there are indeed some cases where the
majority of the sensitivity to a WC is derived from a relatively clear subset
of the analysis bins, the sensitivity to many of the WCs is provided by a
diverse combination of bins across all selection categories. Furthermore, when
characterizing relevant bins, it is also important to keep in mind
interference and correlations among WCs. The following subsections will step
through each of the groups of WCs outlined in Table 0.9.2, discussing the
subsets of bins that provide the leading contributions to the sensitivity and
discussing non-trivial correlations where relevant.

Summary of categories that provide leading contributions to the sensitivity
for subsets of the Wilson coefficients (WCs).  Grouping of WCs WCs Lead
categories 2hq2 ${c^{3(\Pell)}_{Q\Pell}}$, ${c^{-(\Pell)}_{Q\Pell}}$,
${c^{(\Pell)}_{Q\Pe}}$, ${c^{(\Pell)}_{\PQt\Pell}}$, $3\Pell\textrm{ off-}\PZ$
${c^{(\Pell)}_{\PQt\Pe}}$, ${c^{S(\Pell)}_{\PQt}}$, ${c^{T(\Pell)}_{\PQt}}$
4hq ${c^{1}_{\PQQ\PQQ}}$, ${c^{1}_{\PQQ\PQt}}$, ${c^{8}_{\PQQ\PQt}}$,
${c^{1}_{\PQt\PQt}}$ $2\Pell\textrm{ss}$ 2hq2lq “$\ttbar\Pell\PGn$-like”
${c^{11}_{\PQQ\PQq}}$, ${c^{18}_{\PQQ\PQq}}$, ${c^{1}_{\PQt\PQq}}$,
${c^{8}_{\PQt\PQq}}$ $2\Pell\textrm{ss}$ 2hq2lq
“$\PQt\Pell\overline{\Pell}\PQq$-like” ${c^{31}_{\PQQ\PQq}}$,
${c^{38}_{\PQQ\PQq}}$ $3\Pell\textrm{ on-}\PZ$ 2hqV
“$\ttbar\Pell\overline{\Pell}$-like” ${c_{\PQt\PZ}}$, ${c_{\varphi\PQt}}$,
${c^{-}_{\varphi Q}}$ $3\Pell\textrm{ on-}\PZ$ and $2\Pell\textrm{ss}$ 2hqV
“$\PQt\PX\PQq$-like” ${c^{3}_{\varphi Q}}$, ${c_{\varphi\PQt\PQb}}$,
${c_{\PQb\PW}}$ $3\Pell\textrm{ on-}\PZ$ 2hqV (significant impacts ${c_{\PQt
G}}$, ${c_{\PQt\varphi}}$, ${c_{\PQt\PW}}$ $3\Pell$ and $2\Pell\textrm{ss}$ on
many processes)

#### The WCs from the 2hq2 category of operators

Beginning with the WCs in the 2hq2 group, the $3\Pell\textrm{ off-}\PZ$
channels provide the majority of the sensitivity for these WCs, which are
associated with four-fermion vertices that produce pairs of leptons without an
intermediate boson. To quantify the contributions of the off-channels, a fit
is performed with only this subset of bins included. The resulting 2$\sigma$
profiled CIs show that the expected sensitivity is only degraded by about 5–7%
compared to the results when all bins are included.

#### The WCs from the 4hq category of operators

The next group of WCs are those associated with the 4hq operators. The
sensitivity to these WCs is provided primarily by the $2\Pell\textrm{ss}$
bins, with leading contributions from the bins requiring at least three
-tagged jets. Since the $\ttbar\ttbar$ process contributes significantly to
these bins and the four-heavy WCs strongly affect the $\ttbar\ttbar$ process
(both the total cross section and shape of the kinematic distributions), it is
expected that these bins would contribute significantly to the sensitivity. To
obtain a quantitative characterization of the sensitivity provided by the
$2\Pell\textrm{ss}$ bins, we performed a fit with only these bins included.
The resulting 2$\sigma$ CIs are only degraded by about 4–6% (with respect to a
fit with all bins included), showing that the $2\Pell\textrm{ss}$ bins indeed
represent the dominant source of sensitivity to the four-heavy WCs.

#### The WCs from the 2hq2lq category of operators

The next set of WCs are those associated with the 2hq2lq category of
operators. Four of these WCs (${c^{11}_{\PQQ\PQq}}$, ${c^{1}_{\PQt\PQq}}$,
${c^{18}_{\PQQ\PQq}}$, and ${c^{8}_{\PQt\PQq}}$) primarily affect the
$\ttbar\Pell\PGn$ process, so bins populated significantly by
$\ttbar\Pell\PGn$ are expected to provide important contributions to the
sensitivity to these WCs. Performing a fit with only the $2\Pell\textrm{ss}$
bins included, the expected 2$\sigma$ CIs are degraded by only about 6–15%.
The $2\Pell\textrm{ss}$ bins thus provide the primary source of sensitivity
for these WCs, though other bins (, from the off-channels) also contribute to
the sensitivity.

The remaining two WCs from the 2hq2lq group (${c^{31}_{\PQQ\PQq}}$ and
${c^{38}_{\PQQ\PQq}}$) are distinct from the other 2hq2lq WCs in that they
feature $\PQt\PQb\PQq\PQq^{\prime}$ vertices. These vertices allow
${c^{31}_{\PQQ\PQq}}$ and ${c^{38}_{\PQQ\PQq}}$ to significantly impact the
$\PQt\Pell\overline{\Pell}\PQq$ process in the $3\Pell\textrm{ on-}\PZ$ bins
with two -tagged jets and low jet multiplicity (as discussed in Section
0.5.4). The on-bins thus contribute significant sensitivity to these WCs.
While the $2\Pell\textrm{ss}$ and off-categories also contribute to the
sensitivity to these WCs, the $3\Pell\textrm{ on-}\PZ$ bins provide the
leading contribution. The expected 2$\sigma$ CIs for these WCs each widen by
more than 30% when the $3\Pell\textrm{ on-}\PZ$ bins are excluded from the
fit.

#### The WCs from the 2hqV category of operators

The final set of WCs are those associated with the 2hqV category of operators.
These nine WCs impact a broad range of processes, leading to diverse effects
across the full set of 178 analysis bins and making it challenging to
definitively characterize subsets of bins that provide dominant contributions
to the sensitivity. However, the WCs can be classified into three main groups
(as listed in Table 0.9.2) based on the processes they impact most
significantly.

The ${c_{\PQt\PZ}}$, ${c^{-}_{\varphi Q}}$, and ${c_{\varphi\PQt}}$ WCs
feature $\ttbar\PZ$ EFT vertices and primarily affect the
$\ttbar\Pell\overline{\Pell}$ process; the on-bins are thus important for
these WCs. However, these WCs also impact other processes (, $\ttbar\ttbar$),
meaning other categories of bins can also provide important sensitivity.
Furthermore, the $\ttbar\Pell\overline{\Pell}$ process also significantly
populates the $2\Pell\textrm{ss}$ bins (making up about 20% of the total
expected yield), so the $\ttbar\Pell\overline{\Pell}$ effects can also be
relevant in the $2\Pell\textrm{ss}$ bins. Thus, the $3\Pell\textrm{ on-}\PZ$
bins and $2\Pell\textrm{ss}$ bins are important for these WCs. The
$3\Pell\textrm{ off-}\PZ$ bins provide a smaller (though nonzero) contribution
to the sensitivity. Performing a fit with these bins excluded results in an
approximately 6% degradation of the expected 2$\sigma$ CIs for each of these
three WCs.

Next, let us consider ${c^{3}_{\varphi Q}}$, ${c_{\varphi\PQt\PQb}}$, and
${c_{\PQb\PW}}$. These WCs primarily impact $\PQt\Pell\overline{\Pell}\PQq$
and $\PQt\PH\PQq$, and their sensitivity arises from multiple categories of
analysis bins. The $3\Pell\textrm{ on-}\PZ$ bins represent the leading (though
not overwhelmingly dominant) contribution. Performing a fit with only the
$3\Pell\textrm{ on-}\PZ$ bins included, the expected 2$\sigma$ CIs for these
WCs widen by about 2–13% (depending on the WC) compared to a fit with all bins
included.

The final three WCs from the 2hqV group are ${c_{\PQt G}}$,
${c_{\PQt\varphi}}$, and ${c_{\PQt\PW}}$. Impacting multiple processes, these
WCs gain sensitivity from the full spectrum of analysis bins. For example,
${c_{\PQt G}}$ impacts $\ttbar\PH$ (so the $2\Pell\textrm{ss}$ and
$3\Pell\textrm{ off-}\PZ$ bins are important as $\ttbar\PH$ significantly
populates these bins) but also strongly impacts $\ttbar\Pell\overline{\Pell}$
(so the on-and $2\Pell\textrm{ss}$ bins also play an important role). The
${c_{\PQt\varphi}}$ WC significantly impacts $\ttbar\PH$, $\PQt\PH\PQq$, and
$\ttbar\ttbar$; most of the analysis bins provide sensitivity to this WC,
though the on-bins provide only minor contributions (dropping the on-bins only
results in about a 5% effect on the expected 2$\sigma$ profiled CIs for
${c_{\PQt\varphi}}$). Finally, the ${c_{\PQt\PW}}$ WC impacts all signal
processes and derives important sensitivity from many of the analysis bins.
Further complicating the picture, ${c_{\PQt\PW}}$ has significant interference
with ${c_{\PQt\PZ}}$, and the two WCs have a strong linear correlation in the
profiled fit (as shown in Fig. 9). Thus, when we consider the 2$\sigma$
profiled CIs for ${c_{\PQt\PW}}$, it is important to recall that the
${c_{\PQt\PZ}}$ operator is also set to a nonzero value, so bins that are
affected by ${c_{\PQt\PZ}}$ can also be important when considering the
sensitivity to ${c_{\PQt\PW}}$.

##  0.10 Summary

A search for new physics in the production of one or more top quarks with
additional leptons, jets, and jets in the context of effective field theory
(EFT) has been performed. Events from proton-proton collisions with a center-
of-mass energy of 13corresponding to an integrated luminosity of 138are used.
EFT effects are incorporated into the event weights of the simulated samples,
allowing detector-level predictions that account for correlations and
interference effects among EFT operators and between EFT operators and
standard model (SM) processes.

The Wilson coefficients (WCs) corresponding to $26$ EFT operators were
simultaneously fit to the data. Confidence intervals were extracted for the
WCs either individually or in pairs by scanning the likelihood with the other
WCs either profiled or fixed at their SM values of zero. In all cases, the
data are found to be consistent with the SM expectations.

###### 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: SC
(Armenia), 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 Science Committee, project no. 22rl-037 (Armenia); 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 Shota Rustaveli National Science Foundation, grant
FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under
Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and
under project number 400140256 - GRK2497; the Hellenic Foundation for Research
and Innovation (HFRI), Project Number 2288 (Greece); 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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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Hayrapetyan, 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, D. Liko0000-0002-3380-473X, I.
Mikulec0000-0003-0385-2746, J. Schieck20000-0002-1058-8093, R.
Schöfbeck0000-0002-2332-8784, D. Schwarz0000-0002-3821-7331, M.
Sonawane0000-0003-0510-7010, 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, P. Van Mechelen0000-0002-8731-9051

Vrije Universiteit Brussel, Brussel, Belgium E.S. Bols0000-0002-8564-8732, J.
D’Hondt0000-0002-9598-6241, S. Dansana0000-0002-7752-7471, A. De
Moor0000-0001-5964-1935, M. Delcourt0000-0001-8206-1787, H. El
Faham0000-0001-8894-2390, S. Lowette0000-0003-3984-9987, I.
Makarenko0000-0002-8553-4508, A. Morton0000-0002-9919-3492, D.
Müller0000-0002-1752-4527, A.R. Sahasransu0000-0003-1505-1743, S.
Tavernier0000-0002-6792-9522, M. Tytgat40000-0002-3990-2074, S. Van
Putte0000-0003-1559-3606, 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, A. Khalilzadeh, K. Lee0000-0003-0808-4184, M.
Mahdavikhorrami0000-0002-8265-3595, A. Malara0000-0001-8645-9282, S.
Paredes0000-0001-8487-9603, L. Pétré0009-0000-7979-5771, N. Postiau, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden0009-0000-7725-7945, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496

Ghent University, Ghent, Belgium M. De Coen0000-0002-5854-7442, D.
Dobur0000-0003-0012-4866, J. Knolle0000-0002-4781-5704, L.
Lambrecht0000-0001-9108-1560, G. Mestdach, C. Rendón, A. Samalan, K.
Skovpen0000-0002-1160-0621, N. Van Den Bossche0000-0003-2973-4991, L.
Wezenbeek0000-0001-6952-891X

Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke0000-0003-0252-3609, G. Bruno0000-0001-8857-8197, C.
Caputo0000-0001-7522-4808, 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, J.
Lidrych0000-0003-1439-0196, P. Mastrapasqua0000-0002-2043-2367, K.
Mondal0000-0001-5967-1245, T.T. Tran0000-0003-3060-350X, 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, T. Menezes De Oliveira, A.
Moraes0000-0002-5157-5686, P. Rebello Teles0000-0001-9029-8506, M. Soeiro

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, 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, 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, B.
Orzari0000-0003-4232-4743, 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.
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. Keshri0000-0003-3280-2350, S. Thakur0000-0002-1647-0360

Beihang University, Beijing, China T. Cheng0000-0003-2954-9315, Q. Guo, T.
Javaid0009-0007-2757-4054, M. Mittal0000-0002-6833-8521, L.
Yuan0000-0002-6719-5397

Department of Physics, Tsinghua University, Beijing, China G. Bauer8, Z.
Hu0000-0001-8209-4343, K. Yi8,90000-0002-2459-1824

Institute of High Energy Physics, Beijing, China G.M.
Chen100000-0002-2629-5420, H.S. Chen100000-0001-8672-8227, M.
Chen100000-0003-0489-9669, F. Iemmi0000-0001-5911-4051, C.H. Jiang, A.
Kapoor0000-0002-1844-1504, H. Liao0000-0002-0124-6999, Z.-A.
Liu110000-0002-2896-1386, F. Monti0000-0001-5846-3655, R.
Sharma0000-0003-1181-1426, J.N. Song11, J. Tao0000-0003-2006-3490, J.
Wang0000-0002-3103-1083, H. Zhang0000-0001-8843-5209

State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos0000-0002-8953-1232, Y. Ban0000-0002-1912-0374, 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, H. Yang, C. Zhou0000-0001-5904-7258

Sun Yat-Sen University, Guangzhou, China Z. You0000-0001-8324-3291

University of Science and Technology of China, Hefei, China N.
Lu0000-0002-2631-6770

Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam
Application (MOE) - Fudan University, Shanghai, China X.
Gao120000-0001-7205-2318, 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, J.A. Reyes Vega

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, A.
Sculac0000-0001-7938-7559

University of Split, Faculty of Science, Split, Croatia M.
Kovac0000-0002-2391-4599, T. Sculac0000-0002-9578-4105

Institute Rudjer Boskovic, Zagreb, Croatia P. Bargassa0000-0001-8612-3332, V.
Brigljevic0000-0001-5847-0062, B.K. Chitroda0000-0002-0220-8441, D.
Ferencek0000-0001-9116-1202, S. Mishra0000-0002-3510-4833, 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, 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, A.
Stepennov0000-0001-7747-6582

Charles University, Prague, Czech Republic M. Finger0000-0002-7828-9970, M.
Finger Jr.0000-0003-3155-2484, 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 S. Elgammal14, A.
Ellithi Kamel15

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 R.K.
Dewanjee160000-0001-6645-6244, 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 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, K.T.S.
Kallonen0000-0001-9769-7163, 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, 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${}^{\textrm{\textdagger}}$

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 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, 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, G.
Falmagne0000-0002-6762-3937, B.A. Fontana Santos Alves0000-0001-9752-0624, S.
Ghosh0009-0006-5692-5688, A. Gilbert0000-0001-7560-5790, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi0009-0008-2093-8131, B.
Harikrishnan0000-0003-0174-4020, L. Kalipoliti0000-0002-5705-5059, 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, J.-M.
Brom0000-0003-0249-3622, E.C. Chabert0000-0003-2797-7690, C.
Collard0000-0002-5230-8387, S. Falke0000-0002-0264-1632, U.
Goerlach0000-0001-8955-1666, C. Grimault, R. Haeberle, A.-C. Le
Bihan0000-0002-8545-0187, M.A. Sessini0000-0003-2097-7065, P. Van
Hove0000-0002-2431-3381

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S.
Beauceron0000-0002-8036-9267, B. Blancon0000-0001-9022-1509, G.
Boudoul0009-0002-9897-8439, 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, C. Greenberg, G. Grenier0000-0002-1976-5877, B.
Ille0000-0002-8679-3878, I.B. Laktineh, M. Lethuillier0000-0001-6185-2045, L.
Mirabito, S. Perries, M. Vander Donckt0000-0002-9253-8611, P.
Verdier0000-0003-3090-2948, J. Xiao0000-0002-7860-3958

Georgian Technical University, Tbilisi, Georgia D.
Chokheli0000-0001-7535-4186, 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, F.
Engelke0000-0002-9288-8144, M. Erdmann0000-0002-1653-1303, P.
Fackeldey0000-0003-4932-7162, B. Fischer0000-0002-3900-3482, T.
Hebbeker0000-0002-9736-266X, K. Hoepfner0000-0002-2008-8148, F.
Ivone0000-0002-2388-5548, A. Jung0000-0002-2511-1490, M.y.
Lee0000-0002-4430-1695, L. Mastrolorenzo, M. Merschmeyer0000-0003-2081-7141,
A. Meyer0000-0001-9598-6623, 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, F. Rehm,
H. Reithler0000-0003-4409-702X, V. Sarkisovi0000-0001-9430-5419, A.
Schmidt0000-0003-2711-8984, S.C. Schuler, A. Sharma0000-0002-5295-1460, A.
Stein0000-0003-0713-811X, F. Torres Da Silva De Araujo200000-0002-4785-3057,
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, 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, J.
Alimena0000-0001-6030-3191, S. Amoroso, Y. An0000-0003-1299-1879, S.
Baxter0009-0008-4191-6716, M. Bayatmakou0009-0002-9905-0667, H. Becerril
Gonzalez0000-0001-5387-712X, O. Behnke0000-0002-4238-0991, A.
Belvedere0000-0002-2802-8203, S. Bhattacharya0000-0002-3197-0048, 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, 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, V. Guglielmi0000-0003-3240-7393, M.
Guthoff0000-0002-3974-589X, A. Hinzmann0000-0002-2633-4696, A.
Jafari240000-0001-7327-1870, L. Jeppe0000-0002-1029-0318, N.Z.
Jomhari0000-0001-9127-7408, B. Kaech0000-0002-1194-2306, 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. Lipka250000-0002-8427-3748, W.
Lohmann260000-0002-8705-0857, 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, A.
Mussgiller0000-0002-8331-8166, A. Nürnberg0000-0002-7876-3134, Y. Otarid, D.
Pérez Adán0000-0003-3416-0726, E. Ranken0000-0001-7472-5029, A.
Raspereza0000-0003-2167-498X, B. Ribeiro Lopes0000-0003-0823-447X, J.
Rübenach, A. Saggio0000-0002-7385-3317, M. Scham27,230000-0001-9494-2151, 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, L.P. Sreelatha
Pramod0000-0002-2351-9265, D. Stafford, F. Vazzoler0000-0001-8111-9318, A.
Ventura Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, 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, M. Eich, K. El
Morabit0000-0001-5886-220X, Y. Fischer0000-0002-3184-1457, A. Fröhlich, C.
Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, A.
Grohsjean0000-0003-0748-8494, M. Hajheidari, J. Haller0000-0001-9347-7657,
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, 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, T. Quadfasel0000-0003-2360-351X, B.
Raciti0009-0005-5995-6685, M. Rieger0000-0003-0797-2606, D.
Savoiu0000-0001-6794-7475, J. Schindler0009-0006-6551-0660, 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 S.
Brommer0000-0001-8988-2035, M. Burkart, E. Butz0000-0002-2403-5801, T.
Chwalek0000-0002-8009-3723, A. Dierlamm0000-0001-7804-9902, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, A.
Gottmann0000-0001-6696-349X, F. Hartmann280000-0001-8989-8387, M.
Horzela0000-0002-3190-7962, U. Husemann0000-0002-6198-8388, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, M. Link, A.
Lintuluoto0000-0002-0726-1452, S. Maier0000-0001-9828-9778, S.
Mitra0000-0002-3060-2278, M. Mormile0000-0003-0456-7250, Th.
Müller0000-0003-4337-0098, M. Neukum, M. Oh0000-0003-2618-9203, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, 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, F. Wittig, R.
Wolf0000-0001-9456-383X, 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. Papadopoulos28, A.
Stakia0000-0001-6277-7171

National and Kapodistrian University of Athens, Athens, Greece D. Karasavvas,
P. Kontaxakis0000-0002-4860-5979, G. Melachroinos, A. Panagiotou, I.
Papavergou0000-0002-7992-2686, I. Paraskevas0000-0002-2375-5401, 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, G. Karapostoli0000-0002-4280-2541,
K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos0000-0002-8440-0487, E.
Siamarkou, 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. Gadallah290000-0002-8305-6661, Á.
Kadlecsik0000-0001-5559-0106, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, A.J.
Rádl300000-0001-8810-0388, G.I. Veres0000-0002-5440-4356

Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók310000-0002-4440-2701, 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 G. Bencze, S. Czellar,
J. Karancsi310000-0003-0802-7665, J. Molnar, Z. Szillasi

Institute of Physics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari340000-0003-0498-4265, G. Zilizi0000-0002-0480-0000

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

University of Delhi, Delhi, India A. Ahmed0000-0002-4500-8853, A.
Bhardwaj0000-0002-7544-3258, A. Chhetri0000-0001-7495-1923, B.C.
Choudhary0000-0001-5029-1887, A. Kumar0000-0003-3407-4094, M.
Naimuddin0000-0003-4542-386X, K. Ranjan0000-0002-5540-3750, S.
Saumya0000-0001-7842-9518

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, P. Palit0000-0002-1948-029X, G.
Saha0000-0002-6125-1941, B. Sahu380000-0002-8073-5140, S. Sarkar

Indian Institute of Technology Madras, Madras, India P.K.
Behera0000-0002-1527-2266, S.C. Behera0000-0002-0798-2727, S.
Chatterjee0000-0003-0185-9872, P. Jana0000-0001-5310-5170, P.
Kalbhor0000-0002-5892-3743, J.R. Komaragiri390000-0002-9344-6655, D.
Kumar390000-0002-6636-5331, M. Mohammad Mobassir Ameen0000-0002-1909-9843, L.
Panwar390000-0003-2461-4907, R. Pradhan0000-0001-7000-6510, P.R.
Pujahari0000-0002-0994-7212, N.R. Saha0000-0002-7954-7898, A.
Sharma0000-0002-0688-923X, A.K. Sikdar0000-0002-5437-5217, S.
Verma0000-0003-1163-6955

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 A.
Bala0000-0003-2565-1718, S. Banerjee0000-0002-7953-4683, R.M. Chatterjee, 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.
Bahinipati400000-0002-3744-5332, A.K. Das, C. Kar0000-0002-6407-6974, D.
Maity410000-0002-1989-6703, P. Mal0000-0002-0870-8420, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu410000-0003-4671-815X, K. Naskar410000-0003-0638-4378, A.
Nayak410000-0002-7716-4981, P. Sadangi, P. Saha0000-0002-7013-8094, S.K.
Swain0000-0001-6871-3937, S. Varghese410009-0000-1318-8266, D.
Vats410009-0007-8224-4664

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, A.
Rastogi0000-0003-1245-6710, S. Sharma0000-0001-6886-0726

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi420000-0001-5741-3357, E. Khazaie430000-0001-9810-7743, M.
Zeinali440000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S.
Chenarani450000-0002-1425-076X, 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,c,460000-0001-6808-1335, A.
Colaleoa0000-0002-0711-6319, D. Creanzaa,c0000-0001-6153-3044, B. D‘
Anzia,b0000-0002-9361-3142, N. De Filippisa,c0000-0002-0625-6811, M. De
Palmaa,b0000-0001-8240-1913, A. Di Florioa,c0000-0003-3719-8041, W.
Elmetenaweea,b0000-0001-7069-0252, L. Fiorea0000-0002-9470-1320, G.
Iasellia,c0000-0003-2546-5341, 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, G. Ramirez-Sancheza,c0000-0001-7804-5514, D.
Ramosa0000-0002-7165-1017, A. Ranieria0000-0001-7912-4062, 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, A.
Zazaa,b0000-0002-0969-7284

INFN Sezione di Bolognaa, Università di Bolognab, Bologna, Italy C.
Battilanaa,b0000-0002-3753-3068, D. Bonacorsia,b0000-0002-0835-9574, L.
Borgonovia0000-0001-8679-4443, 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, D.
Fasanellaa,b0000-0002-2926-2691, 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.
Ceccarellia0000-0003-3232-9380, 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, A.
Papanastassioua,b, G. Sguazzonia0000-0002-0791-3350, L.
Viliania0000-0002-1909-6343

INFN Laboratori Nazionali di Frascati, Frascati, Italy L.
Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124, S.
Meola490000-0002-8233-7277, D. Piccolo0000-0001-5404-543X

INFN Sezione di Genovaa, Università di Genovab, Genova, Italy P.
Chatagnona0000-0002-4705-9582, F. Ferroa0000-0002-7663-0805, 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.
Brivioa0000-0001-9523-6451, F. Cetorellia0000-0002-3061-1553, 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.
Guzzia0000-0002-3086-8260, 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. Zuoloa0000-0003-3072-1020

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, A. Cagnottaa,b0000-0002-8801-9894, 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,500000-0001-6471-5492, P.
Paoluccia,280000-0002-8773-4781, 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 R. Ardinoa0000-0001-8348-2962, P.
Azzia0000-0002-3129-828X, N. Bacchettaa,510000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, R. Carlina,b0000-0001-7915-1650, T.
Dorigoa0000-0002-1659-8727, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, G. Grossoa, L. Layera,52, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, G.
Marona,530000-0003-3970-6986, A.T. Meneguzzoa,b0000-0002-5861-8140, M.
Michelottoa0000-0001-6644-987X, M. Migliorinia,b0000-0002-5441-7755, 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, A.
Triossia,b0000-0001-5140-9154, S. Venturaa0000-0002-8938-2193, H. Yarara,b, M.
Zanettia,b0000-0003-4281-4582, P. Zottoa,b0000-0003-3953-5996, A.
Zucchettaa,b0000-0003-0380-1172

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,540000-0002-0820-0483, 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. Vaia,b0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778

INFN Sezione di Perugiaa, Università di Perugiab, Perugia, Italy S.
Ajmala,b0000-0002-2726-2858, P. Asenova,550000-0003-2379-9903, 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,550000-0002-7676-3106, 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, R.
Bhattacharyaa0000-0002-7575-8639, L. Bianchinia,b0000-0002-6598-6865, T.
Boccalia0000-0002-9930-9299, E. Bossinia0000-0002-2303-2588, D.
Bruschinia,c0000-0001-7248-2967, R. Castaldia0000-0003-0146-845X, M.A.
Cioccia,b0000-0003-0002-5462, M. Cipriania,b0000-0002-0151-4439, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, S.
Donatoa0000-0001-7646-4977, A. Giassia0000-0001-9428-2296, F.
Ligabuea,c0000-0002-1549-7107, D. Matos Figueiredoa0000-0003-2514-6930, A.
Messineoa,b0000-0001-7551-5613, M. Musicha,b0000-0001-7938-5684, F.
Pallaa0000-0002-6361-438X, S. Paroliaa0000-0002-9566-2490, 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, P. Spagnoloa0000-0001-7962-5203, R.
Tenchinia,b0000-0003-2574-4383, 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, L. Cunqueiro Mendeza,b0000-0001-6764-5370, D.
Del Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, F. Erricoa,b0000-0001-8199-370X, E.
Longoa,b0000-0001-6238-6787, P. Meridiania0000-0002-8480-2259, J.
Mijuskovica,b0009-0009-1589-9980, 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, L. Fincoa0000-0002-2630-5465, M.
Grippoa,b0000-0003-0770-269X, B. Kiania,b0000-0002-1202-7652, F.
Leggera0000-0003-1400-0709, F. Luongoa,b0000-0003-2743-4119, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, A.
Meccaa,b0000-0003-2209-2527, E. Migliorea,b0000-0002-2271-5192, M.
Montenoa0000-0002-3521-6333, R. Mulargiaa0000-0003-2437-013X, 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, F.
Sivieroa,b0000-0002-4427-4076, V. Solaa,b0000-0001-6288-951X, A.
Solanoa,b0000-0002-2971-8214, D. Soldia,b0000-0001-9059-4831, A.
Staianoa0000-0003-1803-624X, C. Tarriconea,b0000-0001-6233-0513, M.
Tornagoa,b0000-0001-6768-1056, D. Trocinoa0000-0002-2830-5872, G.
Umoreta,b0000-0002-6674-7874, A. Vagnerinia,b0000-0001-8730-5031, E.
Vlasova,b0000-0002-8628-2090

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, K. De
Leoa,b0000-0002-8908-409X, G. Della Riccaa,b0000-0003-2831-6982

Kyungpook National University, Daegu, Korea S. Dogra0000-0002-0812-0758, J.
Hong0000-0002-9463-4922, C. Huh0000-0002-8513-2824, B. Kim0000-0002-9539-6815,
D.H. Kim0000-0002-9023-6847, J. Kim, H. Lee, 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 G. Bak0000-0002-0095-8185, P. Gwak0009-0009-7347-1480, H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187

Hanyang University, Seoul, Korea E. Asilar0000-0001-5680-599X, D.
Kim0000-0002-8336-9182, T.J. Kim0000-0001-8336-2434, J.A. Merlin, J.
Park0000-0002-4683-6669

Korea University, Seoul, Korea S. Choi0000-0001-6225-9876, S. Han, B.