SMP-20-005

SMP-20-005

# Measurement of $\PWpm\PGg$ differential cross sections in proton-proton
collisions at $\sqrt{s}=13\TeV$ and effective field theory constraints

###### Abstract

Differential cross section measurements of $\PWpm\PGg$ production in proton-
proton collisions at $\sqrt{s}=13\TeV$ are presented. The data set used in
this study was collected with the CMS detector at the CERN LHC in 2016–2018
with an integrated luminosity of 138. Candidate events containing an electron
or muon, a photon, and missing transverse momentum are selected. The
measurements are compared with standard model predictions computed at next-to-
leading and next-to-next-to-leading orders in perturbative quantum
chromodynamics. Constraints on the presence of -scale new physics affecting
the $\PW\PW\PGg$ vertex are determined within an effective field theory
framework, focusing on the $\mathcal{O}_{3W}$ operator. A simultaneous
measurement of the photon transverse momentum and the azimuthal angle of the
charged lepton in a special reference frame is performed. This two-dimensional
approach provides up to a factor of ten more sensitivity to the interference
between the standard model and the $\mathcal{O}_{3W}$ contribution than using
the transverse momentum alone.

##  0.1 Introduction

The measurement of the properties of vector boson pair production is an
important test of the electroweak sector of the standard model (SM). The
production cross sections of these processes are sensitive to the $\PW\PW\PV$
triple gauge couplings (TGCs), where $\PV=\PGg$ or . Because the strengths of
these TGCs are predicted precisely in the SM, the measurement of an anomalous
value would indicate the presence of physics beyond the SM (BSM).

This paper reports an analysis of $\PWpm\PGg$ production in proton-proton
($\Pp\Pp$) collisions at $\sqrt{s}=13\TeV$ using data recorded with the CMS
detector at the CERN LHC in 2016–2018 with an integrated luminosity of 138.
This process has previously been studied by the CDF and D0 Collaborations [1,
2, 3] at the Fermilab Tevatron in $\Pp\Pap$ collisions at $\sqrt{s}=1.96\TeV$,
and by the CMS [4] and ATLAS [5] Collaborations using $\Pp\Pp$ collision data
collected at $\sqrt{s}=7\TeV$. The CMS Collaboration has also performed the
first $\PWpm\PGg$ measurement at $\sqrt{s}=13\TeV$ [6]. All of these studies
found the measured inclusive cross sections to be compatible with the SM
predictions and set limits on the presence of anomalous TGCs.

In the present analysis, $\PWpm\PGg$ events are selected with one charged
lepton (), which is either an electron or muon, one neutrino (), and one
photon () in the final state. A few leading order (LO) Feynman diagrams are
shown in Fig. 1. The photon can be produced as a result of either initial- or
final-state radiation, in addition to the contribution involving a TGC vertex.

Figure 1: LO Feynman diagrams for $\PWp\PGg$ production showing initial-state
(left) and final-state (center) radiation of the photon, and the $\PW\PW\PGg$
TGC process (right).

Differential cross sections are measured for several observables, including
the transverse momentum $\pt^{\PGg}$ and pseudorapidity $\eta^{\PGg}$ of the
photon, and the transverse mass of the $\Pell\PGn\PGg$ system, denoted
$m_{\mathrm{T}}^{\text{cluster}}$. The cross section is also measured as a
function of the number of additional jets. The measured values are compared
with the SM predictions at next-to-leading order (NLO) and next-to-next-to-
leading order (NNLO) in perturbative quantum chromodynamics (QCD).
Interference between the LO $\PWpm\PGg$ production diagrams results in a cross
section that vanishes in specific phase space regions. This effect is known as
a radiation amplitude zero (RAZ) [7, 8, 9, 10, 11]. A differential cross
section measurement of the pseudorapidity difference between the lepton and
the photon, $\Delta\eta(\Pell,\PGg)$, is used to explore this effect.

Constraints on BSM contributions to the $\PW\PW\PGg$ vertex are determined in
an effective field theory (EFT) framework. For the first time in the study of
this process, both $\pt^{\PGg}$ and the angular properties of the final-state
particles are exploited to increase the sensitivity to the interference
between the SM and BSM amplitudes. This novel approach is referred to as
“interference resurrection” [12, 13].

The paper is organized as follows. The interference resurrection technique is
described in Section 0.2. The CMS detector, data samples, and event simulation
are summarized in Sections 0.3 and 0.4. The object reconstruction and event
selection are described in Sections 0.5 and 0.6. The estimation of the main
backgrounds is given in Section 0.7. The systematic uncertainties are
discussed in Section 0.8, the results presented in Section 0.9, and the paper
summarized in Section 0.10.

##  0.2 Interference resurrection

An EFT approach can be used to study how new physics entering at an energy
scale $\Lambda$, assumed to be much larger than the electroweak scale, leads
to deviations from the SM at an energy regime accessible at the LHC. The SM
EFT is constructed by the addition of higher-dimensional operators,
$\mathcal{O}_{i}$, to the SM Lagrangian,

$\mathcal{L}_{\text{EFT}}=\mathcal{L}_{\text{SM}}+\sum_{i}C_{i}^{(6)}\mathcal{O}_{i}^{(6)}+\sum_{i}C_{i}^{(8)}\mathcal{O}_{i}^{(8)}+\cdots,$
(1)

where $i$ enumerates the set of operators under consideration, $C_{i}^{(D)}$
are Wilson coefficients that scale as $\Lambda^{4-D}$ and $D$ denotes the
operator dimension. The leading deviations from the SM are generally expected
to occur at $D=6$, since $D=5$ operators violate lepton number conservation
[14]. Examples of the relationship between the operators affecting diboson
production and specific BSM scenarios are described in Refs. [15, 16].

The dimension-six operator of interest in this analysis, $\mathcal{O}_{3W}$,
is a $CP$-even modification of the $\PW\PW\PV$ TGC defined in the EFT basis of
Ref. [17] as

$\mathcal{O}_{3W}=\epsilon^{ijk}W_{\mu}^{i\nu}W_{\nu}^{j\rho}W_{\rho}^{k\mu},$
(2)

where $W_{\mu}^{i\nu}$ is the weak isospin field strength tensor and
$\epsilon^{ijk}$ is the totally antisymmetric tensor with $\epsilon^{123}=1$.
The cross section in the presence of this operator can be expressed as

$\sigma(C_{3W})=\sigma^{\text{SM}}+C_{3W}\sigma^{\text{int}}+C_{3W}^{2}\sigma^{\text{BSM}},$
(3)

where $C_{3W}$ is the Wilson coefficient, $\sigma^{\text{int}}$ is the
contribution from the interference between the SM and $\mathcal{O}_{3W}$, and
$\sigma^{\text{BSM}}$ is the pure BSM component.

However, it has been demonstrated [12, 13] that in the high-energy limit,
$E>m_{\PW}$, the $2\to 2$ amplitudes for transverse vector boson production,
$ff\to\PW_{\mathrm{T}}\PV_{\mathrm{T}}$, have different final-state helicity
configurations for the SM ($\pm\mp$) and BSM ($\pm\pm$) components. This means
the effect of the interference is typically not detectable when considering
observables inclusive over the decay angles, for example, the of the photon or
boson. This narrows our sensitivity in such observables to just the pure BSM
contribution at order $C_{3W}^{2}$, which scales as $\Lambda^{-4}$. In this
scenario, the validity of any derived constraints can be limited by the
unknown effect of the leading dimension-eight contributions, which also enter
at order $\Lambda^{-4}$. Therefore, increasing the sensitivity to the
interference, which scales as $\Lambda^{-2}$, is important for improving the
validity of the constraints in any global EFT interpretation [18].

A method has been proposed [12, 19] that gives sensitivity to the SM-BSM
interference by measuring the decay angles of the final-state fermions. A
special coordinate system, illustrated in Fig. 2, is defined event-by-event by
a Lorentz boost to the center-of-mass frame of the $\PWpm\PGg$ system, where
the boost direction is denoted $\hat{r}$. Since the longitudinal component of
the neutrino momentum is not measurable, additional constraints are required
in the calculation of the $\PWpm\PGg$ four-momentum, described in Section
0.6.1. In the boosted frame the boson momenta are back-to-back, and the $z$
axis is taken as the direction of the boson, the $y$ axis direction is given
by $\hat{z}\times\hat{r}$, and the $x$ axis given by $\hat{y}\times\hat{z}$.
The final-state fermions from the boson decay are labeled as $f_{+}$ and
$f_{-}$, referring to the positive and negative fermion helicity states,
respectively.

Figure 2: Scheme of the special coordinate system for $\PWpm\PGg$ production,
defined by a Lorentz boost to the center-of-mass frame along the direction
$\hat{r}$. The $z$ axis is chosen as the boson direction in this frame, and
$y$ is given by $\hat{z}\times\hat{r}$. The boson decay plane is indicated in
blue, where the labels $f_{+}$ and $f_{-}$ refer to positive and negative
helicity final-state fermions. The angles $\phi$ and $\theta$ are the
azimuthal and polar angles of $f_{+}$.

In this frame the angle $\theta$ is the polar angle of the boson decay in its
rest frame, taken as the angle between the three-momenta of the boson and
fermion $f_{+}$. The angle $\phi\in[-\pi,\pi]$ is the azimuthal angle of
$f_{+}$: $\phi=\phi(f_{+})=\phi(f_{-})+\pi$, modulo $2\pi$.

A measurement of $\phi$ is sensitive to the interference of the
$\mathcal{O}_{3W}$ term with the SM contribution. Figure 3 (left) shows the
particle-level distribution in $\phi$ in the LO $2\to 2$ scattering process
for varying values of $C_{3W}$, with only the inclusion of the interference
term. A clear modulation is present, which grows with increasing $C_{3W}$.
Figure 3 (right) shows that the magnitude of this modulation is reduced with
the inclusion of additional jets in the matrix element calculations, with MLM
merging [20] used in the matching to the parton shower. However, a veto on the
presence of additional jets in the event with $\pt>30\GeV$ and
$\abs{\eta}<2.5$ is shown to substantially restore the effect.

Figure 3: Particle-level distributions (in arbitrary units) of the decay angle
$\phi$, comparing the LO $2\to 2$ process (left) to the LO MLM-merged
prediction with up to two additional jets in the matrix element calculations
(right). The black line gives the SM prediction ($C_{3W}=0$) and the red,
green, and blue lines correspond to different nonzero values of $C_{3W}$, for
which only the interference contribution is shown. The black and blue dashed
lines in the right figure give the distributions in the presence of a jet
veto, as described in the text.

##  0.3 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid.

In the barrel section of the ECAL, an energy resolution of about 1% is
achieved for unconverted or late-converting photons in the tens of $\GeV$
energy range. The remaining barrel photons have a resolution of about 1.3% up
to $\abs{\eta}=1$, rising to about 2.5% at $\abs{\eta}=1.4$. In the endcaps,
the resolution of unconverted or late-converting photons is about 2.5%, while
the remaining endcap photons have a resolution of 3–4% [21].

The electron momentum is estimated by combining the energy measurement in the
ECAL with the momentum measurement in the tracker. The momentum resolution for
electrons with $\pt\approx 45\GeV$ from $\PZ\to\Pe\Pe$ decays ranges from 1.7
to 4.5%. It is generally better in the barrel region than in the endcaps, and
also depends on the bremsstrahlung energy emitted by the electron as it
traverses the material in front of the ECAL [22].

Muons are measured in the range $\abs{\eta}<2.4$, with detection planes made
using three technologies: drift tubes, cathode strip chambers, and resistive
plate chambers. Matching muons to tracks measured in the silicon tracker
results in a relative resolution of 1% in the barrel and 3% in the endcaps for
muons with up to 100. The resolution in the barrel is better than 7% for muons
with up to 1 [23].

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 [24]. 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 [25].

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematical variables, can be
found in Ref. [26].

##  0.4 Data samples and event simulation

This analysis uses $\Pp\Pp$ collision data collected by the CMS experiment
between 2016 and 2018 at $\sqrt{s}=13\TeV$ with an integrated luminosity of
138. Simulated event samples are used to model both the $\PWpm\PGg$ signal and
several backgrounds. The $\PWpm\PGg$ signal, with up to one additional jet,
and the $\PZ$+jets, $\PZ\PGg$, $\PGg\PGg$, single-$\PQt$+$\PGg$, $\PW\PW$,
$\PW\PZ$, and $\PZ\PZ$ background samples are generated with [27] v2.3.3–2.6.5
at NLO in QCD. Backgrounds from $\ttbar$+jets and single-$\PQt$ production are
simulated with v2.0 [28, 29, 30, 31, 32, 33]. The $\ttbar\PGg$ background is
simulated with v2.6.0 at LO in QCD. The effect of EFT operators on $\PWpm\PGg$
distributions is modeled using a dedicated sample generated with v2.6.7 at LO
with up to two additional jets. Event weights for varying values of $C_{3W}$
are embedded, utilizing LO matrix element reweighting [34] and the SMEFTsim
UFO model [35, 36].

The NNPDF3.1 [37] NNLO parton distribution functions (PDFs) are used in the
$\PWpm\PGg$ signal simulation. The background samples use either the NNPDF3.0
[38] NLO or NNPDF3.1 NNLO PDFs. All samples are interfaced with v8.230 [39]
for parton showering and underlying event simulation. Samples produced for the
2016 analysis use the CUETP8M1 [40] underlying event tune, with the exception
of the $\ttbar$ samples, which use CUETP8M2T4 [41]. The 2017 and 2018 samples
use the CP5 [42] tune. The CMS detector response is simulated with the package
[43]. Additional $\Pp\Pp$ interactions occurring in both the same and adjacent
bunch crossings (pileup) are simulated as a set of minimum bias interactions
that are mixed with the hard scattering event. In the analysis of each data-
taking year the simulated events are weighted based on the number of pileup
events to match the distribution measured in data.

##  0.5 Event and object reconstruction

The $\Pp\Pp$ collision vertices in an event are reconstructed by grouping
tracks consistent with originating at a common point in the luminous region.
The candidate vertex with the largest value of summed physics-object $\pt^{2}$
is taken to be the primary $\Pp\Pp$ interaction vertex. The physics objects
are the jets, clustered using the anti-jet finding algorithm [44, 45] with all
the tracks assigned to candidate vertices as inputs, and the associated
missing transverse momentum, taken as the negative vector sum of those jets.

The full event reconstruction uses the particle-flow (PF) algorithm [46],
which aims to reconstruct and identify each individual particle in an event,
with an optimized combination of information from the various elements of the
CMS detector. The energy of photons is obtained from the ECAL measurement. The
energy of electrons is determined from a combination of the electron momentum
at the primary interaction vertex as determined by the tracker, the energy of
the corresponding ECAL cluster, and the energy sum of all bremsstrahlung
photons spatially compatible with originating from the electron track. The
energy of muons is obtained from the curvature of the corresponding track. The
energy of charged hadrons is determined from a combination of their momentum
measured in the tracker and the matching ECAL and HCAL energy deposits,
corrected for the response function of the calorimeters to hadronic showers.
Finally, the energy of neutral hadrons is obtained from the corresponding
corrected ECAL and HCAL energies.

For each event, hadronic jets are clustered from these reconstructed particles
using the infrared- and collinear-safe anti-algorithm [44, 45] with a distance
parameter of 0.4. The jet momentum is determined as the vector sum of all
particle momenta in the jet, and is found from simulation to be, on average,
within 5–10% of the true momentum over the entire spectrum and detector
acceptance. Pileup interactions can contribute additional tracks and
calorimetric energy depositions to the jet momentum. To mitigate this effect,
charged particles identified to be originating from pileup vertices are
discarded, and an offset correction is applied to subtract remaining
contributions. Jet energy corrections are derived from simulation to bring the
measured response of jets to that of particle level jets on average. In situ
measurements of the momentum balance in dijet, photon+jet, $\PZ$+jet, and
multijet events are used to correct any residual differences in the jet energy
scale between data and simulation [47].

Events of interest in this analysis are characterized by the presence of a
single isolated charged lepton, either an electron or a muon; an isolated
photon; and missing transverse momentum. Identification and isolation criteria
are used to improve the purity of events containing prompt leptons or photons,
while rejecting candidates that originate from jets, pileup interactions, or
misreconstruction.

The identification criteria for photons [48] include selections on the ratio
of hadronic to electromagnetic energy deposited in calorimeters ($H/E$), a
variable quantifying the lateral extension of the shower
($\sigma_{\eta\eta}$), and a requirement that the photon ECAL cluster is not
matched to an electron track that has hits in the innermost tracker layers.
Electron candidates are required to pass identification criteria related to
the impact parameters of the track with respect to the primary vertex, the
matching between the track and ECAL cluster, the shower shape in the ECAL, and
the same $H/E$ variable as for photons. A veto on electrons originating from
photon conversions is applied [21]. Muon candidates are reconstructed by a
simultaneous track fit to hits in the tracker and in the muon chambers.
Identification criteria are applied to the impact parameters with respect to
the primary vertex, to the number of spatial measurements in the silicon
tracker and the muon system, and to the fit quality of the combined muon
track.

Both lepton and photon candidates are required to be isolated from any other
activity in the event. For electrons and muons, a relative isolation variable
is defined as

$I^{\Pell}_{\text{rel}}=\frac{1}{\pt^{\Pell}}\Bigg{[}\sum_{\text{charged}}\pt+\max\bigg{(}0,\sum_{\text{neutral}}\pt+\sum_{\text{photons}}\pt-\pt^{\text{pileup}}\bigg{)}\Bigg{]},$
(4)

where the sums are over the PF candidates of the indicated type that are
within $\DR<0.4$ of the lepton, with
$\DR=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$. To suppress the
effect of pileup, only charged hadrons compatible with originating at the
primary vertex are included. For the neutral hadron and photon sums, an
estimate of the expected pileup contribution is subtracted [48, 23].
Requirements of $I^{\Pgm}_{\text{rel}}<0.15$ and
$I^{\Pe}_{\text{rel}}<0.05$–$0.09$ are applied, where the latter selection
varies with $\pt^{\Pe}$ and $\abs{\eta^{\Pe}}$. For photons, isolation
requirements are applied separately to each of the three components in Eq.
(4). In addition, a selection is applied on the maximal charged particle
isolation sum $I_{\text{ch}}^{\text{max}}$ computed with respect to any
vertex. A requirement of
$I_{\text{ch}}^{\text{max}}<\min(0.05\pt^{\PGg},6\GeV)$ is applied. This more
than halves the rate of photons from pileup jets, which constitute around 60%
of misidentified photons at low $\pt^{\PGg}$.

For leptons and photons, several corrections are applied to the simulation to
improve the modeling of the data. Correction factors are applied as event
weights for the efficiencies of electrons and muons to be reconstructed, pass
their respective identification and isolation criteria, and pass the single-
lepton trigger requirements. These are determined using the “tag-and-probe”
(T&P) technique [49] with $\PZ\to\Pell\Pell$ data. Similarly, scale factors
for the efficiencies of photons to pass identification and isolation criteria
are applied. These are measured with the T&P technique in $\PZ\to\Pe\Pe$
events, where the probe electron is reconstructed as a photon. The muon
momentum scale in data and simulation is corrected using a calibration derived
in $\PZ\to\PGm\PGm$ events [50, 23]. In simulation this includes an additional
resolution smearing to match the performance in data. Similarly, scale and
resolution corrections are applied to simulated electrons and photons to
better match the data resolution.

The missing transverse momentum vector is computed as the negative vector sum
of all the PF candidates in an event, and its magnitude is denoted as [51].
The vector is modified to account for corrections to the energy scale of the
reconstructed jets. The pileup-per-particle identification algorithm [52] is
applied to reduce the pileup dependence of the observable. In the sum over PF
candidates, each is weighted by the probability for that particle to originate
from the primary interaction vertex.

Anomalous high-events can be due to a variety of reconstruction failures,
detector malfunctions, or noncollision backgrounds. Such events are rejected
by event filters that are designed to identify more than 85% of the spurious
high-events with a mistagging rate of less than 0.1% [51].

##  0.6 Event selection

Single-lepton triggers are used to select data events. These require that
candidates pass loose identification and isolation criteria and have a above
thresholds of 27–32for electrons and 24–27for muons, with the exact values
depending on the data-taking year.

The offline event selection requires the presence either of an electron with
$\pt>35\GeV$ and $\abs{\eta}<2.5$ (electron channel) or a muon with
$\pt>30\GeV$ and $\abs{\eta}<2.4$ (muon channel). The lepton must pass the
identification criteria outlined in Section 0.5 and be spatially matched to
the object that fired the trigger. Electron candidates falling in the
transition region between the ECAL barrel and endcap calorimeters,
$1.44<\abs{\eta}<1.57$, are rejected. Events are vetoed if they contain
additional leptons with $\pt>10\GeV$ that pass looser versions of the
identification criteria. The same photon selection is used for both channels.
The photon is required to have $\pt>30\GeV$ and $\abs{\eta}<2.5$, not fall in
the ECAL transition region, pass the identification and isolation requirements
and be separated from the selected lepton by $\DR>0.7$. Events with more than
one photon passing these requirements are rejected. Hadronic jets in the event
are counted if having $\pt>30\GeV$, $\abs{\eta}<2.5$, and a separation from
both the lepton and photon of $\DR>0.4$.

In both channels, events are required to have $\ptmiss>40\GeV$. This reduces a
large source of background from processes that do not contain any intrinsic ,
for example $\PZ\to\Pell\Pell$. Events with $m_{\Pell\PGg}$, the invariant
mass of the lepton-photon pair, close to $m_{\PZ}$ are rejected. In the
electron channel this veto is on events with $70<m_{\Pe\PGg}<110\GeV$, which
reduces the remaining $\PZ\to\Pe\Pe$ contribution. In the muon channel the
range is $70<m_{\PGm\PGg}<100\GeV$, which rejects events where the photon
originates as final-state radiation, $\PZ\to\Pgm\Pgm\to\Pgm\Pgm\PGg$, and
takes a large fraction of the original muon momentum.

This baseline event selection results in the selection of $72\,798$ electron
channel and $109\,669$ muon channel events, of which $\PWpm\PGg$ production is
expected to account for 53 and 58%, respectively. Figure 4 shows the
$\pt^{\Pell}$, $\pt^{\PGg}$, $\mT(\Pell,\ptmiss)$, and
$m_{\mathrm{T}}^{\text{cluster}}$ distributions in data and simulation, summed
over the electron and muon channels. The $m_{\mathrm{T}}^{\text{cluster}}$
observable is the transverse mass of the $\Pell\PGn\PGg$ system, defined as

$m_{\mathrm{T}}^{\text{cluster}}=\sqrt{\left[\sqrt{m_{\Pell\PGg}^{2}+p_{\mathrm{T},\Pell\PGg}^{2}}+\ptmiss\right]^{2}-p_{\mathrm{T},\Pell\PGg\text{miss}}^{2}},$
(5)

where $p_{\mathrm{T},\Pell\PGg}$ is the transverse momentum of the lepton and
photon system, and $p_{\mathrm{T},\Pell\PGg\text{miss}}$ is the transverse
momentum of the lepton, photon, and system. The baseline selection is used for
the majority of the differential cross section measurements reported in
Section 0.9. For the EFT analysis a tighter selection, the EFT selection, with
$\pt^{\Pell}>80\GeV$ and $\pt^{\PGg}>150\GeV$ is applied, and events
containing hadronic jets, as defined above, are vetoed.

Figure 4: Distributions of the lepton (upper left), the photon (upper right),
$\mT(\Pell,\ptmiss)$ (lower left), and $m_{\mathrm{T}}^{\text{cluster}}$
(lower right), combining the electron and muon channels. The horizontal and
vertical bars associated to the data points correspond to the bin widths and
statistical uncertainties, respectively. The shaded band represents the total
statistical and systematic uncertainty in the signal plus background
expectation.

###  0.6.1 Reconstruction of $\phi$

In the EFT analysis the neutrino four-momentum is required to construct the
$\PWpm\PGg$ center-of-mass frame and determine the azimuthal angle $\phi$. The
reconstruction procedure described in Ref. [12] is followed. The vector in the
event is assumed to be the transverse momentum of the neutrino,
$\vec{p}_{\mathrm{T}}^{\PGn}$. Then by requiring that the invariant mass of
the charged lepton and neutrino system equals the boson pole mass $m_{\PW}$,
it is possible to form an equation for $\eta^{\PGn}$,

$\eta^{\PGn}=\eta^{\Pell}\pm\ln\left[1+\Delta\sqrt{2+\Delta^{2}}+\Delta^{2}\right],$
(6)

where:

$\Delta=\sqrt{\frac{m_{\PW}^{2}-\mT^{2}}{2\pt^{\Pell}\pt^{\PGn}}},$ (7)

$\eta^{\Pell}$ is the pseudorapidity of the lepton, and is the transverse mass
of the $\Pell\PGn$ system. Of the two possible solutions for $\eta^{\PGn}$,
only one will correspond to the unknown true value. One of the two solutions
is picked at random event-by-event. In the limit of high boson momentum, it
can be demonstrated that the two solutions for $\phi$, $\phi^{+}$ and
$\phi^{-}$, are related by $\phi^{+}=\pi-\phi^{-}$, modulo $2\pi$. This
intrinsic ambiguity does not, however, prevent the observation of the
interference effect. This is illustrated in Fig. 3, where the deviation in the
$\phi$ distribution is unaffected by a transformation $\phi\to\pi-\phi$.

One additional complication to this reconstruction is in cases where
$\mT>m_{\PW}$, either because the boson was produced off shell, or because of
mismeasured in the reconstruction. In this case Eq. (6) has no real solutions
and instead $\eta^{\PGn}$ is assumed to equal $\eta^{\Pell}$, which gives a
lepton-neutrino mass as close as possible to $m_{\PW}$. In these events $\phi$
is biased towards $\pm\pi/2$. Figure 5 shows the two-dimensional (2D)
distribution of $\phi^{\text{gen}}$ versus $\phi^{\text{true}}$, where
$\phi^{\text{gen}}$ is the reconstructed $\phi$ angle using particle-level
quantities.

Figure 5: The two-dimensional distribution of $\phi^{\text{gen}}$ versus
$\phi^{\text{true}}$, where the former is reconstructed using the particle-
level lepton and photon momenta and . The off-diagonal components correspond
to events where the incorrect solution for $\eta^{\PGn}$ is chosen, as
described in the text.

Given this ambiguity in $\phi$, the variable $\phi_{f}$ is used in the
subsequent analysis, where

$\phi_{f}=\begin{cases}-(\pi+\phi),&\text{for }\phi<-\frac{\pi}{2};\\\
\phi,&\text{for }\abs{\phi}<\frac{\pi}{2};\\\ \pi-\phi,&\text{for
}\phi>\frac{\pi}{2}.\\\ \end{cases}$ (8)

Furthermore, since the distributions of $\phi_{f}$ shown in Fig. 3 are
symmetric about zero, the magnitude $\abs{\phi_{f}}$ will be used.

##  0.7 Background estimation

The background from processes containing prompt leptons and photons is
estimated from simulation. This includes $\PZ/\PGg^{\ast}(\Pell\Pell)\PGg$,
$\ttbar\PGg$, $\PW\PV\PGg$, and single-$\PQt$+$\PGg$ production, where
$\PV=\PW,\PZ$.

An important background in the electron channel is from events with two prompt
electrons, predominantly $\PZ\to\Pe\Pe$, in which one is misidentified as the
photon candidate. The rate at which electrons, having passed the photon
identification criteria described in Section 0.5, are subsequently not
rejected by the specific electron veto is measured in both data and simulation
using the T&P method. The probability in data is typically 1–5%, depending on
the and $\eta$ of the electron. The resulting correction factors are applied
in all simulated events where the photon candidate is matched to a prompt
electron at the generator level.

The remaining backgrounds originate from other processes where one or both of
the lepton and photon are misidentified, and these are discussed in the
following sections.

###  0.7.1 Jets misidentified as photons

After the baseline selection, the main background in both channels comes from
$\PW$+jets events in which a jet is misidentified as a photon. These jets tend
to have high fractions of energy deposited in the ECAL, and may contain
genuine photons, for example from $\Pgpz\to\PGg\PGg$ decays. Since the
probability for jets to pass the photon identification criteria is not
guaranteed to be well modeled in simulation, this background is estimated
using data.

Independent control regions, with a high purity in misidentified jets, are
defined by inverting some of the photon identification requirements. Two
variables are used: the charged isolation sum defined in Eq. (4), denoted
$I_{\text{ch}}$, and $\sigma_{\eta\eta}$. This exploits the fact that
misidentified jets tend to be less well isolated and have broader showers than
prompt photons. For a given selection and observable bin, the events in the
inverted-$\sigma_{\eta\eta}$ region are used to predict the number of
misidentified jet events in the nominal region, $N_{\text{misid}}$. Each
inverted-$\sigma_{\eta\eta}$ event is assigned an extrapolation factor,
$f_{\PGg}$, corresponding to the expected inverted-to-nominal ratio given the
and $\abs{\eta}$ of the photon candidate. This gives

$N_{\text{misid}}=\sum_{\text{data}}f_{\PGg}(\pt^{\PGg},\eta^{\PGg})-N_{\text{sim}}^{\text{prompt-}\PGg},$
(9)

where the sum runs over all data events in the inverted-$\sigma_{\eta\eta}$
region, and $N_{\text{sim}}^{\text{prompt-}\PGg}$ is the estimated prompt
photon contamination, determined by applying the same extrapolation factors to
simulated events in this region. The $f_{\PGg}$ value for each and
$\abs{\eta}$ bin is determined from a region where $I_{\text{ch}}$ is
inverted. The $\sigma_{\eta\eta}$ extrapolation factor, calculated after the
subtraction of the expected prompt photon contribution, varies linearly with
$I_{\text{ch}}$. Therefore, a linear fit to the ratio is performed, with the
result extrapolated to the nominal $I_{\text{ch}}$ region to give $f_{\PGg}$.

The statistical uncertainty in $f_{\PGg}$ is 2–35%, depending on the and
$\abs{\eta}$ bin. Systematic uncertainties come from the normalization of the
prompt photon contribution that is subtracted, and the bias in the
extrapolated value of $f_{\PGg}$, compared with the true value, when the
method is validated in simulation. The former is typically 10%, and the latter
is up to 15% in a given bin. The total statistical and systematic uncertainty,
which ranges from 10 to 45%, is propagated through all subsequent results.

In the EFT selection, because of the limited number of control region events
for $\pt^{\PGg}>150\GeV$, this method is modified. In the
inverted-$\sigma_{\eta\eta}$ region, the nominal requirement on
$I_{\text{ch}}$ is retained, whereas all other photon identification criteria
are removed and replaced with only a loose requirement of $H/E<0.15$. The
total uncertainty in $f_{\PGg}$ is 20–60% under this selection, and the
statistical component is typically dominant.

###  0.7.2 Misidentified leptons

Another contribution comes from events with a prompt photon but a nonprompt or
misidentified lepton, , within a jet containing a semileptonic meson decay.
The main identifying feature of such events is that the leptons will generally
be less well isolated. The contribution is estimated by applying an inverted
lepton isolation requirement, and applying a per-event extrapolation factor
$f_{\Pell}$, binned in and $\abs{\eta}$, to predict the number of events in
the nominal isolated selection.

The $f_{\Pell}$ factors are measured in a control region in data designed to
increase the purity of events with misidentified leptons. This control region
is obtained by applying the baseline selection in both channels, but vetoing
the presence of an identified photon and instead requiring the presence of a
jet with $\pt>30\GeV$ and $\abs{\eta}<2.5$. A requirement of
$\mT(\Pell,\ptmiss)<30\GeV$ is applied to reject the background from
$\PW$+jets events. The remaining contamination from $\PW$+jets and other
prompt-lepton sources is estimated using simulation and subtracted. The
normalization uncertainty in this prompt subtraction dominates the
uncertainties in the $f_{\Pell}$ factors, which are typically 10–40%.

Both the misidentified lepton and misidentified photon background estimates
predict the contribution from events where both objects are misidentified. To
avoid double-counting these events, their expected contribution is subtracted
from the misidentified lepton background estimate. This is estimated using
events in a region where both the lepton isolation and photon
$\sigma_{\eta\eta}$ requirements are inverted, where the extrapolation factors
for both methods are applied.

##  0.8 Systematic uncertainties

The effects of systematic uncertainties in the signal and background
expectations are incorporated for all measurements in this analysis. The
majority of the uncertainties affect both the shape and normalization of the
predictions, whereas several affect only the normalization. The shape
variations are modeled by a smooth interpolation between the nominal and $\pm
1$ standard deviation predictions in each bin and a linear extrapolation
beyond this. The systematic uncertainties and their typical effect on the
expected yields are summarized in Table 0.8. The experimental sources are as
follows:

Summary of the systematic uncertainties affecting the signal and background
predictions. The table notes whether each uncertainty affects the shape of the
measured observable or just the normalization (Affects shape), and whether the
effect is correlated between the data-taking years (Corr. years). The
normalization effect on the expected yield of the applicable processes is also
given. For some shape uncertainties the values vary significantly across the
observable distribution. In these cases the typical range and maximum values
are given, where the former is the central 68% interval considering all bins.
Uncertainty Affects shape Corr. years Relative effect on expected yield
Experimental Integrated luminosity — Partial 1.6% Pileup modeling ✓ ✓ 0.2–3.1%
L1 trigger ✓ ✓ 0.3–1.1% Electron ID ✓ ✓ 0.7–2.8% Electron ID
($\pt^{\Pe}>200\GeV$) ✓ — 0.1–1.2% Electron trigger — — 0.5% Muon ID (stat) ✓
— 0.1–0.6% Muon ID (syst) ✓ ✓ 0.2–0.7% Muon trigger ✓ — 0.1–0.7% Photon ID ✓ ✓
0.6–6.0% Photon ID ($\pt^{\PGg}>200\GeV$) ✓ — 2.1–4.7% Photon ID (high
extrapolation) ✓ — Typically 3.0–9.0%, max. 14% Photon ($\Pe$ veto) — — 1%
Photon energy scale ✓ ✓ Typically 0.1–4.8%, max. 13% Jet energy scale ✓ — 1–4%
scale ✓ Partial 0.1–10.1% $\Pe\to\PGg$ misidentification ✓ — Typically
6.7–18%, max. 25% $\text{Jet}\to\PGg$ misidentification ✓ — 10–45%
Misidentified $\Pe$ ✓ — Typically 13–36%, max. 75% Misidentified $\Pgm$ ✓ —
Typically 16–42%, max. 70% Theoretical $\PWpm\PGg$ acceptance (scale) ✓ ✓
0.3–1.7% $\PWpm\PGg$ acceptance (PDF) ✓ ✓ Typically 0.5–2.2%, max. 7.6%
$\PWpm\PGg$ out-of-acceptance (scale) ✓ ✓ 5.2–12% $\PWpm\PGg$ parton shower
modeling ✓ ✓ 0.2–1.3% Background normalization (scale) — ✓ 2.0–16% Background
normalization (PDF) — ✓ 4.2–4.8%

  * •

Integrated luminosity: The 2016, 2017, and 2018 data set uncertainties are
1.2%, 2.3%, and 2.5% [53, 54, 55], respectively, applied to all processes
estimated purely with simulation. The total integrated luminosity has an
uncertainty of 1.6%, the improvement in precision reflecting the
(uncorrelated) time evolution of some systematic effects.

  * •

Pileup modeling: The uncertainty in the weighting of the pileup distribution
in simulation is derived by varying the total inelastic cross section by $\pm
5\%$ in the estimate of the distribution in data [56].

  * •

L1 trigger: During the 2016 and 2017 data-taking periods, a gradual shift in
the timing of the inputs of the ECAL L1 trigger in the $\abs{\eta}>2.4$ region
led to a specific inefficiency. A correction amounting to 2–3% is applied to
the simulation along with the corresponding uncertainty in the inefficiency
measurement.

  * •

Lepton ID and trigger efficiencies: The uncertainties in the measured
correction factors are applied to the simulation, which include both
statistical and systematic sources. The latter dominate at low and the former
in the higher range probed in this analysis.

  * •

Photon ID efficiency: The uncertainties in the identification correction
factors, derived in $\PZ\to\Pe\Pe$ events, are applied to the simulation. The
highest measurement bin ($\pt^{\PGg}>200\GeV$) is dominated by the statistical
uncertainty and so is uncorrelated from the uncertainty at lower . An
uncertainty in the extrapolation of the efficiencies to the maximum
$\pt^{\PGg}$ considered, 1.5, is also included.

  * •

Photon energy scale: The uncertainty from the calibration of the scale in data
is determined using $\PZ\to\Pe\Pe$ events. Typical values are 0.1–3% of the
photon energy depending on .

  * •

Jet energy scale corrections: The energies of jets in simulation are varied
within the uncertainties of the scale correction measurements and propagated
to all relevant observables. The magnitude varies with and $\eta$, and is
typically a few percent.

  * •

scale: The uncertainty in the jet energy scale also has a correlated effect on
the calculation. An additional uncertainty is included for the energy scale of
PF candidates not clustered in jets.

  * •

Electrons misidentified as photons: An uncertainty in the correction of the
electron to photon misidentification rate, due to the subtraction of genuine
photon events in the T&P measurement, is derived.

  * •

Jets misidentified as photons: For jets misidentified as photons, the
uncertainties from the method based on control samples in data described in
Section 0.7.1 are applied, independently for each $\pt^{\PGg}$ and
$\abs{\eta^{\PGg}}$ bin that $f_{\PGg}$ is measured in. These uncertainties
are correlated between the electron and muon channels.

  * •

Misidentified leptons: The dominant source of uncertainty in the $f_{\Pell}$
measurement, described in Section 0.7.2, comes primarily from the
normalization of the subtracted prompt lepton contamination in the measurement
region.

  * •

Simulated sample size: The statistical uncertainty due to the finite number of
simulated events per bin is applied following the Barlow–Beeston method [57].

Theoretical uncertainties in the $\PWpm\PGg$ signal prediction because of
missing higher orders in the perturbative cross section calculation are
evaluated by varying the renormalization and factorization scales, denoted
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$, respectively, in simulated events
by factors of 0.5 and 2. The maximum variation among all possible pairings of
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ is considered, with the exception of
the $(0.5,2)$ and $(2,0.5)$ combinations. For constraints on $C_{3W}$, the
full uncertainty from this procedure is applied, which amounts to an 8–11%
normalization effect depending on the bin. For the cross section measurements,
the effect on the predicted cross sections is neglected, whereas the effect on
the reconstruction-level acceptance is retained. These values typically vary
from 0.5 to 2% and are treated as independent for each measured cross section
bin. Conversely, for the contamination of signal events from outside the
fiducial region, the effect on both cross section and acceptance is included.
Uncertainties from the NNPDF3.1 PDF set eigenvectors are also evaluated
following the PDF4LHC prescription [58], and affect the acceptance by up to
2%. For measurements that include a jet veto, an additional migration
uncertainty between the 0-jet and $\geq$1-jet regions is applied, following
the procedure described in Ref. [59]. This results in an 8.6% uncertainty in
the 0-jet cross section.

Normalization uncertainties are assigned for each background process estimated
with simulation to account for missing higher-order corrections and the PDF
choice. These are treated as uncorrelated between different processes, and are
calculated following the same procedures as for the signal. The missing
higher-order correction uncertainties typically range from 2 to 3.5%, and the
PDF uncertainties are up to 4.8%, depending on process.

##  0.9 Results

The results described in this section are obtained using statistical
procedures developed by the ATLAS and CMS Collaborations, detailed in Refs.
[60, 61] and implemented in the RooFit [62] and RooStats [63] frameworks.

The parameters of interest $\vec{\alpha}$ for a particular result are
estimated with their corresponding confidence intervals using a profile
likelihood ratio test statistic $q(\vec{\alpha})$ [64], in which experimental
and theoretical uncertainties are incorporated via nuisance parameters (NPs)
$\vec{\theta}$:

$q(\vec{\alpha})=-2\ln\left(\frac{\mathcal{L}\big{(}\vec{\alpha},\hat{\vec{\theta}}_{\vec{\alpha}}\big{)}}{\mathcal{L}(\hat{\vec{\alpha}},\hat{\vec{\theta}})}\right).$
(10)

The asymptotic approximation, whereby $q(\vec{\alpha})$ is assumed to follow a
$\chi^{2}$ distribution, is used in the determination of the confidence
intervals. The likelihood functions in the numerator and denominator of Eq.
(10) are constructed using products of binned signal and background
probability density functions, as well as constraint terms for the NPs. The
quantities $\hat{\vec{\alpha}}$ and $\hat{\vec{\theta}}$ denote the
unconditional maximum likelihood estimates of the parameter values, while
$\hat{\vec{\theta}}_{\vec{\alpha}}$ denotes the conditional maximum likelihood
estimate for fixed values of the parameters of interest $\vec{\alpha}$. The
binned likelihood function is expressed as

$\mathcal{L}(\text{data}\mid\vec{\alpha},\vec{\theta})=\prod_{i}\text{Poisson}({n}_{i}\mid
s_{i}(\vec{\alpha},\vec{\theta})+b_{i}(\vec{\theta}))p(\widetilde{\vec{\theta}}\mid\vec{\theta}),$
(11)

where $n_{i}$ is the number of observed events in each bin, $s$ and $b$ are
the expected numbers of signal and background events, and
$\widetilde{\vec{\theta}}$ represents the nominal values of the NPs,
determined by external measurements.

For the measurement of a set of fiducial cross sections,
$\vec{\alpha}\equiv\sigma_{j}$, the signal expectation $s$ is constructed as

$s_{i}=\sum_{j}\sigma_{j}R_{ij}(\vec{\theta})L+\sum_{k}\sigma_{k}^{\text{OOA}}R_{ik}(\vec{\theta})L,$
(12)

where $j$ runs over the set of $\PWpm\PGg$ fiducial cross sections being
measured; $k$ runs over a set of unmeasured fiducial cross section bins,
referred to as out-of-acceptance (OOA) and discussed further below; $L$ is the
integrated luminosity of a given data set; and $R_{ij}$ is the response
matrix, parameterized by $\vec{\theta}$, giving the probability for an event
in fiducial bin $j$ to be reconstructed in analysis bin $i$.

In this measurement a significant OOA contribution can come from events that
are within the fiducial acceptance with the sole exception of the requirement,
because is measured with poor resolution compared with the transverse momentum
of the leptons and photons. This component is therefore split into the same
set of fiducial bins $j$ but with a modified selection. Since it is not
possible to measure these OOA cross sections $\sigma_{j}^{\text{miss}}$
simultaneously, they are related to the $\sigma_{j}$ using the predicted
ratios from the $\PWpm\PGg$ simulation:
$\sigma_{j}^{\text{miss}}=(\sigma_{j}^{\text{miss},\text{MC}}/\sigma_{j}^{\text{MC}})\sigma_{j}$.
The remaining OOA contribution $\sigma^{\text{OOA}}$ is small and is fixed to
the value predicted by the simulation. This includes events in which the
electron or muon originates from a tau lepton decay. Equation (12) above can
then be expressed as

$s_{i}=\sum_{j}\left[\sigma_{j}R_{ij}(\vec{\theta})L+\frac{\sigma_{j}^{\text{miss},\text{MC}}}{\sigma_{j}^{\text{MC}}}\sigma_{j}R_{ij}^{\text{miss}}(\vec{\theta})L\right]+\sigma^{\text{OOA}}R^{\text{OOA}}_{i}(\vec{\theta})L.$
(13)

In addition to measuring the absolute $\sigma_{j}$, it is useful to present
the results as fractional cross sections,
$f_{j}=\sigma_{j}/\sigma_{\text{tot}}$, where $\sigma_{\text{tot}}$ is treated
as a free parameter in the likelihood fit and $\sum{f_{j}}$ is constrained to
unity. This facilitates comparisons between different predictions purely on
the basis of observable shape, and reduces the impact of systematic
uncertainties that are uniform across the distribution, for example from the
integrated luminosity.

Tabulated versions of all the results in this section are provided in the
HEPData record for this analysis [65].

###  0.9.1 Differential cross sections

The differential cross sections
$\sigma_{j}(\Pp\Pp\to\PWpm\PGg\to\Pell^{\pm}\PGn\PGg)$, where denotes all
three lepton flavors, are measured in the following fiducial region:

  * •

$\pt^{\Pell}>30\GeV$, $\abs{\eta^{\Pell}}<2.5$,

  * •

$\pt^{\PGg}>30\GeV$, $\abs{\eta^{\PGg}}<2.5$,

  * •

$\ptmiss>40\GeV$,

  * •

$\DR(\Pell,\PGg)>0.7$.

These selections are chosen to match as closely as possible with the
reconstruction-level baseline described in Section 0.6, with two exceptions:
in the electron channel $\pt^{\Pe}>35\GeV$ is required because of the higher
trigger thresholds, and in the muon channel $\abs{\eta^{\PGm}}<2.4$ is applied
to reflect the acceptance of the muon system.

The lepton momenta are completed by adding the four-momenta of any photons
with $\DR(\Pell,\PGg)<0.1$ to the four-momentum of the lepton. A smooth-cone
photon isolation selection is also applied. This is necessary to ensure an
infrared-safe cross section calculation in perturbative QCD. It requires that
for every cone in $\eta$–$\phi$ around the photon direction with radius
$\delta<\delta_{0}$ the total scalar transverse energy sum must be smaller
than $\et^{\text{max}}(\delta)$ [66] where

$\et^{\text{max}}(\delta)=\epsilon\pt^{\PGg}{\left(\frac{1-\cos{\delta}}{1-\cos{\delta_{0}}}\right)}^{n},$
(14)

and the values of the tunable parameters are set as $\delta_{0}=0.4$,
$\epsilon=1.0$, and $n=1$.

Differential cross sections are measured for $\pt^{\PGg}$, $\eta^{\PGg}$,
$\DR(\Pell,\PGg)$, $\Delta\eta(\Pell,\PGg)$, and
$m_{\mathrm{T}}^{\text{cluster}}$ under the baseline selection. The
$\abs{\phi_{f}}$ observable is measured only under the higher-EFT selection,
given in Section 0.9.4, where the resolution is sufficiently improved compared
to the baseline selection.

The signal model is constructed as in Eq. (13), where the OOA contributions
$\sigma_{j}^{\text{miss}}$ are defined for events with $0<\ptmiss<40\GeV$, but
otherwise within the fiducial region for $j$. A simultaneous fit is performed
to the reconstructed distributions of the observables in each year and for
each of the electron and muon channels, where lepton flavor universality in
the boson decay is assumed. These distributions use the same bin boundaries as
the fiducial-level cross section bins. The measurements are compared with the
predictions from the NLO + simulation (mg5_amc+py8) and NNLO predictions from
the matrix [67, 68], [69, 70], and geneva [71, 72, 73, 74] frameworks.

The matrix calculation implements a dedicated $q_{\mathrm{T}}$-subtraction
formalism [75, 76], where $q_{\mathrm{T}}$ is the total transverse momentum of
the colorless final state particles. The framework utilizes amplitudes up to
one-loop level from OpenLoops [77, 78], as well as dedicated two-loop
calculations [79]. The prediction uses the $N$-jettiness slicing method, with
all one- and two-loop amplitudes [79] calculated using analytic formulae. It
is given with and without the inclusion of NLO electroweak corrections, which
have been shown [80, 69] to modify $\PWpm\PGg$ differential distributions by
$\mathcal{O}(10\%)$. The NLO electroweak correction to the $\Pq\Paq$-initiated
process is applied multiplicatively to the NNLO QCD cross section, which is
then summed with the contribution from the distinct photon-induced $\Pq\PGg$
channel. The geneva framework combines the fixed-order NNLO result with a
next-to-next-to-leading logarithmic resummed calculation, in this case using
soft-collinear effective theory to resum the 0-jettiness variable. The output
events are interfaced with 8 [39] for parton showering, hadronization and
underlying event simulation.

Figures 6–8 show the resulting measurements for each observable. For
$\pt^{\PGg}$ in Fig. 6, the uncertainty composition and correlation matrices
are also given.

Figure 6: The measured $\pt^{\PGg}$ absolute (left) and fractional (right)
differential cross sections (upper), compared with the mg5_amc+py8, geneva,
matrix, and predictions, and corresponding uncertainty decomposition (center)
and correlation matrices (lower). In the upper figures, the black vertical
bars give the total uncertainty on each measurement. The predictions are
offset horizontally in each bin to improve visibility, and the corresponding
vertical bars show the missing higher-order correction uncertainties.

Figure 7: The measured absolute (left) and fractional (right) differential
cross sections for $\eta^{\PGg}$ (upper) and $\DR(\Pell,\PGg)$ (lower),
compared with the mg5_amc+py8, geneva, matrix, and predictions. The black
vertical bars give the total uncertainty on each measurement. The predictions
are offset horizontally in each bin to improve visibility, and the
corresponding vertical bars show the missing higher-order correction
uncertainties.

Figure 8: The measured absolute (left) and fractional (right) differential
cross sections for $\Delta\eta(\Pell,\PGg)$ (upper) and
$m_{\mathrm{T}}^{\text{cluster}}$ (lower), compared with the mg5_amc+py8,
geneva, matrix, and predictions. The black vertical bars give the total
uncertainty on each measurement. The predictions are offset horizontally in
each bin to improve visibility, and the corresponding vertical bars show the
missing higher-order correction uncertainties.

The absolute cross section, as a function of $\pt^{\PGg}$, is measured with a
precision of 4–5% at low , dominated by experimental systematic uncertainties,
to 40% in the highest bin, dominated by the statistical uncertainty. The
fractional cross section uncertainty falls to 2–3% at low , owing to a
cancellation of systematic uncertainties. Both measurements show a tendency
towards the lower values of the NNLO predictions at high . Although the NLO
electroweak correction is predicted to reduce the cross section in this region
by up to 30%, the current precision is not sufficient to distinguish this
effect. The correlation matrices illustrate how large positive correlations
between bins in the absolute measurement are reduced significantly when using
the fractional parameterization.

The $\DR(\Pell,\PGg)$ measurement is in agreement with all the predictions in
the bulk of the distribution, but favors the higher NNLO predictions in the
$\DR>4.0$ region. The $\Delta\eta(\Pell,\PGg)$ measurement shows significant
enhancement in the highest $\abs{\Delta\eta}$ bins compared with the
mg5_amc+py8 prediction. The NNLO predictions feature a similar, though less
pronounced effect. The $m_{\mathrm{T}}^{\text{cluster}}$ distribution is
broadly in agreement with the predictions, however with a moderate
disagreement in the shape of the peak in the 100–300region.

###  0.9.2 Cross section as a function of the jet multiplicity

The cross section is measured as a function of the jet multiplicity using the
same fiducial region as the previous results. The fiducial selection for the
jets is chosen to match the experimental selection. Jets are clustered from
final state particles, excluding neutrinos, with the anti-algorithm and a
distance parameter of 0.4. They are required to have $\pt>30\GeV$,
$\abs{\eta}<2.5$, and be separated from both the lepton and photon by
$\DR>0.4$. The measured cross sections and their correlation matrix are shown
in Fig. 9, and the cross section values are listed in Table 0.9.2.

Figure 9: The measured jet multiplicity cross sections (left) and
corresponding correlation matrix (right). In the left figure, the black
vertical bars give the total uncertainty on each measurement. The predictions
are offset horizontally in each bin to improve visibility, and the
corresponding vertical bars show the missing higher-order correction
uncertainties.

Cross sections measured in bins of jet multiplicity and comparison with the
mg5_amc+py8, geneva, matrix, and predictions. The column marked (EW) is the
NNLO QCD prediction combined with NLO electroweak corrections, as described in
the text.  $\sigma(\text{fb})$ Number of jets Best fit Stat Syst mg5_amc+py8
matrix (EW) geneva $=0$ $1400_{-67}^{+71}$ ${}_{-11}^{+11}$ ${}_{-67}^{+70}$
$1650\pm 110$ $1473\pm 19$ $1544\pm 18$ $1471\pm 18$ $1584\pm 26$ $=1$
$1246_{-58}^{+61}$ ${}_{-11}^{+11}$ ${}_{-57}^{+60}$ $1590\pm 120$ $1304\pm
64$ $1397\pm 62$ $1376\pm 62$ $1490\pm 110$ $\geq 2$ $1037_{-79}^{+78}$
${}_{-10}^{+10}$ ${}_{-78}^{+77}$ $820\pm 120$ $950\pm 260$ $990\pm 270$
$950\pm 270$ $790\pm 140$

The measured 0-jet cross section is 5–15% smaller than the various
predictions. A similar trend is observed in the 1-jet measurement, whereas the
$\geq$2-jet measurement is higher than the predictions. Each measurement is
positively correlated with the other two, with the strongest correlation of
$+0.86$ between the 0-jet and 1-jet bins.

###  0.9.3 The radiation amplitude zero effect

In $\Pp\Pp$ collisions the RAZ effect is observed [4] in the rapidity
difference between the charged lepton and the photon; it manifests itself as a
suppression of the cross section near $\Delta\eta(\Pell,\PGg)=0$. This region
is of interest because new physics effects could make the minimum in this
region less pronounced. Several models with resonances that would exhibit this
feature are discussed in Ref. [81]. In the EFT framework this enhancement is
generated by $C_{3W}$, however, the $\Delta\eta(\Pell,\PGg)$ observable is
significantly less sensitive than the approach pursued in this paper, as
described in Section 0.2.

To observe the RAZ effect in data it is necessary to add further requirements
to the baseline selection. A veto on the presence of any jet with $\pt>30\GeV$
and $\abs{\eta}<2.5$ is applied to preferentially select events in the Born
configuration, and a requirement that
$m_{\mathrm{T}}^{\text{cluster}}>150\GeV$. Figure 10 shows the differential
cross section measurements, where a pronounced dip at
$\Delta\eta(\Pell,\PGg)=0$ is observed. The jet veto in this selection gives a
similar difference in normalization between measurement and predictions as for
the 0-jet cross section in the previous section. In addition, the dip at
$\Delta\eta(\Pell,\PGg)=0$ is larger than the predictions, though closer to
the NNLO calculations, and the enhancement in the high
$\abs{\Delta\eta(\Pell,\PGg)}$ region is similar to that observed under the
baseline selection in Fig. 8.

Figure 10: The measured $\Delta\eta(\Pell,\PGg)$ absolute (left) and
fractional (right) differential cross sections, compared with the mg5_amc+py8,
geneva, matrix, and predictions. The black vertical bars give the total
uncertainty on each measurement. The predictions are offset horizontally in
each bin to improve visibility, and the corresponding vertical bars show the
missing higher-order correction uncertainties.

###  0.9.4 EFT constraints

In this section, constraints on the $C_{3W}$ coefficient are derived via a
parameterization of the fiducial cross section in $\pt^{\PGg}$ and
$\abs{\phi_{f}}$, under the EFT selection described in Section 0.6. The bin
boundaries are [150, 200, 300, 500, 800, 1500] $\GeV$ and [0, $\pi/6$,
$\pi/3$, $\pi/2$], respectively. For the corresponding 2D differential cross
section measurement the two highest bins are merged, owing to the small number
of events expected in the highest bin. As for the one-dimensional (1D)
differential measurements the signal model is constructed following Eq. (13),
where $j$ runs over all $\pt^{\PGg}$ and $\abs{\phi_{f}}$ bins.

Constraints on $C_{3W}$ are determined by parameterizing the fiducial cross
section in each bin according to Eq. (3). Table 0.9.4 shows the corresponding
values of the $\sigma^{\text{int}}$ and $\sigma^{\text{BSM}}$ terms, relative
to $\sigma^{\text{SM}}$.

Coefficients of the fiducial cross section scaling terms in all
$\pt^{\PGg}{\times}\abs{\phi_{f}}$ bins. Values are given relative to the SM
prediction: $\mu^{\text{int}}=\sigma^{\text{int}}/\sigma^{\text{SM}}$ and
$\mu^{\text{BSM}}=\sigma^{\text{BSM}}/\sigma^{\text{SM}}$, with the total
relative normalization defined as
$1+C_{3W}\mu^{\text{int}}+C_{3W}^{2}\mu^{\text{BSM}}$.  $\pt^{\PGg}$ bin
($\GeVns$) $0\leq\abs{\phi_{f}}<\pi/6$ $\pi/6\leq\abs{\phi_{f}}<\pi/3$
$\pi/3\leq\abs{\phi_{f}}<\pi/2$ $\mu^{\text{int}}$ $\mu^{\text{BSM}}$
$\mu^{\text{int}}$ $\mu^{\text{BSM}}$ $\mu^{\text{int}}$ $\mu^{\text{BSM}}$
150–200 $-0.19$ 0.52 0.03 0.50 0.23 0.44 200–300 $-0.38$ 2.5 0.02 2.1 0.43 1.9
300–500 $-0.95$ 10.7 0.06 10.3 1.0 11.0 500–800 $-2.2$ 83.0 0.07 82.5 2.4 81.6
800–1500 $-4.9$ 688.5 0.02 651.7 4.9 646.2

Figure 11 shows the expected and observed distributions, summed over the
electron and muon channels, before the maximum likelihood fit is performed.
The change in the expected distribution when $C_{3W}$ is set to either $-0.2$
or $0.2\TeV^{-2}$ is also shown, considering only the SM and interference
terms in Eq. (3).

Figure 11: The expected and observed $\pt^{\PGg}$ distribution in each
$\abs{\phi_{f}}$ region before the maximum likelihood fit is performed,
combining the electron and muon channels. The horizontal and vertical bars
associated to the data points correspond to the bin widths and statistical
uncertainties, respectively. The shaded uncertainty band incorporates all
statistical and systematic uncertainties. The red and blue lines show how the
total expectation changes when $C_{3W}$ is set to $-0.2$ and $0.2\TeV^{-2}$,
respectively. Only the SM and interference terms are included in this example.

The 95% confidence level (CL) intervals on $C_{3W}$ are calculated using the
asymptotic properties of the profile likelihood ratio of Eq. (10). Figure 12
shows scans of this ratio as a function of $C_{3W}$. The intervals are
$[-0.062,0.052]\TeV^{-2}$ with the inclusion of the pure BSM term in Eq. (3)
and $[-0.38,0.17]\TeV^{-2}$ with only the SM and interference terms. The
observation is therefore compatible with the SM prediction of $C_{3W}=0$.
Constraints are also determined as a function of the maximum $\pt^{\PGg}$ bin
that is included in the fit, shown in Fig. 13 (left) and Table 0.9.4. Such a
presentation is useful for interpretations where the highest bins may be
beyond the validity of the EFT or specific BSM model being tested [82]. These
constraints are also determined with and without the inclusion of the pure BSM
term. This shows that with the current amount of data the pure BSM term
dominates for all values of the cutoff. However, the sensitivity to the
interference term is comparable at lower values of $\pt^{\PGg}$.

Figure 12: Scans of the profile likelihood test statistic $q$ as a function of
$C_{3W}$, given with and without the pure BSM term by the dashed red and solid
black lines, respectively. The full set of $\pt^{\PGg}$ and $\abs{\phi_{f}}$
bins, described in the text, are included for these scans.

Figure 13: Best-fit values of $C_{3W}$ and the corresponding 95% CL confidence
intervals as a function of the maximum $\pt^{\PGg}$ bin included in the fit
(left). Measurements with and without the pure BSM term are given by the black
and red lines, respectively. The limits without the pure BSM term given with
and without the binning in $\abs{\phi_{f}}$ are also shown (right), with black
and blue lines, respectively. Please note the different vertical scales; the
black lines in both figures correspond to the same limits.

Best fit values of $C_{3W}$ and corresponding 95% CL confidence intervals as a
function of the maximum $\pt^{\PGg}$ bin included in the fit.  $\pt^{\PGg}$
cutoff ($\GeVns$) Best fit $C_{3W}$ (${\TeVns}^{-2}$) Observed 95% CL
(${\TeVns}^{-2}$) Expected 95% CL (${\TeVns}^{-2}$) SM+int. only SM+int.+BSM
SM+int. only SM+int.+BSM SM+int. only SM+int.+BSM 200 $-0.86$ $-0.24$
$[-2.01,0.38]$ $[-0.76,0.40]$ $[-1.16,1.27]$ $[-0.81,0.71]$ 300 $-0.25$
$-0.17$ $[-0.81,0.34]$ $[-0.39,0.28]$ $[-0.56,0.60]$ $[-0.33,0.33]$ 500
$-0.13$ $-0.025$ $[-0.50,0.25]$ $[-0.15,0.12]$ $[-0.35,0.38]$ $[-0.17,0.16]$
800 $-0.20$ $-0.033$ $[-0.49,0.11]$ $[-0.10,0.08]$ $[-0.29,0.31]$
$[-0.097,0.095]$ 1500 $-0.13$ $-0.009$ $[-0.38,0.17]$ $[-0.062,0.052]$
$[-0.27,0.29]$ $[-0.066,0.065]$

The constraint including the pure BSM term is similar in value to that of Ref.
[6], after accounting for a difference in the normalization of $C_{3W}$ due to
the use of an alternative EFT basis. However, the main feature of this result
is the ability to constrain $C_{3W}$ when only the interference term is
considered. To demonstrate this, the constraints are also determined without
the binning in $\abs{\phi_{f}}$ applied. The same set of $\pt^{\PGg}$ bins are
used, but the three bins in $\abs{\phi_{f}}$ are replaced by a single bin
integrating over the full $\abs{\phi_{f}}$ range. The result is shown in Fig.
13 (right), without the inclusion of the pure BSM term. This demonstrates the
significant improvement, by up to a factor of ten, that the $\abs{\phi_{f}}$
binning gives to this constraint.

The interference-only confidence intervals on $C_{3W}$ can be compared with
measurements of other processes that are sensitive to the $\mathcal{O}_{3W}$
operator. An ATLAS measurement of $\PW\PW$ production [83], in which the
coefficient is denoted $c_{W}$, uses an alternative technique to improve
sensitivity to the interference. The presence of an additional high-jet is
required, which partially mitigates the helicity suppression effect [13], and
gives an interference-only sensitivity that is around a factor of eight lower.
The ATLAS measurement of electroweak boson production in association with two
jets [84] gives comparable sensitivity to our result. It exploits the
distribution of the azimuthal angle between the jets, which is sensitive to
the interference contribution.

The response matrix $R_{ij}$ for the 2D differential cross section measurement
of $\pt^{\PGg}$ and $\abs{\phi_{f}}$ is shown in Fig. 14. Although the
migration between $\pt^{\PGg}$ bins is small, the migration between
$\abs{\phi_{f}}$ bins is larger, owing both to the limited resolution and the
fundamental limitations of the method used to reconstruct the neutrino four-
momentum via the $m_{\PW}$ pole mass constraint.

Figure 14: Response matrix for the differential
$\pt^{\PGg}{\times}\abs{\phi_{f}}$ cross section measurement. The entry in
each bin gives the probability for an event of a given particle-level fiducial
bin to be reconstructed in one of the corresponding reconstruction-level bins.
The inner labels give the $\abs{\phi_{f}}$ bin and the outer labels indicate
the $\pt^{\PGg}$ bin.

The resulting cross section measurements are shown in Fig. 15. The measured
values are compared with the prediction from the NLO mg5_amc+py8 simulation.
The correlation matrix is presented in Fig. 16. Unlike the 1D $\pt^{\PGg}$
cross section, the correlations between different $\pt^{\PGg}$ bins are
relatively small, since these measurements at high $\pt^{\PGg}$ are much more
dominated by the statistical uncertainties. For a given $\pt^{\PGg}$ bin the
(anti-)correlation between $\abs{\phi_{f}}$ bins is larger, owing to the
migration in the response matrix discussed previously.

Figure 15: Measured double-differential $\pt^{\PGg}{\times}\abs{\phi_{f}}$
cross section and comparison to the mg5_amc+py8 NLO prediction. The black
vertical bars give the total uncertainty on each measurement. The red shaded
bands give the missing higher-order correction uncertainties in the
prediction. Figure 16: Correlation matrix for the measured
$\pt^{\PGg}{\times}\abs{\phi_{f}}$ cross sections.

##  0.10 Summary

This paper has presented an analysis of $\PWpm\PGg$ production in
$\sqrt{s}=13\TeV$ proton-proton collisions using data recorded with the CMS
detector at the LHC, corresponding to an integrated luminosity of 138.
Differential cross sections have been measured for several observables and
compared with standard model (SM) predictions computed at next-to-leading and
next-to-next-to-leading orders in perturbative quantum chromodynamics. The
radiation amplitude zero effect, caused by interference between $\PWpm\PGg$
production diagrams, has been studied via a measurement of the pseudorapidity
difference between the lepton and the photon.

Constraints on the presence of -scale new physics affecting the $\PW\PW\PGg$
vertex have been determined using an effective field theory framework.
Confidence intervals on the Wilson coefficient $C_{3W}$, determined at the 95%
confidence level, are $[-0.062,0.052]\TeV^{-2}$ with the inclusion of the
interference and pure beyond the SM contributions, and $[-0.38,0.17]\TeV^{-2}$
when only the interference is considered. A novel two-dimensional approach is
used with the simultaneous measurement of the photon transverse momentum and
the azimuthal angle of the charged lepton in a special reference frame. With
this method, the sensitivity to the interference between the SM and the
$\mathcal{O}_{3W}$ operator is enhanced by up to a factor of ten compared to a
measurement using the transverse momentum alone. This improves the validity of
the result, as the dependence on the missing higher-order contributions in the
EFT expansion is reduced. The technique will also be valuable in the future
when sufficiently small values of $C_{3W}$ are probed such that the
interference contribution will be dominant.

###### 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); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST,
STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 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.
14.W03.31.0026 and no. FSWW-2020-0008, and the Russian Foundation for Basic
Research, project No.19-42-703014 (Russia); the Programa Estatal de Fomento de
la Investigación Científica y Técnica de Excelencia María de Maeztu, grant
MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the
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).

## References

  * [1] CDF Collaboration, “Measurement of $\PW\gamma$ and $\PZ\gamma$ production in $\Pp\Pap$ collisions at $\sqrt{s}=1.96$ TeV”,  Phys. Rev. Lett. 94 (2005) 041803, 10.1103/PhysRevLett.94.041803, arXiv:hep-ex/0410008.
  * [2] D0 Collaboration, “Measurement of the $\Pp\Pap\to\PW\gamma$ \+ $X$ cross section at $\sqrt{s}$ = 1.96 TeV and $\PW\PW\gamma$ anomalous coupling limits”,  Phys. Rev. D 71 (2005) 091108, 10.1103/PhysRevD.71.091108, arXiv:hep-ex/0503048.
  * [3] D0 Collaboration, “First study of the radiation-amplitude zero in $\PW\gamma$ production and limits on anomalous $\PW\PW\gamma$ couplings at $\sqrt{s}$ = 1.96 TeV”,  Phys. Rev. Lett. 100 (2008) 241805, 10.1103/PhysRevLett.100.241805, arXiv:0803.0030.
  * [4] CMS Collaboration, “Measurement of the $\PW\gamma$ and $\PZ\gamma$ inclusive cross sections in $\Pp\Pp$ collisions at $\sqrt{s}=7$ TeV and limits on anomalous triple gauge boson couplings”,  Phys. Rev. D 89 (2014) 092005, 10.1103/PhysRevD.89.092005, arXiv:1308.6832.
  * [5] ATLAS Collaboration, “Measurements of $\PW\gamma$ and $\PZ\gamma$ production in $\Pp\Pp$ collisions at $\sqrt{s}=7\,\mathrm{TeV}$ with the ATLAS detector at the LHC”,  Phys. Rev. D 87 (2013) 112003, 10.1103/PhysRevD.87.112003, arXiv:1302.1283. [Erratum: 10.1103/PhysRevD.91.119901].
  * [6] CMS Collaboration, “Measurement of the W$\gamma$ production cross section in proton-proton collisions at $\sqrt{s}=$ 13 TeV and constraints on effective field theory coefficients”,  Phys. Rev. Lett. 126 (2021) 252002, 10.1103/PhysRevLett.126.252002, arXiv:2102.02283.
  * [7] K. O. Mikaelian, M. A. Samuel, and D. Sahdev, “Magnetic moment of weak bosons produced in $\Pp\Pp$ and $\Pp\Pap$ collisions”,  Phys. Rev. Lett. 43 (1979) 746, 10.1103/PhysRevLett.43.746.
  * [8] C. J. Goebel, F. Halzen, and J. P. Leveille, “Angular zeros of Brown, Mikaelian, Sahdev, and Samuel and the factorization of tree amplitudes in gauge theories”,  Phys. Rev. D 23 (1981) 2682, 10.1103/PhysRevD.23.2682.
  * [9] S. J. Brodsky and R. W. Brown, “Zeros in amplitudes: Gauge theory and radiation interference”,  Phys. Rev. Lett. 49 (1982) 966, 10.1103/PhysRevLett.49.966.
  * [10] R. W. Brown, K. L. Kowalski, and S. J. Brodsky, “Classical radiation zeros in gauge theory amplitudes”,  Phys. Rev. D 28 (1983) 624, 10.1103/PhysRevD.28.624. [Addendum: 10.1103/PhysRevD.29.2100].
  * [11] U. Baur, S. Errede, and G. L. Landsberg, “Rapidity correlations in $\PW\gamma$ production at hadron colliders”,  Phys. Rev. D 50 (1994) 1917, 10.1103/PhysRevD.50.1917, arXiv:hep-ph/9402282.
  * [12] G. Panico, F. Riva, and A. Wulzer, “Diboson interference resurrection”,  Phys. Lett. B 776 (2018) 473, 10.1016/j.physletb.2017.11.068, arXiv:1708.07823.
  * [13] A. Azatov, J. Elias-Miró, Y. Reyimuaji, and E. Venturini, “Novel measurements of anomalous triple gauge couplings for the LHC”,  JHEP 10 (2017) 027, 10.1007/JHEP10(2017)027, arXiv:1707.08060.
  * [14] S. Weinberg, “Baryon- and lepton-nonconserving processes”,  Phys. Rev. Lett. 43 (1979) 1566, 10.1103/PhysRevLett.43.1566.
  * [15] D. Liu, A. Pomarol, R. Rattazzi, and F. Riva, “Patterns of strong coupling for LHC searches”,  JHEP 11 (2016) 141, 10.1007/JHEP11(2016)141, arXiv:1603.03064.
  * [16] D. Marzocca et al., “BSM benchmarks for effective field theories in Higgs and electroweak physics”, 2020. arXiv:2009.01249.
  * [17] B. Grzadkowski, M. Iskrzyński, M. Misiak, and J. Rosiek, “Dimension-six terms in the standard model Lagrangian”,  JHEP 10 (2010) 085, 10.1007/JHEP10(2010)085, arXiv:1008.4884.
  * [18] A. Azatov, R. Contino, C. S. Machado, and F. Riva, “Helicity selection rules and noninterference for BSM amplitudes”,  Phys. Rev. D 95 (2017) 065014, 10.1103/PhysRevD.95.065014, arXiv:1607.05236.
  * [19] A. Azatov, D. Barducci, and E. Venturini, “Precision diboson measurements at hadron colliders”,  JHEP 04 (2019) 075, 10.1007/JHEP04(2019)075, arXiv:1901.04821.
  * [20] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [21] CMS Collaboration, “Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at $\sqrt{s}=8$”,  JINST 10 (2015) P08010, 10.1088/1748-0221/10/08/P08010, arXiv:1502.02702.
  * [22] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [23] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [24] CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [25] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [26] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [27] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [28] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [29] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [30] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [31] S. Frixione, P. Nason, and G. Ridolfi, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,  JHEP 09 (2007) 126, 10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  * [32] R. Frederix, E. Re, and P. Torrielli, “Single-top t-channel hadroproduction in the four-flavour scheme with POWHEG and aMC@NLO”,  JHEP 09 (2012) 130, 10.1007/JHEP09(2012)130, arXiv:1207.5391.
  * [33] E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [34] O. Mattelaer, “On the maximal use of Monte Carlo samples: re-weighting events at NLO accuracy”,  Eur. Phys. J. C 76 (2016) 674, 10.1140/epjc/s10052-016-4533-7, arXiv:1607.00763.
  * [35] I. Brivio, Y. Jiang, and M. Trott, “The SMEFTsim package, theory and tools”,  JHEP 12 (2017) 070, 10.1007/JHEP12(2017)070, arXiv:1709.06492.
  * [36] I. Brivio, “SMEFTsim 3.0 — a practical guide”,  JHEP 04 (2021) 073, 10.1007/JHEP04(2021)073, arXiv:2012.11343.
  * [37] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [38] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [39] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [40] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [41] CMS Collaboration, “Investigations of the impact of the parton shower tuning in Pythia 8 in the modelling of $\mathrm{t\overline{t}}$ at $\sqrt{s}=8$ and 13 TeV”, CMS Physics Analysis Summary CMS-PAS-TOP-16-021, 2016.
  * [42] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [43] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [44] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [45] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [46] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [47] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [48] CMS Collaboration, “Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC”,  JINST 16 (2021) P05014, 10.1088/1748-0221/16/05/p05014, arXiv:2012.06888.
  * [49] CMS Collaboration, “Measurements of inclusive $\PW$ and $\PZ$ cross sections in $\Pp\Pp$ collisions at $\sqrt{s}=7$ TeV”,  JHEP 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.
  * [50] A. Bodek et al., “Extracting muon momentum scale corrections for hadron collider experiments”,  Eur. Phys. J. C 72 (2012) 2194, 10.1140/epjc/s10052-012-2194-8, arXiv:1208.3710.
  * [51] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=13$ TeV using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [52] D. Bertolini, P. Harris, M. Low, and N. Tran, “Pileup per particle identification”,  JHEP 10 (2014) 059, 10.1007/JHEP10(2014)059, arXiv:1407.6013.
  * [53] CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at $\sqrt{s}=$ 13 TeV in 2015 and 2016 at CMS”,  Eur. Phys. J. C 81 (2021) 800, 10.1140/epjc/s10052-021-09538-2, arXiv:2104.01927.
  * [54] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018.
  * [55] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019.
  * [56] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$ TeV”,  JHEP 07 (2018) 161, 10.1007/JHEP07(2018)161, arXiv:1802.02613.
  * [57] R. J. Barlow and C. Beeston, “Fitting using finite Monte Carlo samples”,  Comput. Phys. Commun. 77 (1993) 219, 10.1016/0010-4655(93)90005-W.
  * [58] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”,  J. Phys. G 43 (2016) 023001, 10.1088/0954-3899/43/2/023001, arXiv:1510.03865.
  * [59] I. W. Stewart and F. J. Tackmann, “Theory uncertainties for Higgs mass and other searches using jet bins”,  Phys. Rev. D 85 (2012) 034011, 10.1103/PhysRevD.85.034011, arXiv:1107.2117.
  * [60] The ATLAS Collaboration, The CMS Collaboration, The LHC Higgs Combination Group, “Procedure for the LHC Higgs boson search combination in Summer 2011”, Technical Report CMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011.
  * [61] ATLAS and CMS Collaborations, “Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC $\Pp\Pp$ collision data at $\sqrt{s}$ = 7 and 8 TeV”,  JHEP 08 (2016) 45, 10.1007/JHEP08(2016)045, arXiv:1606.02266.
  * [62] W. Verkerke and D. P. Kirkby, “The RooFit toolkit for data modeling”, in  Proceedings of the 13th International Conference for Computing in High-Energy and Nuclear Physics (CHEP03).  2003\.  arXiv:physics/0306116.  [eConf C0303241, MOLT007].
  * [63] L. Moneta et al., “The RooStats project”, in  13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2010).  2010\.  arXiv:1009.1003.  [PoS(ACAT2010)057]. 10.22323/1.093.0057.
  * [64] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [65] HEPData record for this analysis, 2021.  10.17182/hepdata.115354.
  * [66] S. Frixione, “Isolated photons in perturbative QCD”,  Phys. Lett. B 429 (1998) 369, 10.1016/S0370-2693(98)00454-7, arXiv:hep-ph/9801442.
  * [67] M. Grazzini, S. Kallweit, and M. Wiesemann, “Fully differential NNLO computations with MATRIX”,  Eur. Phys. J. C 78 (2018) 537, 10.1140/epjc/s10052-018-5771-7, arXiv:1711.06631.
  * [68] M. Grazzini, S. Kallweit, and D. Rathlev, “$\PW\gamma$ and $\PZ\gamma$ production at the LHC in NNLO QCD”,  JHEP 07 (2015) 085, 10.1007/JHEP07(2015)085, arXiv:1504.01330.
  * [69] J. M. Campbell, G. De Laurentis, R. K. Ellis, and S. Seth, “The $\Pp\Pp\to\PW(\to\mathrm{l}\nu)+\gamma$ process at next-to-next-to-leading order”,  JHEP 07 (2021) 079, 10.1007/JHEP07(2021)079, arXiv:2105.00954.
  * [70] J. M. Campbell, R. K. Ellis, and C. Williams, “Vector boson pair production at the LHC”,  JHEP 07 (2011) 018, 10.1007/JHEP07(2011)018, arXiv:1105.0020.
  * [71] T. Cridge, M. A. Lim, and R. Nagar, “$\PW\gamma$ production at NNLO+PS accuracy in GENEVA”,  Phys. Lett. B 826 (2022) 136918, 10.1016/j.physletb.2022.136918, arXiv:2105.13214.
  * [72] S. Alioli et al., “Combining higher-order resummation with multiple NLO calculations and parton showers in GENEVA”,  JHEP 09 (2013) 120, 10.1007/JHEP09(2013)120, arXiv:1211.7049.
  * [73] S. Alioli et al., “Drell-Yan production at NNLL’+NNLO matched to parton showers”,  Phys. Rev. D 92 (2015) 094020, 10.1103/PhysRevD.92.094020, arXiv:1508.01475.
  * [74] S. Alioli et al., “Matching NNLO predictions to parton showers using N3LL color-singlet transverse momentum resummation in GENEVA”,  Phys. Rev. D 104 (2021) 094020, 10.1103/PhysRevD.104.094020, arXiv:2102.08390.
  * [75] S. Catani et al., “Vector boson production at hadron colliders: hard-collinear coefficients at the NNLO”,  Eur. Phys. J. C 72 (2012) 2195, 10.1140/epjc/s10052-012-2195-7, arXiv:1209.0158.
  * [76] S. Catani and M. Grazzini, “Next-to-next-to-leading-order subtraction formalism in hadron collisions and its application to Higgs-boson production at the Large Hadron Collider”,  Phys. Rev. Lett. 98 (2007) 222002, 10.1103/PhysRevLett.98.222002, arXiv:hep-ph/0703012.
  * [77] F. Cascioli, P. Maierhöfer, and S. Pozzorini, “Scattering amplitudes with Open Loops”,  Phys. Rev. Lett. 108 (2012) 111601, 10.1103/PhysRevLett.108.111601, arXiv:1111.5206.
  * [78] A. Denner, S. Dittmaier, and L. Hofer, “Collier: a fortran-based complex one-loop library in extended regularizations”,  Comput. Phys. Commun. 212 (2017) 220, 10.1016/j.cpc.2016.10.013, arXiv:1604.06792.
  * [79] T. Gehrmann and L. Tancredi, “Two-loop QCD helicity amplitudes for $\Pq\Paq\to\PWpm\gamma$ and $\Pq\Paq\to\PZz\gamma$”,  JHEP 02 (2012) 004, 10.1007/JHEP02(2012)004, arXiv:1112.1531.
  * [80] A. Denner, S. Dittmaier, M. Hecht, and C. Pasold, “NLO QCD and electroweak corrections to $\mathrm{W}$+$\gamma$ production with leptonic $\mathrm{W}$-boson decays”,  JHEP 04 (2015) 018, 10.1007/JHEP04(2015)018, arXiv:1412.7421.
  * [81] R. M. Capdevilla, R. Harnik, and A. Martin, “The radiation valley and exotic resonances in $\PW\gamma$ production at the LHC”,  JHEP 03 (2020) 117, 10.1007/JHEP03(2020)117, arXiv:1912.08234.
  * [82] R. Contino et al., “On the validity of the effective field theory approach to SM precision tests”,  JHEP 07 (2016) 144, 10.1007/JHEP07(2016)144, arXiv:1604.06444.
  * [83] ATLAS Collaboration, “Measurements of $\PW^{+}\PW^{-}+\geq 1~{}$jet production cross-sections in $\Pp\Pp$ collisions at $\sqrt{s}=13~{}$TeV with the ATLAS detector”,  JHEP 06 (2021) 003, 10.1007/JHEP06(2021)003, arXiv:2103.10319.
  * [84] ATLAS Collaboration, “Differential cross-section measurements for the electroweak production of dijets in association with a $\PZ$ boson in proton–proton collisions at ATLAS”,  Eur. Phys. J. C 81 (2021) 163, 10.1140/epjc/s10052-020-08734-w, arXiv:2006.15458.

##  .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,
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, M.
Spanring0000-0001-6328-7887, 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 F. Blekman0000-0002-7366-7098, E.S. Bols0000-0002-8564-8732,
J. D’Hondt0000-0002-9598-6241, J. De Clercq0000-0001-6770-3040, 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, P. Van Mulders 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. Grebenyuk, A.K. Kalsi0000-0002-6215-0894, K. Lee, M. Mahdavikhorrami, I.
Makarenko0000-0002-8553-4508, L. Moureaux0000-0002-2310-9266, 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, D.
Vannerom0000-0002-2747-5095, L. Wezenbeek 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. Roskas, A.
Samalan, K. Skovpen0000-0002-1160-0621, M. Tytgat0000-0002-3990-2074, W.
Verbeke, B. Vermassen, M. Vit Université Catholique de Louvain, Louvain-la-
Neuve, Belgium 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 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, S. Fonseca De Souza0000-0001-7830-0837, D. Matos
Figueiredo, C. Mora Herrera0000-0003-3915-3170, K. Mota Amarilo, L.
Mundim0000-0001-9964-7805, H. Nogima, P. Rebello Teles0000-0001-9029-8506, A.
Santoro, S.M. Silva Do Amaral0000-0002-0209-9687, A.
Sznajder0000-0001-6998-1108, M. Thiel, F. Torres Da Silva De
Araujo0000-0002-4785-3057, 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, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan
Department of Physics, Tsinghua University, Beijing, China M.
Ahmad0000-0001-9933-995X, G. Bauer, C. Dozen70000-0002-4301-634X, Z.
Hu0000-0001-8209-4343, J. Martins80000-0002-2120-2782, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China E. Chapon0000-0001-6968-9828,
G.M. Chen60000-0002-2629-5420, H.S. Chen60000-0001-8672-8227, M.
Chen0000-0003-0489-9669, F. Iemmi, A. Kapoor0000-0002-1844-1504, D. Leggat, H.
Liao, Z.-A. Liu60000-0002-2896-1386, 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, S. Zhang6, J. Zhao0000-0001-8365-7726 State Key
Laboratory of Nuclear Physics and Technology, Peking University, Beijing,
China A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin0000-0001-9565-4186, Q.
Li0000-0002-8290-0517, X. Lyu, Y. Mao, S.J. Qian, D. Wang0000-0002-9013-1199,
Q. Wang0000-0003-1014-8677, J. Xiao 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 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, A. Sarkar0000-0001-7540-7540, M.A. Segura
Delgado Universidad de Antioquia, Medellin, Colombia J. Mejia Guisao, F.
Ramirez, J.D. Ruiz Alvarez0000-0002-3306-0363, C.A. Salazar
González0000-0002-0394-4870 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.
Starodumov110000-0001-9570-9255, T. Susa0000-0001-7430-2552 University of
Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K. Christoforou, E.
Erodotou, 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.
Finger12, M. Finger Jr.120000-0003-3155-2484, A. Kveton Escuela Politecnica
Nacional, Quito, Ecuador E. Ayala Universidad San Francisco de Quito, Quito,
Ecuador E. Carrera Jarrin0000-0002-0857-8507 Academy of Scientific Research
and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy
Physics, Cairo, Egypt A.A. Abdelalim13,140000-0002-2056-7894, S. Abu
Zeid150000-0002-0820-0483 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, L. Forthomme0000-0002-3302-336X, 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, A.
Givernaud, 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-
Navarro16, 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, M.
Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, P.
Paganini0000-0001-9580-683X, J. Rembser, R. Salerno0000-0003-3735-2707, J.B.
Sauvan0000-0001-5187-3571, Y. Sirois0000-0001-5381-4807, A. Zabi, A.
Zghiche0000-0002-1178-1450 Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France J.-L. Agram170000-0001-7476-0158, 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. Fontaine17, 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 D.
Lomidze0000-0003-3936-6942, I. Lomidze, Z. Tsamalaidze12 RWTH Aachen
University, I. Physikalisches Institut, Aachen, Germany L.
Feld0000-0001-9813-8646, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P.
Rauch, 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, S. Ghosh0000-0001-6717-0803, T. Hebbeker0000-0002-9736-266X, K.
Hoepfner, F. Ivone, H. Keller, 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,
T. Pook0000-0002-9635-5126, A. Pozdnyakov0000-0003-3478-9081, Y. Rath, H.
Reithler, J. Roemer, 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 Ahmad180000-0003-1491-0446, O. Hlushchenko, T. Kress, A.
Nowack0000-0002-3522-5926, C. Pistone, O. Pooth, D. Roy0000-0002-8659-7762, H.
Sert0000-0003-0716-6727, A. Stahl190000-0002-8369-7506, T.
Ziemons0000-0003-1697-2130 Deutsches Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I.
Babounikau0000-0002-6228-4104, S. Baxter, O. Behnke, A. Bermúdez Martínez, S.
Bhattacharya, A.A. Bin Anuar0000-0002-2988-9830, K. Borras20, V. Botta, 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, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez
Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E. Gallo21, A.
Geiser, A. Giraldi, A. Grohsjean0000-0003-0748-8494, M. Guthoff, A.
Jafari220000-0001-7327-1870, N.Z. Jomhari0000-0001-9127-7408, H.
Jung0000-0002-2964-9845, A. Kasem200000-0002-6753-7254, M.
Kasemann0000-0002-0429-2448, H. Kaveh0000-0002-3273-5859, C.
Kleinwort0000-0002-9017-9504, D. Krücker0000-0003-1610-8844, W. Lange, J.
Lidrych0000-0003-1439-0196, K. Lipka, W. Lohmann23, R. Mankel, I.-A. Melzer-
Pellmann0000-0001-7707-919X, J. Metwally, A.B. Meyer0000-0001-8532-2356, M.
Meyer0000-0003-2436-8195, J. Mnich0000-0001-7242-8426, A. Mussgiller, 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. Scham, V.
Scheurer, C. Schwanenberger210000-0001-6699-6662, A. Singh, R.E. Sosa
Ricardo0000-0002-2240-6699, D. Stafford, N. Tonon0000-0003-4301-2688, O.
Turkot0000-0001-5352-7744, M. Van De Klundert0000-0001-8596-2812, R.
Walsh0000-0002-3872-4114, D. Walter, 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, A. Benecke, 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, J. Haller0000-0001-9347-7657, A. Hinzmann0000-0002-2633-4696, G.
Kasieczka, R. Klanner0000-0002-7004-9227, R. Kogler0000-0002-5336-4399, T.
Kramer, V. Kutzner, J. Lange0000-0001-7513-6330, T. Lange0000-0001-6242-7331,
A. Lobanov0000-0002-5376-0877, A. Malara0000-0001-8645-9282, A. Nigamova, K.J.
Pena Rodriguez, O. Rieger, P. Schleper, M. Schröder0000-0001-8058-9828, J.
Schwandt0000-0002-0052-597X, D. Schwarz, J. Sonneveld0000-0001-8362-4414, H.
Stadie, G. Steinbrück, A. Tews, B. Vormwald0000-0003-2607-7287, I.
Zoi0000-0002-5738-9446 Karlsruher Institut fuer Technologie, Karlsruhe,
Germany J. Bechtel0000-0001-5245-7318, T. Berger, 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.
Hartmann190000-0001-8989-8387, C. Heidecker, U. Husemann0000-0002-6198-8388,
I. Katkov24, P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S.
Mitra0000-0002-3060-2278, Th. Müller, M. Neukum, A. Nürnberg, 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, T.
Geralis0000-0001-7188-979X, A. Kyriakis, D. Loukas, A.
Stakia0000-0001-6277-7171 National and Kapodistrian University of Athens,
Athens, Greece M. Diamantopoulou, D. Karasavvas, G. Karathanasis, 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 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. Gadallah250000-0002-8305-6661, S.
Lökös260000-0002-4447-4836, P. Major, K. Mandal0000-0002-3966-7182, A.
Mehta0000-0002-0433-4484, G. Pasztor0000-0003-0707-9762, A.J. Rádl, O.
Surányi, G.I. Veres0000-0002-5440-4356 Wigner Research Centre for Physics,
Budapest, Hungary M. Bartók270000-0002-4440-2701, G. Bencze, C.
Hajdu0000-0002-7193-800X, D. Horvath280000-0003-0091-477X, F.
Sikler0000-0001-9608-3901, V. Veszpremi0000-0001-9783-0315, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary S. Czellar, J. Karancsi270000-0003-0802-7665, J. Molnar, Z.
Szillasi, D. Teyssier Institute of Physics, University of Debrecen, Debrecen,
Hungary P. Raics, Z.L. Trocsanyi290000-0002-2129-1279, B. Ujvari Karoly Robert
Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo300000-0002-9110-9663, F. Nemes30, T. Novak Indian Institute of Science
(IISc), Bangalore, India J.R. Komaragiri0000-0002-9344-6655, D. Kumar, L.
Panwar0000-0003-2461-4907, P.C. Tiwari0000-0002-3667-3843 National Institute
of Science Education and Research, HBNI, Bhubaneswar, India S.
Bahinipati310000-0002-3744-5332, C. Kar0000-0002-6407-6974, P. Mal, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair Bindhu32, A.
Nayak320000-0002-7716-4981, P. Saha, N. Sur0000-0001-5233-553X, S.K. Swain, D.
Vats32 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. Dhingra330000-0002-7200-6204, R. Gupta, A.
Kaur, M. Kaur0000-0002-3440-2767, S. Kaur, 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. Bharti34, R. Bhattacharya, S. Bhattacharya0000-0002-8110-4957, D.
Bhowmik, S. Dutta, S. Dutta, B. Gomber350000-0002-4446-0258, M. Maity36, P.
Palit0000-0002-1948-029X, P.K. Rout0000-0001-8149-6180, G. Saha, B.
Sahu0000-0002-8073-5140, S. Sarkar, M. Sharan, B. Singh34, S. Thakur34 Indian
Institute of Technology Madras, Madras, India P.K. Behera0000-0002-1527-2266,
S.C. Behera, P. Kalbhor0000-0002-5892-3743, A. Muhammad, R. Pradhan, P.R.
Pujahari, A. Sharma0000-0002-0688-923X, A.K. Sikdar Bhabha Atomic Research
Centre, Mumbai, India D. Dutta0000-0002-0046-9568, V. Jha, V.
Kumar0000-0001-8694-8326, D.K. Mishra, K. Naskar37, P.K. Netrakanti, L.M.
Pant, P. Shukla0000-0001-8118-5331 Tata Institute of Fundamental Research-A,
Mumbai, India T. Aziz, S. Dugad, M. Kumar, U. Sarkar0000-0002-9892-4601 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 K. Alpana, S.
Dube0000-0002-5145-3777, B. Kansal, A. Laha, S. Pandey0000-0003-0440-6019, A.
Rane0000-0001-8444-2807, A. Rastogi0000-0003-1245-6710, S.
Sharma0000-0001-6886-0726 Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi380000-0001-5741-3357, M. Zeinali39 Institute for Research in
Fundamental Sciences (IPM), Tehran, Iran S. Chenarani40, 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,410000-0001-6808-1335, 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, 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, J.A. Merlina, 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, A.
Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, 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, 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,42, 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,430000-0001-7901-4189,
S. Costaa,b,430000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,430000-0002-5071-5501, 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,b, 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,
F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,19, 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. 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, S. Ragazzia,b0000-0001-8219-2074, N.
Redaellia0000-0002-0098-2716, T. Tabarelli de Fatisa,b0000-0001-6262-4685, D.
Valsecchia,b,19, 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,b0000-0001-6471-5492, S. Meolaa,d,190000-0002-8233-7277, P.
Paoluccia,190000-0002-8773-4781, 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, S.Y. Hoha,b0000-0003-3233-5123, L. Layera,44,
M. Margonia,b0000-0003-1797-4330, A.T. Meneguzzoa,b0000-0002-5861-8140, J.
Pazzinia,b0000-0002-1118-6205, M. Presillaa,b0000-0003-2808-7315, 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. Aime‘a,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,450000-0003-2379-9903, G.M.
Bileia0000-0002-4159-9123, D. Ciangottinia,b0000-0002-0843-4108, L.
Fanòa,b0000-0002-9007-629X, P. Laricciaa,b, M. Magherinib, G. Mantovania,b, V.
Mariania,b, M. Menichellia0000-0002-9004-735X, F.
Moscatellia,450000-0002-7676-3106, A. Piccinellia,b0000-0003-0386-0527, 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, A.
Messineoa,b0000-0001-7551-5613, 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 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, S.
Comettia0000-0001-6621-7606, M. Costaa,b0000-0003-0156-0790, R.
Covarellia,b0000-0003-1216-5235, N. Demariaa0000-0003-0743-9465, B.
Kiania,b0000-0001-6431-5464, F. Leggera0000-0003-1400-0709, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, E.
Migliorea,b0000-0002-2271-5192, E. Monteila,b0000-0002-2350-213X, M.
Montenoa0000-0002-3521-6333, M.M. Obertinoa,b0000-0002-8781-8192, G.
Ortonaa0000-0001-8411-2971, L. Pachera,b0000-0003-1288-4838, N.
Pastronea0000-0001-7291-1979, M. Pelliccionia0000-0003-4728-6678, G.L. Pinna
Angionia,b, M. Ruspaa,c0000-0002-7655-3475, K.
Shchelinaa,b0000-0003-3742-0693, F. Sivieroa,b0000-0002-4427-4076, V.
Solaa0000-0001-6288-951X, A. Solanoa,b0000-0002-2971-8214, D.
Soldia,b0000-0001-9059-4831, A. Staianoa0000-0003-1803-624X, M. Tornagoa,b, D.
Trocinoa,b0000-0002-2830-5872, A. Vagnerini INFN Sezione di Trieste a,
Trieste, Italy, Università di Trieste b, Trieste, Italy S.
Belfortea0000-0001-8443-4460, V. Candelisea,b0000-0002-3641-5983, M.
Casarsaa0000-0002-1353-8964, F. Cossuttia0000-0001-5672-214X, A. Da
Rolda,b0000-0003-0342-7977, G. Della Riccaa,b0000-0003-2831-6982, G.
Sorrentinoa,b, F. Vazzolera,b0000-0001-8111-9318 Kyungpook National
University, Daegu, Korea S. Dogra0000-0002-0812-0758, C.
Huh0000-0002-8513-2824, B. Kim, D.H. Kim0000-0002-9023-6847, G.N.
Kim0000-0002-3482-9082, J. Kim, J. Lee, S.W. Lee0000-0002-1028-3468, C.S.
Moon0000-0001-8229-7829, Y.D. Oh0000-0002-7219-9931, S.I. Pak, B.C. Radburn-
Smith, S. Sekmen0000-0003-1726-5681, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187 Hanyang University,
Seoul, Korea B. Francois0000-0002-2190-9059, T.J. Kim0000-0001-8336-2434, J.
Park0000-0002-4683-6669 Korea University, Seoul, Korea S. Cho, S.
Choi0000-0001-6225-9876, Y. Go, B. Hong0000-0002-2259-9929, K. Lee, K.S.
Lee0000-0002-3680-7039, J. Lim, J. Park, S.K. Park, J. Yoo Kyung Hee
University, Department of Physics, Seoul, Republic of Korea, Seoul, Korea J.
Goh0000-0002-1129-2083, A. Gurtu Sejong University, Seoul, Korea H.S.
Kim0000-0002-6543-9191, Y. Kim Seoul National University, Seoul, Korea J.
Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee0000-0002-1138-3700, S. Lee, B.H. Oh, M. Oh0000-0003-2618-9203, S.B. Oh, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026 University of
Seoul, Seoul, Korea W. Jang, D. Jeon, D.Y. Kang, Y. Kang, J.H. Kim, S. Kim, B.
Ko, J.S.H. Lee0000-0002-2153-1519, Y. Lee, I.C. Park, Y. Roh, M.S. Ryu, D.
Song, I.J. Watson0000-0003-2141-3413, S. Yang Yonsei University, Department of
Physics, Seoul, Korea S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea M.
Choi, Y. Jeong, H. Lee, Y. Lee, I. Yu0000-0003-1567-5548 College of
Engineering and Technology, American University of the Middle East (AUM),
Egaila, Kuwait, Dasman, Kuwait T. Beyrouthy, Y. Maghrbi Riga Technical
University, Riga, Latvia T. Torims, V. Veckalns460000-0003-3676-9711 Vilnius
University, Vilnius, Lithuania M. Ambrozas, A. Carvalho Antunes De
Oliveira0000-0003-2340-836X, A. Juodagalvis0000-0002-1501-3328, A.
Rinkevicius0000-0002-7510-255X, G. Tamulaitis0000-0002-2913-9634 National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia N. Bin
Norjoharuddeen0000-0002-8818-7476, W.A.T. Wan Abdullah, M.N. Yusli, Z.
Zolkapli Universidad de Sonora (UNISON), Hermosillo, Mexico J.F.
Benitez0000-0002-2633-6712, A. Castaneda Hernandez0000-0003-4766-1546, M. León
Coello, J.A. Murillo Quijada0000-0003-4933-2092, A. Sehrawat, L. Valencia
Palomo0000-0002-8736-440X Centro de Investigacion y de Estudios Avanzados del
IPN, Mexico City, Mexico G. Ayala, H. Castilla-Valdez, E. De La Cruz-
Burelo0000-0002-7469-6974, I. Heredia-De La Cruz470000-0002-8133-6467, R.
Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sánchez
Hernández0000-0001-9548-0358 Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera0000-0001-9724-0016, M. Ramírez
García0000-0002-4564-3822, F. Vazquez Valencia Benemerita Universidad Autonoma
de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
University of Montenegro, Podgorica, Montenegro J. Mijuskovic48, N. Raicevic
University of Auckland, Auckland, New Zealand D. Krofcheck0000-0001-5494-7302
University of Canterbury, Christchurch, New Zealand S. Bheesette, P.H.
Butler0000-0001-9878-2140 National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan,
H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib0000-0001-6791-8252, M.
Waqas0000-0002-3846-9483 AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland V. Avati,
L. Grzanka, M. Malawski National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj0000-0003-1229-1442, B. Boimska0000-0002-4200-1541, M.
Górski, M. Kazana, M. Szleper0000-0002-1697-004X, P. Zalewski Institute of
Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski0000-0002-1280-5493, M.
Konecki0000-0001-9482-4841, J. Krolikowski0000-0002-3055-0236, M.
Walczak0000-0002-2664-3317 Laboratório de Instrumentação e Física Experimental
de Partículas, Lisboa, Portugal M. Araujo, P. Bargassa0000-0001-8612-3332, D.
Bastos, A. Boletti0000-0003-3288-7737, P. Faccioli0000-0003-1849-6692, M.
Gallinaro0000-0003-1261-2277, J. Hollar0000-0002-8664-0134, N.
Leonardo0000-0002-9746-4594, T. Niknejad, M. Pisano, J.
Seixas0000-0002-7531-0842, O. Toldaiev0000-0002-8286-8780, J.
Varela0000-0003-2613-3146 Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov0000-0003-3777-6606, V.
Karjavine, V. Korenkov0000-0002-2342-7862, A. Lanev, A. Malakhov, V.
Matveev49,50, V. Palichik, V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S.
Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B.S. Yuldashev51, A.
Zarubin, I. Zhizhin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia G. Gavrilov0000-0003-3968-0253, V. Golovtcov, Y. Ivanov,
V. Kim520000-0001-7161-2133, E. Kuznetsova53, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov0000-0002-7452-8380, V. Sulimov, L. Uvarov, S. Volkov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia Yu.
Andreev0000-0002-7397-9665, A. Dermenev, S. Gninenko0000-0001-6495-7619, N.
Golubev, A. Karneyeu0000-0001-9983-1004, D. Kirpichnikov0000-0002-7177-077X,
M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov0000-0001-6435-4463, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko54, V.
Popov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov0000-0002-8628-2090, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia T. Aushev
National Research Nuclear University ’Moscow Engineering Physics Institute’
(MEPhI), Moscow, Russia O. Bychkova, M. Chadeeva550000-0003-1814-1218, P.
Parygin, E. Popova, E. Zhemchugov560000-0002-0914-9739 P.N. Lebedev Physical
Institute, Moscow, Russia V. Andreev, M. Azarkin, I.
Dremin0000-0001-7451-247X, M. Kirakosyan, A. Terkulov Skobeltsyn Institute of
Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia A. Belyaev,
E. Boos0000-0002-0193-5073, V. Bunichev, M. Dubinin570000-0002-7766-7175, L.
Dudko0000-0002-4462-3192, A. Ershov, V. Klyukhin0000-0002-8577-6531, O.
Kodolova0000-0003-1342-4251, I. Lokhtin0000-0002-4457-8678, S. Obraztsov, M.
Perfilov, V. Savrin, A. Snigirev0000-0003-2952-6156 Novosibirsk State
University (NSU), Novosibirsk, Russia V. Blinov58, T. Dimova58, L.
Kardapoltsev58, A. Kozyrev58, I. Ovtin58, Y. Skovpen580000-0002-3316-0604
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia I. Azhgirey0000-0003-0528-341X, I. Bayshev, D.
Elumakhov, V. Kachanov, D. Konstantinov0000-0001-6673-7273, P.
Mandrik0000-0001-5197-046X, V. Petrov, R. Ryutin, S.
Slabospitskii0000-0001-8178-2494, A. Sobol, S. Troshin0000-0001-5493-1773, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia A. Babaev, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borshch, V. Ivanchenko0000-0002-1844-5433, E. Tcherniaev0000-0002-3685-0635
University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear
Sciences, Belgrade, Serbia P. Adzic590000-0002-5862-7397, M.
Dordevic0000-0002-8407-3236, P. Milenovic0000-0001-7132-3550, J.
Milosevic0000-0001-8486-4604 Centro de Investigaciones Energéticas
Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain M. Aguilar-Benitez, J.
Alcaraz Maestre0000-0003-0914-7474, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya0000-0001-8057-9152, C.A. Carrillo
Montoya0000-0002-6245-6535, M. Cepeda0000-0002-6076-4083, M. Cerrada, N.
Colino0000-0002-3656-0259, B. De La Cruz, A. Delgado Peris0000-0002-8511-7958,
J.P. Fernández Ramos0000-0002-0122-313X, J. Flix0000-0003-2688-8047, M.C.
Fouz0000-0003-2950-976X, O. Gonzalez Lopez0000-0002-4532-6464, S. Goy
Lopez0000-0001-6508-5090, J.M. Hernandez0000-0001-6436-7547, M.I.
Josa0000-0002-4985-6964, J. León Holgado0000-0002-4156-6460, D. Moran, Á.
Navarro Tobar0000-0003-3606-1780, A. Pérez-Calero
Yzquierdo0000-0003-3036-7965, J. Puerta Pelayo0000-0001-7390-1457, I.
Redondo0000-0003-3737-4121, L. Romero, S. Sánchez Navas, L. Urda
Gómez0000-0002-7865-5010, C. Willmott Universidad Autónoma de Madrid, Madrid,
Spain J.F. de Trocóniz, R. Reyes-Almanza0000-0002-4600-7772 Universidad de
Oviedo, Instituto Universitario de Ciencias y Tecnologías Espaciales de
Asturias (ICTEA), Oviedo, Spain B. Alvarez Gonzalez0000-0001-7767-4810, J.
Cuevas0000-0001-5080-0821, C. Erice0000-0002-6469-3200, J. Fernandez
Menendez0000-0002-5213-3708, S. Folgueras0000-0001-7191-1125, I. Gonzalez
Caballero0000-0002-8087-3199, J.R. González Fernández, E. Palencia
Cortezon0000-0001-8264-0287, C. Ramón Álvarez, J. Ripoll Sau, V. Rodríguez
Bouza0000-0002-7225-7310, A. Trapote, N. Trevisani0000-0002-5223-9342
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria,
Santander, Spain J.A. Brochero Cifuentes0000-0003-2093-7856, I.J. Cabrillo, A.
Calderon0000-0002-7205-2040, J. Duarte Campderros0000-0003-0687-5214, M.
Fernandez0000-0002-4824-1087, C. Fernandez Madrazo0000-0001-9748-4336, P.J.
Fernández Manteca0000-0003-2566-7496, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol0000-0002-7737-5121, F.
Matorras0000-0003-4295-5668, P. Matorras Cuevas0000-0001-7481-7273, J. Piedra
Gomez0000-0002-9157-1700, C. Prieels, T. Rodrigo0000-0002-4795-195X, A. Ruiz-
Jimeno0000-0002-3639-0368, L. Scodellaro0000-0002-4974-8330, I. Vila, J.M.
Vizan Garcia0000-0002-6823-8854 University of Colombo, Colombo, Sri Lanka M.K.
Jayananda, B. Kailasapathy60, D.U.J. Sonnadara, D.D.C. Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka W.G.D.
Dharmaratna0000-0002-6366-837X, K. Liyanage, N. Perera, N. Wickramage CERN,