TOP-21-004

TOP-21-004

#  Measurement of the inclusive and differential $\ttbar\PGg$ cross sections
in the dilepton channel and effective field theory interpretation in proton-
proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

The production cross section of a top quark pair in association with a photon
is measured in proton-proton collisions in the decay channel with two
oppositely charged leptons ($\Pepm\PGmmp$, $\Pep\Pem$, or $\PGmp\PGmm$). The
measurement is performed using 138of proton-proton collision data recorded by
the CMS experiment at $\sqrt{s}=13\TeV$ during the 2016–2018 data-taking
period of the CERN LHC. A fiducial phase space is defined such that photons
radiated by initial-state particles, top quarks, or any of their decay
products are included. An inclusive cross section of $175.2\pm 2.5\stat\pm
6.3\syst\unit{fb}$ is measured in a signal region with at least one jet coming
from the hadronization of a bottom quark and exactly one photon with
transverse momentum above 20. Differential cross sections are measured as
functions of several kinematic observables of the photon, leptons, and jets,
and compared to standard model predictions. The measurements are also
interpreted in the standard model effective field theory framework, and limits
are found on the relevant Wilson coefficients from these results alone and in
combination with a previous CMS measurement of the $\ttbar\PGg$ production
process using the lepton+jets final state.

##  0.1 Introduction

The cross section measurement of top quark pair production in association with
a photon ($\ttbar\PGg$) probes the coupling between the top quark and the
photon, making it both a test of standard model (SM) predictions and providing
sensitivity to new-physics phenomena beyond the SM. With the large amount of
data collected with the CMS detector in proton-proton ($\Pp\Pp$) collisions at
$\sqrt{s}=13\TeV$ during the data-taking period from 2016 to 2018 of the CERN
LHC, precise measurements of relatively small cross sections such as for
$\ttbar\PGg$ production are possible. We present inclusive and differential
measurements of the $\ttbar\PGg$ production cross section in final states with
two oppositely charged (OC) leptons ($\Pepm\PGmmp$, $\Pep\Pem$, or
$\PGmp\PGmm$). The inclusion of differential information improves the
sensitivity of the measurement to new-physics modifications. Additionally, we
perform a model-independent interpretation of the results in terms of the SM
effective field theory (SMEFT).

First evidence for $\ttbar\PGg$ production was found by the CDF Collaboration
in $\Pp\PAp$ collisions [1]. At the LHC, $\ttbar\PGg$ production in $\Pp\Pp$
collisions was first observed by the ATLAS Collaboration at $\sqrt{s}=7\TeV$
[2]. Further measurements were performed by the ATLAS and CMS Collaborations
at 8 [3, 4] and 13 [5, 6, 7]. In the latest CMS measurement presented in Ref.
[7], $\ttbar\PGg$ production is measured in events with exactly one lepton
(electron or muon) and at least three jets (“$\Pell\text{+jets}$”), using the
same data set as the dilepton measurement presented here.

For this measurement, a fiducial phase space is defined for the $\ttbar\PGg$
signal process with criteria on the kinematic properties of the photon,
leptons, and jets at the particle level. Events are included where the photon
is radiated from a top quark, an incoming quark, or any of the charged decay
products of the top quarks. Examples of leading-order (LO) Feynman diagrams
for these processes are shown in Fig. 1.

Figure 1: Examples of leading-order Feynman diagrams for $\ttbar\PGg$
production with two leptons in the final state, where the photon is radiated
by a top quark (left), by an incoming quark (middle), or by one of the charged
decay products of a top quark (right).

Events are selected with two OC leptons, an isolated photon, and at least one
jet. Background contributions without top quarks are suppressed by applying
additional tagging criteria on the selected jets. After the event selection,
the dominant source of background stems from events with nonprompt photons,
photons originating from particles inside hadronic jets or from additional
$\Pp\Pp$ collisions, or hadronic jets misidentified as photons. The nonprompt
background contribution is estimated using control samples in data, while
other background sources (mainly boson and single top quark production in
association with a photon) are estimated from Monte Carlo (MC) event
simulations.

The inclusive cross section is measured with a profile likelihood fit from the
measured distribution of the transverse momentum of the reconstructed photon,
in which the sources of systematic uncertainty are treated as nuisance
parameters. The differential cross sections are measured by subtracting the
estimated background contributions from the measured distributions and
applying an unfolding method to correct for detector resolution effects. To
evaluate the sensitivity to possible modifications of the coupling between the
top quark and the photon, the measured photon distribution is used to
constrain relevant Wilson coefficients in the SMEFT framework [8, 9]. Limits
are derived on the relevant Wilson coefficients from these results alone and
in combination with the $\Pell\text{+jets}$ measurement from Ref. [7].

The SM production of $\ttbar\PGg$ has been studied at next-to-LO (NLO) in
quantum chromodynamics (QCD) and electroweak theory [10, 11, 12, 13, 14, 15,
16], including also the top quark decays and photons radiated from the final-
state particles at full NLO accuracy [17, 18, 19]. The sensitivity to new-
physics modifications has been studied for anomalous dipole moments of the top
quark [20, 21, 22] and in the SMEFT framework [23]. The possibility to study
charge asymmetries in $\ttbar\PGg$ production has been investigated in Refs.
[24, 25].

This paper is organized as follows. The CMS detector is described briefly in
Section 0.2. In Section 0.3, the data and simulated event samples are
discussed. The reconstruction and selection of events is detailed in Section
0.4, followed by an estimation of the background contributions in Section 0.5.
Systematic uncertainties that affect the measurements are discussed in Section
0.6. The results of the inclusive and the differential cross section
measurements are presented in Sections 0.7 and 0.8, respectively. In Section
0.9, the interpretation of the measurements in the SMEFT framework is
provided. The results are summarized in Section 0.10.

Tabulated results are provided in the HEPData record for this analysis [26].

##  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, each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are measured in gas-ionization detectors
embedded in the steel flux-return yoke outside the solenoid. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, can be found in Ref. [27].

Events of interest are selected using a two-tiered trigger system. The first
level (L1), composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed latency of about 4 [28]. 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 [29].

##  0.3 Data and simulated samples

The data sample used in this measurement corresponds to an integrated
luminosity of 138of $\Pp\Pp$ collision events at $\sqrt{s}=13\TeV$ collected
with the CMS detector between 2016 and 2018. To account for the differences in
the LHC running conditions and the CMS detector performance, the data
collected in the three years of data taking are analyzed separately and
appropriate per-year calibrations are applied, before the data are combined
for the final cross section measurements.

Simulated MC events are used for the evaluation of the signal selection
efficiency, the validation of the background estimates from control samples in
data, and the prediction of other background contributions. Three separate
sets of simulated event samples are used, corresponding to the conditions of
the three data-taking years.

For the $\ttbar\PGg$ signal process, simulated events are generated at LO
accuracy in QCD using the 2.6.0 event generator [30] for the $2\to 7$ signal
process, as $\Pp\Pp\to\PQb\Pell^{+}\PGn\PAQb\Pell^{-}\PAGn\PGg$ and similarly
for the other final states. In this way, all contributions with photons
radiated from incoming quarks, top quarks, or any of the charged top quark
decay products are included. For the normalization of the $\ttbar\PGg$ sample,
a $K$-factor evaluated with at NLO accuracy for the $2\to 3$ process
$\Pp\Pp\to\ttbar\PGg$ is applied. The parton distribution functions (PDFs)
used in the simulation of the hard process are the NNPDF3.1 [31] sets.

For the simulation of background events from , $\ttbar\PH$, and single top
quark $t$-channel and $\PQt\PW$ production, the $\POWHEG 2$ event generator
[32, 33, 34, 35, 36, 37, 38, 39] at NLO accuracy in QCD is used. Samples for
$\Pg\Pg\to\PZ\PZ$ production are generated at LO accuracy in QCD with the
7.0.1 event generator [40, 41]. For all other background processes, events are
simulated with the event generator at LO or NLO accuracy in QCD. In the
simulation of the hard process, the NNPDF3.0 [42] (NNPDF3.1) sets are used for
the 2016 (2017–2018) samples.

All event generators are interfaced with the 8.226 (8.230) simulation [43] for
parton showering and hadronization of the 2016 (2017–2018) samples. The
underlying event is modelled using the CP5 tune [44], except for some 2016
background samples for which the CUETP8M1, CUETP8M2, and CUETP8M2T4 tunes [45,
46, 47] are used. For the LO (NLO) samples generated with and interfaced with
$\PYTHIA 8$, jets from matrix element calculations are merged with those from
the parton shower using the MLM (FxFx) [48, 49] matching scheme. A summary of
all simulated samples is given in Table 0.3.

MC event generators used to simulate events for the signal and background
processes. For each simulated process, the order of the cross section
normalization calculation, the MC event generator used, and the perturbative
order in QCD of the generator calculation are shown. The normalization and
perturbative QCD orders are given as LO, NLO, next-to-NLO (NNLO), and
including next-to-next-to-leading-logarithmic (NNLL) corrections. The symbol
refers to and bosons.  Process Cross section Event generator Perturbative
normalization order in QCD $\ttbar\PGg$ NLO LO NNLO [50] LO $\PZ\PGg$,
$\PW\PGg$, $\PV\PV$, $\PV\PV\PV$, $\ttbar\PV$, $\PQt\PZ\PQq$, $\PQt\PW\PZ$,
$\PQt\PH\Pq$, NLO NLO $\PQt\PH\PW$, $\ttbar\PV\PV$, $\ttbar\ttbar$ NNLO+NNLL
[51] NLO single ($t\text{ channel}$) NLO [52, 53] NLO single ($s\text{
channel}$) NLO [52, 53] NLO $\PQt\PW$ NNLO [54] NLO $\ttbar\PH$ NLO NLO
$\Pg\Pg\to\PZ\PZ$ LO LO

An overlap exists between the phase space modelled in the $\ttbar\PGg$ and
samples because of the addition of soft photons to simulated events by the
$\PYTHIA 8$ simulation. To ensure orthogonal phase spaces, simulated events
from the $\ttbar\PGg$ sample are only used if they contain a generated photon
with $\pt>10\GeV$ and $\abs{\eta}<5$ that does not originate from the decay of
a hadron and is separated from any other stable particle (a generated particle
with lifetime larger than 3ns [55]) except neutrinos and photons by
$\DR=\sqrt{(\Delta\eta)^{2}+(\Delta\varphi)^{2}}>0.1$, where $\Delta\eta$ and
$\Delta\varphi$ are the differences in pseudorapidity and azimuthal angle,
respectively, between the directions of the photon and the stable particle.
Conversely, simulated events from the sample are only used if they do not
contain such a photon. Similarly, the overlap between the $\PZ\PGg$ and
$\PZ\text{+jets}$ sample is removed, with modified requirements of
$\pt>15\GeV$ and $\abs{\eta}<2.6$ for the generated photon and a minimum
distance of $\DR>0.05$, corresponding to the different settings in the event
simulation.

The $\ttbar\PGg$ cross section is measured in a fiducial phase space for the
dilepton final state, defined at particle level after the event generation,
parton showering, and hadronization of the $\ttbar\PGg$ event samples.
Electrons and muons, after adding all photons inside a cone of $\DR<0.1$
around the lepton direction, are required to have $\pt>15\GeV$ and
$\abs{\eta}<2.4$. Signal photons must have $\pt>20\GeV$ and $\abs{\eta}<1.44$,
and be isolated from any other stable particle with $\pt>5\GeV$ except for
neutrinos by $\DR>0.1$. Additionally, photons must be separated from electrons
and muons by $\DR>0.4$. Jets are clustered with the anti-algorithm [56] with a
distance parameter $R=0.4$, using all particles excluding neutrinos, and are
required to have $\pt>30\GeV$ and $\abs{\eta}<2.4$. Furthermore, jets must be
isolated from all leptons and photons by $\DR>0.4$ and 0.1, respectively. For
each hadron, an additional collinear four-vector of infinitesimal magnitude is
included in the jet clustering, and each jet that includes such a “ghost” is
identified as jet [57]. The fiducial phase space is defined by requiring
$\ttbar\PGg$ events to have exactly one photon, at least one jet, and exactly
two OC leptons with an invariant mass $m(\Pell\Pell)>20\GeV$, of which at
least one has $\pt>25\GeV$. The fiducial phase space definition is summarized
in Table 0.3. A small fraction of events from the $\ttbar\PGg$ event sample
generated in the $\Pell\text{+jets}$ final state also passes the fiducial
phase space requirements with an additional lepton created in the parton
showering and hadronization, and is included as a signal contribution. The SM
prediction for the $\ttbar\PGg$ cross section in this fiducial phase space,
evaluated from the LO event sample with the NLO $K$-factor as described above,
is $\sigma_{\text{SM}}(\Pp\Pp\to\ttbar\PGg)=155\pm 27\unit{fb}$, where the
uncertainty includes scale variations and the PDF choice.

Summary of the requirements at the particle level on the various physics
objects in the fiducial phase space definition. The two lepton thresholds are
applied to the highest and second-highest lepton, respectively. The “isolated”
definition for the photon requires no stable particle with $\pt>5\GeV$ except
neutrinos within a cone of $\DR=0.1$. The parameters $N_{\Pell}$, $N_{\PGg}$,
and $N_{\PQb}$ represent the numbers of leptons, photons, and jets,
respectively, in the event.  Leptons Photons Jets jets Events $\pt>25$ (15)
$\pt>20\GeV$ $\pt>30\GeV$ $\pt>30\GeV$ $N_{\Pell}=2$ (OC) $\abs{\eta}<2.4$
$\abs{\eta}<1.44$ $\abs{\eta}<2.4$ $\abs{\eta}<2.4$ $N_{\PGg}=1$
$\DR(\PGg,\Pell)>0.4$ $\DR(\text{jet},\Pell)>0.4$ $\DR(\text{jet},\Pell)>0.4$
$N_{\PQb}\geq 1$ isolated $\DR(\text{jet},\PGg)>0.1$
$\DR(\text{jet},\PGg)>0.1$ $m(\Pell\Pell)>20\GeV$ matched to hadron

For all simulated events, the CMS detector response is subsequently modelled
with the toolkit [58]. Additional minimum-bias $\Pp\Pp$ interactions in the
same or nearby bunch crossing, referred to as pileup, are added from
simulation as well. All events in the data and simulated samples are
reconstructed with the same algorithms described in Section 0.4. Corrections
for differences in the selection performance between the data and simulated
samples are applied to simulated events.

##  0.4 Event reconstruction and selection

Information from the various subdetectors is used by the particle-flow
algorithm [59] to reconstruct the particles (photons, electrons, muons,
charged and neutral hadrons) produced in an event. The candidate vertex with
the largest value of summed physics-object $\pt^{2}$ is taken to be the
primary $\Pp\Pp$ interaction vertex (PV). The physics objects used for this
determination are the jets, clustered using the jet finding algorithm [56, 60]
with the tracks assigned to candidate vertices as inputs, and the associated
missing transverse momentum, taken as the negative vector sum of the of those
jets.

Electrons are identified as a primary charged particle track that can have
bremsstrahlung photons emitted along its path through the silicon tracker
material, and when extrapolated to the ECAL, can be associated with many ECAL
energy deposits [61]. Muons are identified as tracks in the silicon tracker
consistent with either a track or several hits in the muon system, and
associated with compatible calorimeter deposits [62].

Electron and muon candidates with $\pt>15\GeV$ and $\abs{\eta}<2.4$ that pass
the identification requirements described in Ref. [63] are selected. These
requirements are optimized to discriminate between “prompt” leptons
originating from decays of heavy vector bosons and top quarks, and “nonprompt”
leptons that originate from hadron decays, or are jets or hadrons
misidentified as leptons. To that end, discriminating variables are combined
into a boosted decision tree discriminant trained with the TMVA toolkit [64].
The discriminating variables are the kinematic properties of the lepton and
the jet closest to the lepton; relative isolation variables defined as scalar
sums of all particles within cones around the lepton direction, divided by the
lepton ; the impact parameter of the lepton tracks with respect to the PV; the
muon segment compatibility [62] in the case of muons; and the discriminant of
a standard electron identification algorithm [61] in the case of electrons.
Additionally, electrons with $1.44<\abs{\eta}<1.57$ in the transition region
between the ECAL barrel and endcaps are removed due to the worse performance
of electron reconstruction in this region.

Photons are identified based on the presence of an energy deposit in the ECAL
with no charged particle tracks pointing towards this deposit. The photon
energy is obtained using this ECAL measurement, to which corrections for the
energy scale and zero suppression are applied in both simulation and data. In
simulated events, smearing corrections are applied to photons to match the
resolution observed in data [61]. Photon candidates are considered with
$\pt>20\GeV$ and $\abs{\eta}<1.44$, inside the barrel region of the detector,
and are required to be isolated from selected leptons by requiring that
$\DR(\PGg,\Pell)>0.4$. Identification criteria are imposed on photon
candidates based on the shape of its electromagnetic shower, the isolation of
the photon, and the absence of hits in the pixel tracker compatible with the
photon direction, which improves the rejection of electrons, misidentified
hadrons, and photons produced within jets [61]. One shower shape criterion is
a requirement on $\sigma_{\eta\eta}$, a measure of the electromagnetic shower
width in units of the ECAL crystal spacing, of $\sigma_{\eta\eta}<0.01$, thus
rejecting $\PGpz\to\PGg\PGg$ decays, which are typically reconstructed as a
single wide shower. One isolation criterion requires
$I_{\text{chg}}<1.14\GeV$, where $I_{\text{chg}}$ is the charged particle
isolation parameter, computed as the scalar sum of all charged hadrons
compatible with the PV and inside a cone of $\DR<0.3$ around the photon
direction. The $\sigma_{\eta\eta}$ and $I_{\text{chg}}$ criteria are not used
to veto any additional reconstructed photons, and the inverted requirements
are applied for the selection of control samples in data for the background
estimation described in Section 0.5.

Jets are clustered using all particle-flow candidates with the anti-algorithm
[56] and a distance parameter of $R=0.4$. To mitigate the impact of pileup on
the jet momentum, tracks identified as originating from pileup vertices are
discarded, and a correction is applied for contributions from neutral pileup
particles and those charged pileup particles that are not associated with a
pileup vertex [65]. Jet energy corrections are derived from simulation studies
so that the average measured energy of jets are made equal to that of
particle-level jets, and are applied in both data and simulation. In situ
measurements of the momentum balance in dijet, photon+jet, +jet, and multijet
events are used to determine any residual differences between the jet energy
scale in data and simulation, which are applied as corrections to the
simulation [66]. Additional selection criteria are applied to each jet to
remove jets potentially dominated by instrumental effects or reconstruction
failures [65]. Reconstructed jets with $\pt>30\GeV$ and $\abs{\eta}<2.4$ are
selected if they are separated from any selected lepton by $\DR>0.4$, and any
selected photon by $\DR>0.1$. Jets originating from the hadronization of
quarks are identified (tagged) with the DeepCSV heavy-flavour jet tagging
algorithm [67], with an efficiency of about 70% and a misidentification rate
of 12 (1)% for jets originating from quarks (light quarks or gluons).

A combination of trigger paths requiring the presence of one or two leptons is
used to select events. The threshold of the single-lepton trigger paths for
electrons and muons was 27 and 24in 2016, and 32 and 27in 2017–2018,
respectively. For the double-lepton trigger paths, the thresholds were 23 and
17for the highest (leading) and 12 and 8for the second-highest (subleading)
electron and muon, respectively. The trigger efficiency for events passing the
lepton selection described below is higher than 95%, as measured from events
selected with independent trigger paths.

Events are selected with exactly two OC leptons, exactly one photon, and at
least one -tagged jet. At least one lepton is required to have $\pt>25\GeV$.
The two leptons are required to have an invariant mass $m(\Pell\Pell)>20\GeV$
to reduce background contributions with nonprompt leptons and from low-mass
quark resonances. The jet requirement reduces background contributions from
processes without top quarks. To further reduce the background from $\PZ\PGg$
production, events with same-flavour leptons where either the two leptons or
the two leptons and the photon have an invariant mass close to the boson mass
are removed. Specifically, $\abs{m(\Pell\Pell)-m_{\PZ}}>15\GeV$ and
$\abs{m(\Pell\Pell\PGg)-m_{\PZ}}>15\GeV$ are required for same-flavour lepton
pairs, where $m_{\PZ}$ is the world-average boson mass [68].

The observed data yields for a selection of observables are shown in Figs. 2
and 3, and compared to the signal yields expected from the simulated event
samples and the predicted background yields, as described in Section 0.5. The
signal and background yields are normalized to the expected values, without
taking the results of the fit to the data described in Section 0.7 into
account. The signal yields are normalized to the SM prediction and a
normalization uncertainty of 18% is included in the systematic uncertainty
bands to account for the uncertainty in the prediction.

Figure 2: The observed (points) and predicted (shaded histograms) signal and
background yields as functions of the number of jets (upper left) and -tagged
jets (upper right), the (middle left) and $\abs{\eta}$ (middle right) of the
photon, the dilepton flavours (lower left), and the of the leading jet
$\mathrm{j}_{1}$ (lower right), after applying the signal selection.
Distributions are shown for the three lepton flavour channels combined, with
all relevant corrections applied. The predictions are normalized to the
expected yields, without taking the results of the fit to the data into
account. The vertical bars on the points show the statistical uncertainties in
the data, and the hatched bands the systematic uncertainty in the predictions.
The lower panels show the ratio of the event yields in data to the overall sum
of the predictions.

Figure 3: The observed (points) and predicted (shaded histograms) signal and
background yields as functions of the scalar sum (upper left) and $\phi$
difference (upper right) of the two leptons, the smallest $\DR$ between the
photon and any lepton (lower left), and between any lepton and any jet (lower
right), after applying the signal selection. Details can be found in the
caption of Fig. 2.

While an overall normalization difference, consistent with the result of the
fit to the data performed for the inclusive cross section measurement
discussed in Section 0.7, is found between the observed and predicted yields,
the shapes of the distributions are generally well described with two
exceptions. The observed jet multiplicity is larger than expected from the LO
event samples for the $\ttbar\PGg$ signal process, likely explained by the
missing simulation of additional radiation in the matrix element calculation.
Similarly, the observed minimal $\DR$ separation between the photon and any
lepton being larger than expected from the simulated $\ttbar\PGg$ event
samples hints at a mismodelling of different photon origins in the LO
simulation, as discussed in Ref. [19].

##  0.5 Background estimation

Background contributions to the final state with two leptons, one photon, and
at least one -tagged jet arise from several SM processes. A distinction is
made between events with a prompt or a nonprompt photon. For simulated events
of all considered processes, the reconstructed photon is matched to the
closest generated stable particle with $\DR<0.3$ for which the reconstructed
and generated agree within 50%. A reconstructed photon is classified as
“prompt” if it is matched to a generated photon that is radiated from a
lepton, quark, or heavy boson. If the match is to a photon originating from
other types of particles (photons from $\smash{\PGpz\to\PGg\PGg}$ decays or
other hadronic sources), or it is matched to other types of particles
(electrons misidentified as a photon), or no match is found (photons from
pileup interactions), it is classified as “nonprompt”.

Background contributions with prompt photons are estimated from simulated
event samples. The most important contributions arise from $\PZ\text{+jets}$
and single top quark production ($t\text{ channel}$, $s\text{ channel}$, and
$\PQt\PW$) in association with a photon (referred to as $\PZ\PGg$ and
$\PQt\PGg$, respectively). These two categories are treated separately, while
all other processes with prompt photons are grouped as “other+”. Normalization
uncertainties of 5, 10, and 30% are assigned to the $\PZ\PGg$, $\PQt\PGg$, and
other+background predictions, respectively, based on the precision of
corresponding cross section measurements [69, 70, 71]. To improve the
modelling of the $\PZ\PGg$ prediction at high jet multiplicity, correction
factors are derived from data samples orthogonal to the signal selection, as
detailed in Section 0.5.1. For all background contributions with nonprompt
photons, an estimation based on data control samples is used, as described in
Section 0.5.2.

After applying the event selection described in Section 0.4, about 13% of all
selected events contain a nonprompt photon. Considering only the estimated
background contributions as determined from simulation and the data control
samples, prompt and nonprompt photon events contribute about equally.

###  0.5.1 The $\PZ\PGg$ control region

Prompt photons in $\PZ\text{+jets}$ production events originate either by
initial-state radiation (ISR) from an incoming quark or final-state radiation
(FSR) from one of the leptons in the boson decay. To validate the prediction
of this $\PZ\PGg$ contribution from simulated event samples, a control region
in data is defined using the inverted requirement
$\abs{m(\Pell\Pell\PGg)-m_{\PZ}}<15\GeV$ for events with a same-flavour lepton
pair ($\Pep\Pem$ or $\PGmp\PGmm$), which enriches the selection of events
where the reconstructed photon originates from FSR in $\PZ\text{+jets}$
production. By keeping the requirement $\abs{m(\Pell\Pell)-m_{\PZ}}>15\GeV$
unchanged, the contribution from $\PZ\text{+jets}$ production events with
nonprompt photons remains small.

Figure 4: The observed (points) and predicted (shaded histograms) event yields
as a function of $m(\Pell\Pell\PGg)$ (upper left), $m(\Pell\Pell)$ (upper
right), photon (lower left), and the number of jets j and -tagged jets (lower
right), after applying the event selection for the $\PZ\PGg$ control region.
The vertical lines on the points show the statistical uncertainties in the
data, and the hatched bands the systematic uncertainty in the predictions. The
lower panels show the ratio of the event yields in data to the sum of the
predictions.

In Fig. 4, the measured and predicted yields are compared as functions of
$m(\Pell\Pell\PGg)$, $m(\Pell\Pell)$, the reconstructed photon , and the
numbers of jets and -tagged jets ($N_{\mathrm{j}}$ and $N_{\PQb}$,
respectively). The photon shape is well described, but a clear mismodelling is
observed in the distributions of jet and jet multiplicity. We derive
correction factors as functions of $N_{\mathrm{j}}$ and $N_{\PQb}$, which are
applied in the signal selection to the $\PZ\PGg$ background yields. No
correction factors are applied to $\Pepm\PGmmp$ events since the $\PZ\PGg$
yield is negligible for events with different-flavour leptons.

The data yields are found in bins of $(N_{\mathrm{j}},N_{\PQb})$, and the
statistical uncertainty in each bin yield is propagated to the correction
factor uncertainties. Additionally, the normalization of the signal
contamination in the $\PZ\PGg$ control region is considered as a source of
systematic uncertainty. With a cross section normalization uncertainty of 18%
and a fraction of $\ttbar\PGg$ events in the control region of about 10%, the
resulting normalization uncertainty in the $\PZ\PGg$ yield in the signal
region is 1.8%.

The correction factors significantly improve the precision of the prediction
for FSR photons in $\PZ\text{+jets}$ events. After the signal selection, about
70% of the $\PZ\PGg$ background events are from FSR photons. The contribution
of ISR photons to the $\PZ\PGg$ background is not constrained by using the
control region, and thus retains a normalization uncertainty of 5% [70],
resulting in an additional uncertainty of 1.5% in the normalization of the
total $\PZ\PGg$ yield.

###  0.5.2 Nonprompt-photon background

The contribution of background processes with nonprompt photons is estimated
from control samples in data with a “tight-to-loose ratio” method. A transfer
factor $f$ is defined as the ratio of the number of nonprompt-photon events
where the photon passes the identification criteria to the number where it
fails. It is used to predict the number $N_{\text{SR}}$ of nonprompt-photon
events in the signal region, defined by the selection requirements discussed
in Section 0.4, from the observed number $N_{\text{SB}}$ of events in a
sideband region enriched with nonprompt-photon events as
$N_{\text{SR}}=fN_{\text{SB}}$.

The sideband region contains events that pass the signal selection except for
having a photon that fails the $\sigma_{\eta\eta}$ identification criterion,
which enriches the sideband region in nonprompt photon events. To increase the
fraction of nonprompt-photon events in the sideband region to more than 95%,
the photon is required to have $\sigma_{\eta\eta}>0.012$.

For the evaluation of $f$, a measurement region with nonisolated-photon events
is defined by inverting the signal region requirement on the charged particle
isolation parameter of the reconstructed photon to
$1.14<I_{\text{chg}}<15\GeV$. To increase the amount of data in the
measurement region, the $N_{\PQb}\geq 1$ requirement is replaced by the looser
requirement of $N_{\mathrm{j}}\geq 1$ for $\Pep\Pem$ and $\PGmp\PGmm$ events,
and no jet requirement is imposed for $\Pepm\PGmmp$ events. The sideband
region of the measurement region, defined by inverting both the
$\sigma_{\eta\eta}$ and $I_{\text{chg}}$ requirements of the signal region,
has a fraction of nonprompt-photon events larger than 99.5%.

The transfer factor is measured separately in bins of and $\abs{\eta}$ of the
reconstructed photon. Contributions from prompt-photon events in the
measurement region and both sideband regions are estimated from simulated
event samples and subtracted before the evaluation of $N_{\text{SR}}$.

We validate the performance of the nonprompt-photon background estimate with
simulated event samples. The transfer factors are measured from and
$\PZ\text{+jets}$ samples since the measurement region is enriched in these
two processes. In the signal selection, however, the contribution from
$\PZ\text{+jets}$ production is minimal, and thus the nonprompt-photon
background contribution in the signal selection is estimated from samples
alone in the sideband region, and compared to the direct prediction of the
samples for the signal region. The comparison is displayed in Fig. 5, and
shows good agreement between the two predictions at the level of 5%, which is
assigned as a constant systematic uncertainty. Only at large reconstructed
photon does the use of the transfer factor result in an overprediction of the
nonprompt-photon background. Hence, we assign an additional uncertainty of 50%
to the predicted event yields where the reconstructed photon has $\pt>80\GeV$.

Figure 5: Event yields in the signal region predicted from a simulated event
sample (shaded histogram) and estimated from applying the transfer factor to
the event yields of the same sample in the sideband region (points), as a
function of the lepton flavour (left) and the photon (right). The vertical
lines on the points show the statistical uncertainties from the simulated
event samples, and the hatched bands the total systematic uncertainty assigned
to the nonprompt-photon background estimate. The lower panels show the ratio
of the two predictions.

##  0.6 Systematic uncertainties

Systematic uncertainties affect the signal selection efficiency, the predicted
and measured background yields, and the measured distributions. For each
source of systematic uncertainty, variations of the predicted signal and
background yields are evaluated in the relevant distributions, and either used
to construct nuisance parameters in the fits employed for the inclusive cross
section measurement and the EFT interpretation, or to repeat the differential
cross section measurement and evaluate the uncertainty in the unfolded
distribution. A summary of all systematic uncertainties and the estimated
impact on the measured inclusive cross section is given in Table 0.6.
Additionally, the table indicates the treatment of the uncertainties between
the 2016, 2017, and 2018 data sets as uncorrelated, partially correlated, or
fully correlated.

Summary of the systematic uncertainty sources in the $\ttbar\PGg$ cross
section measurements. The first column lists the source of the uncertainty.
The second column indicates the treatment of correlations between the
uncertainties in the three data-taking years, where $\checkmark$ means fully
correlated, $\sim$ means partially correlated, and $\times$ means
uncorrelated. For each systematic source, the “prefit” uncertainty is
estimated from a cut-and-count analysis of the expected and observed event
yields separately in bins of $\pt(\PGg)$ and for the three data-taking years
using the input variations; the typical range across the three years is shown
in the third column and can be compared between the different uncertainty
sources. The last column gives the impact of each uncertainty source on the
measured inclusive $\ttbar\PGg$ cross section after the fit to the data
(“postfit”). The last two rows give the statistical and total uncertainty in
the measured cross section.  Source Correlation Uncertainty [%] Prefit range
Postfit Experimental Integrated luminosity $\sim$ 1.3–3.2 1.7 Pileup
$\checkmark$ 0.1–1.4 0.7 Trigger efficiency $\times$ 0.6–1.7 0.6 Electron
selection efficiency $\sim$ 1.0–1.3 1.0 Muon selection efficiency $\sim$
0.3–0.5 0.5 Photon selection efficiency $\sim$ 0.4–3.6 1.1 Electron & photon
energy $\checkmark$ 0.0–1.1 0.1 Jet energy scale $\sim$ 0.1–1.3 0.5 Jet energy
resolution $\checkmark$ 0.0–0.6 $<$0.1 tagging efficiency $\sim$ 0.9–1.4 1.1
L1 prefiring $\checkmark$ 0.0–0.8 0.3 Theoretical Values of $\mu_{\mathrm{F}}$
and $\mu_{\mathrm{R}}$ $\checkmark$ 0.3–3.5 1.3 PDF choice $\checkmark$
0.3–4.5 0.3 PS modelling: ISR & FSR scale $\checkmark$ 0.3–3.5 1.3 PS
modelling: colour reconnection $\checkmark$ 0.0–8.4 0.2 PS modelling:
fragmentation $\checkmark$ 0.0–2.2 0.7 Underlying-event tune $\checkmark$ 0.5
0.5 Background $\PZ\PGg$ correction & normalization $\checkmark$ 0.0–0.2 0.1
$\PQt\PGg$ normalization $\checkmark$ 0.0–0.9 0.8 Other+normalization
$\checkmark$ 0.3–1.0 0.8 Nonprompt normalization $\checkmark$ 0.0–1.8 0.7 Size
of background samples $\times$ 1.5–7.6 0.9 Total systematic uncertainty 3.6
Statistical uncertainty 1.4 Total uncertainty 3.9

The integrated luminosities of the 2016, 2017, and 2018 data-taking periods
are individually known with uncertainties of 1.2, 2.3, and 2.5%, respectively
[72, 73, 74]. Some systematic effects in the calibration of the luminosity
measurements are correlated, such that the uncertainty in the integrated
luminosity of the combined data set is 1.6%. The uncertainty in the integrated
luminosity affects both the normalization of the background contributions
predicted from simulated event samples, as well as the extraction of the
measured cross section from the final estimate of the number of signal events.

Simulated events are reweighted such that the simulated distribution of the
number of interactions in each bunch crossing matches the expected
distribution, assuming a total inelastic $\Pp\Pp$ cross section of 69.2mb
[65]. Changes in this cross section by 4.6% are used to produce varied signal
and background predictions, fully correlated between the three data-taking
years.

The efficiency of the trigger selection is corrected in simulated events to
match the efficiency in data measured from two separate classes of independent
trigger paths based on hadronic activity or missing transverse momentum
signatures. The scale factors depend on the momentum of the two selected
leptons and deviate from unity by up to 20% for low-electrons. In addition to
the statistical uncertainty in the trigger efficiency measurement, the
difference between the trigger efficiencies measured from the two classes of
independent trigger paths is included as a source of systematic uncertainty.
Both the statistical and systematic uncertainty in the trigger efficiency
measurement are treated as uncorrelated between the three data-taking years.

For the reconstruction, identification, and isolation of electrons, muons, and
photons, the efficiencies are measured with the “tag-and-probe” method [61,
62] separately in data and simulation. Scale factors are applied to simulated
events to correct for differences, and uncertainties are evaluated by varying
the scale factors, separately for electrons, muons, and photons. Additionally,
systematic uncertainties in the energy scale and resolution of electrons and
photons are measured with electrons from boson decays. Statistical
(systematic) sources of uncertainty in these measurements are treated as
uncorrelated (correlated) between the three data-taking years.

Uncertainties in the jet energy scale and resolution are evaluated by varying
the of the reconstructed jets in simulated events separately for the several
uncertainty sources described in Ref. [66]. For each source, two separate
variations that are either fully correlated or uncorrelated between the three
data-taking years are considered.

Differences in the tagging efficiency between data and simulated events are
corrected by applying scale factors to simulated events. Uncertainties are
evaluated by varying the scale factors separately for light- and heavy-flavour
jets, where both correlated and uncorrelated variations between the three
data-taking years are considered [67].

During the 2016 and 2017 data-taking periods, the ECAL L1 trigger in the
forward endcap region ($\abs{\eta}>2.4$) exhibited a gradual shift in the
timing of its inputs, leading to a specific inefficiency known as “prefiring”.
The effect was found to be most relevant in events with jets reconstructed
with $2.4<\abs{\eta}<3.0$ and $\pt>100\GeV$, affecting also measurements that
do not directly rely on such jets. A correction determined from an unbiased
data sample is applied, and 20% of the correction is assigned as the
associated uncertainty.

Several theoretical uncertainties are considered in the simulation of both
signal and background contributions. To evaluate the impact in the choice of
the factorization scale $\mu_{\mathrm{F}}$ and the renormalization scale
$\mu_{\mathrm{R}}$, these two parameters are scaled up and down by a factor of
2, individually and simultaneously, and the envelope of the variations is
taken to estimate the uncertainty. Following the procedure described in Ref.
[75], the choice of the PDF set is evaluated by using the MC replicas in the
NNPDF PDFs [42, 31] and taking the root-mean-square of the variations as an
estimate of the uncertainty. The choice of $\mu_{\mathrm{F}}$ for ISR and FSR
in the parton shower simulation is separately varied up and down by a factor
of 2. The default colour reconnection model in the parton shower simulation of
the $\ttbar\PGg$ signal samples is replaced by three alternative models [76,
77], and only a small impact on the cross section results is found. The
uncertainty in the fragmentation function is evaluated by varying the
parameters of the Bowler–Lund function [78]. Finally, the uncertainty in the
underlying-event tune [44, 47] is evaluated for the $\ttbar\PGg$ signal
sample, which results in a 0.5% variation of the predicted signal yield. All
theoretical uncertainties are treated as correlated between the three data-
taking years.

For all background predictions, normalization uncertainties as detailed in
Section 0.5 are included in the measurements, and treated as correlated
between the three data-taking years. The statistical uncertainty in the
background samples, both simulated event samples and data control samples, is
included as a systematic uncertainty as well.

##  0.7 Inclusive cross section measurement

The inclusive cross section is extracted following the statistical procedure
described in Refs. [79, 80] from a profile likelihood fit to the reconstructed
photon distribution. The binned likelihood function $L(r,\theta)$ is
constructed as the product of the Poisson probabilities to obtain the observed
yields given the predicted signal and background estimates in each event
category, and includes terms to account for the systematic uncertainties and
their correlation pattern, as described in Section 0.6. The signal strength
modifier $r$ scales the normalization of the predicted signal estimate, and
$\theta$ denotes a full set of nuisance parameters representing the systematic
uncertainties, including the normalization of the background predictions. The
event categories are the bins of the photon distribution separately for each
year of data taking and for the three lepton-flavour channels.

The quantities $\hat{r}$ and $\hat{\theta}$ denote the signal strength and
nuisance parameter set that simultaneously maximize the likelihood function.
Similarly, $\hat{\theta}_{r}$ maximizes the likelihood function for a fixed
value of $r$. The observed cross section is extracted using a limit
calculation in which the test statistic $q(r)=-2\ln
L(r,\hat{\theta}_{r})/L(\hat{r},\hat{\theta})$, which is based on the profile
likelihood function, is simplified using an asymptotic approximation [79, 80].

The reconstructed photon distributions for each dilepton flavour, combined for
the three data-taking years, are shown in Fig. 6 after the fit to the data.
The fiducial cross section for $\ttbar\PGg$ production in the dilepton final
state is measured to be

$\sigma_{\text{fid}}(\Pp\Pp\to\ttbar\PGg)=175.2\pm 2.5\stat\pm
6.3\syst\unit{fb}.$

Consistent results are found when fitting the flavour channels separately, as
shown in Fig. 7.

Figure 6: The observed (points) and predicted (shaded histograms) event yields
as a function of the reconstructed photon after applying the signal selection,
for the $\Pepm\PGmmp$ (upper left), $\Pep\Pem$ (upper right), and $\PGmp\PGmm$
(lower) channels, after the values of the normalizations and nuisance
parameters obtained in the fit to the data are applied. The vertical bars on
the points show the statistical uncertainties in data, and the hatched bands
the systematic uncertainty in the predictions. The lower panels of each plot
show the ratio of the event yields in data to the predictions. Figure 7: The
measured inclusive fiducial $\ttbar\PGg$ production cross section in the
dilepton final state for the different dilepton-flavour channels and combined.
The thick and thin lines on the points show the statistical and total
uncertainties, respectively. The vertical line represents the SM prediction
obtained with the (MG5) interfaced with $\PYTHIA 8$, as described in the text.
The shaded band shows the theoretical uncertainty in the prediction.

The impact of the different systematic uncertainty sources on the inclusive
cross section measurements are summarized in Table 0.6. The measurement from
the fit of all the nuisance parameters describing the background yield
normalizations and systematic uncertainty sources are consistent with their
initial values within the obtained uncertainties. The leading contributions to
the overall systematic uncertainty arise from the uncertainty in the
integrated luminosity, the values of $\mu_{\mathrm{F}}$ and $\mu_{\mathrm{R}}$
in the signal simulation, the PS modelling, and the tagging and electron
selection efficiencies.

Compared to the CMS measurement of the inclusive production cross section in
the dilepton final state using 35.9of data collected in 2016 [81], this result
profits significantly from improvements with respect to the two leading
systematic uncertainties in the measurement. First, the uncertainty in the
integrated luminosity was reduced from 2.5 to 1.7%, mainly due to the improved
luminosity measurement described in Ref. [72] and the combination of three
data sets with only partially correlated luminosity uncertainties. Second, the
uncertainty in the lepton identification efficiency was improved from 2.0 to
1.1%, due to the use of the identification discriminant presented in Ref.
[63].

The predicted cross section of $\sigma_{\text{SM}}(\Pp\Pp\to\ttbar\PGg)=155\pm
27\unit{fb}$ is in agreement with the measured cross section within the
uncertainty, with a central value about 12% smaller than the measurement,
corresponding to a difference of 0.7 standard deviations. As discussed in
Section 0.3, the prediction is normalized with an NLO $K$-factor for the $2\to
3$ process $\Pp\Pp\to\ttbar\PGg$, which does not take into account the
radiation of photons from charged decay products of the top quarks. This
source of photons plays a significant role in the NLO calculation [19] of the
cross section, so neglecting it could be a cause for the theoretical
prediction to be low.

The simulated $\ttbar\PGg$ samples used in the cross section measurement were
generated assuming a top quark mass of 172.5. With a higher top quark mass in
the event generation, we would find a higher signal selection efficiency and
thus a lower measured cross section. For a quantitative evaluation of this
effect, we reweight the simulated events to match the Breit–Wigner shapes
corresponding to the varied top quark mass. For an increase (decrease) of the
top quark mass by the current experimental uncertainty of 0.3 [68], the
measured cross section decreases (increases) by 0.4%.

##  0.8 Differential cross section measurement

The differential $\ttbar\PGg$ cross sections in the fiducial phase space are
measured as functions of twelve observables defined in Table 0.8. For each
observable, variable bin sizes are chosen to optimize the statistical
uncertainty in the measured event yields and limit the number of bin-to-bin
migrations. In each bin of the reconstructed distributions, the yield is
determined by subtracting the predicted background yields from the measured
event yields, after which the expected fraction of $\ttbar\PGg$ events outside
of the fiducial phase space is removed as well.

Definition of the observables used in the differential cross section
measurement.  Symbol Definition $\pt(\PGg)$ Transverse momentum of the photon
$\abs{\eta}(\PGg)$ Absolute value of the pseudorapidity of the photon
$\min\DR(\PGg,\Pell)$ Angular separation between the photon and the closest
lepton $\DR(\PGg,\Pell_{1})$ Angular separation between the photon and the
leading lepton $\DR(\PGg,\Pell_{2})$ Angular separation between the photon and
the subleading lepton $\min\DR(\PGg,\PQb)$ Angular separation between the
photon and the closest jet $\abs{\Delta\eta(\Pell\Pell)}$ Pseudorapidity
difference between the two leptons $\Delta\varphi(\Pell\Pell)$ Azimuthal angle
difference between the two leptons $\pt(\Pell\Pell)$ Transverse momentum of
the dilepton system $\pt(\Pell_{1})+\pt(\Pell_{2})$ Scalar sum of the
transverse momenta of the two leptons $\min\DR(\Pell,\mathrm{j})$ Smallest
angular separation between any of the selected leptons and jets
$\pt(\mathrm{j}_{1})$ Transverse momentum of the leading jet

Both detector response and acceptance effects are represented by response
matrices derived from the $\ttbar\PGg$ simulated event samples, using half the
number of bins for the fiducial phase space observables as used in the
reconstructed distributions to limit the bin-to-bin migrations, and including
the same corrections and scale factors as used in the inclusive cross section
measurement. The differential cross section is then evaluated by applying an
unfolding procedure. The resolutions of the considered observables are found
to be good, with a very small number of events migrating from one bin to
another. Under such conditions, matrix inversion without regularization is an
unbiased and stable method to correct for detector response and acceptance
[82]. We apply the TUnfold package [83] to evaluate the differential cross
sections from the background-subtracted measured event yields and the response
matrices.

All systematic uncertainties described in Section 0.6 are applied in the
differential cross section measurement. The theoretical uncertainties are
evaluated by repeating the unfolding procedure with varied response matrices.
For the experimental uncertainties, the relative variation of the total signal
and background predictions is evaluated, and this variation is then applied to
the data before the subtraction of the varied background predictions. The
background normalization uncertainties are evaluated by varying the background
subtraction.

Figure 8: Absolute differential $\ttbar\PGg$ production cross sections as
functions of $\pt(\PGg)$ (upper left), $\abs{\eta}(\PGg)$ (upper right),
$\min\DR(\PGg,\Pell)$ (middle left), $\DR(\PGg,\Pell_{1})$ (middle right),
$\DR(\PGg,\Pell_{2})$ (lower left), and $\min\DR(\PGg,\PQb)$ (lower right).
The data are represented by points, with inner (outer) vertical bars
indicating the statistical (total) uncertainties. The predictions obtained
with the event generator interfaced with $\PYTHIA 8$ (solid lines) and
$\HERWIG 7$ (dotted lines) parton shower simulations are shown as horizontal
lines. The theoretical uncertainties in the prediction using $\PYTHIA 8$ are
indicated by shaded bands. The lower panels display the ratios of the
predictions to the measurement. The values of the $\chi^{2}$ divided by the
number of degrees of freedom (dof) quantifying the agreement between the
measurement and the prediction using $\PYTHIA 8$ are indicated in the legends.

Figure 9: Absolute differential $\ttbar\PGg$ production cross sections as
functions of $\abs{\Delta\eta(\Pell\Pell)}$ (upper left),
$\Delta\varphi(\Pell\Pell)$ (upper right), $\pt(\Pell\Pell)$ (middle left),
$\pt(\Pell_{1})+\pt(\Pell_{2})$ (middle right), $\min\DR(\Pell,\mathrm{j})$
(lower left), and $\pt(\mathrm{j}_{1})$ (lower right). Details can be found in
the caption of Fig. 8.

Figure 10: Normalized differential $\ttbar\PGg$ production cross sections as
functions of $\pt(\PGg)$ (upper left), $\abs{\eta}(\PGg)$ (upper right),
$\min\DR(\PGg,\Pell)$ (middle left), $\DR(\PGg,\Pell_{1})$ (middle right),
$\DR(\PGg,\Pell_{2})$ (lower left), and $\min\DR(\PGg,\PQb)$ (lower right).
Details can be found in the caption of Fig. 8.

Figure 11: Normalized differential $\ttbar\PGg$ production cross sections as
functions of $\abs{\Delta\eta(\Pell\Pell)}$ (upper left),
$\Delta\varphi(\Pell\Pell)$ (upper right), $\pt(\Pell\Pell)$ (middle left),
$\pt(\Pell_{1})+\pt(\Pell_{2})$ (middle right), $\min\DR(\Pell,\mathrm{j})$
(lower left), and $\pt(\mathrm{j}_{1})$ (lower right). Details can be found in
the caption of Fig. 8.

The resulting absolute differential cross sections are shown in Figs. 8 and 9,
and normalized differential cross sections are shown in Figs. 10 and 11. For a
given observable, the normalized differential cross section is obtained by
dividing the absolute differential cross section by the sum of the binned
cross section measurements over all bins of the observable. The measurements
are compared to two cross section predictions obtained at particle level,
generated with the event generator interfaced with parton shower simulations
provided by $\PYTHIA 8$ with the CP5 tune and by [84] v7.1.4 with the CH3 tune
[85]. The prediction using $\PYTHIA 8$ is shown with its uncertainty from
scale variations and the PDF choice. The scale variations affect the
normalization of the predictions but have negligible impact on their shapes.
The agreement between the measured distribution and the prediction using
$\PYTHIA 8$ is evaluated by calculating a $\chi^{2}$ value, which is indicated
on the figures. The $\chi^{2}$ value computation takes into account the
statistical and systematic covariance matrices of the measurement, as well as
the uncertainty in the prediction. For all distributions, no significant
deviation between the measured and predicted distributions is observed.

The measurement of the kinematic properties of the photon, $\pt(\PGg)$ and
$\abs{\eta}(\PGg)$, provides sensitivity to the coupling between the top quark
and the photon. Both predictions show good agreement with the observed
distribution, with only one bin in each observable having a disagreement
larger than the uncertainty in the measurement. The $\chi^{2}/\text{dof}$
values of about $5/5$ ($3/6$) for the normalized differential cross sections
of $\pt(\PGg)$ ($\abs{\eta}(\PGg)$) confirm that there is no significant
disagreement.

The angular separation variables between the photon and the top quark decay
products provide sensitivity to the origin of the photon in $\ttbar\PGg$
events [19]. With $\chi^{2}/\text{dof}$ values for the normalized
distributions of about $8/6$ for $\min\DR(\PGg,\Pell)$ and
$\DR(\PGg,\Pell_{1})$, $1/6$ for $\DR(\PGg,\Pell_{2})$, and $4/4$ for
$\min\DR(\PGg,\PQb)$, no statistically significant disagreement is observed.
The apparent trends in all four distributions for the separation to be smaller
in the prediction than in the measurement hint at a mismodelling of the photon
origins in the LO simulation of the $\ttbar\PGg$ signal process, but more
precise measurements are required to draw a clear conclusion.

The observables that do not involve the photon provide sensitivity to the
modelling of the top quark decay. The pseudorapidity and azimuthal angle
differences between the two leptons, $\abs{\Delta\eta(\Pell\Pell)}$ and
$\Delta\varphi(\Pell\Pell)$, are of special interest due to their sensitivity
to spin correlations [86, 87]. Their predictions show some difference with
respect to the measurement that are, with a $\chi^{2}/\text{dof}$ value of
about $11/6$ for the normalized differential cross section of
$\abs{\Delta\eta(\Pell\Pell)}$, compatible within the uncertainties. The
predictions for $\pt(\Pell\Pell)$, $\pt(\Pell_{1})+\pt(\Pell_{2})$,
$\min\DR(\Pell,\mathrm{j})$, and $\pt(\mathrm{j}_{1})$ show good agreement
with the observed differential cross sections except for the last bins of the
distributions where the statistical uncertainty in the measurement becomes
very large. The largest difference between the two MC predictions is seen in
the $\pt(\mathrm{j}_{1})$ distribution.

##  0.9 Effective field theory interpretation

Many theories of new physics beyond the SM predict the existence of new
particles and mechanisms characterized by an energy scale $\Lambda$ that is
well above the energy reach of the LHC. Such theories may still lead to
observable deviations in well-established processes through quantum-loop
corrections. In the SMEFT approach, such corrections are parameterized in a
coherent and model-independent way by the extension of the SM Lagrangian with
new, effective interactions between the SM fields, characterized by higher-
order operators [8]. The interaction strength of such an operator of dimension
$d$ is proportional to $\Lambda^{4-d}$ and thus suppressed, which implies that
the impact of new interactions can be approximated with a finite set of lower-
order operators. Under the assumption of lepton number conservation, the
lowest order of SMEFT operators to contribute have dimension six [88]. The
SMEFT Lagrangian can thus be written as:

$\mathcal{L}_{\text{SMEFT}}=\mathcal{L}_{\text{SM}}+\sum_{i}\frac{c_{i}}{\Lambda^{2}}\mathcal{O}_{i},$

where $\mathcal{L}_{\text{SM}}$ is the SM Lagrangian, $\mathcal{O}_{i}$ are
the dimension-six operators, and $c_{i}$ the corresponding Wilson
coefficients.

We follow the recommendations of Ref. [89] and adopt the Warsaw basis [9] with
59 baryon-number-conserving dimension-six operators. Of those, 15 are found to
be relevant for top quark interactions [90, 91]. While anomalous interactions
between the top quark and the gluon are strongly constrained by measurements
of production [86, 87], the measurement of $\ttbar\PGg$ production provides
sensitivity to the electroweak dipole moments of the top quark, denoted by
$c_{\PQu\PB}^{(33)}$ and $c_{\PQu\PW}^{(33)}$. Because of the SM gauge
symmetries, the sensitivity to $\ttbar\PGg$ production is complementary to
that provided by $\ttbar\PZ$ production [20, 21, 22]. The coefficients
describing the modifications of the $\ttbar\PZ$ interaction vertex,
$c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$, and of the $\ttbar\PGg$
interaction vertex, $c_{\PQt\PGg}$ and $c_{\PQt\PGg}^{\mathrm{I}}$, are
expressed in the Warsaw basis as linear combinations of $c_{\PQu\PB}^{(33)}$
and $c_{\PQu\PW}^{(33)}$:

$\displaystyle c_{\PQt\PZ}$
$\displaystyle=\mathrm{Re}\left(-\sin\theta_{\PW}c_{\PQu\PB}^{(33)}+\cos\theta_{\PW}c_{\PQu\PW}^{(33)}\right),$
$\displaystyle c_{\PQt\PZ}^{\mathrm{I}}$
$\displaystyle=\mathrm{Im}\left(-\sin\theta_{\PW}c_{\PQu\PB}^{(33)}+\cos\theta_{\PW}c_{\PQu\PW}^{(33)}\right),$
$\displaystyle c_{\PQt\PGg}$
$\displaystyle=\mathrm{Re}\left(\cos\theta_{\PW}c_{\PQu\PB}^{(33)}-\sin\theta_{\PW}c_{\PQu\PW}^{(33)}\right),$
$\displaystyle c_{\PQt\PGg}^{\mathrm{I}}$
$\displaystyle=\mathrm{Im}\left(\cos\theta_{\PW}c_{\PQu\PB}^{(33)}-\sin\theta_{\PW}c_{\PQu\PW}^{(33)}\right).$

The modification of the $\PW\PQt\PQb$ vertex is better probed in measurements
of helicity fractions [92]. Under the assumption of an SM $\PW\PQt\PQb$ vertex
with $c_{\PQu\PW}^{(33)}=0$, the $\ttbar\PZ$ and $\ttbar\PGg$ modifications
are dependent. We choose to parameterize the new-physics hypothesis in terms
of $c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$, and set the Wilson
coefficients of all other operators to zero.

The effect of these modifications is probed in the measured distribution of
the photon at the reconstructed level, which is also used in the inclusive
cross section measurement. The other observables studied in the differential
cross section measurement are found to be largely insensitive to these new-
physics effects. With a fixed SMEFT expansion parameter of $\Lambda=1\TeV$,
the expected SMEFT modifications for nonzero values of $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$ are estimated at the particle level and used to
calculate per-event weights corresponding to the ratio of the predicted SM and
SMEFT cross sections in bins of photon . In the parameterization used in the
calculation, both linear and quadratic terms in the coefficients are included.
The nominal simulation is then reweighted after applying the full analysis
selection criteria to the reconstructed events to estimate the expected SMEFT
modifications at the reconstructed level.

The similar measurement by CMS using final states with one lepton and jets
($\Pell\text{+jets}$) presented in Ref. [7] probes an orthogonal fiducial
phase space of the $\ttbar\PGg$ production process defined for the single-
lepton decay channel of the system. While the measurement presented here
profits from very small background contributions, the measurement in Ref. [7]
has a significantly larger number of signal events at large photon values,
which increases the sensitivity to modifications described by the studied
Wilson coefficients. To further improve the constraints on the Wilson
coefficients, a combined EFT interpretation of both measurements is also
performed.

The constraints on the Wilson coefficients are measured from a profile
likelihood fit constructed in the same way as for the inclusive cross section
measurement. The set of nuisance parameters is extended by the uncertainty in
the $\ttbar\PGg$ signal normalization. The minimized likelihood value obtained
in a fit using the SMEFT-predicted photon distribution is compared to the
corresponding likelihood value using the SM prediction. A combined profile
likelihood function is also constructed using the photon distributions from
both this analysis and the previous $\Pell\text{+jets}$ measurement, and the
correlations between the nuisance parameters applied in both analyses are
treated appropriately.

One-dimensional scans of the negative log-likelihood value difference to the
best fit value, obtained either using the photon distribution in dilepton
events alone or from the combined interpretation, are shown in Fig. 12 for
each Wilson coefficient, where the other Wilson coefficient is set to zero in
the fit. The corresponding 68 and 95% confidence level () intervals are listed
in Table 0.9. Fits are also performed where both Wilson coefficients are
varied simultaneously in the fit. The result of the two-dimensional scans and
the corresponding 68 and 95% contours are shown in Fig. 13.

Figure 12: Distributions of the observed (solid line) and expected (dashed
line) negative log-likelihood difference from the best fit value for the one-
dimensional scans of the Wilson coefficients $c_{\PQt\PZ}$ (upper) and
$c_{\PQt\PZ}^{\mathrm{I}}$ (lower), using the photon distribution from this
analysis (left) or the combination of this analysis with the
$\Pell\text{+jets}$ analysis from Ref. [7] (right). In the scans, the other
Wilson coefficient is set to zero. The green (orange) bands indicate the 68
(95)% limits on the Wilson coefficients.

Summary of the one-dimensional 68 and 95% intervals obtained for the Wilson
coefficients $c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$ using the photon
distribution from this analysis or the combination of this analysis with the
$\Pell\text{+jets}$ analysis from Ref. [7]. The profiled results correspond to
the fits where the other Wilson coefficient is left free in the fit, otherwise
it is set to zero.  Wilson coefficient Dilepton result Dilepton &
$\Pell\text{+jets}$ combination 68% interval 95% interval 68% interval 95%
interval [$(\Lambda/\TeVns)^{2}$] [$(\Lambda/\TeVns)^{2}$]
[$(\Lambda/\TeVns)^{2}$] [$(\Lambda/\TeVns)^{2}$] Expected $c_{\PQt\PZ}$
$c_{\PQt\PZ}^{\mathrm{I}}=0$ [$-0.28$, $0.35$] [$-0.42$, $0.49$] [$-0.15$,
$0.19$] [$-0.25$, $0.29$] profiled [$-0.28$, $0.35$] [$-0.42$, $0.49$]
[$-0.15$, $0.19$] [$-0.25$, $0.29$] $c_{\PQt\PZ}^{\mathrm{I}}$ $c_{\PQt\PZ}=0$
[$-0.33$, $0.30$] [$-0.47$, $0.45$] [$-0.17$, $0.18$] [$-0.27$, $0.27$]
profiled [$-0.33$, $0.30$] [$-0.47$, $0.45$] [$-0.18$, $0.18$] [$-0.27$,
$0.27$] Observed $c_{\PQt\PZ}$ $c_{\PQt\PZ}^{\mathrm{I}}=0$ [$-0.43$, $-0.09$]
[$-0.53$, $0.52$] [$-0.30$, $-0.13$] [$-0.36$, $0.31$] profiled [$-0.43$,
$0.17$] [$-0.53$, $0.51$] [$-0.30$, $0.00$] [$-0.36$, $0.31$]
$c_{\PQt\PZ}^{\mathrm{I}}$ $c_{\PQt\PZ}=0$ [$-0.47$, $-0.03$] $\cup$ [$0.07$,
$0.38$] [$-0.58$, $0.52$] [$-0.32$, $-0.13$] $\cup$ [$0.16$, $0.29$] [$-0.38$,
$0.36$] profiled [$-0.43$, $0.33$] [$-0.56$, $0.51$] [$-0.28$, $0.23$]
[$-0.36$, $0.35$]

Figure 13: Result from the two-dimensional scan of the Wilson coefficients
$c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$ using the photon distribution
from this analysis (left) or the combination of this analysis with the
$\Pell\text{+jets}$ analysis from Ref. [7] (right). The shading quantified by
the colour scale on the right reflects the negative log-likelihood difference
with respect to the best fit value that is indicated by the red diamond. The
68% (dashed curve) and 95% (solid curve) contours are shown for the observed
result. The orange circle indicates the SM prediction.

In the two-dimensional scans, the best fit point, shown by the red diamonds in
Fig. 13, is found at $(c_{\PQt\PZ},c_{\PQt\PZ}^{\mathrm{I}})=(-0.28,-0.02)$
for the dilepton-only interpretation, and at $(-0.22,-0.05)$ for the combined
interpretation. The SM expectation of zero for both Wilson coefficients is 0.7
(1.2) standard deviations away from the best fit point for the dilepton-only
(combined) interpretation. The SM expectation is compatible with the 95%
limits for both the one- and two-dimensional scans from this and the combined
analysis.

Other measurements [93, 94, 95, 96, 97] and global SMEFT fits [98, 99, 100,
101, 102] have also derived constraints on these Wilson coefficients. A
comparison of the 95% limits on $c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$
from a selection of these results and this analysis are shown in Fig. 14.

Figure 14: Comparison of observed 95% intervals for the Wilson coefficients
$c_{\PQt\PZ}$ (upper panel) and $c_{\PQt\PZ}^{\mathrm{I}}$ (lower panel). For
the CMS $\ttbar\PZ$ [95], $\ttbar\PGg$ $\Pell\text{+jets}$ [7] and
$\ttbar\PGg$ dilepton results, the limits are shown for the case where all
other considered Wilson coefficients are fixed to zero. The dashed lines
indicate the corresponding result of the $\ttbar\PGg$ $\Pell\text{+jets}$ and
dilepton combination. For the CMS result based on $\ttbar\PZ$ and
$\PQt\PZ\PQq$ events [97], as well as from a global fit to the LHC, LEP, and
Tevatron data [100], the limits where all other considered Wilson coefficients
are fixed to zero are shown with solid lines, and the marginalized limits from
the full fits are shown with dashed-and-dotted lines.

The CMS $\ttbar\PZ$ measurement using an integrated luminosity of 77.5 [95]
had 95% limits on $c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$ about three
times less strict than the combined limits presented here, while the limit on
$c_{\PQt\PZ}$ from a recent CMS result on $\ttbar\PZ$ and $\PQt\PZ\PQq$
production using 138 [97] was a factor of two less strict. Comparing with
other experiments, the combined result presented here provides the best limits
on the Wilson coefficients $c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$ yet
published.

##  0.10 Summary

A measurement of the inclusive and differential cross sections for top quark
pair production in association with a photon ($\ttbar\PGg$) has been
presented, using 138of proton-proton ($\Pp\Pp$) collision data at
$\sqrt{s}=13\TeV$ recorded with the CMS detector at the LHC. The analysis is
performed in a fiducial phase space defined at the particle level by the
requirement of exactly one isolated photon, exactly two oppositely charged
leptons, and at least one jet coming from the hadronization of a bottom quark,
including the $\Pepm\PGmmp$, $\Pep\Pem$, and $\PGmp\PGmm$ channels of the
decay. The inclusive cross section is extracted with a profile likelihood fit
to the transverse momentum distribution of the reconstructed photon, and is
measured to be $\sigma_{\text{fid}}(\Pp\Pp\to\ttbar\PGg)=175.2\pm 2.5\stat\pm
6.3\syst\unit{fb}$, in agreement with the standard model (SM) prediction of
$\sigma_{\text{SM}}(\Pp\Pp\to\ttbar\PGg)=155\pm 27\unit{fb}$.

Differential cross sections are measured as functions of various kinematic
properties of the photon, leptons, and jets, and unfolded to the particle
level. The comparison to SM predictions is performed using different parton
shower algorithms. No significant deviations from the SM predictions are
found.

The measurements are also interpreted in terms of the SM effective field
theory. Constraints are derived on the Wilson coefficients $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$ describing the modifications of the $\ttbar\PZ$ and
$\ttbar\PGg$ interaction vertices, from these results alone and in combination
with another CMS measurement of $\ttbar\PGg$ production using the lepton+jets
final state and the same data set. From the combined interpretation, the best
experimental limits on these Wilson coefficients to date are derived.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI
(Greece); 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); MCIN/AEI and PCTI (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, 758316, 765710, 824093, 884104, and COST
Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 -
GRK2497; the Lendület (“Momentum”) Program and the János Bolyai Research
Scholarship of the Hungarian Academy of Sciences, the New National Excellence
Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850,
125105, 128713, 128786, and 129058 (Hungary); the Council of Science and
Industrial Research, India; the Latvian Council of Science; the Ministry of
Science and Higher Education and the National Science Center, contracts Opus
2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the Fundação para a
Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National
Priorities Research Program by Qatar National Research Fund; the Ministry of
Science and Higher Education, projects no. 0723-2020-0041 and no.
FSWW-2020-0008, and the Russian Foundation for Basic Research, project
No.19-42-703014 (Russia); MCIN/AEI/10.13039/501100011033, ERDF “a way of
making Europe”, and the Programa Estatal de Fomento de la Investigación
Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and
Programa Severo Ochoa del Principado de Asturias (Spain); the Stavros Niarchos
Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship,
Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century
Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia
Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Tumasyan Institut für
Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341, J.W.
Andrejkovic, T. Bergauer0000-0002-5786-0293, S. Chatterjee0000-0003-2660-0349,
K. Damanakis, M. Dragicevic0000-0003-1967-6783, A. Escalante Del
Valle0000-0002-9702-6359, R. Frühwirth1, M. Jeitler10000-0002-5141-9560, N.
Krammer, L. Lechner0000-0002-3065-1141, D. Liko, I. Mikulec, P. Paulitsch,
F.M. Pitters, J. Schieck10000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784,
D. Schwarz, S. Templ0000-0003-3137-5692, W. Waltenberger0000-0002-6215-7228,
C.-E. Wulz10000-0001-9226-5812 Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko0000-0002-8406-8605 Universiteit
Antwerpen, Antwerpen, Belgium M.R. Darwish2, E.A. De Wolf, T.
Janssen0000-0002-3998-4081, T. Kello3, A. Lelek0000-0001-5862-2775, H. Rejeb
Sfar, P. Van Mechelen0000-0002-8731-9051, S. Van Putte, N. Van
Remortel0000-0003-4180-8199 Vrije Universiteit Brussel, Brussel, Belgium E.S.
Bols0000-0002-8564-8732, J. D’Hondt0000-0002-9598-6241, M. Delcourt, H. El
Faham0000-0001-8894-2390, S. Lowette0000-0003-3984-9987, S.
Moortgat0000-0002-6612-3420, A. Morton0000-0002-9919-3492, D.
Müller0000-0002-1752-4527, A.R. Sahasransu0000-0003-1505-1743, S.
Tavernier0000-0002-6792-9522, W. Van Doninck, D. Vannerom0000-0002-2747-5095
Université Libre de Bruxelles, Bruxelles, Belgium D. Beghin, B.
Bilin0000-0003-1439-7128, B. Clerbaux0000-0001-8547-8211, G. De Lentdecker, L.
Favart0000-0003-1645-7454, A.K. Kalsi0000-0002-6215-0894, K. Lee, M.
Mahdavikhorrami, I. Makarenko0000-0002-8553-4508, L.
Moureaux0000-0002-2310-9266, S. Paredes0000-0001-8487-9603, L. Pétré, A.
Popov0000-0002-1207-0984, N. Postiau, E. Starling0000-0002-4399-7213, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496 Ghent University,
Ghent, Belgium T. Cornelis0000-0001-9502-5363, D. Dobur, J.
Knolle0000-0002-4781-5704, L. Lambrecht, G. Mestdach, M.
Niedziela0000-0001-5745-2567, C. Rendón, C. Roskas, A. Samalan, K.
Skovpen0000-0002-1160-0621, M. Tytgat0000-0002-3990-2074, B. Vermassen, L.
Wezenbeek Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke, A. Bethani0000-0002-8150-7043, G. Bruno, F. Bury0000-0002-3077-2090,
C. Caputo0000-0001-7522-4808, P. David0000-0001-9260-9371, C.
Delaere0000-0001-8707-6021, I.S. Donertas0000-0001-7485-412X, A.
Giammanco0000-0001-9640-8294, K. Jaffel, Sa. Jain0000-0001-5078-3689, V.
Lemaitre, K. Mondal0000-0001-5967-1245, J. Prisciandaro, A. Taliercio, M.
Teklishyn0000-0002-8506-9714, T.T. Tran, P. Vischia0000-0002-7088-8557, S.
Wertz0000-0002-8645-3670 Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil G.A. Alves0000-0002-8369-1446, C. Hensel, A.
Moraes0000-0002-5157-5686, P. Rebello Teles0000-0001-9029-8506 Universidade do
Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, M. Alves Gallo Pereira0000-0003-4296-7028, M.
Barroso Ferreira Filho, H. Brandao Malbouisson, W.
Carvalho0000-0003-0738-6615, J. Chinellato4, E.M. Da Costa0000-0002-5016-6434,
G.G. Da Silveira50000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680,
V. Dos Santos Sousa, S. Fonseca De Souza0000-0001-7830-0837, C. Mora
Herrera0000-0003-3915-3170, K. Mota Amarilo, L. Mundim0000-0001-9964-7805, H.
Nogima, A. Santoro, S.M. Silva Do Amaral0000-0002-0209-9687, A.
Sznajder0000-0001-6998-1108, M. Thiel, F. Torres Da Silva De
Araujo60000-0002-4785-3057, A. Vilela Pereira0000-0003-3177-4626 Universidade
Estadual Paulista (a), Universidade Federal do ABC (b), São Paulo, Brazil C.A.
Bernardes50000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
D.S. Lemos0000-0003-1982-8978, P.G. Mercadante0000-0001-8333-4302, S.F.
Novaes0000-0003-0471-8549, Sandra S. Padula0000-0003-3071-0559 Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria A. Aleksandrov, G. Antchev0000-0003-3210-5037, R. Hadjiiska, P.
Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia,
Sofia, Bulgaria A. Dimitrov, T. Ivanov, L. Litov0000-0002-8511-6883, B.
Pavlov, P. Petkov, A. Petrov Beihang University, Beijing, China T.
Cheng0000-0003-2954-9315, T. Javaid7, M. Mittal, L. Yuan Department of
Physics, Tsinghua University, Beijing, China M. Ahmad0000-0001-9933-995X, G.
Bauer, C. Dozen80000-0002-4301-634X, Z. Hu0000-0001-8209-4343, J.
Martins90000-0002-2120-2782, Y. Wang, K. Yi10,11 Institute of High Energy
Physics, Beijing, China E. Chapon0000-0001-6968-9828, G.M.
Chen70000-0002-2629-5420, H.S. Chen70000-0001-8672-8227, M.
Chen0000-0003-0489-9669, F. Iemmi, A. Kapoor0000-0002-1844-1504, D. Leggat, H.
Liao, Z.-A. Liu70000-0002-2896-1386, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, R. Sharma0000-0003-1181-1426, J.
Tao0000-0003-2006-3490, J. Thomas-Wilsker, J. Wang0000-0002-4963-0877, H.
Zhang0000-0001-8843-5209, J. Zhao0000-0001-8365-7726 State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China A. Agapitos,
Y. An, Y. Ban, C. Chen, A. Levin0000-0001-9565-4186, Q. Li0000-0002-8290-0517,
X. Lyu, Y. Mao, S.J. Qian, D. Wang0000-0002-9013-1199, J. Xiao, H. Yang Sun
Yat-Sen University, Guangzhou, China M. Lu, Z. You0000-0001-8324-3291
Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam
Application (MOE) - Fudan University, Shanghai, China X. Gao3, H.
Okawa0000-0002-2548-6567, Y. Zhang0000-0002-4554-2554 Zhejiang University,
Hangzhou, China, Zhejiang, China Z. Lin0000-0003-1812-3474, M.
Xiao0000-0001-9628-9336 Universidad de Los Andes, Bogota, Colombia C.
Avila0000-0002-5610-2693, A. Cabrera0000-0002-0486-6296, C.
Florez0000-0002-3222-0249, J. Fraga Universidad de Antioquia, Medellin,
Colombia J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez0000-0002-3306-0363
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia D. Giljanovic, N.
Godinovic0000-0002-4674-9450, D. Lelas0000-0002-8269-5760, I.
Puljak0000-0001-7387-3812 University of Split, Faculty of Science, Split,
Croatia Z. Antunovic, M. Kovac, T. Sculac0000-0002-9578-4105 Institute Rudjer
Boskovic, Zagreb, Croatia V. Brigljevic0000-0001-5847-0062, D.
Ferencek0000-0001-9116-1202, D. Majumder0000-0002-7578-0027, M. Roguljic, A.
Starodumov120000-0001-9570-9255, T. Susa0000-0001-7430-2552 University of
Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K. Christoforou, A.
Ioannou, G. Kole0000-0002-3285-1497, M. Kolosova, S. Konstantinou, J.
Mousa0000-0002-2978-2718, C. Nicolaou, F. Ptochos0000-0002-3432-3452, P.A.
Razis, H. Rykaczewski, H. Saka0000-0001-7616-2573 Charles University, Prague,
Czech Republic M. Finger13, M. Finger Jr.130000-0003-3155-2484, A. Kveton
Escuela Politecnica Nacional, Quito, Ecuador E. Ayala Universidad San
Francisco de Quito, Quito, Ecuador E. Carrera Jarrin0000-0002-0857-8507
Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt Y. Assran14,15, A.
Ellithi Kamel16 Center for High Energy Physics (CHEP-FU), Fayoum University,
El-Fayoum, Egypt M.A. Mahmoud0000-0001-8692-5458, Y.
Mohammed0000-0001-8399-3017 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia S. Bhowmik0000-0003-1260-973X, R.K.
Dewanjee0000-0001-6645-6244, K. Ehataht, M. Kadastik, S. Nandan, C. Nielsen,
J. Pata, M. Raidal0000-0001-7040-9491, L. Tani, C. Veelken Department of
Physics, University of Helsinki, Helsinki, Finland P.
Eerola0000-0002-3244-0591, H. Kirschenmann0000-0001-7369-2536, K.
Osterberg0000-0003-4807-0414, M. Voutilainen0000-0002-5200-6477 Helsinki
Institute of Physics, Helsinki, Finland S. Bharthuar, E.
Brücken0000-0001-6066-8756, F. Garcia0000-0002-4023-7964, J.
Havukainen0000-0003-2898-6900, M.S. Kim0000-0003-0392-8691, R. Kinnunen, T.
Lampén, K. Lassila-Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén, M. Lotti, L. Martikainen, M. Myllymäki, J. Ott0000-0001-9337-5722, H.
Siikonen, E. Tuominen0000-0002-7073-7767, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland P. Luukka0000-0003-2340-4641,
H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola0000-0002-4359-836X, M. Besancon, F. Couderc0000-0003-2040-4099, M.
Dejardin, D. Denegri, J.L. Faure, F. Ferri0000-0002-9860-101X, S. Ganjour, P.
Gras, G. Hamel de Monchenault0000-0002-3872-3592, P. Jarry, B.
Lenzi0000-0002-1024-4004, E. Locci, J. Malcles, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro17, M. Titov0000-0002-1119-6614, G.B. Yu0000-0001-7435-2963 Laboratoire
Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de
Paris, Palaiseau, France S. Ahuja0000-0003-4368-9285, F.
Beaudette0000-0002-1194-8556, M. Bonanomi0000-0003-3629-6264, A. Buchot
Perraguin, P. Busson, A. Cappati, C. Charlot, O. Davignon, B. Diab, G.
Falmagne0000-0002-6762-3937, S. Ghosh, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi, I. Kucher0000-0001-7561-5040, J.
Motta, M. Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, P.
Paganini0000-0001-9580-683X, J. Rembser, R. Salerno0000-0003-3735-2707, U.
Sarkar0000-0002-9892-4601, J.B. Sauvan0000-0001-5187-3571, Y.
Sirois0000-0001-5381-4807, A. Tarabini, A. Zabi, A. Zghiche0000-0002-1178-1450
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram180000-0001-7476-0158, J. Andrea, D. Apparu, D. Bloch0000-0002-4535-5273,
G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard0000-0002-5230-8387, D.
Darej, J.-C. Fontaine18, U. Goerlach, C. Grimault, A.-C. Le Bihan, E.
Nibigira0000-0001-5821-291X, P. Van Hove0000-0002-2431-3381 Institut de
Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France E.
Asilar0000-0001-5680-599X, S. Beauceron0000-0002-8036-9267, C.
Bernet0000-0002-9923-8734, G. Boudoul, C. Camen, A. Carle, N.
Chanon0000-0002-2939-5646, D. Contardo, P. Depasse0000-0001-7556-2743, H. El
Mamouni, J. Fay, S. Gascon0000-0002-7204-1624, M.
Gouzevitch0000-0002-5524-880X, B. Ille, I.B. Laktineh, H.
Lattaud0000-0002-8402-3263, A. Lesauvage0000-0003-3437-7845, M.
Lethuillier0000-0001-6185-2045, L. Mirabito, S. Perries, K. Shchablo, V.
Sordini0000-0003-0885-824X, L. Torterotot0000-0002-5349-9242, G. Touquet, M.
Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia I.
Bagaturia19, I. Lomidze, Z. Tsamalaidze13 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany V. Botta, L. Feld0000-0001-9813-8646,
K. Klein, M. Lipinski, D. Meuser, A. Pauls, N. Röwert, J. Schulz, M.
Teroerde0000-0002-5892-1377 RWTH Aachen University, III. Physikalisches
Institut A, Aachen, Germany A. Dodonova, D. Eliseev, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B. Fischer, T.
Hebbeker0000-0002-9736-266X, K. Hoepfner, F. Ivone, L. Mastrolorenzo, M.
Merschmeyer0000-0003-2081-7141, A. Meyer0000-0001-9598-6623, G. Mocellin, S.
Mondal, S. Mukherjee0000-0001-6341-9982, D. Noll0000-0002-0176-2360, A. Novak,
A. Pozdnyakov0000-0003-3478-9081, Y. Rath, H. Reithler, A.
Schmidt0000-0003-2711-8984, S.C. Schuler, A. Sharma0000-0002-5295-1460, L.
Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany C. Dziwok, G. Flügge, W. Haj
Ahmad200000-0003-1491-0446, O. Hlushchenko, T. Kress, A.
Nowack0000-0002-3522-5926, O. Pooth, D. Roy0000-0002-8659-7762, A.
Stahl210000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A. Zotz Deutsches
Elektronen-Synchrotron, Hamburg, Germany H. Aarup Petersen, M. Aldaya Martin,
P. Asmuss, S. Baxter, M. Bayatmakou, O. Behnke, A. Bermúdez Martínez, S.
Bhattacharya, A.A. Bin Anuar0000-0002-2988-9830, F.
Blekman0000-0002-7366-7098, K. Borras22, D. Brunner, A.
Campbell0000-0003-4439-5748, A. Cardini0000-0003-1803-0999, C. Cheng, F.
Colombina, S. Consuegra Rodríguez0000-0002-1383-1837, G. Correia Silva, V.
Danilov, M. De Silva, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez
Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E. Gallo23, A.
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Melzer-Pellmann0000-0001-7707-919X, M. Mendizabal Morentin, J. Metwally, A.B.
Meyer0000-0001-8532-2356, M. Meyer0000-0003-2436-8195, J.
Mnich0000-0001-7242-8426, A. Mussgiller, A. Nürnberg, Y. Otarid, D. Pérez
Adán0000-0003-3416-0726, D. Pitzl, A. Raspereza, B. Ribeiro Lopes, J.
Rübenach, A. Saggio0000-0002-7385-3317, A. Saibel0000-0002-9932-7622, M.
Savitskyi0000-0002-9952-9267, M. Scham26, V. Scheurer, S. Schnake, P. Schütze,
C. Schwanenberger230000-0001-6699-6662, M. Shchedrolosiev, R.E. Sosa
Ricardo0000-0002-2240-6699, D. Stafford, N. Tonon0000-0003-4301-2688, M. Van
De Klundert0000-0001-8596-2812, F. Vazzoler0000-0001-8111-9318, R.
Walsh0000-0002-3872-4114, D. Walter, Q. Wang0000-0003-1014-8677, Y.
Wen0000-0002-8724-9604, K. Wichmann, L. Wiens, C. Wissing, S.
Wuchterl0000-0001-9955-9258 University of Hamburg, Hamburg, Germany R.
Aggleton, S. Albrecht0000-0002-5960-6803, S. Bein0000-0001-9387-7407, L.
Benato0000-0001-5135-7489, P. Connor0000-0003-2500-1061, K. De
Leo0000-0002-8908-409X, M. Eich, F. Feindt, A. Fröhlich, C.
Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, P. Gunnellini, M.
Hajheidari, J. Haller0000-0001-9347-7657, A. Hinzmann0000-0002-2633-4696, G.
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Lange0000-0001-7513-6330, T. Lange0000-0001-6242-7331, A.
Lobanov0000-0002-5376-0877, A. Malara0000-0001-8645-9282, A.
Mehta0000-0002-0433-4484, A. Nigamova, K.J. Pena Rodriguez, M.
Rieger0000-0003-0797-2606, O. Rieger, P. Schleper, M.
Schröder0000-0001-8058-9828, J. Schwandt0000-0002-0052-597X, J.
Sonneveld0000-0001-8362-4414, H. Stadie, G. Steinbrück, A. Tews, I.
Zoi0000-0002-5738-9446 Karlsruher Institut fuer Technologie, Karlsruhe,
Germany J. Bechtel0000-0001-5245-7318, S. Brommer, M. Burkart, E.
Butz0000-0002-2403-5801, R. Caspart0000-0002-5502-9412, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El Morabit, N.
Faltermann0000-0001-6506-3107, M. Giffels, J.O. Gosewisch, A. Gottmann, F.
Hartmann210000-0001-8989-8387, C. Heidecker, U. Husemann0000-0002-6198-8388,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra0000-0002-3060-2278,
Th. Müller, M. Neukum, G. Quast0000-0002-4021-4260, K.
Rabbertz0000-0001-7040-9846, J. Rauser, D. Savoiu0000-0001-6794-7475, M.
Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich0000-0002-2535-402X, J.
Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber0000-0002-3639-2267, S.
Wieland, R. Wolf0000-0001-9456-383X, S. Wozniewski, S. Wunsch Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, A. Kyriakis, D. Loukas, A.
Stakia0000-0001-6277-7171 National and Kapodistrian University of Athens,
Athens, Greece M. Diamantopoulou, D. Karasavvas, P.
Kontaxakis0000-0002-4860-5979, C.K. Koraka, A. Manousakis-Katsikakis, A.
Panagiotou, I. Papavergou, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K. Vellidis,
E. Vourliotis National Technical University of Athens, Athens, Greece G.
Bakas, K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos, G. Tsipolitis, A.
Zacharopoulou University of Ioánnina, Ioánnina, Greece K. Adamidis, I.
Bestintzanos, I. Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios, P.
Katsoulis, P. Kokkas, N. Manthos, I. Papadopoulos0000-0002-9937-3063, J.
Strologas0000-0002-2225-7160 MTA-ELTE Lendület CMS Particle and Nuclear
Physics Group, Eötvös Loránd University, Budapest, Hungary M.
Csanad0000-0002-3154-6925, K. Farkas, M.M.A. Gadallah270000-0002-8305-6661, S.
Lökös280000-0002-4447-4836, P. Major, K. Mandal0000-0002-3966-7182, G.
Pasztor0000-0003-0707-9762, A.J. Rádl, O. Surányi, G.I.
Veres0000-0002-5440-4356 Wigner Research Centre for Physics, Budapest, Hungary
M. Bartók290000-0002-4440-2701, G. Bencze, C. Hajdu0000-0002-7193-800X, D.
Horvath30,310000-0003-0091-477X, F. Sikler0000-0001-9608-3901, V.
Veszpremi0000-0001-9783-0315 Institute of Nuclear Research ATOMKI, Debrecen,
Hungary S. Czellar, D. Fasanella0000-0002-2926-2691, F.
Fienga0000-0001-5978-4952, J. Karancsi290000-0003-0802-7665, J. Molnar, Z.
Szillasi, D. Teyssier Institute of Physics, University of Debrecen, Debrecen,
Hungary P. Raics, Z.L. Trocsanyi320000-0002-2129-1279, B. Ujvari Karoly Robert
Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo330000-0002-9110-9663, F. Nemes33, T. Novak National Institute of
Science Education and Research, HBNI, Bhubaneswar, India S.
Bahinipati340000-0002-3744-5332, C. Kar0000-0002-6407-6974, P. Mal, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair Bindhu35, A.
Nayak350000-0002-7716-4981, P. Saha, N. Sur0000-0001-5233-553X, S.K. Swain, D.
Vats35 Panjab University, Chandigarh, India S. Bansal0000-0003-1992-0336, S.B.
Beri, V. Bhatnagar0000-0002-8392-9610, G. Chaudhary0000-0003-0168-3336, S.
Chauhan0000-0001-6974-4129, N. Dhingra360000-0002-7200-6204, R. Gupta, A.
Kaur, H. Kaur, M. Kaur0000-0002-3440-2767, P. Kumari0000-0002-6623-8586, M.
Meena, K. Sandeep0000-0002-3220-3668, J.B. Singh0000-0001-9029-2462, A.K.
Virdi0000-0002-0866-8932 University of Delhi, Delhi, India A. Ahmed, A.
Bhardwaj0000-0002-7544-3258, B.C. Choudhary0000-0001-5029-1887, M. Gola, S.
Keshri0000-0003-3280-2350, A. Kumar0000-0003-3407-4094, M.
Naimuddin0000-0003-4542-386X, P. Priyanka0000-0002-0933-685X, K. Ranjan, A.
Shah0000-0002-6157-2016 Saha Institute of Nuclear Physics, HBNI, Kolkata,
India M. Bharti37, R. Bhattacharya, S. Bhattacharya0000-0002-8110-4957, D.
Bhowmik, S. Dutta, S. Dutta, B. Gomber380000-0002-4446-0258, M. Maity39, P.
Palit0000-0002-1948-029X, P.K. Rout0000-0001-8149-6180, G. Saha, B.
Sahu0000-0002-8073-5140, S. Sarkar, M. Sharan Indian Institute of Technology
Madras, Madras, India P.K. Behera0000-0002-1527-2266, S.C. Behera, P.
Kalbhor0000-0002-5892-3743, J.R. Komaragiri400000-0002-9344-6655, D. Kumar40,
A. Muhammad, L. Panwar400000-0003-2461-4907, R. Pradhan, P.R. Pujahari, A.
Sharma0000-0002-0688-923X, A.K. Sikdar, P.C. Tiwari400000-0002-3667-3843
Bhabha Atomic Research Centre, Mumbai, India K. Naskar41 Tata Institute of
Fundamental Research-A, Mumbai, India T. Aziz, S. Dugad, M. Kumar Tata
Institute of Fundamental Research-B, Mumbai, India S.
Banerjee0000-0002-7953-4683, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar,
G. Majumder, K. Mazumdar, S. Mukherjee0000-0003-3122-0594 Indian Institute of
Science Education and Research (IISER), Pune, India A. Alpana, S.
Dube0000-0002-5145-3777, B. Kansal, A. Laha, S. Pandey0000-0003-0440-6019, A.
Rastogi0000-0003-1245-6710, S. Sharma0000-0001-6886-0726 Isfahan University of
Technology, Isfahan, Iran A. Gholami42, E. Khazaie43, M. Zeinali44 Institute
for Research in Fundamental Sciences (IPM), Tehran, Iran S. Chenarani45, S.M.
Etesami0000-0001-6501-4137, M. Khakzad0000-0002-2212-5715, M. Mohammadi
Najafabadi0000-0001-6131-5987 University College Dublin, Dublin, Ireland M.
Grunewald0000-0002-5754-0388 INFN Sezione di Bari a, Bari, Italy, Università
di Bari b, Bari, Italy, Politecnico di Bari c, Bari, Italy M.
Abbresciaa,b0000-0001-8727-7544, R. Alya,b,460000-0001-6808-1335, C. Arutaa,b,
A. Colaleoa0000-0002-0711-6319, D. Creanzaa,c0000-0001-6153-3044, N. De
Filippisa,c0000-0002-0625-6811, M. De Palmaa,b0000-0001-8240-1913, A. Di
Florioa,b, A. Di Pilatoa,b0000-0002-9233-3632, W.
Elmetenaweea,b0000-0001-7069-0252, F. Erricoa,b0000-0001-8199-370X, L.
Fiorea0000-0002-9470-1320, A. Gelmia,b0000-0002-9211-2709, M.
Gula0000-0002-5704-1896, G. Iasellia,c0000-0003-2546-5341, M.
Incea,b0000-0001-6907-0195, S. Lezkia,b0000-0002-6909-774X, G.
Maggia,c0000-0001-5391-7689, M. Maggia0000-0002-8431-3922, I. Margjekaa,b, V.
Mastrapasquaa,b0000-0002-9082-5924, S. Mya,b0000-0002-9938-2680, S.
Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, D. Ramosa,
A. Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, Ü.
Sözbilira, R. Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631
INFN Sezione di Bologna a, Bologna, Italy, Università di Bologna b, Bologna,
Italy G. Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia, L. Brigliadoria, R.
Campaninia,b0000-0002-2744-0597, P. Capiluppia,b0000-0003-4485-1897, A.
Castroa,b0000-0003-2527-0456, F.R. Cavalloa0000-0002-0326-7515, C.
Cioccaa0000-0003-0080-6373, M. Cuffiania,b0000-0003-2510-5039, G.M.
Dallavallea0000-0002-8614-0420, T. Diotalevia,b0000-0003-0780-8785, F.
Fabbria0000-0002-8446-9660, A. Fanfania,b0000-0003-2256-4117, P.
Giacomellia0000-0002-6368-7220, L. Giommia,b0000-0003-3539-4313, C.
Grandia0000-0001-5998-3070, L. Guiduccia,b, S. Lo Meoa,47, L. Lunertia,b, S.
Marcellinia0000-0002-1233-8100, G. Masettia0000-0002-6377-800X, F.L.
Navarriaa,b0000-0001-7961-4889, A. Perrottaa0000-0002-7996-7139, F.
Primaveraa,b0000-0001-6253-8656, A.M. Rossia,b0000-0002-5973-1305, T.
Rovellia,b0000-0002-9746-4842, G.P. Sirolia,b0000-0002-3528-4125 INFN Sezione
di Catania a, Catania, Italy, Università di Catania b, Catania, Italy S.
Albergoa,b,480000-0001-7901-4189, S. Costaa,b,480000-0001-9919-0569, A. Di
Mattiaa0000-0002-9964-015X, R. Potenzaa,b, A.
Tricomia,b,480000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153 INFN Sezione
di Firenze a, Firenze, Italy, Università di Firenze b, Firenze, Italy G.
Barbaglia0000-0002-1738-8676, A. Cassesea0000-0003-3010-4516, R.
Ceccarellia,b, V. Ciullia,b0000-0003-1947-3396, C.
Civininia0000-0002-4952-3799, R. D’Alessandroa,b0000-0001-7997-0306, E.
Focardia,b0000-0002-3763-5267, G. Latinoa,b0000-0002-4098-3502, P.
Lenzia,b0000-0002-6927-8807, M. Lizzoa,b, M. Meschinia0000-0002-9161-3990, S.
Paolettia0000-0003-3592-9509, R. Seiditaa,b, G. Sguazzonia0000-0002-0791-3350,
L. Viliania0000-0002-1909-6343 INFN Laboratori Nazionali di Frascati,
Frascati, Italy L. Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124,
D. Piccolo0000-0001-5404-543X INFN Sezione di Genova a, Genova, Italy,
Università di Genova b, Genova, Italy M. Bozzoa,b0000-0002-1715-0457, F.
Ferroa0000-0002-7663-0805, R. Mulargiaa, E. Robuttia0000-0001-9038-4500, S.
Tosia,b0000-0002-7275-9193 INFN Sezione di Milano-Bicocca a, Milano, Italy,
Università di Milano-Bicocca b, Milano, Italy A. Benagliaa0000-0003-1124-8450,
G. Boldrini0000-0001-5490-605X, F. Brivioa,b, F. Cetorellia,b, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, A.
Ghezzia,b0000-0002-8184-7953, P. Govonia,b0000-0002-0227-1301, L.
Guzzia,b0000-0002-3086-8260, M.T. Lucchinia,b0000-0002-7497-7450, M.
Malbertia, S. Malvezzia0000-0002-0218-4910, A. Massironia0000-0002-0782-0883,
D. Menascea0000-0002-9918-1686, L. Moronia0000-0002-8387-762X, M.
Paganonia,b0000-0003-2461-275X, D. Pedrinia0000-0003-2414-4175, B.S. Pinolini,
S. Ragazzia,b0000-0001-8219-2074, N. Redaellia0000-0002-0098-2716, T.
Tabarelli de Fatisa,b0000-0001-6262-4685, D. Valsecchia,b,21, D.
Zuoloa,b0000-0003-3072-1020 INFN Sezione di Napoli a, Napoli, Italy,
Università di Napoli ’Federico II’ b, Napoli, Italy, Università della
Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy S.
Buontempoa0000-0001-9526-556X, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, A. De Iorioa,b0000-0002-9258-1345, F.
Fabozzia,c0000-0001-9821-4151, A.O.M. Iorioa,b0000-0002-3798-1135, L.
Listaa,b,490000-0001-6471-5492, S. Meolaa,d,210000-0002-8233-7277, P.
Paoluccia,210000-0002-8773-4781, B. Rossia0000-0002-0807-8772, C.
Sciaccaa,b0000-0002-8412-4072 INFN Sezione di Padova a, Padova, Italy,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia0000-0002-3129-828X, N. Bacchettaa0000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, R. Carlina,b0000-0001-7915-1650, P.
Checchiaa0000-0002-8312-1531, T. Dorigoa0000-0002-1659-8727, U.
Dossellia0000-0001-8086-2863, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, G. Grosso, L. Layera,50, E.
Lusiani0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, A.T.
Meneguzzoa,b0000-0002-5861-8140, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, H. Yarara,b, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679 INFN Sezione di Pavia a, Pavia, Italy,
Università di Pavia b, Pavia, Italy C. Aimèa,b, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b, S.P. Rattia,b, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778 INFN Sezione di Perugia a, Perugia, Italy,
Università di Perugia b, Perugia, Italy P. Asenova,510000-0003-2379-9903, G.M.
Bileia0000-0002-4159-9123, D. Ciangottinia,b0000-0002-0843-4108, L.
Fanòa,b0000-0002-9007-629X, M. Magherinib, G. Mantovania,b, V. Mariania,b, M.
Menichellia0000-0002-9004-735X, F. Moscatellia,510000-0002-7676-3106, A.
Piccinellia,b0000-0003-0386-0527, M. Presillaa,b0000-0003-2808-7315, A.
Rossia,b0000-0002-2031-2955, A. Santocchiaa,b0000-0002-9770-2249, D.
Spigaa0000-0002-2991-6384, T. Tedeschia,b0000-0002-7125-2905 INFN Sezione di
Pisa a, Pisa, Italy, Università di Pisa b, Pisa, Italy, Scuola Normale
Superiore di Pisa c, Pisa, Italy, Università di Siena d, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, V.
Bertacchia,c0000-0001-9971-1176, L. Bianchinia0000-0002-6598-6865, T.
Boccalia0000-0002-9930-9299, E. Bossinia,b0000-0002-2303-2588, R.
Castaldia0000-0003-0146-845X, M.A. Cioccia,b0000-0003-0002-5462, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, M.R. Di
Domenicoa,d0000-0002-7138-7017, S. Donatoa0000-0001-7646-4977, A.
Giassia0000-0001-9428-2296, F. Ligabuea,c0000-0002-1549-7107, E.
Mancaa,c0000-0001-8946-655X, G. Mandorlia,c0000-0002-5183-9020, D. Matos
Figueiredo, A. Messineoa,b0000-0001-7551-5613, M. Musicha, F.
Pallaa0000-0002-6361-438X, S. Paroliaa,b, G. Ramirez-Sancheza,c, A.
Rizzia,b0000-0002-4543-2718, G. Rolandia,c0000-0002-0635-274X, S. Roy
Chowdhurya,c, A. Scribanoa, N. Shafieia,b0000-0002-8243-371X, P.
Spagnoloa0000-0001-7962-5203, R. Tenchinia0000-0003-2574-4383, G.
Tonellia,b0000-0003-2606-9156, N. Turinia,d0000-0002-9395-5230, A.
Venturia0000-0002-0249-4142, P.G. Verdinia0000-0002-0042-9507 INFN Sezione di
Roma a, Rome, Italy, Sapienza Università di Roma b, Rome, Italy P.
Barriaa0000-0002-3924-7380, M. Campanaa,b, F. Cavallaria0000-0002-1061-3877,
D. Del Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, E. Longoa,b0000-0001-6238-6787, P.
Meridiania0000-0002-8480-2259, G. Organtinia,b0000-0002-3229-0781, F.
Pandolfia, R. Paramattia,b0000-0002-0080-9550, C. Quarantaa,b, S.
Rahatloua,b0000-0001-9794-3360, C. Rovellia0000-0003-2173-7530, F.
Santanastasioa,b0000-0003-2505-8359, L. Soffia0000-0003-2532-9876, R.
Tramontanoa,b INFN Sezione di Torino a, Torino, Italy, Università di Torino b,
Torino, Italy, Università del Piemonte Orientale c, Novara, Italy N.
Amapanea,b0000-0001-9449-2509, R. Arcidiaconoa,c0000-0001-5904-142X, S.
Argiroa,b0000-0003-2150-3750, M. Arneodoa,c0000-0002-7790-7132, N.
Bartosika0000-0002-7196-2237, R. Bellana,b0000-0002-2539-2376, A.
Belloraa,b0000-0002-2753-5473, J. Berenguer Antequeraa,b0000-0003-3153-0891,
C. Biinoa0000-0002-1397-7246, N. Cartigliaa0000-0002-0548-9189, M.
Costaa,b0000-0003-0156-0790, R. Covarellia,b0000-0003-1216-5235, N.
Demariaa0000-0003-0743-9465, B. Kiania,b0000-0001-6431-5464, F.
Leggera0000-0003-1400-0709, C. Mariottia0000-0002-6864-3294, S.
Masellia0000-0001-9871-7859, E. Migliorea,b0000-0002-2271-5192, E.
Monteila,b0000-0002-2350-213X, M. Montenoa0000-0002-3521-6333, M.M.
Obertinoa,b0000-0002-8781-8192, G. Ortonaa0000-0001-8411-2971, L.
Pachera,b0000-0003-1288-4838, N. Pastronea0000-0001-7291-1979, M.
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Chonnam National University, Institute for Universe and Elementary Particles,
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Seoul, Seoul, Korea W. Jang, D.Y. Kang, Y. Kang, S. Kim, B. Ko, J.S.H.
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Physics, Seoul, Korea S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea M.
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Technology, American University of the Middle East (AUM), Egaila, Kuwait,
Dasman, Kuwait T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga,
Latvia K. Dreimanis0000-0003-0972-5641, V. Veckalns520000-0003-3676-9711
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Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, C. Oropeza
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Autonoma de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen, C. Uribe
Estrada University of Montenegro, Podgorica, Montenegro J. Mijuskovic54, N.
Raicevic University of Auckland, Auckland, New Zealand D.
Krofcheck0000-0001-5494-7302 University of Canterbury, Christchurch, New
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Azam University, Islamabad, Pakistan A. Ahmad, M.I. Asghar, A. Awais, M.I.M.
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Waqas0000-0002-3846-9483 AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland V. Avati,
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Varela0000-0003-2613-3146 Joint Institute for Nuclear Research, Dubna, Russia
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Zhizhin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia G. Gavrilov0000-0003-3968-0253, V. Golovtcov, Y. Ivanov, V.