TOP-19-001

TOP-19-001

# Search for new physics in top quark production with additional leptons in
proton-proton collisions at $\sqrt{s}=13\TeV$ using effective field theory

###### Abstract

Events containing one or more top quarks produced with additional prompt
leptons are used to search for new physics within the framework of an
effective field theory (EFT). The data correspond to an integrated luminosity
of $41.5\fbinv$ of proton-proton collisions at a center-of-mass energy of 13at
the LHC, collected by the CMS experiment in 2017. The selected events are
required to have either two leptons with the same charge or more than two
leptons; jets, including identified bottom quark jets, are also required, and
the selected events are divided into categories based on the multiplicities of
these objects. Sixteen dimension-six operators that can affect processes
involving top quarks produced with additional charged leptons are considered
in this analysis. Constructed to target EFT effects directly, the analysis
applies a novel approach in which the observed yields are parameterized in
terms of the Wilson coefficients (WCs) of the EFT operators. A simultaneous
fit of the 16 WCs to the data is performed and two standard deviation
confidence intervals for the WCs are extracted; the standard model
expectations for the WC values are within these intervals for all of the WCs
probed.

##  0.1 Introduction

There are numerous motivations to search for new particles or interactions at
the CERN LHC. The strong evidence for dark matter [1, 2] and dark energy [3]
suggests the possibility that the full list of nature’s constituents has not
yet been discovered. Likewise, the observed asymmetry between matter and
antimatter in the universe implies an additional source of CP violation [4].
Many explanations for the hierarchy between the Planck mass and the
electroweak scale set by the vacuum expectation value of the Higgs field also
include new particles [5, 6, 7]. Nonetheless, there is no guarantee that new
particles exist in the mass range directly accessible at the LHC. To extend
the discovery reach of the LHC, it is therefore important to consider not only
direct searches for new particles, but also indirect means of probing higher
energy scales.

One flexible framework for undertaking such indirect probes is that of
effective field theory (EFT) [8, 9, 10]. An EFT is a low-energy approximation
for a more fundamental theory involving interactions at a mass scale
$\Lambda$. Treating the standard model (SM) as a low-energy approximation of a
higher-energy theory, one can add additional higher-order terms to the
Lagrangian consistent with the symmetries and conservation laws that expand
the possibilities compatible with new physics at higher mass scales. The
additional terms are constructed from operators—products of fields and their
derivatives—that involve only SM fields. The EFT Lagrangian can then be
written as

$\mathcal{L}_{\mathrm{EFT}}=\mathcal{L}_{\mathrm{SM}}+\sum\limits_{d,i}\dfrac{c^{d}_{i}}{\Lambda^{d-4}}\mathcal{O}^{d}_{i},$
(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 dimensionless
parameters known as Wilson coefficients (WCs) that characterize the strength
of the interactions at dimension $d$. The contribution of an operator of
dimension $d$ to the Lagrangian is suppressed by a factor of $1/\Lambda^{d-4}$
implying that the focus should be placed on operators of the lowest possible
dimension. However, all operators of odd dimension violate baryon or lepton
number [11], so dimension-five operators are neglected, and dimension-six
operators provide the leading contribution from new physics [9]. The first
sub-leading contributions that conserve baryon and lepton number arise from
dimension-eight operators and are not considered in this analysis.

While the impact of EFT operators can in principle be detected in a large
variety of experimental observables, it is particularly interesting to
consider their impact on the production of one or more top quarks in
association with additional leptons. In the SM, the leading contribution to
this signature arises from the production of top quarks in association with a
, , or Higgs boson () [4]. It has long been speculated that the large mass of
the top quark [12, 13], and hence its large coupling to the Higgs boson, might
be an indication of a special relationship between the top quark and the
physics of electroweak symmetry breaking. If so, the production of top quarks
along with electroweak or Higgs bosons may shed some light on possible new
dynamics. Only recently have experimental measurements started to test
directly the coupling of the top quark to [14, 15] and [16, 17, 18] bosons.
The current and future LHC data therefore provide intriguing opportunities to
study these processes in more detail. Furthermore, because of the new terms
added within the EFT expansion, it is possible to consider the production of
top quarks with additional leptons directly through four-fermion operators
that do not contain , , or bosons. Such contributions are also probed as part
of this analysis.

Collisions producing one or more top quarks and additional leptons generate a
variety of signatures involving multiple leptons and jets, including jets that
are initiated from the hadronization of bottom quarks, referred to as jets.
Top quarks decay with almost 100% branching fraction to a bottom quark and a
boson [4], which can decay either leptonically, to a charged lepton and a
neutrino, or hadronically, to two jets. If , , or bosons are produced in
association with the top quarks, they may also decay in various ways involving
quarks (including quarks, especially in the case of the Higgs boson) or
leptons. Ultimately, the final-state signatures are primarily determined by
the decay modes of the bosons, either hadronic or leptonic. Final states in
which multiple bosons decay leptonically present a number of experimental
advantages. Multiple leptons provide an efficient trigger strategy, which
remains viable even at large instantaneous luminosities. Furthermore, for
final states involving either a same-charge dilepton pair or more than two
leptons with additional jets (including jets), the contributions from
background processes are small compared to the size of the signals. These
final states are the focus of this analysis and are denoted multilepton final
states hereafter.

Focusing on multilepton final states leads to unique challenges that have not
been encountered by previous LHC analyses employing EFT methods to search for
new physics associated with top quark production [16, 19, 20, 21, 22, 23, 24,
18, 25, 26, 27]. First, multilepton final states receive contributions from
multiple processes, and it is not possible to isolate high-purity samples from
each contribution. For example, both $\PQt\PAQt\cPZ$ and electroweak
$\PQt\cPZ\cPq$ events contribute to the three-lepton final state where two of
the leptons form a same-flavor, oppositely charged pair with an invariant mass
near the boson mass peak. Likewise, same-charge dilepton and trilepton final
states outside the peak originate with comparable probability from SM
$\PQt\PAQt\PW$ and $\PQt\PAQt\PH$ production. Since the multiple processes
cannot be reliably disentangled, this analysis cannot be constructed as a
reinterpretation of either a set of inclusive or differential cross section
measurements. Second, there are numerous EFT operators capable of impacting
one or more of the processes contributing to multilepton final states; a
priori, there is no reason to assume that new physics would manifest only
through the contribution of a single operator. It is therefore important to
analyze the effects of these operators simultaneously across all components of
the data set.

A new approach is implemented to address these challenges. Designed to target
EFT effects directly, this approach does not aim to isolate specific physical
processes and extract high-level observables; rather, it relies on detector-
level observables, namely the number of events observed in a set of distinct
categories defined by the multiplicities of final-state objects. For each
category, a different admixture of physics processes will contribute to the
observed event yield. Sensitivity to the EFT operators is obtained by
parameterizing the predicted yields in terms of the WCs of all relevant
operators simultaneously. To procure these predicted yields, we use simulated
events with weights parameterized to represent the effects of the EFT
operators. These weighted, simulated events are then analyzed to obtain the
necessary predictions of the observed event yields, as functions of the EFT
parameters. Parameterizing the event weights in terms of the WCs represents
the key enabling concept of this approach, as it allows all relevant
interference effects—both interference between new physics and the SM and
interference among new physics operators—to be incorporated into the
prediction. The effects of multiple EFT operators on multiple physical
processes contributing to a single final-state signature are therefore
accounted for in a straightforward and rigorous manner. EFT operators can also
impact the kinematical properties of the events, so this approach allows the
full effect on the detector acceptance and efficiency to be appropriately
described. Correlations among statistical and systematic uncertainties can
also be accounted for, and, where possible, fully leveraged. For example, this
approach should provide enhanced sensitivity when EFT operators impact the
contribution of multiple relevant physics processes, since the observables
used are sensitive to the sum of the effects. The main drawback of this
approach is that, because it relies on detector-level observation and fully
simulated events, theoretical updates cannot be incorporated without repeating
the analysis. This is the first time such an approach has been applied to LHC
data; ultimately, the technique can be applied to differential kinematical
distributions, but for this initial analysis, we take a more inclusive
approach.

The detailed strategy employed in this analysis is as follows. Multilepton
events are divided into categories based on the number and the sign of the
charge sum of the reconstructed leptons; the lepton categories are then
subdivided according to the number of jets. Within each lepton and jet
category, the event yields are characterized as a function of the number of
jets. For oppositely charged, same-flavor lepton pairs in three-lepton events,
the data are divided based on whether the invariant mass of the lepton pair
falls in a window around the boson mass ($m_{\cPZ}$). This strategy results in
35 nonoverlapping categories. These event yields define the observables for
the analysis and are compared against predictions that incorporate the effects
of EFT operators. Contributions involving primarily prompt leptons—including
signal processes—are modeled using simulated events. Where relevant, the
predicted yields for processes sensitive to EFT operators are parameterized in
terms of the WCs for those operators. Predictions for backgrounds involving
primarily nonprompt leptons (, leptons from bottom or charmed hadron decays or
misidentified leptons) are based on extrapolations from control regions in
data. The WCs are varied to determine the best fit of the predictions to data,
as well as to establish the range over which the predicted yields are
consistent with the observation.

The sections of the paper are organized in the following order. A brief
overview of the CMS detector and triggering system is outlined in Section 0.2.
Section 0.3 describes the simulation of signal and background processes,
including a discussion of the parameterization of the predicted yields in
terms of the WCs. The event reconstruction and event selection are covered in
Sections 0.4 and 0.5, respectively, while Section 0.6 discusses the background
estimation. In Section 0.7, the signal extraction is explained. Sources of
systematic uncertainties affecting this analysis are described in Section 0.8.
Section 0.9 presents the results, and Section 0.10 provides a summary of the
analysis.

##  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 coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid. A more detailed description
of the CMS detector and its performance, together with a definition of the
coordinate system and the kinematic variables used in the analysis, can be
found in Ref. [28].

Events of interest are selected using a two-tiered trigger system [29]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed time interval of about 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

##  0.3 Data samples and simulation

The data used in this analysis comprise proton-proton ($\Pp\Pp$) collisions at
$\sqrt{s}=13\TeV$ collected with the CMS detector in 2017, corresponding to a
total integrated luminosity of $41.5\fbinv$ [30]. The events have been
recorded using a combination of single-, double-, and triple-lepton triggers.

Simulations are used to estimate the event yields of the signal processes and
some sources of background. The signal samples incorporate EFT effects and are
generated at leading order (LO), while all background samples are generated at
next-to-leading order (NLO) and do not include EFT effects. The simulated
samples used to estimate the backgrounds include $\PQt\cPaqt\PGg$, diboson,
and triboson production. Used for validation purposes, additional samples are
also generated to simulate SM background processes that are estimated from
data; these include $\cPZ\text{+jets}$, $\PW\text{+jets}$,
$\ttbar\text{+jets}$, and single top quark processes ($s$ channel, $t$
channel, and $\PQt\PW$). The background samples are generated using matrix
elements (MEs) implemented either in the [31, 32, 33] (version 2.4.2) or the
v2 [34, 35, 36, 37, 38, 39, 40] programs. The simulation of the signal
processes is described in Section 0.3.1

Parton showering and hadronization for all of the samples is done by [41]
(version 8.226 was used for the signal samples), and the Lund fragmentation
model is employed [42]. The parameters for the underlying event description
correspond to the CP5 tune [43] and the proton structure is described by the
NNPDF3.1 [44] set of parton distribution functions (PDFs). Minimum bias
$\Pp\Pp$ interactions occurring in the same or nearby bunch crossings (pileup)
are generated with and overlaid on all simulated events, according to the
luminosity profile of the analyzed data. Finally, all generated events are
passed through a detailed simulation of the CMS apparatus, based on [45], and
are reconstructed using the same event reconstruction software as used for
data.

###  0.3.1 Simulation of the signal processes

The signal events are generated at LO with (version 2.6.0). The signal
processes include those in which one or more top quarks are produced along
with multiple charged leptons: $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$, and
$\PQt\PAQt\Pl\PGn$ (where l indicates a charged lepton and $\nu$ indicates a
neutrino). We also include $\PQt\PAQt\PH$ and $\PQt\PH\cPq$, as these
processes can produce signal events when the Higgs boson decays into one or
more leptons. The decays of the Higgs bosons are handled by since it would be
computationally expensive to produce samples for each decay mode and difficult
to separate them from the other signal processes. An example diagram for each
signal process is shown in Fig. 1. We note that the signal processes include
contributions from lepton pairs produced from on-shell and bosons, as well as
those from nonresonant processes; this is important so that effects from EFT
four-fermion operators can also be included in these samples. Furthermore, we
note that the $\PQt\PAQt\Pl\PAl$ sample includes the production of top quark
pairs in association with virtual photons.

Figure 1: Example diagrams for the five signal processes considered in this
analysis: $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PAl$, $\PQt\PAQt\Pl\PGn$,
$\PQt\Pl\PAl\cPq$, and $\PQt\PH\cPq$.

Sixteen operators are considered in this analysis. Chosen because they are
expected to have a relatively large impact on the signal processes but not on
the background process, the operators can be classified into two categories:
operators that involve two quarks and one or more bosons, and operators that
involve two quarks and two leptons. Table 0.3.1 lists the 16 operators and
corresponding WCs; all couplings are assumed to involve only quarks of the
third generation. The operators that require a Hermitian conjugate term in the
Lagrangian (marked with a double dagger in Table 0.3.1) can have complex WCs;
however, the imaginary coefficients lead to CP violation, and, as outlined in
Ref. [46], are generally already constrained. Consequently, only the real
components are considered in this analysis. More details about the operators
listed in Table 0.3.1 can be found in Ref. [46]. For the purpose of
illustrating which processes are most strongly affected by the operators
considered, Table 0.3.1 lists the leading signal processes affected by each
operator. To determine whether a process is to be listed in this column, we
check whether the cross section of the process is scaled by more than five
times the SM cross section when the WC associated with the operator is set to
$16\pi^{2}$ or $-16\pi^{2}$ [11]. For the operators that are associated with
multiple WCs, if any of the WCs causes the process to be scaled by more than
five times the SM cross section, the process is considered to be affected by
that operator. The choice to use a factor of five as the criterion for this
determination is arbitrary; if it were changed, the list of processes listed
in the column would potentially change as well. However, changing these
criteria would have no influence on the analysis, as the effects of all
operators on all process are fully considered. The diagrams in Fig. 2
illustrate two examples of interactions that arise from one of the operators
we consider; these interactions can affect the signal process $\PQt\PAQt\PH$.

List of operators that have effects on $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PAl$,
$\PQt\PAQt\Pl\PGn$, $\PQt\Pl\PAl\cPq$, and $\PQt\PH\cPq$ processes at order
$1/\Lambda^{2}$ that are considered in this analysis. The couplings are
assumed to involve only third-generation quarks. The quantity
$T^{A}=\frac{1}{2}\lambda^{A}$ denotes the eight Gell-Mann matrices, and
$\tau^{I}$ are the Pauli matrices. The field $\varphi$ is the Higgs boson
doublet, and $\tilde{\varphi}=\varepsilon\varphi^{*}$, where
$\varepsilon\equiv i\tau^{2}$. The $\ell$ and represent the left-handed lepton
and quark doublets, respectively, while represents the right-handed lepton,
and and represent the right-handed quark singlets. Furthermore,
$(\varphi^{\dagger}i\overleftrightarrow{D}_{\mu}\varphi)\equiv\varphi^{\dagger}(iD_{\mu}\varphi)-(iD_{\mu}\varphi^{\dagger})\varphi$
and
$(\varphi^{\dagger}i\overleftrightarrow{D}^{I}_{\mu}\varphi)\equiv\varphi^{\dagger}\tau^{I}(iD_{\mu}\varphi)-(iD_{\mu}\varphi^{\dagger})\tau^{I}\varphi$.
The boson field strength is
$\PW_{\mu\nu}^{I}=\partial_{\mu}\PW_{\nu}^{I}-\partial_{\nu}\PW^{I}_{\mu}+g\varepsilon_{IJK}\PW^{J}_{\mu}\PW^{K}_{\nu}$,
and
$G_{\mu\nu}^{A}=\partial_{\mu}G_{\nu}^{A}-\partial_{\nu}G^{A}_{\mu}+g_{s}f^{ABC}G^{B}_{\mu}G^{C}_{\nu}$
is the gluon field strength. The abbreviations $s_{\mathrm{W}}$ and
$c_{\mathrm{W}}$ denote the sine and cosine of the weak mixing angle (in the
unitary gauge), respectively. The leading processes affected by the operators
are also listed (the details of the criteria used for this determination are
described in the text).

Operators involving two quarks and one or more bosons
---
Operator |  Definition |  WC |  Lead processes affected
${}^{\ddagger}{{O}^{(ij)}_{\PQu\varphi}}$ |  $\PAQq_{i}\PQu_{j}\tilde{\varphi}\>(\varphi^{\dagger}\varphi)$ |  ${c_{\PQt\varphi}}+i{c^{I}_{\PQt\varphi}}$ |  $\PQt\PAQt\PH$, $\PQt\PH\cPq$
${{O}^{1(ij)}_{\varphi\cPq}}$ |  $(\varphi^{\dagger}i\\!\\!\overleftrightarrow{D}_{\mu}\varphi)(\PAQq_{i}\gamma^{\mu}\cPq_{j})$ |  ${c^{-}_{\varphi Q}}+{c^{3}_{\varphi Q}}$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\PH\cPq$, $\PQt\Pl\PAl\cPq$
${{O}^{3(ij)}_{\varphi\cPq}}$ |  $(\varphi^{\dagger}i\\!\\!\overleftrightarrow{D}^{I}_{\mu}\varphi)(\PAQq_{i}\gamma^{\mu}\tau^{I}\cPq_{j})$ |  ${c^{3}_{\varphi Q}}$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\PH\cPq$, $\PQt\Pl\PAl\cPq$
${{O}^{(ij)}_{\varphi\PQu}}$ |  $(\varphi^{\dagger}i\\!\\!\overleftrightarrow{D}_{\mu}\varphi)(\PAQu_{i}\gamma^{\mu}\PQu_{j})$ |  ${c_{\varphi\PQt}}$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$
${}^{\ddagger}{{O}^{(ij)}_{\varphi\PQu\PQd}}$ |  $(\tilde{\varphi}^{\dagger}iD_{\mu}\varphi)(\PAQu_{i}\gamma^{\mu}\PQd_{j})$ |  ${c_{\varphi\PQt\PQb}}+i{c^{I}_{\varphi\PQt\PQb}}$ |  $\PQt\PAQt\PH$, $\PQt\Pl\PAl\cPq$, $\PQt\PH\cPq$
${}^{\ddagger}{{O}^{(ij)}_{\PQu\PW}}$ |  $(\PAQq_{i}\sigma^{\mu\nu}\tau^{I}\PQu_{j})\>\tilde{\varphi}\PW_{\mu\nu}^{I}$ |  ${c_{\PQt\PW}}+i{c^{I}_{\PQt\PW}}$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\PH\cPq$, $\PQt\Pl\PAl\cPq$
${}^{\ddagger}{{O}^{(ij)}_{\PQd\PW}}$ |  $(\PAQq_{i}\sigma^{\mu\nu}\tau^{I}\PQd_{j})\>{\varphi}\PW_{\mu\nu}^{I}$ |  ${c_{\PQb\PW}}+i{c^{I}_{\PQb\PW}}$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PAl$, $\PQt\PH\cPq$, $\PQt\Pl\PAl\cPq$
${}^{\ddagger}{{O}^{(ij)}_{\PQu\PB}}$ |  $(\PAQq_{i}\sigma^{\mu\nu}\PQu_{j})\>\tilde{\varphi}\PB_{\mu\nu}$ |  $(c_{\mathrm{W}}{c_{\PQt\PW}}-{c_{\PQt\cPZ}})/s_{\mathrm{W}}+i(c_{\mathrm{W}}{c^{I}_{\PQt\PW}}-{c^{I}_{\PQt\cPZ}})/s_{\mathrm{W}}$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\PH\cPq$, $\PQt\Pl\PAl\cPq$
${}^{\ddagger}{{O}^{(ij)}_{\PQu G}}$ |  $(\PAQq_{i}\sigma^{\mu\nu}T^{A}\PQu_{j})\>\tilde{\varphi}G_{\mu\nu}^{A}$ |  $\mathrm{g}_{\mathrm{s}}({c_{\PQt G}}+i{c^{I}_{\PQt G}})$ |  $\PQt\PAQt\PH$, $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\PH\cPq$, $\PQt\Pl\PAl\cPq$
Operators involving two quarks and two leptons
Operator |  Definition |  WC |  Lead processes affected
${{O}^{1(ijkl)}_{\ell\cPq}}$ |  $(\overline{\ell}_{i}\gamma^{\mu}\ell_{j})(\PAQq_{k}\gamma^{\mu}\cPq_{\ell})$ |  ${c^{-(\ell)}_{Q\ell}}+{c^{3(\ell)}_{Q\ell}}$ |  $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$
${{O}^{3(ijkl)}_{\ell\cPq}}$ |  $(\overline{\ell}_{i}\gamma^{\mu}\tau^{I}\ell_{j})(\PAQq_{k}\gamma^{\mu}\tau^{I}\cPq_{\ell})$ |  ${c^{3(\ell)}_{Q\ell}}$ |  $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$
${{O}^{(ijkl)}_{\ell\PQu}}$ |  $(\overline{\ell}_{i}\gamma^{\mu}\ell_{j})(\PAQu_{k}\gamma^{\mu}\PQu_{\ell})$ |  ${c^{(\ell)}_{\PQt\ell}}$ |  $\PQt\PAQt\Pl\PAl$
${{O}^{(ijkl)}_{\Pe\PAQq}}$ |  $(\overline{\Pe}_{i}\gamma^{\mu}\Pe_{j})(\PAQq_{k}\gamma^{\mu}\cPq_{\ell})$ |  ${c^{(\ell)}_{Q\Pe}}$ |  $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$
${{O}^{(ijkl)}_{\Pe\PQu}}$ |  $(\overline{\Pe}_{i}\gamma^{\mu}\Pe_{j})(\PAQu_{k}\gamma^{\mu}\PQu_{\ell})$ |  ${c^{(\ell)}_{\PQt\Pe}}$ |  $\PQt\PAQt\Pl\PAl$
${}^{\ddagger}{{O}^{1(ijkl)}_{\ell\Pe\cPq\PQu}}$ |  $(\overline{\ell}_{i}\Pe_{j})\;\varepsilon\;(\PAQq_{k}\PQu_{\ell})$ |  ${c^{S(\ell)}_{\PQt}}+i{c^{SI(\ell)}_{\PQt}}$ |  $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$
${}^{\ddagger}{{O}^{3(ijkl)}_{\ell\Pe\cPq\PQu}}$ |  $(\overline{\ell}_{i}\sigma^{\mu\nu}\Pe_{j})\;\varepsilon\;(\PAQq_{k}\sigma_{\mu\nu}\PQu_{\ell})$ |  ${c^{T(\ell)}_{\PQt}}+i{c^{TI(\ell)}_{\PQt}}$ |  $\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$
Figure 2: Example diagrams showing two of the vertices associated with the
${{O}_{\PQu G}}$ operator. This operator, whose definition can be found in
Table 0.3.1, gives rise to vertices involving top quarks, gluons, and the
Higgs boson; as illustrated here, these interactions can contribute to the
$\PQt\PAQt\PH$ process.

The signal events are generated using an approach similar to the one described
in Ref. [46]. Like the framework presented there, the model used in this
analysis adopts the Warsaw basis of gauge-invariant dimension-six operators
[9], focuses on operators that give rise to interactions involving at least
one top quark, and only allows for tree-level generation. To allow to properly
handle the emission of gluons from ${{O}_{\PQu G}}$ vertices, an extra factor
of the strong coupling is applied to the coefficients arising from the
${{O}_{\PQu G}}$ operator, as indicated in Table 0.3.1. Since only tree-level
generation is possible with this model, the $\PQt\PAQt\PH$,
$\PQt\PAQt\Pl\PGn$, and $\PQt\PAQt\Pl\PAl$ signal samples are generated with
an extra parton in the final state, improving accuracy and allowing some
operators to contribute more significantly to processes upon which they would
otherwise have a weaker effect.

For the samples generated with an additional parton, a matching procedure is
applied to account for the overlap in phase space between the ME and parton
shower (PS) contributions; in this analysis, we use the MLM scheme [33]. Since
EFT effects are included in the ME contribution but not in the PS
contribution, the validity of the matching procedure must be examined for
operators that give rise to vertices involving a gluon. The only such operator
considered by this analysis is ${{O}_{\PQu G}}$, and contributions from this
operator to the soft and collinear regime are suppressed; therefore, the phase
space overlap with the SM contribution from the PS is small, and the effects
of this potential issue are mitigated [47]. The $\PQt\Pl\PAl\cPq$ and
$\PQt\PH\cPq$ signal samples are not generated with an extra final state
parton since, when using the MLM scheme with LO , jet matching cannot be
correctly performed between the ME and the PS for single top quark processes
in the $t$ channel.

###  0.3.2 Parameterization of the yields in terms of the WCs

In order to discern the effects of new physics on the observed yields, the
predicted yields must be parameterized in terms of the WCs. First, the ME can
be written as the sum of SM and new physics components:

$\mathcal{M}=\mathcal{M}_{\mathrm{SM}}+\sum_{i}\frac{c_{i}}{\Lambda^{2}}\mathcal{M}_{i},$
(2)

where $\mathcal{M}_{\mathrm{SM}}$ is the SM ME, $\mathcal{M}_{i}$ are the MEs
corresponding to the new physics components, and $c_{i}$ are the WCs. Any
cross section—inclusive or differential—is proportional to the square of the
ME. The SM contribution to the cross section is necessarily independent of the
WC values, while the new EFT operators have contributions that depend linearly
on the coefficients arising from the interference with the SM and
contributions quadratic in the coefficients from pure EFT terms. The latter
can originate from the effects of an individual operator or interference
between the EFT operators. Since this analysis considers 16 WCs, the expected
cross section could therefore be parameterized as a 16-dimensional (16D)
quadratic function of the WCs.

In principle, the 16D quadratic terms could be fully determined by evaluating
the cross section at multiple points in WC space and solving for the
coefficients; it would be impractical, however, to produce the large number of
simulated samples required. Instead, we use the event generator’s ability to
assign weights to the generated events in order to effectively account for the
variation of the differential cross section in an infinitesimal part of phase
space occupied by an individual event. Each event weight, just like the
inclusive or differential cross section, can be parameterized by a polynomial
of second order in the WCs. In order to determine the coefficients of each
event’s 16D quadratic weight function, the weight is calculated at multiple
randomly selected points in the 16D WC phase space. Once enough weights have
been calculated to constrain the 16D quadratic function, we can solve for the
coefficients and therefore obtain the parameterization for the weight function
of each event in terms of the WCs. The weight functions $w_{i}$ for a given
event event $i$ can then be written as follows:

$w_{i}\left(\frac{\vec{c}}{\Lambda^{2}}\right)=s_{0i}+\sum_{j}s_{1ij}\frac{c_{j}}{\Lambda^{2}}+\sum_{j}s_{2ij}\frac{c_{j}^{2}}{\Lambda^{4}}+\sum_{j,k}s_{3ijk}\frac{c_{j}}{\Lambda^{2}}\frac{c_{k}}{\Lambda^{2}},$
(3)

where $\vec{c}$ represents the set of WCs, the sum over $j$ and $k$
corresponds to the sum over the WCs, and the values $s_{0}$, $s_{1}$, $s_{2}$,
and $s_{3}$ represent the coefficients in the quadratic parameterization of
the weight from the SM, interference between EFT and SM, pure EFT, and
interference between two EFT contributions, respectively.

The weighted events can then be used to calculate predicted yields for an
arbitrary set of WC values; the yield for a given event selection category (as
discussed in Section 0.5) is found by summing the weight functions for events
that meet the selection requirements of the given category. Thus, summing Eq.
(3) over $i$, we find the predicted yield $N$:

$\displaystyle N\left(\dfrac{\vec{c}}{\Lambda^{2}}\right)$
$\displaystyle=\sum_{i}w_{i}\left(\frac{\vec{c}}{\Lambda^{2}}\right)$ (4)
$\displaystyle=\sum_{i}\left(s_{0i}+\sum_{j}s_{1ij}\frac{c_{j}}{\Lambda^{2}}+\sum_{j}s_{2ij}\frac{c_{j}^{2}}{\Lambda^{4}}+\sum_{j,k}s_{3ijk}\frac{c_{j}}{\Lambda^{2}}\frac{c_{k}}{\Lambda^{2}}\right)$
$\displaystyle=\left(\sum_{i}s_{0i}\right)+\sum_{j}\left(\sum_{i}s_{1ij}\right)\frac{c_{j}}{\Lambda^{2}}+\sum_{j}\left(\sum_{i}s_{2ij}\right)\frac{c_{j}^{2}}{\Lambda^{4}}+\sum_{j,k}\left(\sum_{i}s_{3ijk}\right)\frac{c_{j}}{\Lambda^{2}}\frac{c_{k}}{\Lambda^{2}}$
$\displaystyle=S_{0}+\sum_{j}S_{1j}\frac{c_{j}}{\Lambda^{2}}+\sum_{j}S_{2j}\frac{c_{j}^{2}}{\Lambda^{4}}+\sum_{j,k}S_{3jk}\frac{c_{j}}{\Lambda^{2}}\frac{c_{k}}{\Lambda^{2}}.$

The predicted yield has therefore been expressed as a quadratic function of
the WCs, where the quadratic coefficients of the yield parameterization were
found by summing the quadratic coefficients of the weights, ,
$S_{1j}=\sum_{i}s_{1ij}$. Since the parameterized yields should be consistent
with the SM when all WCs are set to zero, we normalize the yields to the NLO
predictions [48].

##  0.4 Event reconstruction

The CMS global (also called particle-flow (PF) [49]) event reconstruction aims
to reconstruct and identify each individual particle in an event, with an
optimized combination of all subdetector information. In this process, the
identification (ID) of the particle type (photon, electron, muon, charged or
neutral hadron) plays an important role in the determination of the particle
direction and energy. Photons are identified as ECAL energy clusters not
linked to the extrapolation of any charged particle trajectory to the ECAL.
Electrons 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 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 charged 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 missing transverse momentum vector () is computed as the negative vector
sum of the transverse momenta of all the PF candidates in an event, and its
magnitude is denoted as [50]. The vector is modified to account for
corrections to the energy scale of the reconstructed jets in the event.

The candidate vertex with the largest value of the sum of squared physics-
object transverse momentum () is taken to be the primary $\Pp\Pp$ interaction
vertex. The physics objects are the jets, clustered using the anti-jet finding
algorithm [51, 52] with the tracks assigned to the vertex as inputs, and the
associated . More details are given in Section 9.4.1 of Ref. [53]. Lepton
candidates, which are subsequently reconstructed, are required to be
compatible with originating from the selected primary vertex.

Electrons are reconstructed by matching tracks in the silicon tracker to the
energy deposit in the ECAL, without any significant energy deposit in the HCAL
[54]. Genuine electrons are distinguished from hadrons mimicking an electron
signature by a multivariate algorithm using the quality of the electron track,
the shape of the electron cluster, and the matching between the track momentum
and direction with the sum and position of energy deposits in the ECAL.
Furthermore, to reject electrons produced in photon conversions, candidates
with missing hits in the innermost tracking layers or matched to a conversion
secondary vertex are discarded.

Muon candidates are reconstructed by combining information from the silicon
tracker and the outer muon spectrometer of CMS in a global fit [55]. The
quality of the geometrical matching between the individual measurements in the
tracker and the muon system is used to improve the ID of genuine prompt muons
by substantially reducing misidentification due to hadrons punching through
the calorimeters or from muons produced through in-flight decays of kaons and
pions.

The electron and muon selection criteria described above define the “loose”
lepton selection. Additional selection criteria are applied to discriminate
leptons produced in the decays of and bosons and leptons from leptons produced
in the decays of or light hadrons, or from misidentified jets. We will refer
to the former as “prompt” leptons and to the latter as “nonprompt” leptons.
Isolation criteria are also applied to all leptons.

A multivariate analysis (MVA) approach based on boosted decision trees (BDTs),
referred to as the prompt lepton MVA, is used for this selection. Each BDT
takes as inputs the lepton kinematic, ID, and impact parameter information, as
well as kinematic and tagging information about the nearest jet to the lepton.
Two versions of the lepton MVA are trained, one for electrons and one for
muons, which differ based on the inclusion of one additional observable for
each of the two versions. For electrons, the extra input is the multivariate
discriminant developed via dedicated analysis for electron ID [54]. For muons,
the extra input is a similar discriminant that classifies the compatibility of
track segments in the muon system with the pattern expected from a muon
ionization [56]. The BDT inputs have been checked in control regions in data
to ensure that they are well modeled by the simulation. A more detailed
description of the lepton MVA can be found in [57].

In the signal region, lepton candidates are required to exceed a given
discriminant threshold, referred to as the “tight” lepton selection. A looser
selection, referred to as the “relaxed” selection, is defined by relaxing the
above lepton MVA discriminant requirement for the purpose of estimating
background processes, as discussed in Section 0.6. The efficiency of the
triggers based on the presence of one, two, or three electrons or muons is
measured in data in $\cPZ/\PGg^{*}\to\Pe\Pe$ and $\cPZ/\PGg^{*}\to\Pgm\Pgm$
events, respectively. These events are also used to measure the efficiency for
electrons or muons to pass the lepton reconstruction, ID, and isolation
criteria [54, 55].

For each event, hadronic jets are clustered from PF candidates using the anti-
algorithm with a distance parameter of 0.4. Jet momentum is determined as the
vectorial sum of all particle momenta in the jet, and is found from simulation
to be, on average, within 5 to 10% of the true momentum over the whole $\pt$
spectrum and detector acceptance. Pileup can contribute additional tracks and
calorimetric energy depositions to the jet momentum. To mitigate this effect,
charged particles originating from pileup vertices are discarded and an offset
correction is applied to account for the remaining contributions. Jet energy
corrections are derived from simulation to bring measured response of jets to
that of particle level jets on an average. In situ measurements of the
momentum balance in dijet, $\text{photon}{+}\text{jet}$, $\cPZ\text{+jets}$,
and multijet events are used to account for any residual differences in jet
energy scale in data and simulation [58]. The jet energy resolution amounts
typically to 15–20% at 30, 10% at 100, and 5% at 1 [58]. Jets are rejected if
the distance in $\eta$–$\phi$ space (where the $\phi$ is azimuthal angle in
radians) between the jet and the closest lepton is less than 0.4.

Jets originating from the hadronization of quarks are identified by two
“combined secondary vertex” algorithms [59, 60], namely CSVv2 and DeepCSV,
which exploit observables related to the long lifetime of hadrons and to the
higher particle multiplicity and mass of jets compared to light-quark and
gluon jets. DeepCSV is used for jet selection, while CSVv2 is used to aid
lepton ID as an input to the prompt lepton MVA. The analysis uses two levels
of selection based on DeepCSV, with a loose and a medium working point. The
medium (loose) working point has approximately 70 (85)% efficiency for tagging
jets from quarks, with a misidentification probability of $1.0(10)\%$ for
light quark and gluon jets [60].

##  0.5 Event selection

The goal of the event selection is to retain $\PQt\PAQt\PH$,
$\PQt\PAQt\Pl\PGn$, $\PQt\PAQt\Pl\PAl$, $\PQt\Pl\PAl\cPq$, and $\PQt\PH\cPq$
events while excluding as many contributions from background processes as
possible. The analysis is split into categories with two same-sign leptons
($2\ell\text{ss}$), three leptons ($3\ell$), and four leptons ($4\ell$), where
$\ell$ refers to either a or . Events must also have a minimum number of jets,
as well as jets, with additional requirements that depend on the lepton flavor
and multiplicity. Single- and double-lepton triggers are used to collect
events containing two leptons. For events with three or more leptons, a
combination of single-, double-, and triple-lepton triggers are used.

All events are required to have at least two leptons passing the tight
selection. Events where a pair of tight leptons with an invariant mass less
than $12\GeV$ is found are rejected, to avoid backgrounds from light
resonances. In addition, events must have at least two jets with $\pt>30\GeV$
to be reconstructed in the pseudorapidity ($\eta$) region, $\abs{\eta}<2.4$.
One or more of the jets also need to pass the medium working point of the
DeepCSV tagging algorithm, as at least one top quark whose decay produces a
bottom quark jet is present in all signal events. No explicit identification
requirements are placed on leptons, which are allowed to enter the event
selection via their decay products.

###  0.5.1 $2\ell\text{ss}$ category

The $2\ell\text{ss}$ category primarily targets $\PQt\PAQt\PH$ and
$\PQt\PAQt\Pl\PGn$ signal events in which the $\PQt\cPaqt$ system decays
semileptonically, with an additional, identically charged lepton coming from
the decay of a boson produced in association with a top quark pair (in the
case of $\PQt\PAQt\Pl\PGn$), or coming from the decay of a boson or a lepton
produced in the decay of the Higgs boson (in the case of $\PQt\PAQt\PH$). In
addition, the $\PQt\PAQt\Pl\PAl$ process may contribute with events in which
there is at least one lepton that is not reconstructed or does not pass the
selection. Selected events are required to contain exactly two same-sign
leptons passing the tight criteria, with the lepton of higher (lower) $\pt$
required to have $\pt>25$ ($15$). Residual $\ttbar\text{+jets}$ background
contributions are suppressed by requiring that the charge of all electrons and
muons, which pass the relaxed object selection criteria, is well measured.
Electrons are required to pass two conditions which test the consistency
between the independent measurements of the electron charge obtained from the
position of the ECAL cluster and from its track, while muons must satisfy the
condition that the estimated uncertainty on the $\pt$ of the muon track is
below $20\%$. The events are required to contain at least four jets with
$\pt>30\GeV$ and $\abs{\eta}<2.4$. At least two of these jets must be jets,
one of which must satisfy the medium working point of the DeepCSV tagging
algorithm, while the second is allowed to pass the loose working point. Events
containing more than two tight leptons are vetoed to avoid overlap with the
$3\ell$ and $4\ell$ categories.

###  0.5.2 $3\ell$ category

The $3\ell$ category selects $\PQt\PAQt\Pl\PGn$ events in which all three
bosons decay leptonically; $\PQt\PAQt\Pl\PAl$ events in which the $\PQt\cPaqt$
system decays semileptonically and the boson decays to two charged leptons;
$\PQt\Pl\PAl\cPq$ events in which the top quark decays leptonically and the
decays to two charged leptons; and $\PQt\PAQt\PH$ events in which the $\PH$
decays to , or particles, at least one of which then decays leptonically (with
one or more charged leptons also coming from the decay of the $\PQt\cPaqt$
system). Selected events are required to contain exactly three charged leptons
passing the tight object selection criteria. The three charged leptons are
required to have $\pt>25$, $15$, and $10\GeV$. In the case that the third
lepton is an electron, the requirement on it is instead $\pt>15\GeV$ in order
to stay above the trigger thresholds and keep the contributions from nonprompt
electrons under control. Two or more jets with $\pt>30\GeV$ and
$\abs{\eta}<2.4$ are required, at least one of which must satisfy the medium
working point of the DeepCSV tag algorithm. Two subcategories are defined
according to whether a second jet passing the medium tag is present. This
avoids incorrectly promoting $\PQt\Pl\PAl\cPq$ events into the subcategory
requiring two jets, due to misidentification.

###  0.5.3 $4\ell$ category

The $4\ell$ category targets $\PQt\PAQt\Pl\PAl$ events in which all the and
bosons decay leptonically, and $\PQt\PAQt\PH$ events in which the $\PH$ decays
into a pair of bosons, where all bosons decay leptonically; bosons, where at
least one decays leptonically; or leptonically decaying leptons. Events
selected in this category are required to contain four or more leptons passing
the tight object selection criteria and passing $\pt$ thresholds of $\pt>25$,
$15$, $10$, and $10\GeV$ for the lepton of highest, second, third, and fourth
highest , respectively. In the case of electrons, the requirement on the third
or fourth lepton is instead $\pt>15\GeV$ for the same reasons as in the
$3\ell$ category. Two or more jets with $\pt>30\GeV$ and $\abs{\eta}<2.4$ are
required. As in the $2\ell\text{ss}$ category, at least two of these jets must
be jets, one of which must satisfy the medium working point of the DeepCSV
tagging algorithm, while the second is allowed to pass the loose working
point.

###  0.5.4 Separation into subcategories

Events in the $2\ell\text{ss}$, $3\ell$, and $4\ell$ categories are further
separated based on a number of criteria, as summarized in Table 1. In the
$2\ell\text{ss}$ category, events are further separated based on lepton
charge. This allows us to take advantage of the fact that the
$\PQt\PAQt\PW^{+}$ cross section is roughly a factor of 2 larger than that of
the $\PQt\PAQt\PW^{-}$ cross section, so it is enhanced in $2\ell\text{ss}$
events where both leptons are positively charged. In the $3\ell$ category, we
consider separately events which contain a same-flavor, oppositely charged
pair of leptons with an invariant mass that falls within $10\GeV$ of the
$m_{\cPZ}$, which primarily serves to create a region where the contribution
from (on-shell) $\PQt\PAQt\cPZ$ is enhanced. For $3\ell$ events that do not
fall within this region, a classification based on the sum of lepton charges
is used, considering events where the charge sum is positive separately from
those where it is negative. This again exploits the difference in cross
section between $\PQt\PAQt\PW^{+}$ and $\PQt\PAQt\PW^{-}$. In all $3\ell$
events, a classification is done based on whether the event contains exactly
one jet passing the medium DeepCSV working point, or two or more jets passing
the medium DeepCSV working point; the contribution from $\PQt\Pl\PAl\cPq$ is
enhanced in the former case. For the $4\ell$ category, events are not split
based on the invariant dilepton mass due to a small population of events in
those bins.

Table 1: Requirements for the different event categories. Requirements
separated by commas indicate a division into subcategories. The jet
requirement on individual jets varies based on the lepton category, as
described in the text.

Selection | 2$\ell$ss | $3\ell$ |  $\geq$4$\ell$
---|---|---|---
Leptons | Exactly 2 leptons | Exactly 3 leptons |  $\geq$4 leptons
Charge requirements | $\sum_{\ell}q<0,\sum_{\ell}q>0$ | $\sum_{\ell}q<0,\sum_{\ell}q>0$ |  |
Jet multiplicity | 4, 5, 6, $\geq$7 jets | 2, 3, 4, $\geq$5 jets | 2, 3, 4, $\geq$5 jets | 2, 3, $\geq$4 jets
Number of $\PQb$ jets |  $\geq$2 $\PQb$ jets | 1, $\geq$2 $\PQb$ jets | 1, $\geq$2 $\PQb$ jets |  $\geq$2 $\PQb$ jets
Dilepton mass |  | $\abs{m_{\ell\ell}-m_{\cPZ}}>10\GeV$ | $\abs{m_{\ell\ell}-m_{\cPZ}}\leq 10\GeV$ |

Events in all categories are then separated into jet-multiplicity bins, which
are used to fit to data and place limits on EFT parameters, as described in
Section 0.7. The $2\ell\text{ss}$ and $3\ell$ categories are split into four
jet multiplicity bins, resulting in 8 and 24 subcategories respectively. The
$4\ell$ category is split into three jet bins, bringing the total to 35
distinct signal region subcategories.

##  0.6 Background estimation

Contributions to the selected event yields arise from a variety of background
processes, which can be categorized as reducible or irreducible and are either
estimated from data control regions or modeled using simulation. A background
is considered as reducible in case one or more of the reconstructed electrons
or muons passing the tight object selection are not due to genuine prompt
leptons, as defined in Section 0.4.

In the $2\ell\text{ss}$ categories, a reducible background arises from events
containing lepton pairs of opposite charge, mostly from $\ttbar\text{+jets}$
production, in which the charge of one lepton is mismeasured. Both background
contributions are determined from data using the same methods as in Ref. [57].
A summary of these procedures is given in Sections 0.6.1 and 0.6.2.

The dominant irreducible background processes are diboson production and (to a
lesser extent) triboson production and are estimated using simulations. In the
$3\ell$ and $4\ell$ categories in particular, diboson production is the
dominant overall background (among both reducible and irreducible sources).

The production of a top quark pair in association with a photon represents an
additional, minor source of background. It is typically due to an asymmetric
photon conversion into an electron-positron pair, where one of the produced
particles carries most of the photon energy while the other one is not
reconstructed. Even though this is a reducible background source, its
contribution is estimated using simulation, since the isolated leptons arising
from asymmetric conversion are well described in simulation.

###  0.6.1 Background from misidentified leptons

The background from nonprompt leptons is estimated from control samples in
data, applying the measured rate at which nonprompt leptons pass the lepton
selection criteria. This rate, referred to as the fake rate, is measured from
a multijet sample dominated by nonprompt leptons. The data analyzed in this
region are collected with single lepton triggers, except at low muon , where
the presence of an additional jet with $\pt>40\GeV$ is required in the
trigger. The misidentification rate is defined as the probability for a lepton
candidate that passes a relaxed lepton cut to pass the tight lepton selection.
The misidentification rate is extracted separately for electrons and muons and
is measured as a function of the $\pt$ and $\eta$ of the nonprompt lepton.
Contamination from prompt leptons in the multijet sample is subtracted based
on expectations from simulation.

Once the misidentification rates have been determined, they are applied to a
selection called the application region (AR), which is identical to the signal
region except that one or more of the leptons are required to fail the tight
selection but pass the relaxed lepton selection instead. An estimate of the
misidentified-lepton background in the SR is obtained by applying
appropriately chosen weights to the events selected in the AR. Further details
on the nonprompt-lepton background estimation technique can be found in Ref.
[57].

###  0.6.2 Background from lepton charge mismeasurement

The lepton charge mismeasurement background in the $2\ell\text{ss}$ categories
is dominated by events with two prompt leptons in which the sign of either
prompt lepton is mismeasured. This background contribution is estimated from
data, following a strategy similar to the one used for the estimation of the
nonprompt background. It is found to be relevant only for electrons. The
electron charge is defined using the majority method, which takes the charge
to be the one shared by at least two of the three charge estimate methods
[54]. An AR is defined for the $2\ell\text{ss}$ categories, requiring that the
two selected leptons are of opposite charge. The events in the AR are then
weighted using the probability to mismeasure the electron charge, derived in a
$\cPZ/\PGg^{*}\to\Pe\Pe$ sample and parameterized as a function of the
electron $\pt$ and $\eta$. The probability for mismeasuring the sign of
electrons ranges from 0.02% for electrons in the barrel to 0.2% for electrons
in the endcaps, after all the object selection criteria. The sum of the charge
mismeasurement probabilities of the two lepton candidates is used to determine
the overall background.

##  0.7 Signal extraction

As stated in Section 0.5.4, the analysis is split into 35 statistically
independent categories, based on the sum of lepton charges, number of tagged
jets, and jet multiplicity. A likelihood function is built based on
independent bins following Poisson statistics. The event yields are a function
of the WCs and a set of nuisance parameters (NPs) which contain the effects of
systematic uncertainties (see Section 0.8). The WCs are parameterized by the
quadratic form for the cross section as explained in Section 0.3. In order to
fit this parameterization to the data, we scan over all WCs. The boundaries of
the scans roughly correspond to values of the chosen operator that result in a
five-fold increase in the SM cross section for at least one signal process. At
each point in the scan, the negative log profiled-likelihood is computed, and
the best fit is evaluated as the WC which minimizes the negative log
likelihood. Confidence intervals of 1 and 2 standard deviations ($\sigma$) are
calculated by finding where the negative log likelihood curve crosses twice
the value of one and four respectively. In principle, this scan can be
performed in the 16D WC space. However, fitting this hypersurface is time-
consuming, and the results are difficult to interpret. Instead, we perform the
fit for a single WC in two scenarios: when the other 15 WCs are treated as
unconstrained NPs (profiled); and when the other 15 WCs are fixed to their SM
value of zero.

##  0.8 Systematic uncertainties

There are two types of systematic effects considered: those that affect only
the rates of signal or background processes, and those that affect both the
rate and the shape which refers to changes in the relative expected yield of
the jet and/or jet bins. In the latter case, the rate and shape effects are
treated simultaneously so that they are considered fully correlated. Each
systematic uncertainty is correlated across all analysis bins by using a
single NP per physical process. The sources of systematic uncertainties
considered are: the integrated luminosity; the jet energy scale (JES); jet
tagging scale factors; the theoretical cross section; the PDF shape
variations; the renormalization ($\mu_{\mathrm{R}}$) and factorization
($\mu_{\mathrm{F}}$) scales; the PS; the parton matching; the additional
radiation; the muon and electron ID, isolation, trigger efficiency; the
pileup; the the misidentified-lepton rate estimate; and the charge
misreconstruction estimate.

All variations listed are applied equally to the signal and background
samples, and are treated as 100% correlated across all samples. Unless
otherwise stated, all systematic uncertainties are considered independent and
thus uncorrelated with each other.

  * •

Integrated luminosity The measured uncertainty on the LHC integrated
luminosity estimate is 2.3% [30].

  * •

Jet Energy Scale The JES is adjusted via scale factors to account for pileup,
nonuniform detector response, and any residual differences between the data
and simulation. The resulting effect of the JES uncertainty on the yields is
determined by shifting the jet energy correction up and down by 1$\sigma$ ,
and propagating the changes through the object ID and event selection.

  * •

jet tagging scale factors In order to use the DeepCSV tagger for identifying
jets, scale factors are applied to the simulated samples to bring them in
agreement with data [60]. There are three types of systematic uncertainties
associated with the use of these scale factors: the JES, the purity of the
control samples used to derive the scale factors, and the size of all the
samples used to derive the scale factors. The JES dependence is calculated
simultaneously with the JES systematic uncertainty. The purity component is
treated by assigning NPs for the yields from both the light (, , , ) and heavy
(, ) flavors. The jet tagging uncertainty is used to remove jets potentially
mistagged as jets. Finally, the statistical uncertainty of the samples used to
derive the scale factors is taken into account with four NPs: two for the
light-flavor (LF) case and two for the heavy-flavor (HF) case. The two NPs for
each case are: an NP for the overall tilt that would be consistent with the
statistical uncertainties on the SFs, while the second NP controls distortions
of a more complicated nature, where the upper and lower ends of the
distribution change relative to the center. These NPs account for
discrepancies in the shape of the tagging discriminant distributions, which
are consistent with the uncertainty of the scale factors.

  * •

Theoretical cross section The expected yields for signal and background are
derived from theoretical predictions of at least NLO accuracy. There are
associated uncertainties on the $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$
scales of the process and the PDF. Table • ‣ 0.8 summarizes these
uncertainties. For signal processes, this uncertainty is considered on the
whole process (SM+EFT). These uncertainties do not vary with the WCs, so the
uncertainties are of the same magnitude as when fixing the model to the SM
component alone.

Cross section (rate) uncertainties used for the fit. Each column in the table
is an independent source of uncertainty. Uncertainties in the same column for
different processes (different rows) are fully correlated.  Process
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ scales PDF $\PQt\PAQt\PH$ $-$9.2%
$+$5.8% $\pm$3.6% $\PQt\Pl\PAl\cPq$ $\pm$1% $\pm$4% $\PQt\PAQt\Pl\PGn$ $-$12%
$+$13% $\pm$2% $\PQt\PAQt\Pl\PAl$ $-$12% $+$10% $\pm$3% $\PQt\PH\cPq$ $-$8%
$+$6% $\pm$3.7% Diboson $\pm$2% $\pm$2% Triboson $\pm$2.6% $\pm$4.2%
$\PQt\PAQt\PGg$ +jets $\pm$10% $\pm$5%

  * •

PDF shape variations The shape variation of the final fitting variable
distributions due to the uncertainty on the PDF is estimated by reweighting
the spectra according to 100 replica sets. The total uncertainty is measured
using the standard PDF4LHC [61] recommendation.

  * •

Renormalization and factorization scales Uncertainties due to the
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ scales in the ME generator are
modeled by varying the scales independently by a factor of $1/2$ or 2 and
propagating the changes to the final fitting variable distribution in the fit.
An uncertainty envelope is then calculated from these two systematic
uncertainties. This is accommodated via weights obtained directly from the
generator information. Since the normalization uncertainties of the ME
generators are covered by the cross section uncertainties (listed in Table • ‣
0.8), only the impact on the kinematic shape of the process in question are
considered. These shape effects primarily enter as changes in the acceptance
and efficiency for events to fall into a particular event selection category.
The bounds of the envelope are determined by taking the maximum of the
$\mu_{\mathrm{F}}$ uncertainties, the $\mu_{\mathrm{R}}$ uncertainties, and
their sum. The $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ effects on the WCs
range between 1 and 5% depending on the bin.

  * •

Parton shower The uncertainty in the PS simulation is estimated by varying the
$\mu_{\mathrm{R}}$ for initial- and final-state radiation (ISR/FSR) up and
down in by multiplying/dividing the scale by a factor of 2 for ISR and
$\sqrt{2}$ for FSR. A dedicated SM sample (produced without EFT effects) is
used to determine this systematic variation; the values obtained through this
study are then applied to the full analysis samples.

  * •

Parton matching This uncertainty only applies to $\PQt\PAQt\PH$,
$\PQt\PAQt\PW$, and $\PQt\PAQt\cPZ$ processes, since matching is only
performed for processes that include an extra parton. Determined by varying
the matching scale value between the extra partons generated in and jets
produced in , this uncertainty is computed bin-by-bin. A dedicated SM sample
(produced without any EFT effects) was used to perform this study. The nominal
scale is 19, and is shifted up to 25and down to 15.

  * •

Additional radiation Since an extra final-state parton was not included in the
LO single top quark processes ($\PQt\Pl\PAl\cPq$ and $\PQt\PH\cPq$), they are
not expected to be as sensitive to varying the WCs as the LO $\PQt\PAQt\PH$,
$\PQt\PAQt\PW$, and $\PQt\PAQt\cPZ$ samples. A comparison of the LO
$\PQt\Pl\PAl\cPq$ sample to the NLO $\PQt\cPZ\cPq$ sample, reveals a
discrepancy in the event yield, which is not covered by the existing
systematic uncertainties. We therefore introduced a new systematic uncertainty
specifically for the $\PQt\Pl\PAl\cPq$ sample to cover this disagreement. The
same systematic uncertainty is applied to $\PQt\PH\cPq$, since the uncertainty
accounts for the fact that cannot handle the matching for these extra partons
for any $t$-channel process. These uncertainties are typically around 20%, but
can reach as high as 80% for the high jet multiplicity bins with few events.

  * •

Muon and electron ID and isolation Scale factors are used to correct the
tracking efficiency, electron and muon ID efficiency, and isolation in the
simulation to match that in data, which are derived with a “tag-and-probe”
method [55, 54, 62]. The impacts of these quantities are estimated by varying
the scale factors within their uncertainties. The resulting systematic
uncertainties are typically of the order of 1–2% per lepton.

  * •

Trigger efficiency The impact due to the trigger efficiency [55] is estimated
by varying the trigger scale factors within their uncertainties, which are in
the range of 2–5%.

  * •

Pileup Effects due to the uncertainty in the distribution of the number of
pileup interactions are evaluated by varying the total inelastic $\Pp\Pp$
cross section used to predict the number of pileup interactions in the
simulation by 4.6% from its nominal value, which corresponds to a 1$\sigma$
variation [63]. This effect typically ranges from less than 1–3%.

  * •

Misidentified-lepton rate estimate Several sources of systematic uncertainty
are considered. The measurement of the misidentified-lepton weights is
affected by the small population in the measurement region, subtraction of
prompt lepton contamination in this region, as well as the uncertainty in the
background jet composition in this region (dominated by multijet background)
and the AR (dominated by $\ttbar\text{+jets}$ background). The effect on the
misidentified lepton rate due to the overall uncertainty of the misidentified
leptons is taken into account by varying the entire map of misidentified
lepton weights up or down by 1$\sigma$. This is the largest source of
uncertainty on the misidentified lepton rate, and amounts to approximately
25–30%, depending on the jet multiplicity bin. In addition, the limited
population in the AR of the misidentified lepton method have a significant
effect on the estimate of the misidentified lepton rate and must be considered
as a separate source of uncertainty. This again varies with jet multiplicity
bin, and amounts to approximately 10–30%.

  * •

Charge misidentification probability The yield of the misreconstructed
background in the 2$\ell\text{ss}$ categories is known with an uncertainty of
30%, and is included as a rate systematic uncertainty. The uncertainty due to
the limited population in the corresponding AR is negligible and is not
considered.

Table 0.8 summarizes the systematic uncertainties assessed in the signal and
backgrounds, and how each systematic uncertainty is treated in the fit used to
extract the amount of signal present in the data sample. We note that it is
possible for the statistical and systematic uncertainties to depend on the
choice of the initial WC values (used to evaluate the quadratic fit
parameters). To examine this, simulations are generated at the boundaries of
the measured 2$\sigma$ confidence interval, and the SM point (, all WCs set to
zero), and no difference is observed within the current level of precision. A
summary of the percentage effect (change in WC divided by the symmetrized
confidence interval) for ${c_{\PQt\PW}}$, ${c_{\PQt\varphi}}$,
${c^{-(\ell)}_{Q\ell}}$, and ${c^{(\ell)}_{\PQt\ell}}$ is provided in the last
four columns of Table 0.8 to illustrate the range of values we observe for
each systematic variation. This table is related to the change in the WCs do
to a single NP—correlations among NPs are not taken into account—and is
therefore a conservative estimate.

Summary for the systematic uncertainties. Here “shape” means that the
systematic uncertainty causes a change in the relative expected yield of the
jet and/or jet bins. Except where noted, each row in this table will be
treated as a single, independent NP. Impacts of various systematic variations
on a subset of WCs are also quoted. Percentages represent the change in a WC
divided by the symmetrized 2$\sigma$ confidence interval. A value of 100%
indicates the particular systematic variation adds an uncertainty equal to the
WC interval. The percentages for the and jet tags are the sum of all their
respective subcategories.  Source Type ${c_{\PQt\PW}}$ ${c_{\PQt\varphi}}$
${c^{-(\ell)}_{Q\ell}}$ ${c^{(\ell)}_{\PQt\ell}}$ Integrated luminosity rate
6% 2% 1% $<$1% JES rate+shape 6% 2% 1% $<$1% jet tag 1% 5% 8% $<$1% jet tag HF
fraction rate+shape jet tag HF stats (linear) rate+shape jet tag HF stats
(quadratic) rate+shape jet tag LF fraction rate+shape jet tag LF stats
(linear) rate+shape jet tag LF stats (quadratic) rate+shape jet mistag $<$1%
12% 8% 2% jet tag charm (linear) rate+shape jet tag charm (quadratic)
rate+shape PDF ($\Pg\Pg$) rate 1% $<$1% $<$1% $<$1% PDF
($\Pg\Pg_{\PQt\PAQt\PH}$) rate $<$1% 1% $<$1% $<$1% PDF () rate 1% $<$1% $<$1%
$<$1% PDF ($\cPq\Pg_{\PQt\PH\cPq}$) rate $<$1% $<$1% $<$1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\PQt\PAQt\PH$) rate 2% 5% $<$1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\PQt\PAQt\PGg$) rate 1% 1% $<$1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\ttbar\mathrm{V}$) rate 15% 4% 1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\PQt\PH\cPq$) rate 1% 1% $<$1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\mathrm{V}$) rate $<$1% $<$1% $<$1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\mathrm{VV}$) rate $<$1% $<$1% $<$1% $<$1%
$\mu_{\mathrm{R,F}}$ scale ($\mathrm{VVV}$) rate $<$1% $<$1% $<$1% $<$1% PDF
shape 2% 1% $<$1% $<$1% $\mu_{\mathrm{R,F}}$ scales shape $<$1% 6% 1% $<$1%
FSR rate+shape 1% 11% 7% 2% ISR rate+shape $<$1% 8% 3% $<$1% Parton matching
rate+shape 1% 10% 5% 1% Additional radiation rate+shape 11% 3% 1% $<$1% Lepton
ident./isol. rate+shape 4% 2% $<$1% $<$1% Trigger efficiency rate+shape 2% 1%
$<$1% 1% Pileup rate+shape 1% 1% $<$1% $<$1% Lepton misident. rate+shape 2%
70% 29% $<$1% Lepton misident. rate+shape Charge misident. rate 3% 2% $<$1%
$<$1%

##  0.9 Results

The number of events selected in different categories is compared to the
expected contributions of the signal processes and of the different background
processes before (prefit) and after simultaneously fitting all 16 WCs, and the
NPs, to minimize the negative log-likelihood (postfit) in Fig. 0.3.1. The
prefit scenario corresponds to the SM where the values of the WCs are all
assumed to be zero. The simultaneous fit is equivalent to the 16 best fit
points from the profiled fits: the profiled fit will always find the global
minimum for each of the 15 profiled WCs. The hatched region in the stack plot
and the shaded region in the ratio plot show the sum of all systematic
uncertainties. The large increase in the $\PQt\PH\cPq$ event yields is a
consequence of the low sensitivity to this particular process coupled with the
fact that $\PQt\PH\cPq$ receives relatively large enhancements from the EFT
operators considered. The fit finds a combination of WCs that is able to
enhance $\PQt\PH\cPq$, which helps improve agreement in the $2\ell\text{ss}$
and $3\ell$ (non-Z) categories, without spoiling the agreement elsewhere.
Despite the large increase, $\PQt\PH\cPq$ is still a smaller contribution than
$\PQt\PAQt\PH$ and $\PQt\PAQt\Pl\PGn$ in these categories. There is also a
large increase in the $\PQt\Pl\PAl\cPq$ event yields, which we are also
insensitive to.

Table 0.3.1 shows the 2$\sigma$ confidence intervals for each WC. Intervals
are given for two scenarios. These results are displayed graphically in Fig.
4, along with their 1$\sigma$ confidence intervals (thicker lines). The
confidence intervals for a single WC (solid bars) are calculated while the
other 15 WCs are profiled. An alternative determination of the confidence
interval for a single WC is performed by fixing the other 15 WCs to their SM
values of zero (dashed bars). For the profiled scenario, the confidence
interval for all WCs includes the SM. Occasionally, when fixing the other 15
WCs to zero the SM point falls just outside the 2$\sigma$ confidence interval
(, ${c_{\PQt\PW}}$). This is not surprising because in the cases where all WCs
but one are fixed to zero, that single WC must account entirely for any
deviation between observed data and expectation. In contrast, in the profiled
case, all 16 WCs can work together to accommodate any deviations, resulting in
a best fit point that is closer to the SM, leading to the SM point falling
inside the 2$\sigma$ confidence interval. It is also sometimes possible for
the profiled case to produce a more narrow 2$\sigma$ confidence interval, as
can be seen for ${c^{S(\ell)}_{\PQt}}$, ${c^{(\ell)}_{\PQt\Pe}}$,
${c^{(\ell)}_{\PQt\ell}}$, ${c^{(\ell)}_{Q\Pe}}$, and ${c^{-(\ell)}_{Q\ell}}$.
It is important to note that these five parameters each have disjoint nonzero
1$\sigma$ confidence interval when the other 15 parameters are frozen. This
will inherently broaden the profiled likelihood curve, resulting in a larger
interval. Note that as mentioned in Section 0.3.1, the definition of
${{O}_{\PQu G}}$ 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 3: Expected yields prefit (left) and postfit (right). The postfit
values of the WCs are obtained from performing the fit over all WCs
simultaneously. “Conv.” refers to the photon conversion background, “Charge
misid.” is the lepton charge mismeasurement background, and “Misid. leptons”
is the background from misidentified leptons. The jet multiplicity bins have
been combined here, however, the fit is performed using all 35 event
categories outlined in Section 0.5.4. The lower panel is the ratio of the
observation over the prediction.

The 2$\sigma$ confidence intervals on the WCs. The intervals are found by
scanning over a single WC while either treating the other 15 profiled, or
fixing the other 15 to the SM value of zero.  WC$/\Lambda^{2}$[$\TeV^{-2}$]
2$\sigma$ interval (others profiled) 2$\sigma$ interval (others fixed to SM)
${c_{\PQt\PW}}$ [$-$3.08, 2.87] [$-$2.15, $-$0.29]$\cup$[0.21, 1.96]
${c_{\PQt\cPZ}}$ [$-$3.32, 3.15] [$-$2.14, 2.19] ${c_{\PQt\varphi}}$
[$-$16.98, 44.26] [$-$14.12, $-$1.46]$\cup$[32.30, 44.48] ${c^{-}_{\varphi
Q}}$ [$-$7.59, 21.65] [$-$3.45, 3.33] ${c_{\PQt G}}$ [$-$1.38, 1.18] [$-$1.26,
$-$0.69]$\cup$[0.08, 0.79] ${c_{\PQb\PW}}$ [$-$4.95, 4.95] [$-$4.12, 4.09]
${c^{3}_{\varphi Q}}$ [$-$7.37, 3.48] [$-$7.21, 2.25] ${c_{\varphi\PQt\PQb}}$
[$-$12.72, 12.63] [$-$9.87, 9.67] ${c_{\varphi\PQt}}$ [$-$18.62, 12.31]
[$-$20.91, $-$14.10]$\cup$[$-$6.52, 4.24] ${c^{3(\ell)}_{Q\ell}}$ [$-$9.67,
8.97] [$-$9.91, 9.50] ${c^{-(\ell)}_{Q\ell}}$ [$-$4.02, 4.99] [$-$4.76, 5.83]
${c^{(\ell)}_{Q\Pe}}$ [$-$4.38, 4.59] [$-$5.20, 5.36]
${c^{(\ell)}_{\PQt\ell}}$ [$-$4.29, 4.82] [$-$5.15, 5.51]
${c^{(\ell)}_{\PQt\Pe}}$ [$-$4.24, 4.86] [$-$4.97, 5.80]
${c^{S(\ell)}_{\PQt}}$ [$-$6.52, 6.52] [$-$7.70, 7.70] ${c^{T(\ell)}_{\PQt}}$
[$-$0.84, 0.84] [$-$1.01, 1.01]

Figure 4: Observed WC 1$\sigma$ (thick line) and 2$\sigma$ (thin line)
confidence intervals (CIs). Solid lines correspond to the other WCs profiled,
while dashed lines correspond to the other WCs fixed to the SM value of zero.
In order to make the figure more readable, the ${c_{\varphi\PQt}}$ interval is
scaled by $1/2$, the ${c_{\PQt G}}$ interval is scaled by 2, the
${c^{-}_{\varphi Q}}$ interval is scaled by $1/2$, and the ${c_{\PQt\varphi}}$
interval is scaled by $1/5$.

Additional validation of the results was done with simultaneous scans in two
WCs. These are illustrated in Figs. 5–10. This subset of figures are the only
ones which show clear signs of correlations between pairs of WCs. The diamond
marker corresponds to the SM theoretical prediction. The contours correspond
to the 1$\sigma$ (solid lines), 2$\sigma$ (thick dashed lines), and 3$\sigma$
(dash-dotted lines) confidence intervals. Figure 5 indicates minimal
correlation between the WCs ${c^{-(\ell)}_{Q\ell}}$ and ${c^{(\ell)}_{Q\Pe}}$.
This is a common case for many coefficient pairs, especially when the other 14
are profiled. The left panel of Fig. 6 shows a more rectangular contour for
${c_{\varphi\PQt\PQb}}$ and ${c^{3(\ell)}_{Q\ell}}$. This indicates that these
two WCs are not correlated, and the rectangular shape comes from a double
minima in ${c^{3(\ell)}_{Q\ell}}$. Comparing the left and right panels of Fig.
6 shows ${c^{3(\ell)}_{Q\ell}}$ has little preference over profiling the other
14 WCs versus fixing them to zero, while the ${c_{\varphi\PQt\PQb}}$ range is
cut almost in half when fixing the other 14 coefficients to zero. Figure 7
shows a similar behavior for ${c^{3}_{\varphi Q}}$ and ${c_{\PQb\PW}}$. The
horizontal scales on the right panels of Figs. 8 and 9 were modified to
facilitate the display of the 1$\sigma$ confidence interval contour. The right
panel of Fig. 10 shows a preference in ${c_{\PQt\PW}}$ and ${c_{\PQt\cPZ}}$
for nonzero values when the other 14 WCs are fixed to zero. When the other 14
WCs are profiled (Fig. 10 left), the preference vanishes. This is indicative
of the complex interplay between all 16 WCs. Exploring the 16D hypersurface
provides a unique handle on the WCs, which was not previously utilized in
single parameter analyses such as [16, 19, 20, 21, 22, 23, 24, 25, 26, 27]. As
a result, any differences compared to other analyses should not be surprising,
and may indicate our robust technique more accurately captures the nuances of
the EFT.

Figure 0.3.1 shows the fractional variation in expected yields for a given
process and category after the fit and relative to the SM expectation. The
nominal points correspond to the best fit values obtained from the
simultaneous fit of all WCs and are therefore the same in each plot. The
vertical bars represent the maximum variation in the expected yield within the
corresponding 2$\sigma$ confidence interval for the given WC. The variations
are found by profiling all other WCs and NPs, that is by re-running the
simultaneous fit for the other WCs and NPs, at each point in the scan for the
WC of interest. As the quadratic parameterization for a given WC need not be
the same for each process or for each bin of a given process, the extrema of
the vertical bars do not necessarily correspond to the same WC values;
furthermore, the edges of the vertical bars need not correspond to the
2$\sigma$ limits of the WC in question. The value for $\PQt\Pl\PAl\cPq$ in the
4$\ell$ category is shown off-scale to preserve the legibility for the rest of
the plot. Despite the large ratio, the expected yield in this category is
still exceptionally small after the fit and has a negligible contribution to
the 2$\sigma$ confidence intervals.

Figure 5: The observed 1$\sigma$, 2$\sigma$, and 3$\sigma$ confidence contours
of a 2D scan for ${c^{-(\ell)}_{Q\ell}}$ and ${c^{(\ell)}_{Q\Pe}}$ with the
other WCs profiled (left), and fixed to their SM values (right). Diamond
markers are shown for the SM prediction.

Figure 6: The observed 1$\sigma$, 2$\sigma$, and 3$\sigma$ confidence contours
of a 2D scan for ${c_{\varphi\PQt\PQb}}$ and ${c^{3(\ell)}_{Q\ell}}$ with the
other WCs profiled (left), and fixed to their SM values (right). Diamond
markers are shown for the SM prediction.

Figure 7: The observed 1$\sigma$, 2$\sigma$, and 3$\sigma$ confidence contours
of a 2D scan for ${c^{3}_{\varphi Q}}$ and ${c_{\PQb\PW}}$ with the other WCs
profiled (left), and fixed to their SM values (right). Diamond markers are
shown for the SM prediction.

Figure 8: The observed 1$\sigma$, 2$\sigma$, and 3$\sigma$ confidence contours
of a 2D scan for ${c_{\PQt G}}$ and ${c^{-}_{\varphi Q}}$ with the other WCs
profiled (left), and fixed to their SM values (right). Diamond markers are
shown for the SM prediction. The range on the right plot is modified to
emphasize the 1$\sigma$ contour.

Figure 9: The observed 1$\sigma$, 2$\sigma$, and 3$\sigma$ confidence contours
of a 2D scan for ${c_{\PQt\varphi}}$ and ${c_{\varphi\PQt}}$ with the other
WCs profiled (left), and fixed to their SM values (right). Diamond markers are
shown for the SM prediction. The range on the right plot is modified to
emphasize the 1$\sigma$ contour.

Figure 10: The observed 1$\sigma$, 2$\sigma$, and 3$\sigma$ confidence
contours of a 2D scan for ${c_{\PQt\cPZ}}$ and ${c_{\PQt\PW}}$ with the other
WCs profiled (left), and fixed to their SM values (right). Diamond markers are
shown for the SM prediction. Figure 11: Plots showing the relative change in
the expected yield for the signal processes in each event category. The
“$\Delta$Yield/prefit” is the difference in expected yield before fit (prefit)
and after fit (postfit), normalized to the prefit yield of the process in the
corresponding category. The vertical bars represent the maximum variation for
a given WC within the corresponding 2$\sigma$ confidence interval. The values
in upper right of each plot are to indicate the variation for
$\PQt\Pl\PAl\cPq$ in the $4\ell$ category.

##  0.10 Summary

A search for new physics has been performed in the production of at least one
top quark in association with additional leptons, jets, and jets, in the
context of an effective field theory. The events were produced in proton-
proton collisions corresponding to an integrated luminosity of $41.5\fbinv$.
The expected yield in each category was parameterized in terms of 16 Wilson
coefficients (WCs) associated with effective field theory operators relevant
to the dominant processes in the data.

A simultaneous fit was performed of the 16 WCs to the data. For each WC, an
interval over which the model predictions agree with the observed yields at
the 2 standard deviation level was extracted by either keeping the other WCs
fixed to zero or treating the other WCs as unconstrained nuisance parameters.
Two-dimensional contours were produced for some of the WCs, to illustrate
correlations between various WCs. The results from fitting the WCs in the
dimension-six model to the data were consistent with the standard model at the
level of 2 standard deviations.

###### Acknowledgements.

We thank Adam Martin and Jeong Han Kim for their theoretical guidance in
configuring and debugging the EFT model used to generate the signal samples in
this analysis. We congratulate our colleagues in the CERN accelerator
departments for the excellent performance of the LHC and thank the technical
and administrative staffs at CERN and at other CMS institutes for their
contributions to the success of the CMS effort. In addition, we gratefully
acknowledge the computing centers and personnel of the Worldwide LHC Computing
Grid for delivering so effectively the computing infrastructure essential to
our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC and the CMS detector provided by the
following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium);
CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS,
MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of
Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and
HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
724704, 752730, and 765710 (European Union); the Leventis Foundation; the A.P.
Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal
Science Policy Office; the Fonds pour la Formation à la Recherche dans
l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor
Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and
FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n.
30820817; the Beijing Municipal Science & Technology Commission, No.
Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the
Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany’s
Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lendület
(“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia);
the Tomsk Polytechnic University Competitiveness Enhancement Program; the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the
Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

## References

  * [1] J. L. Feng, “Dark matter candidates from particle physics and methods of detection”,  Ann. Rev. Astron. Astrophys. 48 (2010) 495, 10.1146/annurev-astro-082708-101659, arXiv:1003.0904.
  * [2] T. A. Porter, R. P. Johnson, and P. W. Graham, “Dark matter searches with astroparticle data”,  Ann. Rev. Astron. Astrophys. 49 (2011) 155, 10.1146/annurev-astro-081710-102528, arXiv:1104.2836.
  * [3] P. J. E. Peebles and B. Ratra, “The cosmological constant and dark energy”,  Rev. Mod. Phys. 75 (2003) 559, 10.1103/RevModPhys.75.559, arXiv:astro-ph/0207347.
  * [4] Particle Data Group, “Review of particle physics”,  PTEP 2020 (2020) 083C01, 10.1093/ptep/ptaa104.
  * [5] E. Witten, “Dynamical breaking of supersymmetry”,  Nucl. Phys. B 188 (1981) 513, 10.1016/0550-3213(81)90006-7.
  * [6] J. Alimena et al., “Searching for long-lived particles beyond the standard model at the Large Hadron Collider”,  J. Phys. G 47 (2020) 090501, 10.1088/1361-6471/ab4574, arXiv:1903.04497.
  * [7] O. Matsedonskyi, G. Panico, and A. Wulzer, “Light top partners for a light composite Higgs”,  JHEP 01 (2013) 164, 10.1007/JHEP01(2013)164, arXiv:1204.6333.
  * [8] W. Buchmuller and D. Wyler, “Effective Lagrangian analysis of new interactions and flavor conservation”,  Nucl. Phys. B 268 (1986) 621, 10.1016/0550-3213(86)90262-2.
  * [9] B. Grzadkowski, M. Iskrzynski, M. Misiak, and J. Rosiek, “Dimension-six terms in the standard model Lagrangian”,  JHEP 10 (2010) 085, 10.1007/JHEP10(2010)085, arXiv:1008.4884.
  * [10] A. Falkowski and R. Rattazzi, “Which EFT”,  JHEP 10 (2019) 255, 10.1007/JHEP10(2019)255, arXiv:1902.05936.
  * [11] C. Degrande et al., “Effective field theory: a modern approach to anomalous couplings”,  Annals Phys. 335 (2013) 21, 10.1016/j.aop.2013.04.016, arXiv:1205.4231.
  * [12] CDF Collaboration, “Observation of top quark production in $\bar{p}p$ collisions”,  Phys. Rev. Lett. 74 (1995) 2626, 10.1103/PhysRevLett.74.2626, arXiv:hep-ex/9503002.
  * [13] D0 Collaboration, “Observation of the top quark”,  Phys. Rev. Lett. 74 (1995) 2632, 10.1103/PhysRevLett.74.2632, arXiv:hep-ex/9503003.
  * [14] CMS Collaboration, “Observation of $\mathrm{t\overline{t}}$H production”,  Phys. Rev. Lett. 120 (2018) 231801, 10.1103/PhysRevLett.120.231801, arXiv:1804.02610.
  * [15] ATLAS Collaboration, “Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector”,  Phys. Lett. B 784 (2018) 173, 10.1016/j.physletb.2018.07.035, arXiv:1806.00425.
  * [16] CMS Collaboration, “Measurement of the cross section for top quark pair production in association with a W or Z boson in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 08 (2018) 011, 10.1007/JHEP08(2018)011, arXiv:1711.02547.
  * [17] CMS Collaboration, “Observation of single top quark production in association with a $Z$ boson in proton-proton collisions at $\sqrt{s}$ =13 TeV”,  Phys. Rev. Lett. 122 (2019) 132003, 10.1103/PhysRevLett.122.132003, arXiv:1812.05900.
  * [18] ATLAS Collaboration, “Measurement of the $t\bar{t}Z$ and $t\bar{t}W$ cross sections in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Rev. D 99 (2019) 072009, 10.1103/PhysRevD.99.072009, arXiv:1901.03584.
  * [19] CMS Collaboration, “Measurements of $\mathrm{t\overline{t}}$ differential cross sections in proton-proton collisions at $\sqrt{s}=$ 13 TeV using events containing two leptons”,  JHEP 02 (2019) 149, 10.1007/JHEP02(2019)149, arXiv:1811.06625.
  * [20] CMS Collaboration, “Search for new physics in top quark production in dilepton final states in proton-proton collisions at $\sqrt{s}$ = 13 TeV”,  Eur. Phys. J. C 79 (2019) 886, 10.1140/epjc/s10052-019-7387-y, arXiv:1903.11144.
  * [21] CMS Collaboration, “Measurement of the top quark polarization and $\mathrm{t\bar{t}}$ spin correlations using dilepton final states in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Rev. D 100 (2019) 072002, 10.1103/PhysRevD.100.072002, arXiv:1907.03729.
  * [22] CMS Collaboration, “Measurement of top quark pair production in association with a Z boson in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2020) 056, 10.1007/JHEP03(2020)056, arXiv:1907.11270.
  * [23] ATLAS Collaboration, “Search for flavour-changing neutral currents in processes with one top quark and a photon using 81 fb-1 of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS experiment”,  Phys. Lett. B 800 (2020) 135082, 10.1016/j.physletb.2019.135082, arXiv:1908.08461.
  * [24] ATLAS Collaboration, “Search for flavour-changing neutral current top-quark decays $t\to qZ$ in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  JHEP 07 (2018) 176, 10.1007/JHEP07(2018)176, arXiv:1803.09923.
  * [25] A. Buckley et al., “Constraining top quark effective theory in the LHC Run II era”,  JHEP 04 (2016) 015, 10.1007/JHEP04(2016)015, arXiv:1512.03360.
  * [26] N. P. Hartland et al., “A Monte Carlo global analysis of the standard model effective field theory: the top quark sector”,  JHEP 04 (2019) 100, 10.1007/JHEP04(2019)100, arXiv:1901.05965.
  * [27] I. Brivio et al., “O new physics, where art thou? A global search in the top sector”,  JHEP 02 (2020) 131, 10.1007/JHEP02(2020)131, arXiv:1910.03606.
  * [28] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [29] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [30] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018\.
  * [31] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [32] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [33] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [34] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [35] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [36] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [37] R. Frederix, E. Re, and P. Torrielli, “Single-top t-channel hadroproduction in the four-flavour scheme with POWHEG and aMC@NLO”,  JHEP 09 (2012) 130, 10.1007/JHEP09(2012)130, arXiv:1207.5391.
  * [38] E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [39] T. Melia, P. Nason, R. Rontsch, and G. Zanderighi, “W+W-, WZ and ZZ production in the POWHEG BOX”,  JHEP 11 (2011) 078, 10.1007/JHEP11(2011)078, arXiv:1107.5051.
  * [40] S. Frixione, P. Nason, and G. Ridolfi, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,  JHEP 09 (2007) 126, 10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  * [41] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [42] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y, arXiv:1404.5630.
  * [43] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [44] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [45] Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [46] D. Barducci et al., “Interpreting top-quark LHC measurements in the standard-model effective field theory”,  2018\.  arXiv:1802.07237.
  * [47] R. Goldouzian et al., “Matching in $pp\to t\bar{t}W/Z/h+$ jet SMEFT studies”, (2020). arXiv:2012.06872. Submitted to JHEP.
  * [48] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 4. deciphering the nature of the Higgs sector”,  CERN (2016) 10.23731/CYRM-2017-002, arXiv:1610.07922.
  * [49] CMS Collaboration, “Particle-flow reconstruction and global event description with the cms detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [50] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=13$ TeV using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [51] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-$k_{\mathrm{t}}$ jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [52] M. Cacciari, G. P. Salam, and G. Soyez, “user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [53] CMS Collaboration, “Technical Proposal for the Phase-II Upgrade of the CMS Detector”, Technical Report CERN-LHCC-2015-010. LHCC-P-008. CMS-TDR-15-02, CERN, 2015.
  * [54] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [55] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [56] CMS Collaboration, “Performance of CMS muon reconstruction in $\Pp\Pp$ collision events at $\sqrt{s}=7$ TeV”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [57] CMS Collaboration, “Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying $\tau$ leptons at $\sqrt{s}=$ 13 TeV”,  JHEP 08 (2018) 066, 10.1007/JHEP08(2018)066, arXiv:1803.05485.
  * [58] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [59] CMS Collaboration, “Identification of $\cPqb$ quark jets with the CMS experiment”,  JINST 8 (2013) P04013, 10.1088/1748-0221/8/04/P04013, arXiv:1211.4462.
  * [60] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [61] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”,  J. Phys. G 43 (2016) 023001, 10.1088/0954-3899/43/2/023001, arXiv:1510.03865.
  * [62] CMS Collaboration, “Measurements of inclusive $\PW$ and $\PZ$ cross sections in $\Pp\Pp$ collisions at $\sqrt{s}=7$ TeV”,  JHEP 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.
  * [63] ATLAS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$ TeV with the ATLAS detector at the LHC”,  Phys. Rev. Lett. 117 (2016) 182002, 10.1103/PhysRevLett.117.182002, arXiv:1606.02625.

##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A.M.
Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria W. Adam, T. Bergauer, M. Dragicevic, A.
Escalante Del Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D.
Liko, I. Mikulec, F.M. Pitters, N. Rad, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for
Nuclear Problems, Minsk, Belarus V. Chekhovsky, A. Litomin, V. Makarenko, J.
Suarez Gonzalez Universiteit Antwerpen, Antwerpen, Belgium M.R. Darwish2, E.A.
De Wolf, X. Janssen, T. Kello3, A. Lelek, M. Pieters, H. Rejeb Sfar, H. Van
Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit
Brussel, Brussel, Belgium F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J.
De Clercq, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, A. Morton,
D. Müller, Q. Python, S. Tavernier, W. Van Doninck, P. Van Mulders Université
Libre de Bruxelles, Bruxelles, Belgium D. Beghin, B. Bilin, B. Clerbaux, G. De
Lentdecker, B. Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, I. Makarenko, L.
Moureaux, L. Pétré, A. Popov, N. Postiau, E. Starling, L. Thomas, C. Vander
Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, G. Mestdach, M. Niedziela,
C. Roskas, K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université
Catholique de Louvain, Louvain-la-Neuve, Belgium G. Bruno, F. Bury, C. Caputo,
P. David, C. Delaere, M. Delcourt, I.S. Donertas, A. Giammanco, V. Lemaitre,
K. Mondal, J. Prisciandaro, A. Taliercio, M. Teklishyn, P. Vischia, S. Wertz,
S. Wuyckens Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H.
BRANDAO MALBOUISSON, W. Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa,
G.G. Da Silveira6, D. De Jesus Damiao, S. Fonseca De Souza, J. Martins7, D.
Matos Figueiredo, M. Medina Jaime8, C. Mora Herrera, L. Mundim, H. Nogima, P.
Rebello Teles, L.J. Sanchez Rosas, A. Santoro, S.M. Silva Do Amaral, A.
Sznajder, M. Thiel, F. Torres Da Silva De Araujo, A. Vilela Pereira
Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,
Brazil C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria A. Aleksandrov, G. Antchev, I. Atanasov, R.
Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov
University of Sofia, Sofia, Bulgaria A. Dimitrov, T. Ivanov, L. Litov, B.
Pavlov, P. Petkov, A. Petrov Beihang University, Beijing, China T. Cheng, W.
Fang3, Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University,
Beijing, China M. Ahmad, G. Bauer, Z. Hu, Y. Wang, K. Yi9,10 Institute of High
Energy Physics, Beijing, China E. Chapon, G.M. Chen11, H.S. Chen11, M. Chen,
T. Javaid11, A. Kapoor, D. Leggat, H. Liao, Z.-A. LIU11, R. Sharma, A.
Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S. Zhang11, J. Zhao
State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu,
X. Lyu, Y. Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University,
Guangzhou, China Z. You Institute of Modern Physics and Key Laboratory of
Nuclear Physics and Ion-beam Application (MOE) - Fudan University, Shanghai,
China X. Gao3 Zhejiang University, Hangzhou, China M. Xiao Universidad de Los
Andes, Bogota, Colombia C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar,
M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia J. Jaramillo,
J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar González, N.
Vanegas Arbelaez University of Split, Faculty of Electrical Engineering,
Mechanical Engineering and Naval Architecture, Split, Croatia D. Giljanovic,
N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty of Science,
Split, Croatia Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic,
Zagreb, Croatia V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A.
Starodumov12, T. Susa University of Cyprus, Nicosia, Cyprus M.W. Ather, A.
Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou, J.
Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka, D.
Tsiakkouri Charles University, Prague, Czech Republic M. Finger13, M. Finger
Jr.13, A. Kveton, J. Tomsa Escuela Politecnica Nacional, Quito, Ecuador E.
Ayala Universidad San Francisco de Quito, Quito, Ecuador E. Carrera Jarrin
Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt A.A. Abdelalim14,15, S.
Abu Zeid16, S. Elgammal17 Center for High Energy Physics (CHEP-FU), Fayoum
University, El-Fayoum, Egypt A. Lotfy, M.A. Mahmoud National Institute of
Chemical Physics and Biophysics, Tallinn, Estonia S. Bhowmik, A. Carvalho
Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M. Kadastik, J. Pata, M.
Raidal, C. Veelken Department of Physics, University of Helsinki, Helsinki,
Finland P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland E. Brücken, F. Garcia, J.
Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T. Lampén, K. Lassila-Perini,
S. Lehti, T. Lindén, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland P. Luukka, T. Tuuva IRFU, CEA,
Université Paris-Saclay, Gif-sur-Yvette, France C. Amendola, M. Besancon, F.
Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour, A.
Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, B. Lenzi, E. Locci, J.
Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-Navarro18, M. Titov,
G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique,
Institut Polytechnique de Paris, Palaiseau, France S. Ahuja, F. Beaudette, M.
Bonanomi, A. Buchot Perraguin, P. Busson, C. Charlot, O. Davignon, B. Diab, G.
Falmagne, R. Granier de Cassagnac, A. Hakimi, I. Kucher, A. Lobanov, C. Martin
Perez, M. Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B.
Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC
UMR 7178, Strasbourg, France J.-L. Agram19, J. Andrea, D. Bloch, G. Bourgatte,
J.-M. Brom, E.C. Chabert, C. Collard, J.-C. Fontaine19, D. Gelé, U. Goerlach,
C. Grimault, A.-C. Le Bihan, P. Van Hove Université de Lyon, Université Claude
Bernard Lyon 1, CNRS-IN2P3, Institut de Physique Nucléaire de Lyon,
Villeurbanne, France E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen,
A. Carle, N. Chanon, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S.
Gascon, M. Gouzevitch, B. Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, K. Shchablo, L. Torterotot, G.
Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia T. Toriashvili20, Z. Tsamalaidze13 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany L. Feld, K. Klein, M. Lipinski, D.
Meuser, A. Pauls, M.P. Rauch, J. Schulz, M. Teroerde RWTH Aachen University,
III. Physikalisches Institut A, Aachen, Germany D. Eliseev, M. Erdmann, P.
Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin, S. Mondal, S. Mukherjee,
D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath, H. Reithler, J. Roemer, A.
Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski RWTH Aachen
University, III. Physikalisches Institut B, Aachen, Germany C. Dziwok, G.
Flügge, W. Haj Ahmad21, O. Hlushchenko, T. Kress, A. Nowack, C. Pistone, O.
Pooth, D. Roy, H. Sert, A. Stahl22, T. Ziemons Deutsches Elektronen-
Synchrotron, Hamburg, Germany H. Aarup Petersen, M. Aldaya Martin, P. Asmuss,
I. Babounikau, S. Baxter, O. Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K.
Borras23, V. Botta, D. Brunner, A. Campbell, A. Cardini, P. Connor, S.
Consuegra Rodríguez, V. Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, L.I. Estevez Banos, E. Gallo24,
A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari25, N.Z.
Jomhari, H. Jung, A. Kasem23, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle,
D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann26, T.
Madlener, R. Mankel, I.-A. Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer,
J. Mnich, A. Mussgiller, V. Myronenko, Y. Otarid, D. Pérez Adán, S.K.
Pflitsch, D. Pitzl, A. Raspereza, A. Saggio, A. Saibel, M. Savitskyi, V.
Scheurer, C. Schwanenberger, A. Singh, R.E. Sosa Ricardo, N. Tonon, O. Turkot,
A. Vagnerini, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C.
Wissing, S. Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg,
Germany R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A.
Ebrahimi, M. Eich, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, V. Kutzner, J. Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A.
Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S. Schumann, J.
Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbrück, A. Tews, B.
Vormwald, I. Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany J.
Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A. Dierlamm,
A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M. Giffels, A. Gottmann, F.
Hartmann22, C. Heidecker, U. Husemann, I. Katkov27, P. Keicher, R.
Koppenhöfer, S. Maier, M. Metzler, S. Mitra, Th. Müller, M. Musich, G. Quast,
K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M. Schnepf, M. Schröder, D.
Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Wassmer, M. Weber, R. Wolf, S.
Wozniewski Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos,
Aghia Paraskevi, Greece G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis,
A. Kyriakis, D. Loukas, G. Paspalaki, A. Stakia National and Kapodistrian
University of Athens, Athens, Greece M. Diamantopoulou, D. Karasavvas, G.
Karathanasis, P. Kontaxakis, C.K. Koraka, A. Manousakis-katsikakis, A.
Panagiotou, I. Papavergou, N. Saoulidou, K. Theofilatos, E. Tziaferi, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A.
Zacharopoulou University of Ioánnina, Ioánnina, Greece I. Evangelou, C.
Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, K. Manitara, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary M. Bartók28, M. Csanad,
M.M.A. Gadallah29, S. Lökös30, P. Major, K. Mandal, A. Mehta, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary G.
Bencze, C. Hajdu, D. Horvath31, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary S. Czellar, J. Karancsi28, J. Molnar, Z. Szillasi, D.
Teyssier Institute of Physics, University of Debrecen, Debrecen, Hungary P.
Raics, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University, Karoly Robert
Campus, Gyongyos, Hungary T. Csorgo33, F. Nemes33, T. Novak Indian Institute
of Science (IISc), Bangalore, India S. Choudhury, J.R. Komaragiri, D. Kumar,
L. Panwar, P.C. Tiwari National Institute of Science Education and Research,
HBNI, Bhubaneswar, India S. Bahinipati34, D. Dash, C. Kar, P. Mal, T. Mishra,
V.K. Muraleedharan Nair Bindhu, A. Nayak35, N. Sur, S.K. Swain Panjab
University, Chandigarh, India S. Bansal, S.B. Beri, V. Bhatnagar, G.
Chaudhary, S. Chauhan, N. Dhingra36, R. Gupta, A. Kaur, S. Kaur, P. Kumari, M.
Meena, K. Sandeep, S. Sharma, J.B. Singh, A.K. Virdi University of Delhi,
Delhi, India A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S.
Keshri, A. Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute
of Nuclear Physics, HBNI, Kolkata, India M. Bharti37, R. Bhattacharya, S.
Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber38, M. Maity39, S.
Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S. Sarkar, M. Sharan, B.
Singh37, S. Thakur37 Indian Institute of Technology Madras, Madras, India P.K.
Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari, A.
Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India D. Dutta, V.
Kumar, K. Naskar40, P.K. Netrakanti, L.M. Pant, P. Shukla Tata Institute of
Fundamental Research-A, Mumbai, India T. Aziz, M.A. Bhat, S. Dugad, R. Kumar
Verma, G.B. Mohanty, U. Sarkar Tata Institute of Fundamental Research-B,
Mumbai, India S. Banerjee, S. Bhattacharya, S. Chatterjee, R. Chudasama, M.
Guchait, S. Karmakar, S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy
Indian Institute of Science Education and Research (IISER), Pune, India S.
Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran H. Bakhshiansohi41,
M. Zeinali42 Institute for Research in Fundamental Sciences (IPM), Tehran,
Iran S. Chenarani43, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi
University College Dublin, Dublin, Ireland M. Felcini, M. Grunewald INFN
Sezione di Bari a, Università di Bari b, Politecnico di Bari c, Bari, Italy M.
Abbresciaa,b, R. Alya,b,44, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy G.
Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Lo Meoa,45, S. Marcellinia, G. Masettia, F.L.
Navarriaa,b, A. Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P.
Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di Catania b,
Catania, Italy S. Albergoa,b,46, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,46, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C.
Civininia, R. D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P.
Lenzia,b, M. Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G.
Sguazzonia, L. Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia,
S. Tosia,b INFN Sezione di Milano-Bicocca a, Università di Milano-Bicocca b,
Milano, Italy A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V.
Cirioloa,b,22, F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A.
Ghezzia,b, P. Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, A.
Massironia, D. Menascea, F. Montia,b, L. Moronia, M. Paganonia,b, D. Pedrinia,
S. Ragazzia,b, T. Tabarelli de Fatisa,b, D. Valsecchia,b,22, D. Zuoloa,b INFN
Sezione di Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy,
Università della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma,
Italy S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,22, P. Paoluccia,22, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy P. Azzia, N. Bacchettaa, D. Biselloa,b,
P. Bortignona, A. Bragagnoloa,b, R. Carlina,b, P. Checchiaa, P. De Castro
Manzanoa, T. Dorigoa, F. Gasparinia,b, U. Gasparinia,b, S.Y. Hoha,b, L.
Layera,47, M. Margonia,b, A.T. Meneguzzoa,b, M. Presillaa,b, P. Ronchesea,b,
R. Rossina,b, F. Simonettoa,b, G. Stronga, M. Tosia,b, H. YARARa,b, M.
Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G. Zumerlea,b INFN Sezione di Pavia
a, Università di Pavia b, Pavia, Italy C. Aime‘a,b, A. Braghieria, S.
Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN Sezione
di Perugia a, Università di Perugia b, Perugia, Italy M. Biasinia,b, G.M.
Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G. Mantovania,b, V.
Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b, A. Rossia,b, A.
Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy, Università di Siena d,
Siena, Italy K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L.
Bianchinia, T. Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di
Domenicoa,b, S. Donatoa, L. Gianninia,c, A. Giassia, M.T. Grippoa, F.
Ligabuea,c, E. Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G.
Ramirez-Sancheza,c, A. Rizzia,b, G. Rolandia,c, S. Roy Chowdhurya,c, A.
Scribanoa, N. Shafieia,b, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N.
Turinia, A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza
Università di Roma b, Rome, Italy F. Cavallaria, M. Cipriania,b, D. Del Rea,b,
E. Di Marcoa, M. Diemoza, E. Longoa,b, P. Meridiania, G. Organtinia,b, F.
Pandolfia, R. Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b, R. Tramontanoa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c,
N. Bartosika, R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C.
Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M. Costaa,b, R.
Covarellia,b, N. Demariaa, B. Kiania,b, F. Leggera, C. Mariottia, S. Masellia,
E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, G.
Ortonaa, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b,
M. Ruspaa,c, R. Salvaticoa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D.
Soldia,b, A. Staianoa, M. Tornagoa,b, D. Trocinoa,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy S. Belfortea, V. Candelisea,b, M.
Casarsaa, F. Cossuttia, A. Da Rolda,b, G. Della Riccaa,b, F. Vazzolera,b
Kyungpook National University, Daegu, Korea S. Dogra, C. Huh, B. Kim, D.H.
Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, B.C. Radburn-
Smith, S. Sekmen, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea H. Kim, D.H. Moon Hanyang
University, Seoul, Korea B. Francois, T.J. Kim, J. Park Korea University,
Seoul, Korea S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim,
J. Park, S.K. Park, J. Yoo Kyung Hee University, Department of Physics, Seoul,
Republic of Korea J. Goh, A. Gurtu Sejong University, Seoul, Korea H.S. Kim,
Y. Kim Seoul National University, Seoul, Korea J. Almond, J.H. Bhyun, J. Choi,
S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, K. Lee, S. Lee, K. Nam,
B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon University of Seoul,
Seoul, Korea D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song,
I.J. Watson Yonsei University, Department of Physics, Seoul, Korea H.D. Yoo
Sungkyunkwan University, Suwon, Korea Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y.
Lee, I. Yu College of Engineering and Technology, American University of the
Middle East (AUM), Kuwait Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns48 Vilnius University, Vilnius, Lithuania A. Juodagalvis, A.
Rinkevicius, G. Tamulaitis, A. Vaitkevicius National Centre for Particle
Physics, Universiti Malaya, Kuala Lumpur, Malaysia W.A.T. Wan Abdullah, M.N.
Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo, Mexico J.F.
Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz49,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno,
C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico J. Eysermans, I. Pedraza, H.A.
Salazar Ibarguen, C. Uribe Estrada Universidad Autónoma de San Luis Potosí,
San Luis Potosí, Mexico A. Morelos Pineda University of Montenegro, Podgorica,
Montenegro J. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New
Zealand D. Krofcheck University of Canterbury, Christchurch, New Zealand S.
Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam University,
Islamabad, Pakistan A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R.
Hoorani, W.A. Khan, S. Qazi, M.A. Shah, M. Waqas AGH University of Science and
Technology Faculty of Computer Science, Electronics and Telecommunications,
Krakow, Poland V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear
Research, Swierk, Poland H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M.
Górski, M. Kazana, M. Szleper, P. Traczyk, P. Zalewski Institute of
Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M.
Gallinaro, J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia S.
Afanasiev, A. Baginyan, M. Gavrilenko, A. Golunov, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, I. Kashunin, V. Korenkov, A. Lanev, A. Malakhov, V.
Matveev50,51, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin Petersburg Nuclear Physics
Institute, Gatchina (St. Petersburg), Russia G. Gavrilov, V. Golovtcov, Y.
Ivanov, V. Kim52, E. Kuznetsova53, V. Murzin, V. Oreshkin, I. Smirnov, D.
Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev Institute for Nuclear
Research, Moscow, Russia Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A.
Karneyeu, M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko54, V.
Popov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin
Moscow Institute of Physics and Technology, Moscow, Russia T. Aushev National
Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI),
Moscow, Russia R. Chistov55, M. Danilov56, A. Oskin, P. Parygin, S.
Polikarpov56 P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M.
Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn Institute of Nuclear
Physics, Lomonosov Moscow State University, Moscow, Russia A. Belyaev, E.
Boos, V. Bunichev, M. Dubinin57, L. Dudko, A. Gribushin, V. Klyukhin, N.
Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P. Volkov
Novosibirsk State University (NSU), Novosibirsk, Russia V. Blinov58, T.
Dimova58, L. Kardapoltsev58, I. Ovtin58, Y. Skovpen58 Institute for High
Energy Physics of National Research Centre ‘Kurchatov Institute’, Protvino,
Russia I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V.
Petrov, R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov
National Research Tomsk Polytechnic University, Tomsk, Russia A. Babaev, A.
Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University, Tomsk, Russia V.
Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia P. Adzic59,
M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones Energéticas
Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain M. Aguilar-Benitez, J.
Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, Cristina
F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La
Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez
Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León Holgado, D. Moran, Á.
Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L.
Romero, S. Sánchez Navas, M.S. Soares, L. Urda Gómez, C. Willmott Universidad
Autónoma de Madrid, Madrid, Spain C. Albajar, J.F. de Trocóniz, R. Reyes-
Almanza Universidad de Oviedo, Instituto Universitario de Ciencias y
Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain B. Alvarez Gonzalez,
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau, V. Rodríguez
Bouza, S. Sanchez Cruz, A. Trapote Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain J.A. Brochero Cifuentes, I.J.
Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M. Fernandez,
P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez Rivero, P.
Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-
Tam, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, I. Vila, J.M. Vizan Garcia
University of Colombo, Colombo, Sri Lanka MK Jayananda, B. Kailasapathy60,
D.U.J. Sonnadara, DDC Wickramarathna University of Ruhuna, Department of
Physics, Matara, Sri Lanka W.G.D. Dharmaratna, K. Liyanage, N. Perera, N.
Wickramage CERN, European Organization for Nuclear Research, Geneva,
Switzerland T.K. Aarrestad, D. Abbaneo, E. Auffray, G. Auzinger, J. Baechler,
P. Baillon, A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci,
E. Bossini, E. Brondolin, T. Camporesi, M. Capeans Garrido, G. Cerminara, L.
Cristella, D. d’Enterria, A. Dabrowski, N. Daci, A. David, A. De Roeck, M.
Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita61, D. Fasanella, S. Fiorendi, A. Florent, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L.
Gouskos, M. Guilbaud, M. Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T.
James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S.
Laurila, P. Lecoq, K. Long, C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli,
F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L.
Orsini, F. Pantaleo22, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G.
Petrucciani, A. Pfeiffer, M. Pierini, T. Quast, D. Rabady, A. Racz, M. Rieger,
M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C. Schäfer, C. Schwick,
M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas62, S. Summers, V.R.
Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem, A. Vartak, M. Verzetti, K.A.
Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland L.
Caminada63, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,
U. Langenegger, M. Missiroli, T. Rohe ETH Zurich - Institute for Particle
Physics and Astrophysics (IPA), Zurich, Switzerland M. Backhaus, P. Berger, A.
Calandri, N. Chernyavskaya, A. De Cosa, G. Dissertori, M. Dittmar, M. Donegà,
C. Dorfer, T. Gadek, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann,
A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F. Micheli, F. Nessi-Tedaldi, J.
Niedziela, F. Pauss, V. Perovic, G. Perrin, S. Pigazzini, M.G. Ratti, M.
Reichmann, C. Reissel, T. Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz
Becerra, M. Schönenberger, V. Stampf, J. Steggemann64, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland C. Amsler65, C. Botta, D. Brzhechko,
M.F. Canelli, R. Del Burgo, J.K. Heikkilä, M. Huwiler, A. Jofrehei, B.
Kilminster, S. Leontsinis, A. Macchiolo, P. Meiring, V.M. Mikuni, U.
Molinatti, I. Neutelings, G. Rauco, A. Reimers, P. Robmann, K. Schweiger, Y.
Takahashi National Central University, Chung-Li, Taiwan C. Adloff66, C.M. Kuo,
W. Lin, A. Roy, T. Sarkar39, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li,
R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand B.
Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey F. Boran, S.
Damarseckin67, Z.S. Demiroglu, F. Dolek, C. Dozen68, I. Dumanoglu69, E. Eskut,
G. Gokbulut, Y. Guler, E. Gurpinar Guler70, I. Hos71, C. Isik, E.E. Kangal72,
O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir73, A. Polatoz,
A.E. Simsek, B. Tali74, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez
Middle East Technical University, Physics Department, Ankara, Turkey B.
Isildak75, G. Karapinar76, K. Ocalan77, M. Yalvac78 Bogazici University,
Istanbul, Turkey B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya79, O. Kaya80, Ö.
Özçelik, S. Tekten81, E.A. Yetkin82 Istanbul Technical University, Istanbul,
Turkey A. Cakir, K. Cankocak69, Y. Komurcu, S. Sen83 Istanbul University,
Istanbul, Turkey F. Aydogmus Sen, S. Cerci74, B. Kaynak, S. Ozkorucuklu, D.
Sunar Cerci74 Institute for Scintillation Materials of National Academy of
Science of Ukraine, Kharkov, Ukraine B. Grynyov National Scientific Center,
Kharkov Institute of Physics and Technology, Kharkov, Ukraine L. Levchuk
University of Bristol, Bristol, United Kingdom E. Bhal, S. Bologna, J.J.
Brooke, E. Clement, D. Cussans, H. Flacher, J. Goldstein, G.P. Heath, H.F.
Heath, L. Kreczko, B. Krikler, S. Paramesvaran, T. Sakuma, S. Seif El Nasr-
Storey, V.J. Smith, N. Stylianou84, J. Taylor, A. Titterton Rutherford
Appleton Laboratory, Didcot, United Kingdom K.W. Bell, A. Belyaev85, C. Brew,
R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K. Harder, S. Harper, J. Linacre, K.
Manolopoulos, D.M. Newbold, E. Olaiya, D. Petyt, T. Reis, T. Schuh, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams Imperial College,
London, United Kingdom R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S.
Breeze, O. Buchmuller, A. Bundock, V. Cepaitis, G.S. Chahal86, D. Colling, P.
Dauncey, G. Davies, M. Della Negra, G. Fedi, G. Hall, G. Iles, J. Langford, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash87, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, A. Tapper, K. Uchida, T. Virdee22, N. Wardle, S.N.
Webb, D. Winterbottom, A.G. Zecchinelli Brunel University, Uxbridge, United
Kingdom J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA S. Abdullin, A. Brinkerhoff,
K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R. Kanuganti, C. Madrid, B.
McMaster, N. Pastika, S. Sawant, C. Smith, J. Wilson Catholic University of
America, Washington, DC, USA R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas
Hernandez The University of Alabama, Tuscaloosa, USA A. Buccilli, O. Charaf,
S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson, C.U. Perez, P. Rumerio,
C. West Boston University, Boston, USA A. Akpinar, A. Albert, D. Arcaro, C.
Cosby, Z. Demiragli, D. Gastler, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart,
I. Suarez, S. Yuan, D. Zou Brown University, Providence, USA G. Benelli, B.
Burkle, X. Coubez23, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz, J.M. Hogan88,
K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M. Narain, S. Sagir89,
R. Syarif, E. Usai, W.Y. Wong, D. Yu, W. Zhang University of California,
Davis, Davis, USA R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca
Sanchez, M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores,
G. Funk, F. Jensen, W. Ko${}^{\textrm{\textdagger}}$, O. Kukral, R. Lander, M.
Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y.
Yao, F. Zhang University of California, Los Angeles, USA M. Bachtis, R.
Cousins, A. Dasgupta, D. Hamilton, J. Hauser, M. Ignatenko, M.A. Iqbal, T.
Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B. Stone,
V. Valuev University of California, Riverside, Riverside, USA K. Burt, Y.
Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long, N.
Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang University of
California, San Diego, La Jolla, USA J.G. Branson, P. Chang, S. Cittolin, S.
Cooperstein, N. Deelen, J. Duarte, R. Gerosa, D. Gilbert, V. Krutelyov, J.
Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F.
Würthwein, A. Yagil University of California, Santa Barbara - Department of
Physics, Santa Barbara, USA N. Amin, C. Campagnari, M. Citron, A. Dorsett, V.
Dutta, J. Incandela, M. Kilpatrick, B. Marsh, H. Mei, A. Ovcharova, H. Qu, M.
Quinnan, J. Richman, U. Sarica, D. Stuart, S. Wang California Institute of