TOP-18-010

TOP-18-010

# Measurement of the inclusive and differential $\ttbar\gamma$ cross sections
in the single-lepton channel and EFT interpretation at $\sqrt{s}=13\TeV$

###### Abstract

The production cross section of a top quark pair in association with a photon
is measured in proton-proton collisions at a center-of-mass energy of 13. The
data set, corresponding to an integrated luminosity of 137, was recorded by
the CMS experiment during the 2016–2018 data taking of the LHC. The
measurements are performed in a fiducial volume defined at the particle level.
Events with an isolated, highly energetic lepton, at least three jets from the
hadronization of quarks, among which at least one is tagged, and one isolated
photon are selected. The inclusive fiducial $\ttbar\gamma$ cross section, for
a photon with transverse momentum greater than 20and pseudorapidity
$\abs{\eta}<1.4442$, is measured to be $798\pm 7\stat\pm
48\syst\mbox{$\,\text{fb}$}$, in good agreement with the prediction from the
standard model at next-to-leading order in quantum chromodynamics. The
differential cross sections are also measured as a function of several
kinematic observables and interpreted in the framework of the standard model
effective field theory (EFT), leading to the most stringent direct limits to
date on anomalous electromagnetic dipole moment interactions of the top quark
and the photon.

##  0.1 Introduction

The large amount of proton-proton ($\Pp\Pp$) collision data at a center-of-
mass energy of 13at the LHC allows for precision measurements of standard
model (SM) processes with small production rates. Among these, top quark
production provides a testing ground for the SM predictions and for phenomena
beyond the SM (BSM). In particular, precise measurements of the inclusive and
differential cross sections of top quark pair production in association with a
high-energy photon ($\ttbar\gamma$) constrain anomalous $\ttbar\gamma$
electroweak interactions [1, 2, 3, 4].

The CDF Collaboration at the Fermilab Tevatron measured the $\ttbar\gamma$
production cross section using proton-antiproton collisions at
$\sqrt{s}=1.96\TeV$ [5], while at the LHC the measurement was performed in
$\Pp\Pp$ collisions at 7by the ATLAS [6], and at 8by both the ATLAS [7] and
CMS [8] Collaborations. At 13, the ATLAS Collaboration measured inclusive and
differential $\ttbar\gamma$ production cross sections in leptonic [9] and in
the $\Pe\Pgm$ [10] final states. All of these results are in agreement with
the SM.

In this paper, the inclusive and differential $\ttbar\gamma$ production cross
sections are measured in $\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$. The
analysis uses a data sample recorded with the CMS detector during Run 2
(2016–2018) of the LHC, which corresponds to an integrated luminosity of 137.
The measurement is performed in the single-lepton (electron or muon) final
state in a fiducial region defined at particle level. The inclusive fiducial
$\ttbar\gamma$ cross section is measured for a selection on the photon
transverse momentum of $\pt(\gamma)>20\GeV$ and the pseudorapidity of
$\abs{\eta(\gamma)}<1.4442$, corresponding to the barrel region of the CMS
electromagnetic calorimeter (ECAL). Differential cross sections are measured
in the same fiducial region as a function of $\pt(\gamma)$,
$\abs{\eta(\gamma)}$, and the angular separation between the lepton and the
photon, $\Delta R(\ell,\gamma)$. The observations are interpreted in the
context of the SM effective field theory (SM-EFT) [11], where the
$c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$ operators, defined in Ref. [12],
are constrained using the measurement of the distribution of $\pt(\gamma)$.
Tabulated results are provided in HEPDATA [13]. Examples of Feynman diagrams
at leading order (LO) contributing to the $\ttbar\gamma$ signal topology are
shown in Fig. 1.

Figure 1: Representative LO Feynman diagrams for the $\ttbar\gamma$ signal
process in the single-lepton channel, where the highly energetic photon
originates from the top quark (left), or is emitted from a lepton (right). The
$\ttbar\gamma$ interaction vertex is indicated by a circle.

This paper is organized as follows. The CMS detector is briefly introduced in
Section 0.2. Details on the simulation of the signal and background processes
and their modeling are provided in Section 0.3. The online selection, event
reconstruction, and object definitions are described in Section 0.4. The
fiducial phase space definition and photon categorization are described in
Section 0.5. The event selection and the statistical treatment are discussed
in Section 0.6. The procedures to estimate the backgrounds are described in
Section 0.7 and the systematic uncertainties are discussed in Section 0.8. The
obtained results and the interpretation of the measurements in the context of
SM-EFT are presented in Section 0.9. Finally, a summary is provided in Section
0.10.

##  0.2 The CMS detector

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

Events of interest are selected using a two-tiered trigger system [14]. The
first level trigger (L1) [15], 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. 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
[14]. A more detailed description of the CMS detector, together with a
definition of the coordinate system used and the relevant kinematic variables,
can be found in Ref. [16].

##  0.3 Simulated event samples

Multiple Monte Carlo (MC) event generators are used to simulate the background
and signal contributions, matching the varying conditions for each data-taking
period. The , $t$-channel single top quark, $\PQt\PW$, and $\PW\PW$ background
processes are simulated at next-to-LO (NLO) in perturbative quantum
chromodynamics (QCD) with the v2 [17, 18, 19, 20, 21, 22, 23] event generator.
The QCD multijet processes are generated with v8.226 (8.230) [24] for the 2016
(2017, 2018) data-taking period. All other background processes are simulated
with v2.6.0 [25] at LO or NLO accuracy. The simulation is normalized to a
cross section of $832\pm 42\mbox{$\,\text{pb}$}$ calculated with the Top++
v2.0 program [26] at next-to-NLO (NNLO), including resummation of next-to-
next-to-leading-logarithm (NNLL) soft-gluon terms [27, 28, 29, 30, 31, 32].
Events with an $s$\- or $t$-channel produced top quark are normalized to NLO
cross sections [33, 34], while the normalizations of $\PW\PW$ and $\PQt\PW$
are at NNLO [35]. The overlap of the $\PQt\PW$ and simulation is removed using
the diagram removal technique [36]. Drell–Yan and $\PW\text{+jets}$ events are
generated with up to four extra partons in the matrix element calculations
with at LO and are normalized to NNLO cross sections [37, 38, 39] including
electroweak corrections at NLO [40, 41]. The $\PW\PZ$, $\PZ\PZ$, $\PZ\gamma$,
and $\ttbar\PW$ samples are simulated at NLO precision with one extra parton
at ME level. The $\PW\gamma$ sample is simulated at LO precision with up to
three extra partons. The $\ttbar\PZ$, $\ttbar\PW$, $\PQt\PZ\Pq$, $\PQt\gamma$,
$\PW\gamma$, $\PZ\gamma$, and other diboson processes ($\PW\PZ$ or $\PZ\PZ$)
are normalized to the most precise cross sections available [42, 43].

The $\ttbar\gamma$ signal is generated with v2.6.0 at LO as a doubly resonant
$2\to 7$ process including all decay channels of the intermediate bosons. It
includes events where the photon is radiated from the intermediate top quarks,
the intermediate bosons and their decay products, the quarks, and, in the case
of quark-antiquark annihilation, radiation from initial-state quarks. The
photon is required to satisfy $\pt>10\GeV$ and $\abs{\eta}<5$, while the
lepton must pass $\abs{\eta}<5$. The angular separation between the photon and
any of the seven final-state particles is required to be greater than 0.1,
where $\DR=\sqrt{\smash[b]{(\Delta\eta)}^{2}+\smash[b]{(\Delta\phi)}^{2}}$ and
$\phi$ denotes the azimuthal angle. The renormalization scale
($\mu_{\mathrm{R}}$) and factorization scale ($\mu_{\mathrm{F}}$) are set to
$\frac{1}{2}\sum_{i}\sqrt{\smash[b]{m_{i}}^{2}+\smash[b]{p_{\mathrm{T},i}}^{2}}$,
where the sum runs over all final-state particles generated at the matrix-
element (ME) level. Although no photons are simulated at the ME level in the
process, initial- and final-state photon radiation is accounted for in the
showering algorithm. We remove double counting of the and $\ttbar\gamma$
samples by excluding events from the sample with a generated photon passing
the photon requirements of the $\ttbar\gamma$ signal sample. The overlap
between $\PW\gamma$ and $\PW\text{+jets}$, $\PZ\gamma$ and Drell–Yan, and
$\PQt\gamma$ and the single top quark $t$-channel process is removed
analogously.

Event generator, perturbative order in QCD of the simulation, and perturbative
order of the cross section normalization for each process.  Process Event
generator Perturbative Cross section order of simulation normalization
$\ttbar\gamma$ LO NLO NLO NNLO+NNLL [27, 28, 29, 30, 31, 32, 26] Single
($t$-channel) NLO NLO [33, 34] Single ($s$-channel) NLO NLO [33, 34] $\PQt\PW$
NLO NNLO [35] Drell–Yan, $\PW\text{+jets}$ LO NNLO [37, 38, 39, 40, 41]
$\PW\gamma$ LO NLO $\PW\PW$ NLO NNLO [44] $\PQt\gamma$, $\PZ\gamma$, $\PW\PZ$,
$\PZ\PZ$ NLO NLO $\ttbar\PZ$, $\ttbar\PW$, $\PQt\PZ\Pq$ Multijet LO LO

The event generators are interfaced with v8.226 (8.230) using the CP5 tune
[45, 46, 47] for the 2016 (2017, 2018) samples to simulate multiparton
interactions, fragmentation, parton shower, and hadronization of partons in
the initial and final states, along with the underlying event. The NNPDF
parton distribution functions (PDFs) v3.1 [48] are used according to the
different perturbative order in QCD at the ME level. For the 2016 data-taking
period, the CUETP8M1 tune [46] and the NNPDF PDFs v3.0 [49] are used for the
Drell–Yan, $\PW\text{+jets}$, $\PQt\gamma$, $\PZ\gamma$, $\PW\gamma$, diboson,
$\ttbar\PW$, $\ttbar\PZ$, $\PQt\PZ\Pq$, and multijet processes. Double
counting of the partons generated with and is removed using the MLM [50] and
the FxFx [51] matching schemes for LO and NLO samples, respectively. The
events are subsequently processed with a -based simulation model [52] of the
CMS detector. All simulated samples include the effects of additional $\Pp\Pp$
collisions in the same or adjacent bunch crossings (pileup), and are
reweighted according to the observed distribution of the number of
interactions in each bunch crossing [53]. In the following, to simplify the
notation, the single top quark, , and $\PQt\gamma$ processes are grouped in
the $\PQt/\ttbar$ category, and furthermore, the $\PQt\PZ\Pq$, $\ttbar\PW$,
$\ttbar\PZ$, $\PW\PW$, $\PW\PZ$, and $\PZ\PZ$ processes in a category labeled
“other”. A summary of the event samples is provided in Table 0.3.

##  0.4 Event reconstruction

Events are selected at the high-level trigger by the algorithms that require
the presence of at least one lepton ($\ell=\Pe$ or ). The trigger threshold on
the leading muon is 27 (24)in the 2017 (2016 and 2018) LHC running period. For
electrons, the trigger threshold in the 2016 (2017–2018) period is 27 (32).

The particle-flow (PF) algorithm [54] 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 candidate vertex with the
largest value of summed physics-object $\pt^{2}$ is taken to be the primary
$\Pp\Pp$ interaction vertex (PV). The energy of charged PF hadrons is
determined from a combination of the track momentum and the matching ECAL and
HCAL energy deposits, corrected for zero-suppression effects and for the
response function of the calorimeters to hadronic showers. The energy of
neutral PF hadrons is obtained from the corresponding corrected ECAL and HCAL
energies.

The energy of electrons is determined from a combination of the electron
momentum at the PV 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. Electron
candidates are required to satisfy $\pt>35\GeV$ and $\abs{\eta}<2.4$,
excluding the transition region between the barrel and endcap of the ECAL,
$1.4442<\abs{\eta}<1.5660$. The electron identification is performed using
shower shape variables, track-cluster matching variables, and track quality
variables. To reject electrons originating from photon conversion inside the
detector, electrons are required to miss at most one possible hit in the
innermost tracker layer and to be incompatible with any conversion-like
secondary vertices.

The momentum of muons is obtained from the curvature of the corresponding
track. Muon candidates are selected having $\pt>30\GeV$ and $\abs{\eta}<2.4$.
The identification of muon candidates is performed using the quality of the
geometrical matching between the measurements of the tracker and muon system
[55].

The energy of photons is obtained from the ECAL measurement. Photon candidates
are required to satisfy $\pt(\gamma)>20\GeV$ and to fall within the barrel of
the ECAL, $\abs{\eta}<1.4442$. The identification of photons is based on
isolation and shower shape information as a function of and $\eta$, and takes
into account pileup effects [56, 57]. In particular, the lateral shower
extension must satisfy $\sigma_{\eta\eta}(\gamma)<0.01$ for the chosen
“medium” photon working point. It is defined as the second moment of a log-
weighted distribution of crystal energies in $\eta$, calculated in the
$5\times 5$ matrix around the most energetic crystal in the photon’s
supercluster [58]. Because of the reduced power of the
$\sigma_{\eta\eta}(\gamma)$ observable in the ECAL endcap region in rejecting
nonprompt photons, we find that excluding this $\abs{\eta}$ range improves the
uncertainties in the measurements.

All lepton and photon candidates are required to be isolated from other
objects by selecting the reconstructed charged and neutral PF candidates in a
cone around the candidate. A radius $\DR=0.3~{}(0.4)$ is used for electron
(muon) candidates. For electron candidates, - and $\eta$-dependent thresholds
are set on the pileup corrected scalar sum of photons and neutral and charged
hadrons reconstructed by the PF algorithm ($I_{\textrm{rel}}(\Pe)$) in the
range of 5–10%. The chosen “tight” electron working point has a 70% efficiency
while rejecting electron candidates originating from jets [58]. A muon
candidate is isolated if it satisfies $I_{\textrm{rel}}(\Pgm)<0.15$. The
efficiency of the chosen “tight” working point is 90–95%, depending on and
$\eta$ of the muon candidate [55]. For photon candidates, the scalar sum of
the charged particles within a cone of $\DR=0.3$, denoted as the photon
charged-hadron isolation, must satisfy $I_{\textrm{chg}}(\gamma)\leq
1.141\GeV$. Depending on the photon candidate , there are separate
requirements on the photon neutral-hadron and total isolation [58]. The photon
reconstruction and selection efficiency for the chosen “medium” working point
in simulation is on average 80%. The electron, muon, and photon reconstruction
efficiencies are corrected as a function of the and $\eta$ of the
reconstructed object to match the efficiency observed in data.

Furthermore, “loose” selection criteria are used to define control regions and
to veto events with additional reconstructed leptons and photons. With respect
to the tight electron selection, the transverse momentum requirement is
relaxed to $\pt>15\GeV$, the threshold on $I_{\textrm{rel}}(\Pe)$ to the range
of 20–25%, depending on $\pt$ and $\eta$ of the electron candidate, and two
(three) missed hits in the innermost tracker layers are allowed for electrons
in the barrel (endcap) region. The loose muon selection is based on Ref. [59]
with $\pt>15\GeV$ and $I_{\textrm{rel}}(\Pgm)<0.25$. The loose photon
selection is defined by the “medium” photon working point without the
$I_{\textrm{chg}}(\gamma)$ and $\sigma_{\eta\eta}(\gamma)$ requirements [58].

Jets are reconstructed by clustering PF candidates using the anti-algorithm
[60, 61] with a distance parameter of 0.4. Selected jets are required to
satisfy $\pt>30\GeV$ and $\abs{\eta}<2.4$. Contributions to the clustered
energy from pileup interactions are corrected for by requiring charged-hadron
candidates to be associated with the PV and an offset correction for the
contribution from neutral hadrons falling within the jet area is subtracted
from the jet energy. Corrections to the jet energy scale (JES) are applied in
simulation and data. The jet energy resolution (JER) is corrected in
simulation to match the resolution observed in data [62].

Jets originating from the hadronization of quarks are identified ( tagged)
with a deep neural network algorithm [63] based on tracking and secondary
vertex information. A working point is chosen such that the efficiency to
identify the jet is 55–70% for a jet of 20–400. The misidentification rate in
this range is 1–2% for light-flavor and gluon jets, and up to 12% for charm
quark jets. A correction is applied to the simulation to match the tagging
efficiencies observed in data.

The missing transverse momentum vector, , is defined as the projection onto
the plane perpendicular to the beams of the negative vector momentum sum of
all PF candidates in an event. The JES and JER corrections are included in the
computation. Its magnitude is referred to as .

##  0.5 Fiducial phase space definition and photon classification

The fiducial region of the analysis is defined at the particle level by
applying an event selection to the stable particles after the event
generation, parton showering, and hadronization, but before the detector
simulation.

Electrons (muons) must have $\pt>35~{}(30)\GeV$ and $\abs{\eta}<2.4$, and must
not originate from hadron decays. To account for final-state photon radiation,
the four-momenta of photons inside a cone of $\DR=0.1$ are added to the lepton
before the lepton selection [64]. Events with leptonically decaying $\tau$
leptons in the decay chain of the top quark are considered signal.

Photons are selected if they do not originate from hadron decays, satisfy
$\pt(\gamma)>20\GeV$ and $\abs{\eta(\gamma)}<1.4442$, and are found outside a
cone of $\DR=0.4$ around the leptons. An isolation requirement is applied by
removing photons with stable particles (except neutrinos) found within a cone
of $\DR=0.1$ that satisfy $\pt>5\GeV$.

Particle-level jets are clustered using the anti-algorithm with a distance
parameter of 0.4, using all final-state particles, excluding neutrinos. Jets
must satisfy $\pt>30\GeV$ and $\abs{\eta}<2.4$. A ghost matching method [65]
is used to determine the flavor of the jets, with those matched to hadrons
tagged as jets. Finally, the overlap of jets and other candidates is removed
by excluding jets with $\DR\leq 0.4~{}(0.1)$ to lepton (photon) candidates. A
summary of the object definitions at particle level is provided in Table 0.5.

The fiducial region is constructed by requiring exactly one photon, exactly
one lepton, and three or more jets among which at least one must be tagged.
The inclusive fiducial cross section, predicted with at NLO in QCD, is $773\pm
135$ fb. The NLO effects in the decay of the top quarks are not included in
this calculation.

Overview of the definition of fiducial regions for various objects at particle
level. A photon is isolated, if there are no stable particles (except
neutrinos) with $\pt>5\GeV$ within a cone of $\DR=0.1$.  Photon () Jet jet
$\pt>20\GeV$ $\pt>35~{}(30)\GeV$ $\pt>30\GeV$ $\pt>30\GeV$ $\abs{\eta}<1.4442$
$\abs{\eta}<2.4$ $\abs{\eta}<2.4$ $\abs{\eta}<2.4$ no hadronic origin no
hadronic origin $\DR(\text{jet},\ell)>0.4$ $\DR(\PQb~{}\text{jet},\ell)>0.4$
$\Delta R(\ell,\gamma)>0.4$ $\DR(\text{jet},\gamma)>0.1$
$\DR(\PQb~{}\text{jet},\gamma)>0.1$ isolated matched to hadrons

To facilitate the estimation of backgrounds with nonprompt and misidentified
photons, a photon categorization is based on the matching between the
reconstructed photon and simulated particles. Reconstructed photons are
matched in to the corresponding generator-level particle from the primary
interaction. The maximum considered for matching is 0.3 and the $\pt(\gamma)$
is required to be within 50% of the matched particle. Simulated events with a
reconstructed photon are subsequently classified into three categories based
on the matched generator particle. In the “genuine photon” category, the
reconstructed photon is matched to a generated photon that originates from a
lepton, a boson, or a quark. In the “misidentified electron” category, the
photon is matched to an electron. The “nonprompt photon” category is comprised
of events where the photon is matched to a generated photon that originates
from a hadron (71%), or in absence of a match to a generated photon or
electron. This category thus includes contributions with misidentified photons
and photons that originate from pileup interactions (29%).

##  0.6 Analysis strategy

###  0.6.1 Signal and control region definitions

The $\ttbar\gamma$ process typically produces events with several jets, up to
two -tagged jets, and an isolated photon with large . The measurement is
performed in signal regions with exactly one lepton ($N_{\ell}=1$), exactly
one photon ($N_{\gamma}=1$), and at least three jets ($N_{\text{j}}\geq 3$),
among which at least one is tagged ($N_{\PQb}\geq 1$). Events with additional
leptons or photons passing the loose selection are removed. The measurement is
performed in the $N_{\text{j}}=3$ and $\geq$4 signal selections, denoted by
SR3 and SR4p, respectively. Signal events with a jet failing the
identification criteria thus enter the SR3 region. The $N_{\text{j}}\geq 3$
selection is denoted by SR3p. For illustration, Fig. 2 shows some kinematic
distributions in the SR3p region where the simulated event samples are
categorized according to the origin of the photon. The backgrounds are
normalized according to the methods described in Sec. 0.7 and the pre-fit
systematic uncertainties are shown as a hatched band. In this figure, $M_{3}$
denotes the invariant mass of the three-jet combination among all identified
jets that maximizes the magnitude of the vector sum [66]. This choice
preferentially captures the hadronic top quark decay products.

Figure 2:  Distribution of $\pt(\gamma)$, the transverse mass $\mT(\PW)$ of
the boson candidate, the three-jet invariant mass $M_{3}$ (upper row); the
invariant mass $m(\ell,\gamma)$ of the lepton and the photon, the angular
separation $\Delta R(\ell,\gamma)$ of the lepton and the photon, and the
minimal angular separation $\textrm{min}\,\DR(j,\gamma)$ of the photon and all
jets (lower row) in the SR3p region. The backgrounds are normalized according
to the methods described in Sec. 0.7 and the pre-fit systematic uncertainties
are shown as a hatched band. The lower panels show the ratio of the observed
to the predicted event yields.

Overview of signal and control regions.  Region $N_{\ell}$ $N_{\text{j}}$
$N_{\PQb}$ $N_{\gamma}$ Other requirements SR3p SR3 1 3 $\geq$1 1 SR4p 1
$\geq$4 $\geq$1 1 LM3p LM3 1 3 0 1 $m(\Pe,\gamma)<m_{\PZ}-10\GeV$,
$m(\Pgm,\gamma)<m_{\PZ}$ LM4p 1 $\geq$4 0 1 $m(\Pe,\gamma)<m_{\PZ}-10\GeV$,
$m(\Pgm,\gamma)<m_{\PZ}$ HM3p HM3 1 3 0 1 $m(\Pe,\gamma)>m_{\PZ}+10\GeV$,
$m(\Pgm,\gamma)>m_{\PZ}$ HM4p 1 $\geq$4 0 1 $m(\Pe,\gamma)>m_{\PZ}+10\GeV$,
$m(\Pgm,\gamma)>m_{\PZ}$ misDY3p misDY3 1 3 0 1
$\abs{m(\Pe,\gamma)-m_{\PZ}}\leq 10\GeV$ misDY4p 1 $\geq$4 0 1
$\abs{m(\Pe,\gamma)-m_{\PZ}}\leq 10\GeV$

The data-based estimation procedures for the dominant background sources are
described in Sec. 0.7. The simulation predicts a significant background
contribution from nonprompt photons (23%), misidentified electrons (19%), and
a small contribution from multijet events in the SR3p region. The nonprompt
photon contribution is estimated using background-enriched control regions
with relaxed criteria on $I_{\textrm{chg}}(\gamma)$ and
$\sigma_{\eta\eta}(\gamma)$. The multijet contributions to the signal and
control regions are estimated by rescaling suitable normalized distributions
(templates) obtained from background-enriched high-$I_{\textrm{rel}}(\ell)$
sidebands. The misidentified electron background is estimated in a
$N_{\PQb}=0$ region where the invariant mass of the electron and photon
candidates ($m(\Pe,\gamma)$) is consistent with the boson hypothesis [67]
within 10, , $\abs{m(\Pe,\gamma)-m_{\PZ}}\leq 10\GeV$, where $m_{\PZ}$ is the
boson mass. The control region is denoted by misDY3 (misDY4p) for
$N_{\text{j}}=3$ ($\geq$4). The $\PW\gamma$ and the $\PZ\gamma$ processes
contribute events with genuine photons to both the signal regions and the
misDY3 and misDY4p control regions. In the electron channel, their
contribution is constrained in “low mass” (LM) and “high mass” (HM) regions,
defined by $m(\Pe,\gamma)<m_{\PZ}-10\GeV$ and $m(\Pe,\gamma)>m_{\PZ}+10\GeV$,
respectively. In the muon channel, the LM (HM) region is defined by
$m(\Pgm,\gamma)<m_{\PZ}$ ($m(\Pgm,\gamma)>m_{\PZ}$), where $m(\Pgm,\gamma)$
denotes the invariant mass of the muon and the photon. Table 0.6.1 provides a
summary of the kinematic requirements in the signal and control regions.

###  0.6.2  Statistical treatment

The signal cross section is extracted from signal and control regions using
the statistical procedure detailed in Refs. [68, 69]. The observed yields,
signal and background estimates in each analysis category, and the systematic
uncertainties are used to construct a binned likelihood function $L(r,\theta)$
as the product of Poisson probabilities of all bins. The nuisances related to
the systematic uncertainties in the experiment and in the modeling of signal
and background processes are described by log-normal probability density
functions. The parameter $r$ is the signal strength modifier, , the ratio
between the measured cross section and an arbitrary reference value of 773 fb,
chosen as the nominal prediction for the inclusive fiducial cross section. The
symbol $\theta$ represents the set of nuisance parameters describing the
systematic uncertainties. The number of reconstructed $\ttbar\gamma$ signal
events generated outside the fiducial phase space is scaled with the same
value of $r$, , no independent production cross section is assumed for this
part of the signal.

The used test statistic is the profile likelihood ratio, $q(r)=-2\ln
L(r,\hat{\theta}_{\textrm{r}})/L(\hat{r},\hat{\theta})$, where
$\hat{\theta}_{\textrm{r}}$ reflects the values of the nuisance parameters
that maximize the likelihood function for a signal strength modifier $r$. The
quantities $\hat{r}$ and $\hat{\theta}$ are the values that simultaneously
maximize $L$. A multi-dimensional fit is used to extract the observed cross
section of the signal process, the nuisance parameters, and the uncertainties
in the nuisance parameters [68, 69].

The LM3, LM4p, HM3, HM4p, misDY3 and misDY4p control regions enter the
likelihood fit separately for each data-taking period and lepton flavor. In
order to extract the $\pt(\gamma)$ dependence of the background with
misidentified electrons, the misDY3 and misDY4p control regions are split into
7 bins separated by the $\pt(\gamma)$ thresholds 20, 35, 50, 65, 80, 120, and
160. The LM3, LM4p, HM3, and HM4p regions are similarly separated into 3 bins
defined by the $\pt(\gamma)$ thresholds 20, 65, and 160. The binning is chosen
to obtain a statistical uncertainty in simulated background yields of less
than 15%.

The likelihood fit is performed for the inclusive cross section measurements,
and separately for the differential measurement. For the extraction of the
inclusive cross section, the SR3 and SR4p signal region are divided in three
$M_{3}$ bins in the ranges 0–280, 280–420, and $>$420\. The binning in
$\pt(\gamma)$, $\abs{\eta(\gamma)}$, and $\Delta R(\ell,\gamma)$ in the SR3
and SR4p signal regions for the differential measurements is provided in
Section 0.9.2. The estimation of contributions from various background
processes is performed using control regions binned in $\pt(\gamma)$, which
are used in all inclusive and differential measurements.

##  0.7 Background estimation

###  0.7.1 Multijet background

The probability for a multijet event to mimic the final state of the signal
process is small and subject to large uncertainties. Therefore, the background
from multijet events, comprising events with misidentified and nonprompt
leptons, is estimated with a data-based procedure in sideband regions with
loosened isolation criteria. For each $N_{\text{j}}$ requirement, a sideband
region is defined by $N_{\PQb}=0$ and requiring the lepton to pass the
isolation criterion of the loose lepton working point and to fail the tight
lepton selection. The $N_{\gamma}=1$ requirement is kept. The resulting
selection is dominated by multijet events. After electroweak backgrounds and
backgrounds with top quarks ($\PW\text{+jets}$, $\PQt/\ttbar$, and Drell–Yan)
are subtracted based on the expectation from simulation, templates for the
distributions of kinematic observables are extracted.

Figure 3:  Fit result of the $\mT(\PW)$ multijet distribution in the selection
with $N_{\text{j}}=2$, $N_{\PQb}=0$, and tightly isolated electrons (left) and
muons (right). The template obtained from the selection with loosely isolated
leptons (green) and the total normalization of the electroweak and top quark
background are floating in the fit. The lower panels show the ratio of the
observed to the predicted event yields. The pre-fit systematic uncertainties
are shown as a hatched band.

The template normalization is evaluated from a transfer factor (“TF”), defined
as the ratio of the multijet event yield with tightly isolated lepton
candidates to the yield with loosely isolated lepton candidates. It is
obtained in a selection with $N_{\text{j}}=2$ and $N_{\gamma}=0$ by fitting
the distribution of the transverse mass of the boson candidate, calculated
from the formula

$\mT(\PW)=\sqrt{2\pt^{\ell}\ptmiss[1-\cos(\Delta\phi_{\ell,\ptvecmiss})]}$ (1)

where $\ell$ indicates the lepton considered in the event. The distribution is
taken from data in a $N_{\PQb}=0$ selection with loosely isolated leptons, and
electroweak and top quark backgrounds are subtracted. The fit is then
performed in the selection with tightly isolated leptons where the total
normalization of the electroweak and top quark background is left floating,
while its shape is again taken from simulation. For illustration, the fit
result for the $N_{\text{j}}=2$ and $N_{\PQb}=0$ region, including the
$\mT(\PW)$ multijet distribution from the selection with loosely isolated
leptons, is shown in Fig. 3.

Because the efficiency of the tight lepton selection in multijet events
depends on and $\eta$ of the lepton, the estimation procedure, including the
TF fit, is performed in a total of 24 bins defined in these observables.
Depending on and $\eta$ of the lepton, the TFs vary in the range of 0.9–3.1
(0.1–0.3) for the channel and 2.0–3.7 (0.6–1.0) for the channel, for
$N_{\PQb}=0$ ($\geq$1). A correction based on simulated multijet events
accounts for the TF dependence on $N_{\text{j}}$. Finally, the multijet
estimate is obtained by scaling the $N_{\PQb}=0$ sideband templates with the
corresponding TFs and accumulating the resulting predictions in the 24 bins in
lepton and $\eta$. The total multijet yield is estimated at 12 (8)% in the ()
channel in the LM3p, HM3p and misDY3p control regions and below 0.5% in the
signal regions.

###  0.7.2 Nonprompt photon background

The nonprompt photon background component is estimated from data by exploiting
the difference between its distribution in the plane defined by the weakly
correlated variables $\sigma_{\eta\eta}(\gamma)$ with
$I_{\textrm{chg}}(\gamma)$, and the corresponding distribution for genuine
photons. In a sideband with a requirement of $\sigma_{\eta\eta}(\gamma)\geq
0.011$ on the photon candidate, the expected yields with genuine photons,
misidentified electrons, and multijet events are subtracted. The sideband is
used to obtain the normalization factor $r_{\textrm{SB}}$, defined as the
ratio of the yield passing the $I_{\textrm{chg}}(\gamma)<1.141\GeV$
requirement to the event yield failing it. The estimation is obtained by
multiplying $r_{\textrm{SB}}$ with the yield in the normalization region,
defined by the nominal $\sigma_{\eta\eta}(\gamma)$ requirement and the
inverted criteria on the photon charged hadron isolation,
$I_{\textrm{chg}}(\gamma)>1.141\GeV$. The expected yields with genuine
photons, misidentified electrons, and multijet events are subtracted from the
observation in the normalization region. The procedure is carried out
separately for lepton flavors, $N_{\text{j}}$ selections, data-taking period,
and for each bin of the differential cross section. The deviation from unity
of the double-ratio of $r_{\textrm{SB}}$ to the corresponding ratio in the
nominal $\sigma_{\eta\eta}(\gamma)$ selection, stemming from the residual
correlation between the two variables, is computed from simulation and it
amounts to 18%. This value is used to correct the prediction.

###  0.7.3 Misidentified electron and genuine photon backgrounds

The background from electrons that are misidentified as photons is obtained
from control regions defined by the requirements of
$\abs{m(\Pe,\gamma)-m_{\PZ}}\leq 10\GeV$, and exactly three (misDY3), or four
or more (misDY4p) jets. In the simulation, these event samples have a combined
purity of 58% of Drell–Yan events with $\PZ\to\Pe\Pe$, where one of the
electrons passes the photon selection criteria. The simulated yield of the
background component with a misidentified electron is multiplied by the scale
factor (SF) defined below, separately for each of the three data-taking
periods.

Figure 4:  Distribution of the invariant mass of the lepton and the photon,
$m(\ell,\gamma)$, in the ${N_{\text{j}}\geq 3}$, ${N_{\PQb}=0}$ selection for
the channel (left) and the channel (right). The genuine photon contributions
of $\PW\gamma$ and $\PZ\gamma$ are visualized separately. The lower panels
show the ratio of the observed to the predicted event yields. The pre-fit
systematic uncertainties are shown as a hatched band.

The $\PW\gamma$ ($\PZ\gamma$) process contributes to the LM3p regions for both
lepton flavors and has a purity of 41% (21%). In the HM3p regions, the
$\PW\gamma$ background is dominant with a purity of 51%. The SFs for the
misidentified electron background and the normalization of the $\PW\gamma$ and
$\PZ\gamma$ processes are obtained from the likelihood fit as described in
Section 0.6.2. The fit includes the data-based multijet estimates. The
normalization of the $\PW\gamma$ process is left floating and the
normalization of the $\PZ\gamma$ process is allowed to vary within its
uncertainty. The resulting $m(\ell,\gamma)$ distributions are shown in Fig. 4
in the $N_{\text{j}}\geq 3$ control regions. The background with misidentified
electrons is dominant in the misDY3 and misDY4p regions close to the $m_{\PZ}$
peak. A correction of 15% to the normalization of the Drell–Yan process is
measured in a data sample with two well-identified leptons satisfying
$\abs{m(\ell,\ell)-m_{\PZ}}\leq 10\GeV$ and $N_{\text{j}}\geq 3$, and is
included in these results.

A summary of the extracted SF values for the misidentified electron background
and the normalization of the $\PZ\gamma$ and $\PW\gamma$ processes, obtained
from a profile likelihood fit excluding the signal regions, is provided in
Table 0.7.3. The observed differences in the SFs for misidentified electrons
are a result of the pixel detector replacement in 2017 and its operating
conditions in the three data-taking periods. The stability of the procedure to
estimate the yields of misidentified electrons and genuine photons is assessed
by repeating the fit on individual data-taking periods and separately for the
$N_{\text{j}}=3$ and $\geq$4 selections. The extracted SFs from these checks
agree within the uncertainties. For the measurements of the inclusive and
differential cross sections, as well as for setting EFT limits, the SFs are
determined in situ by performing the fit simultaneously with the signal
regions.

Extracted SFs and the total uncertainty obtained from the likelihood fit for
the contribution from misidentified electrons for the three data-taking
periods, and for the normalization of the $\PZ\gamma$ and $\PW\gamma$
background components.  Scale factor Value Misidentified electrons (2016) 2.25
$\pm$ 0.29 Misidentified electrons (2017) 2.00 $\pm$ 0.27 Misidentified
electrons (2018) 1.52 $\pm$ 0.17 $\PZ\gamma$ normalization 1.01 $\pm$ 0.10
$\PW\gamma$ normalization 1.13 $\pm$ 0.08

##  0.8 Systematic uncertainties

The systematic uncertainties affecting the signal selection efficiency and
background yields are summarized in Table 0.8. The table shows the range of
variations in the different bins of the analysis caused by each systematic
uncertainty in the signal and background yields, as well as an estimate of the
impact of each uncertainty in the measured inclusive cross section. The table
also indicates whether the uncertainties are treated as uncorrelated or fully
correlated among the data-taking periods.

Breakdown of the total uncertainty in its statistical and systematic
components in the different signal regions. The first column indicates the
source of the uncertainty. The second column shows the correlation between the
data-taking periods. The third column shows the typical pre-fit uncertainties
in the total simulated yields in the signal region. The last column gives the
corresponding systematic uncertainty in the $\ttbar\gamma$ cross section from
the fit to the data.  Source Correlation Uncertainty [%] yield
$\sigma(\ttbar\gamma)$ Experimental Integrated luminosity partial 2.3–2.5 1.8
Pileup 100% 0.5–2.0 $<$0.5 Trigger efficiency $<$0.5 $<$0.5 Electron
reconstruction and identification 100% 0.2–1.7 $<$0.5 Muon reconstruction and
identification partial 0.5–0.7 0.7 Photon reconstruction and identification
100% 0.4–1.4 1.1 $\pt(\Pe)$ and $\pt(\gamma)$ reconstruction 100% 0.1–1.2
$<$0.5 JES partial 1.0–4.1 1.9 JER 0.4–1.6 0.6 tagging 100% (2017/2018)
0.8–1.6 1.1 L1 prefiring 100% (2016/2017) 0.3–0.9 $<$0.5 Theoretical Tune 100%
0.1–1.9 $<$0.5 Color reconnection 100% 0.4–3.6 $<$0.5 ISR/FSR 100% 1.0–5.6 1.9
PDF 100% $<$0.5 $<$0.5 ME scales $\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$ 100%
0.4–4.7 $<$0.5 Background Multijet normalization 100% 1.3–6.5 0.9 Nonprompt
photon background 100% 1.2–2.7 1.8 Misidentified  2.5–8.0 1.8 $\PZ\gamma$
normalization 100% 0.6–2.5 0.5 $\PW\gamma$ normalization 100% 1.0–3.5 2.3 DY
normalization 100% 0.1–1.1 1.0 /normalization 100% 1.0–1.9 0.8 $\PQt\PW\gamma$
modeling 100% 1.6–4.4 1.6 “Other” bkg. normalization 100% 0.3–1.0 $<$0.5 Total
systematic uncertainty 6.0 Statistical uncertainty 0.9 Total 6.0

The integrated luminosities of the 2016, 2017, and 2018 data-taking periods
are individually known with uncertainties in the 2.3–2.5% range [70, 71, 72],
while the total Run 2 (2016–2018) integrated luminosity has an uncertainty of
1.8%, the improvement in precision reflecting the (uncorrelated) time
evolution of some systematic effects. The uncertainty in the inclusive cross
section from these sources is, therefore, 1.8%. Simulated events are
reweighted according to the distribution of the number of interactions in each
bunch crossing corresponding to a total inelastic $\Pp\Pp$ cross section of
69.2mb [53]. The uncertainty in the total inelastic $\Pp\Pp$ cross section is
4.6% [73] and affects the pileup estimate. The uncertainty due to the pileup
effect is about 2% for the expected yields and less than 0.5% for the
inclusive cross section.

The uncertainties in the SFs used to match the simulated trigger selection
efficiencies to the ones observed in data are propagated to the results. From
the “tag-and-probe” measurement [59, 56], an uncertainty of up to 0.5% is
assigned to the yields obtained in simulation. Lepton selection efficiencies
are measured in bins of lepton and $\eta$, and are found to be in the range
50–80 (75–85)% for electrons (muons). These measurements are performed
separately in data and simulation and their ratio is used to scale the yields
obtained in the simulation. The impact of these uncertainties on the inclusive
cross section is 0.5 (0.7)% for the electron (muon) channel.

In the barrel section of the ECAL, an energy resolution of about 1% is
achieved for unconverted or late-converting highly energetic photons in the
tens of energy range. The energy resolution of the remaining barrel photons is
about 1.3% up to $\abs{\eta}=1$, changing to about 2.5% at $\abs{\eta}=1.4$
[57]. Uncertainties in the photon energy scale and resolution are measured
with electrons from boson decays, reconstructed using information exclusively
from the ECAL [57, 58]. Additionally, an event sample enriched in
$\Pgmp\Pgmm\gamma$ events is used to measure an SF correcting the efficiency
of the electron veto [74]. The total uncertainty in the photon energy and
identification amounts to 1.1% for the inclusive cross section, and reaches 2%
for $\pt(\gamma)>100\GeV$.

The jet energy calibration accounts for the effects of pileup, the uniformity
of the detector response, and residual data-simulation jet energy scale
differences corrected using Drell–Yan, dijet, and $\gamma$+jet events.
Uncertainties in the JES are estimated by shifting the jet energy corrections
in simulation up and down by one standard deviation. Depending on and $\eta$,
the uncertainty in JES varies in the range 2–5%, leading to uncertainties in
the predicted signal and background yields of 1.0–4.1% and an impact on the
inclusive cross section of 1.9%. The dominant components originate from the
uncertainty in the jet-flavor composition in the Drell–Yan and dijet
selections (JES–FlavorQCD) and the absolute jet energy scale (JES–Absolute)
[62]. For the signal and background processes modeled via simulation, the
uncertainty in the measurement is determined from the observed differences in
the yields with and without the shift in jet energy corrections. The same
technique is used to calculate the uncertainties from the JER, which are found
to be less than 1% [62]. The tagging efficiency in the simulation is corrected
using SFs determined from data, separately for jets, jets, and
$\cPqu\cPqd\cPqs\cPg$ jets [75, 63]. These are estimated separately for
correctly and incorrectly identified jets, and each results in an uncertainty
of about 0.8–1.6% in the yields in the signal regions, depending on
$N_{\PQb}$.

During the 2016 and 2017 data-taking periods, a gradual shift in the timing of
the inputs of the ECAL L1 trigger in the forward endcap region
($\abs{\eta}>2.4$) led to a specific inefficiency (labeled “L1 prefiring” in
Table 0.8). A correction for this effect was determined using an unbiased data
sample and is found to be relevant in events with jets with
$2.4<\abs{\eta}<3.0$ and $\pt>100\GeV$. While no reconstructed objects at this
$\eta$ directly enter the measurements, it can affect the observable. A
systematic variation of 20% of this correction for affected objects leads to
an uncertainty of 0.3–0.9% in the predicted yields.

To estimate the theoretical uncertainties from missing higher-order
corrections in the signal cross section calculation, the choice of
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ are varied independently up and down
by a factor of 2. The acceptance variations are taken as the systematic
uncertainty in each bin and are found to be smaller than 4.7%. Two independent
nuisance parameters are used for the uncertainty in the choice of
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$, and their impact on the inclusive
cross section measurement in the profile likelihood fit is less than 0.5%. A
test with a single nuisance parameter, associated with the envelope of the
uncertainties related to the choice of $\mu_{\mathrm{R}}$ and
$\mu_{\mathrm{F}}$, leads to negligible differences. The different sets in the
NNPDF PDF [49] are used to estimate the corresponding uncertainty in the
acceptance for the cross section measurement, which is less than 0.5%. The
scale, PDF, and uncertainties in the inclusive fiducial cross section of the
$\ttbar\gamma$ process, evaluated with at NLO in QCD, amount to 17.5%. The
limited number of available simulated events is considered by performing the
fit using the Barlow–Beeston method [76].

In the parton shower simulation, the uncertainty from the choice of
$\mu_{\mathrm{F}}$ is estimated by varying the scale of initial- and final-
state radiation (ISR/FSR) up and down by factors of 2 and $\sqrt{2}$,
respectively, as suggested in Ref. [45]. The default configuration in includes
a model of color reconnection based on multiple parton interactions (MPI) with
early resonance decays switched off. To estimate the uncertainty from this
choice of model, the variations of the simulated yields with different color
reconnection schemes within are treated as systematic uncertainties: the MPI-
based scheme with early resonance decays switched on, a gluon-move scheme
[77], and a QCD-inspired scheme [78]. The total uncertainty from color
reconnection modeling is estimated by taking the maximum deviation from the
nominal result and amounts to less than 0.5% in the inclusive cross section.

The $\PQt\PW\gamma$ background component amounts to at most 3.3% of the total
event yield in the SR3 and SR4p signal regions and is predicted by the
$\PQt\PW$ sample, simulated with at NLO precision. To account for
uncertainties in the $\PQt\PW\gamma$ modeling, we treat the difference between
the nominal prediction from the parton shower in the $\PQt\PW$ sample,
normalized to NNLO, and a prediction obtained from at LO for the $2\to 3$
process as an uncertainty. For the SR3 (SR4p) signal regions, the differences
of the total $\PQt\PW\gamma$ contribution are less than 44% (30%) in the
$\pt(\gamma)$ bins, less than 34% (27%) in the $\abs{\eta(\gamma)}$ bins, and
less than 19% (17%) in the $\Delta R(\ell,\gamma)$ bins and lead to an
uncertainty of 1.6% in the inclusive fiducial cross section.

The uncertainty in the normalization of the QCD multijet component is based on
the variation of the TF with $N_{\text{j}}$ for different $N_{\PQb}$ and
amounts to 50%. Independent uncertainties are considered for the contributions
to the $N_{\PQb}=0$ and $\geq$1 yields. These have a significant impact only
in the LM3, LM4p, HM3, and HM4p control regions, and lead to an uncertainty of
0.9% in the measured inclusive cross section.

The uncertainty in the nonprompt photon prediction is based on the modeling of
the $I_{\textrm{chg}}(\gamma)$ distribution for different requirements on
$\sigma_{\eta\eta}(\gamma)$ and leads to an uncertainty of 1.8% in the
inclusive cross section. The normalization of the $\PW\gamma$ process is left
floating in the profile likelihood. To account for the uncertainty in the
$N_{\text{j}}$ modeling of the $\PZ\gamma$ process, we include an uncertainty
of 30% in its normalization. In the signal region, the contribution of
$\PW\gamma$ and $\PZ\gamma$ background events generated with additional or
quarks is 30%, and we assign an uncertainty of 20% in its normalization.
Moreover, 40 (20)% uncertainty is assigned to the normalization of the
$\PZ\gamma$ ($\PW\gamma$ and misidentified electron) background in the
$N_{\text{j}}\geq 4$ signal and control regions. The corresponding impact of
the normalization of the $\PZ\gamma$ and $\PW\gamma$ contributions are 0.5 and
2.3%, respectively. The component with misidentified electrons leads to an
uncertainty of up to 8% in the predicted background yields with an impact on
the inclusive cross section of 1.8%. The 8% uncertainty in the normalization
of the Drell–Yan process, the 5% uncertainty in the /normalization, and the
uncertainties in the normalization of other small background components lead
to additional uncertainties below 1%.

##  0.9 Results

###  0.9.1 Inclusive cross section measurement

Figure 5: Fitted and observed yields in the LM3, LM4p, HM3, HM4p, misDY3, and misDY4p control regions using the post-fit values of the nuisance parameters. The lower panel shows the ratio of the observed to the predicted event yields. The post-fit systematic uncertainties are shown as a hatched band.  Figure 6: Fitted and observed yields in the SR3 and SR4p signal regions using the post-fit values of the nuisance parameters. The lower panel shows the ratio of the observed to the predicted event yields. The post-fit systematic uncertainties are shown as a hatched band.  Table 1: The observed number of events for the SR3 and SR4p signal regions in the and channels, and the predicted yields and total post-fit uncertainties in each background component. Process | SR3 |  | SR4p
---|---|---|---
|  |  |  |  |  |
$\ttbar\gamma$ | 4995 |  $\pm$ | 168 |  | 7821 |  $\pm$ | 251 |  | 6174 |  $\pm$ | 192 |  | 9495 |  $\pm$ | 280
Misid. e | 3710 |  $\pm$ | 200 |  | 3322 |  $\pm$ | 220 |  | 1904 |  $\pm$ | 134 |  | 2015 |  $\pm$ | 153
Nonprompt $\gamma$ | 2621 |  $\pm$ | 107 |  | 4077 |  $\pm$ | 161 |  | 2315 |  $\pm$ | 124 |  | 3580 |  $\pm$ | 149
Other | 1136 |  $\pm$ | 102 |  | 1866 |  $\pm$ | 159 |  | 857 |  $\pm$ | 110 |  | 1360 |  $\pm$ | 166
$\PW\gamma$ | 1082 |  $\pm$ | 77 |  | 1486 |  $\pm$ | 108 |  | 585 |  $\pm$ | 48 |  | 864 |  $\pm$ | 74
Multijet | 560 |  $\pm$ | 104 |  | 762 |  $\pm$ | 140 |  | 302 |  $\pm$ | 65 |  | 472 |  $\pm$ | 102
$\PZ\gamma$ | 356 |  $\pm$ | 38 |  | 640 |  $\pm$ | 68 |  | 189 |  $\pm$ | 25 |  | 306 |  $\pm$ | 40
Total | 14459 |  $\pm$ | 178 |  | 19976 |  $\pm$ | 196 |  | 12326 |  $\pm$ | 150 |  | 18093 |  $\pm$ | 173
Observed | 14479 |  | 19885 |  | 12305 |  | 18184

The observed data, as well as the predicted signal and background yields
resulting from the likelihood fit to all signal and control regions, are shown
in Figs. 5 and 6. In these figures, the contributions from the three data-
taking periods are summed, accounting for the correlation of the systematic
uncertainties. The signal cross section is extracted from these categories
using the statistical procedure detailed in Section 0.6.2. In the fit,
nuisance parameters for the various systematic uncertainties and the
normalization of background processes, as described in Section 0.8, are
included. The theoretical uncertainty in the inclusive fiducial cross section
does not enter the likelihood fit for the inclusive or differential cross
section measurements. Using three bins in $M_{3}$ reduces the uncertainty in
the backgrounds without a hadronically decaying top quark, , the misidentified
electron background and the $\PW\gamma$ and $\PZ\gamma$ processes, and
decreases the total relative uncertainty in the inclusive cross section from
6.7 to 6.0%. The observed number of events for the SR3 and SR4p signal regions
in the and channels, and the predicted yields and total uncertainties in each
background component are listed in Table 1.

Figure 7:  Ranking of the systematic uncertainties from the profile likelihood
fit used in the inclusive cross section measurement. For each uncertainty, the
red and blue bands indicate the post-fit impact on the fit result. The black
dots indicate the post-fit values of the nuisance parameters and the numerical
values provide the extracted SFs for the misidentified electron background and
the normalization of the $\PW\gamma$ process. The black lines represent the
post-fit uncertainties normalized to the pre-fit uncertainties (constraints).

Figure 7 shows the ranking of the leading systematic uncertainties according
to their post-fit impact on the measured inclusive cross section. The
normalization of the $\PW\gamma$ background is the largest individual
contribution to the uncertainty in the inclusive cross section measurement and
amounts to about 2.3%. The post-fit values of the nuisance parameters (pulls)
are also shown and are found to lie within the pre-fit uncertainties. The
extracted SFs and the normalizations of the $\PZ\gamma$ and $\PW\gamma$
backgrounds also agree with the fit result in Table 0.7.3, obtained
exclusively from the control regions. Besides the extraction of the nuisance
parameters related to the normalization of the misidentified electron
component and the $\PZ\gamma$ and $\PW\gamma$ backgrounds, the only mild
constraints are 35% for the JES–FlavorQCD nuisance and 25% for the scale of
ISR. They reflect the improvement of the uncertainty in the inclusive cross
section induced by the binning in $M_{3}$.

The combined inclusive cross section of the $N_{\text{j}}=3$ and $\geq$4
channels within the fiducial phase space is measured to be

$\sigma(\ttbar\gamma)=798\pm 7\stat\pm 48\syst\mbox{$\,\text{fb}$}$ (2)

in good agreement with the SM expectation of
$\sigma^{\textrm{NLO}}(\ttbar\gamma)=773\pm 135$ fb. The measured value of the
signal strength modifier is

$r=1.032\pm 0.009\stat\pm 0.062\syst.$ (3)

A comparison of the measured cross sections and the SM prediction is shown in
Fig. 8, providing also the measurements for different choices of
$N_{\text{j}}$ and the lepton flavor. For the latter results, the likelihood
fit is performed separately in the corresponding set of signal regions and the
full set of control regions.

Figure 8: Summary of the measured cross section ratios with respect to the NLO
cross section prediction for $N_{\text{j}}=3$, $\geq$4, and combined signal
regions in the electron channel, muon channel, and the combined single-lepton
channel. The orange band indicates the theory uncertainty in the prediction.

###  0.9.2 Differential cross section measurement

The differential cross section is measured as a function of $\pt(\gamma)$,
$\abs{\eta(\gamma)}$, and $\Delta R(\ell,\gamma)$. Results are obtained
simultaneously for the electron and muon channels, the 3 jet and $\geq$4 jet
bins, and for the three data-taking periods. The binning in the SR3 and SR4p
selections for the measurement of the differential distributions at the
reconstruction level is shown in Table 0.9.2.

Binning choices in the differential measurements at the reconstruction level.
$\pt(\gamma)$ 20, 35, 50, 65, 80, 100, 120, 140, 160, 200, 260, $\geq$320
$\abs{\eta(\gamma)}$ 0, 0.15, 0.30, 0.45, 0.60, 0.75, 0.90, 1.05, 1.20, 1.35,
1.4442 $\Delta R(\ell,\gamma)$ 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0,
2.2, 2.4, 2.6, 2.8, $\geq$3.0

As described in Section 0.6.2, the same control regions are used for the
inclusive and differential cross section measurements. The signal strength is
left floating in the profile likelihood fit separately for each of the
differential bins, the $N_{\text{j}}$ selection, the lepton flavor, and the
data-taking period. The procedure has been tested to reproduce ad-hoc
modifications of the simulated signal prediction within the numerical
accuracy. The fit is performed separately for each differential distribution.

The distributions of the observables after background subtraction are further
unfolded to the fiducial particle level phase space defined in Section 0.5.
The unfolded differential cross section is defined in the same phase space as
the inclusive cross section reported above, , in the phase space where the top
quark pair is produced in association with a photon satisfying
$\pt(\gamma)>20\GeV$ and $\abs{\eta(\gamma)}<1.4442$. Signal events that are
not generated within the fiducial region amount to 5–10% and are subtracted
based on simulation. In the simulation, $\pt(\gamma)$ is taken as the
transverse momentum after accounting for the effects of QCD and electroweak
radiation.

Figure 9: The unfolded differential cross sections for $\pt(\gamma)$ (upper
left), $\abs{\eta(\gamma)}$ (upper right), and $\Delta R(\ell,\gamma)$ (lower)
compared with simulation obtained from the event generator interfaced to (red,
solid), 7 (black, dashed) and (green, dotted) for the parton shower and
hadronization. For $\pt(\gamma)$ and $\Delta R(\ell,\gamma)$, the last bin
includes the overflow. The lower panel displays the ratio of simulation to the
observation. The inner and outer bands show the statistical and total
uncertainties, respectively. Photons radiated from leptons and satisfying
$\Delta R(\ell,\gamma)>0.4$ are included in the signal and contribute
significantly to the first bin of the differential $\Delta R(\ell,\gamma)$
cross section.

Figure 10: The correlation matrices of systematic uncertainties for the
unfolded differential measurement for $\pt(\gamma)$ (upper left),
$\abs{\eta(\gamma)}$ (upper right), and $\Delta R(\ell,\gamma)$ (lower).

The $\ttbar\gamma$ MC sample is used to construct a response matrix that takes
into account both detector response and acceptance corrections. The same
corrections, SFs, and uncertainties as used in the inclusive cross section are
applied. Because of the high momentum- and angular resolutions of photons and
leptons, the fraction of events migrating from a specific momentum region at
the particle level to another one at the reconstruction level is small for all
unfolded distributions. Under such conditions, and with the chosen bin size,
no regularization term is required [79]. The TUnfold package [80] is used to
obtain the results for the three measured observables using matrix inversion.
The binning in the fiducial region is chosen such that two bins at the
reconstruction level correspond to one bin in the fiducial region for most
cases. This choice provides stability to the unfolding algorithm. The
linearity of the unfolding procedure is tested by unfolding suitably
reweighted simulated reconstruction-level yields. Differences between the
unfolded reweighted distributions and the distributions resulting from the
reweighting applied at the fiducial level are found to be negligible.

Uncertainties in the estimated signal yields are propagated through the
unfolding procedure, including the effects on the response matrix.
Experimental uncertainties from the detector response and efficiency, such as
the photon identification, JES, and tagging uncertainties, are applied as a
function of the reconstructed observable. The differential cross sections,
obtained by this procedure, are shown in Fig. 9. It includes a comparison with
simulation obtained from interfaced to [81] v2.7.1 with the EE5C tune [46] and
to 7 v7.1.4 with the CH3 tune [82] for the parton shower and hadronization.
The inclusive fiducial cross section predicted by (7) is 8.3% (5.4%) lower
than for the nominal simulation.

The bin efficiency, defined as the fraction of generated events that are
reconstructed in the corresponding bins at reconstruction level, is in the
range of 20–30%. The bin purity, defined as the fraction of reconstructed
events that originate from the corresponding bin at the particle level, is in
the range of 85–90%. For $\pt(\gamma)>120\GeV$, the uncertainties in the JES,
the photon identification efficiency, and the color reconnection modeling are
the largest sources of systematic uncertainty. The correlation matrices of the
systematic uncertainties for the unfolded differential measurements are shown
in Fig. 10. The correlations are lower in the tail of $\pt(\gamma)$ due to
larger statistical uncertainties in the simulation. The first bin of the
$\Delta R(\ell,\gamma)$ measurement is less affected by uncertainties in the
normalization of backgrounds, resulting in slightly lower correlations in this
case. All correlations from statistical uncertainties originating from the
data are below 7%. Including the uncertainty in the fiducial signal cross
section, we perform a compatibility test of the unfolded distribution and the
nominal prediction. The corresponding $\chi^{2}$ test statistic evaluates to
12.0 with 9 degrees of freedom (dof) for the $\pt(\gamma)$ distribution, 5.2
with 5 dof for $\abs{\eta(\gamma)}$, and 6.3 with 7 dof for $\Delta
R(\ell,\gamma)$.

###  0.9.3 Effective field theory interpretation

Many BSM models predict anomalous couplings of the top quark to the
electroweak gauge bosons [83, 84, 85, 86, 87, 88, 89]. The differential cross
section measurement is interpreted at the reconstruction level in SM-EFT in
the Warsaw basis [90], formed by 59 baryon number conserving dimension-six
Wilson coefficients. Among them, 15 are relevant for top quark interactions
[91]. Anomalous interactions between the top quark and the gluon
(chromomagnetic and chromoelectric dipole moment interactions) are tightly
constrained by the +jets measurements [92, 93]. Similarly, the modification of
the $\PW\PQt\Pb$ vertex is best constrained by measurements of the helicity
fractions in top quark pair production [94] and in $t$-channel single top
quark production [95].

The Wilson coefficients in the Warsaw basis inducing electroweak dipole
moments are denoted by $C_{\PQu\cmsSymbolFace{B}}^{(33)}$ and
$C_{\PQu\PW}^{(33)}$ [12]. The SM gauge symmetry provides the $\ttbar\PZ$ and
the $\ttbar\gamma$ final states with complementary constraining power [1, 2,
3, 4]. The linear relations

$\displaystyle c_{\PQt\PZ}$
$\displaystyle=\mathrm{Re}\left(-\sin\theta_{\PW}C_{\PQu\cmsSymbolFace{B}}^{(33)}+\cos\theta_{\PW}C_{\PQu\PW}^{(33)}\right),$
$\displaystyle c_{\PQt\PZ}^{\mathrm{I}}$
$\displaystyle=\mathrm{Im}\left(-\sin\theta_{\PW}C_{\PQu\cmsSymbolFace{B}}^{(33)}+\cos\theta_{\PW}C_{\PQu\PW}^{(33)}\right),$
$\displaystyle c_{\PQt\gamma}$
$\displaystyle=\mathrm{Re}\left(\cos\theta_{\PW}C_{\PQu\cmsSymbolFace{B}}^{(33)}+\sin\theta_{\PW}C_{\PQu\PW}^{(33)}\right),$
$\displaystyle c_{\PQt\gamma}^{\mathrm{I}}$
$\displaystyle=\mathrm{Im}\left(\cos\theta_{\PW}C_{\PQu\cmsSymbolFace{B}}^{(33)}+\sin\theta_{\PW}C_{\PQu\PW}^{(33)}\right),$

express the modifications of the $\ttbar\PZ$ interaction vertex, $c_{\PQt\PZ}$
and $c_{\PQt\PZ}^{\mathrm{I}}$, and of the $\ttbar\gamma$ interaction vertex,
$c_{\PQt\gamma}$ and $c_{\PQt\gamma}^{\mathrm{I}}$, in the Warsaw basis. The
constraint $C_{\PQu\PW}^{(33)}=0$ ensures a SM $\PW\PQt\PQb$ vertex. Under
this assumption, $c_{\PQt\PZ}$ ($c_{\PQt\PZ}^{\mathrm{I}}$) and
$c_{\PQt\gamma}$ ($c_{\PQt\gamma}^{\mathrm{I}}$) are dependent and we choose
the former to parametrize the BSM hypothesis.

The spectrum of $\pt(\gamma)$ is a sensitive probe to such modifications.
Other observables, , $\abs{\eta(\gamma)}$ or $\Delta R(\ell,\gamma)$, are
found to be largely insensitive. Wilson coefficients that are not considered
in this work are kept at their SM values and the SM-EFT expansion parameter is
set to a mass scale $\Lambda=1\TeV$. Using the SM-EFT parametrization from
Ref. [12], simulated samples at the particle level are produced with nonzero
values of the Wilson coefficients $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$. The $\ttbar\gamma$ signal process and all
background processes affected by $c_{\PQt\PZ}$ or $c_{\PQt\PZ}^{\mathrm{I}}$
at the ME level are included in the simulation. These samples are used to
reweight the nominal simulation in the fiducial phase space using the
quadratic parametrization detailed in Ref. [96]. The reweighting procedure is
validated at the reconstruction level with a reduced set of statistically
independent samples for nonzero values of $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$ and excellent agreement is found.

The SR3 and SR4p signal regions and the $\pt(\gamma)$ boundaries defining the
bins in Table 0.9.2 are used to construct a binned likelihood function
$L(\theta)$ as a product of Poisson probabilities from the yields in the
signal and control regions. The nuisance parameters are labeled by $\theta$
and the profile likelihood ratio
$q=-2\ln(L(\hat{\theta},\vec{C})/L(\hat{\theta}_{\text{max}}))$ is the test
statistic. Here, $\hat{\theta}$ is the set of nuisance parameters maximizing
the likelihood function at a BSM point defined by the Wilson coefficients
collectively denoted by $\vec{C}$. In the denominator,
$\hat{\theta}_{\text{max}}$ maximizes the likelihood function in the BSM
parameter space. The $\ttbar\gamma$ signal is normalized according to the SM
expectation at NLO in QCD and its uncertainty is included as a nuisance.

Figure 11 shows the result of the fit for the SR3 and SR4p signal regions and
separately for each lepton flavor. No deviations from the SM expectations are
observed. The best fit point is found at ($c_{\PQt\PZ}$,
$c_{\PQt\PZ}^{\mathrm{I}}$) = ($-0.28$, $-0.02$) and the corresponding
spectrum is overlaid together with the ones from several other choices for
nonzero values of the Wilson coefficients. Figure 12 displays the one-
dimensional scans of the coefficients. In the upper row, one Wilson
coefficient is scanned, while the other is profiled. The lower row shows the
scans, where the second Wilson coefficient is set to zero. The second local
minima in the scans of the log-likelihood as a function of $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$, visible in Fig. 12 (lower row), is the result of a
mild tension with the SM hypothesis in conjunction with the similarity of the
predictions for Wilson coefficients with opposite sign. The corresponding one-
dimensional intervals at 68 and 95% confidence interval () are listed in Table
0.9.3 and are more stringent than previous limits obtained from $\ttbar\PZ$
final states [97, 98]. Models with nonzero electroweak dipole moments predict
a harder $\pt(\gamma)$ spectrum that is not observed in data. Figure 13 shows
the best fit result in the two-dimensional plane spanned by $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$ and the log-likelihood scan. The SM prediction is
within the 95% of the best fit value of the $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$ coefficients.

In Fig.14, the 95% intervals are compared with the previous CMS results based
on the inclusive [99] and differential [97] $\ttbar\PZ$ cross section
measurement, a CMS result based on in final states with additional leptons
[96], and the most recent ATLAS result [98] (lower). The result of a global
SM-EFT analysis, including results from Ref. [97], is also shown [100]. The
present result improves upon the previous constraints by about a factor of
2.5.

Figure 11:  The observed (points) and predicted (shaded histograms) post-fit
yields for the combined Run 2 data set in the SR3 (upper) and SR4p (lower)
signal regions for the electron (left) and muon channel (right). The vertical
bars on the points give the statistical uncertainties in the data. The lower
panel displays the ratio of the data to the predictions and the hatched
regions show the total uncertainty. The solid line shows the SM-EFT best fit
prediction and the dashed lines show different predictions for nonzero Wilson
coefficients, $c_{\PQt\PZ}=0.45$ (light blue), $c_{\PQt\PZ}^{\mathrm{I}}=0.45$
(green), and $c_{\PQt\PZ}=-0.45$ (dark blue), where $\Lambda$ is set to 1.

Figure 12: Results of the one-dimensional scans of the Wilson coefficients
$c_{\PQt\PZ}$ (left) and $c_{\PQt\PZ}^{\mathrm{I}}$ (right). In the upper row,
the other Wilson coefficient is profiled, while in the lower row it is set to
zero. The green and orange bands indicate the 68 and 95% contours on the
Wilson coefficients, respectively.  Figure 13: Result of the two-dimensional
scan of the Wilson coefficients $c_{\PQt\PZ}$ and $c_{\PQt\PZ}^{\mathrm{I}}$.
The shading quantified by the color scale on the right reflects the negative
log-likelihood ratio with respect to the best fit value that is designated by
the star. The green and orange lines indicate the 68 and 95% contours from the
fit, respectively. The allowed areas are those between the two green contours
and that inside the orange contour. The dot shows the SM prediction.  Figure
14:  The observed 95% intervals for the Wilson coefficients from this
measurement with the other Wilson coefficient set to zero, the previous CMS
results based on the inclusive [99] and differential [97] $\ttbar\PZ$ cross
section measurement, a CMS result based on in final states with additional
leptons [96], and the most recent ATLAS result [98]. The result of a global
SM-EFT analysis, including results from Ref. [97], is also shown [100]. The
vertical line displays the SM prediction.

Summary of the one-dimensional intervals at 68 and 95% .  Wilson coefficient
68% interval 95% interval $(\Lambda/\TeV)^{2}$ $(\Lambda/\TeV)^{2}$ Expected
$c_{\PQt\PZ}$ $c_{\PQt\PZ}^{\mathrm{I}}=0$ [$-0.19$, $0.20$] [$-0.29$, $0.31$]
profiled [$-0.19$, $0.20$] [$-0.29$, $0.31$] $c_{\PQt\PZ}^{\mathrm{I}}$
$c_{\PQt\PZ}=0$ [$-0.20$, $0.20$] [$-0.30$, $0.30$] profiled [$-0.20$, $0.20$]
[$-0.30$, $0.30$] Observed $c_{\PQt\PZ}$ $c_{\PQt\PZ}^{\mathrm{I}}=0$
[$-0.36$, $-0.17$] [$-0.43$, $0.38$] profiled [$-0.36$, $0.04$] [$-0.43$,
$0.38$] $c_{\PQt\PZ}^{\mathrm{I}}$ $c_{\PQt\PZ}=0$ [$-0.36$, $-0.16$],
[$0.18$, $0.35$] [$-0.43$, $0.43$] profiled [$-0.32$, $0.31$] [$-0.42$,
$0.42$]

##  0.10 Summary

A measurement of the cross section for the top quark pair production in
association with a photon using a data sample of proton-proton collisions at
$\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of 137, collected
with the CMS detector at the LHC has been presented. It is the first result of
the CMS Collaboration on measurements in the $\ttbar\gamma$ final state using
13data. The analysis has been performed in the single-lepton channel with
events with exactly three and four or more jets among which at least one is
tagged. Background components with misidentified electrons, photons
originating in the hadronization of jets, the multijet component, and prompt
photons from the $\PW\gamma$ and $\PZ\gamma$ processes are estimated from
data. The measured inclusive cross section in a fiducial region with photon
transverse momentum $\pt(\gamma)>20\GeV$ and jet multiplicity greater than
three is measured to be $798\pm 7\stat\pm 48\syst\mbox{$\,\text{fb}$}$, in
good agreement with the standard model prediction at next-to-leading order in
quantum chromodynamics.

Differential cross sections for $\pt(\gamma)$ and absolute value of the photon
pseudorapidity, as well as for the angular separation of the lepton and the
photon, have been measured and unfolded to particle level in the same fiducial
volume. The comparison to simulation was performed using different showering
algorithms. The measurements are also interpreted in terms of limits on the
Wilson coefficients in the context of the standard model effective field
theory. The confidence intervals for the Wilson coefficients $c_{\PQt\PZ}$ and
$c_{\PQt\PZ}^{\mathrm{I}}$ are the most stringent to date.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES (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); GSRT
(Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST,
STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (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 National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Higher Education, project no. 0723-2020-0041 (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 Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the
Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, M. Dragicevic, A.
Escalante Del Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D.
Liko, I. Mikulec, P. Paulitsch, F.M. Pitters, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1 Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, M. Delcourt, H. El Faham, S.
Lowette, S. Moortgat, A. Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, L. Favart, A. Grebenyuk,
A.K. Kalsi, K. Lee, M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. Pétré,
A. Popov, N. Postiau, E. Starling, L. Thomas, M. Vanden Bemden, C. Vander
Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, J. Knolle, L. Lambrecht, G. Mestdach, M. Niedziela, C.
Roskas, A. Samalan, K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I.S. Donertas,
A. Giammanco, K. Jaffel, Sa. Jain, V. Lemaitre, K. Mondal, J. Prisciandaro, A.
Taliercio, M. Teklishyn, T.T. Tran, P. Vischia, S. Wertz Centro Brasileiro de
Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, M. Alves Gallo Pereira, M. Barroso Ferreira Filho, H.
BRANDAO MALBOUISSON, W. Carvalho, J. Chinellato4, E.M. Da Costa, G.G. Da
Silveira5, D. De Jesus Damiao, S. Fonseca De Souza, D. Matos Figueiredo, C.
Mora Herrera, K. Mota Amarilo, L. Mundim, H. Nogima, P. Rebello Teles, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a,5, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan Department of
Physics, Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, C. Dozen7, Z. Hu, J. Martins8, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen6, H.S. Chen6, M. Chen, F. Iemmi, A. Kapoor, D. Leggat, H.
Liao, Z.-A. LIU6, V. Milosevic, F. Monti, R. Sharma, J. Tao, J. Thomas-
wilsker, J. Wang, H. Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear
Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, X. Lyu, Y. Mao, S.J.
Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou, China
M. Lu, Z. You Institute of Modern Physics and Key Laboratory of Nuclear
Physics and Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
Z. Lin, M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar González
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
A. Attikis, K. Christoforou, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski,
H. Saka Charles University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton Escuela Politecnica Nacional, Quito,
Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla13, S. Khalil14 Center for High Energy Physics (CHEP-FU), Fayoum
University, El-Fayoum, Egypt
A. Lotfy, M.A. Mahmoud National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, S. Nandan, C. Nielsen, J. Pata, M. Raidal, L. Tani, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
S. Bharthuar, E. Brücken, F. Garcia, J. Havukainen, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, M. Lotti, L. Martikainen, M.
Myllymäki, J. Ott, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
P. Luukka, H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-
Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro15, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, A.
Cappati, C. Charlot, O. Davignon, B. Diab, G. Falmagne, S. Ghosh, R. Granier
de Cassagnac, A. Hakimi, I. Kucher, M. Nguyen, C. Ochando, P. Paganini, J.
Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de
Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram16, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine16, U. Goerlach, C. Grimault,
A.-C. Le Bihan, E. Nibigira, P. Van Hove Institut de Physique des 2 Infinis de
Lyon (IP2I ), Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L. Mirabito, S.
Perries, K. Shchablo, V. Sordini, L. Torterotot, G. Touquet, M. Vander Donckt,
S. Viret Georgian Technical University, Tbilisi, Georgia
I. Lomidze, T. Toriashvili17, Z. Tsamalaidze12 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, N. Röwert, J.
Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
A. Dodonova, D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T.
Hebbeker, K. Hoepfner, F. Ivone, H. Keller, L. Mastrolorenzo, M. Merschmeyer,
A. Meyer, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A.
Pozdnyakov, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A.
Sharma, L. Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad18, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl19, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, S. Bhattacharya, A.A. Bin Anuar, K. Borras20, V.
Botta, D. Brunner, A. Campbell, A. Cardini, C. Cheng, F. Colombina, S.
Consuegra Rodríguez, G. Correia Silva, V. Danilov, L. Didukh, G. Eckerlin, D.
Eckstein, L.I. Estevez Banos, O. Filatov, E. Gallo21, A. Geiser, A. Giraldi,
A. Grohsjean, M. Guthoff, A. Jafari22, N.Z. Jomhari, H. Jung, A. Kasem20, M.
Kasemann, H. Kaveh, C. Kleinwort, D. Krücker, W. Lange, J. Lidrych, K. Lipka,
W. Lohmann23, R. Mankel, I.-A. Melzer-Pellmann, J. Metwally, A.B. Meyer, M.
Meyer, J. Mnich, A. Mussgiller, Y. Otarid, D. Pérez Adán, D. Pitzl, A.
Raspereza, B. Ribeiro Lopes, J. Rübenach, A. Saggio, A. Saibel, M. Savitskyi,
M. Scham, V. Scheurer, C. Schwanenberger21, A. Singh, R.E. Sosa Ricardo, D.
Stafford, N. Tonon, O. Turkot, M. Van De Klundert, R. Walsh, D. Walter, Y.
Wen, K. Wichmann, L. Wiens, C. Wissing, S. Wuchterl University of Hamburg,
Hamburg, Germany
R. Aggleton, S. Albrecht, S. Bein, L. Benato, A. Benecke, P. Connor, K. De
Leo, M. Eich, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini,
J. Haller, A. Hinzmann, G. Kasieczka, R. Klanner, R. Kogler, T. Kramer, V.
Kutzner, J. Lange, T. Lange, A. Lobanov, A. Malara, A. Nigamova, K.J. Pena
Rodriguez, O. Rieger, P. Schleper, M. Schröder, J. Schwandt, D. Schwarz, J.
Sonneveld, H. Stadie, G. Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher
Institut fuer Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El Morabit, N.
Faltermann, M. Giffels, J.o. Gosewisch, A. Gottmann, F. Hartmann19, C.
Heidecker, U. Husemann, I. Katkov24, P. Keicher, R. Koppenhöfer, S. Maier, M.
Metzler, S. Mitra, Th. Müller, M. Neukum, A. Nürnberg, G. Quast, K. Rabbertz,
J. Rauser, D. Savoiu, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich, J. Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R.
Wolf, S. Wozniewski, S. Wunsch Institute of Nuclear and Particle Physics
(INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, A. Stakia
National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, K. Farkas, M.M.A. Gadallah25, S. Lökös26, P. Major, K. Mandal, A.
Mehta, G. Pasztor, A.J. Rádl, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
M. Bartók27, G. Bencze, C. Hajdu, D. Horvath28, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi27, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi29, B. Ujvari Karoly Robert Campus, MATE Institute of
Technology
T. Csorgo30, F. Nemes30, T. Novak Indian Institute of Science (IISc),
Bangalore, India
J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National Institute of
Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati31, A.K. Das, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu32, A. Nayak32, P. Saha, N. Sur, S.K. Swain, D. Vats32 Panjab
University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra33, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh,
A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, M. Gola, S. Keshri, A. Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear Physics,
HBNI, Kolkata, India
M. Bharti34, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Dutta,
B. Gomber35, M. Maity36, P. Palit, P.K. Rout, G. Saha, B. Sahu, S. Sarkar, M.
Sharan, B. Singh34, S. Thakur34 Indian Institute of Technology Madras, Madras,
India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar37, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, M. Kumar, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar, G. Majumder, K.
Mazumdar, S. Mukherjee Indian Institute of Science Education and Research
(IISER), Pune, India
K. Alpana, S. Dube, B. Kansal, A. Laha, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi38, M. Zeinali39 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani40, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Grunewald INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,41, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pellecchiaa,b, A. Pompilia,b, G.
Pugliesea,c, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R.
Vendittia, P. Verwilligena INFN Sezione di Bologna a, Università di Bologna b,
Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, L.
Brigliadoria, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A. Fanfania,b,
P. Giacomellia, L. Giommia,b, C. Grandia, L. Guiduccia,b, S. Lo Meoa,42, L.
Lunertia,b, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A. Perrottaa, F.
Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b INFN Sezione di
Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,43, S. Costaa,b,43, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,43, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M. Lizzoa,b, M.
Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L. Viliania INFN
Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,19, F. De Guioa,b,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D. Menascea, L. Moronia,
M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, N. Redaellia, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,19, D. Zuoloa,b INFN Sezione di Napoli a, Università
di Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c,
Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, F. Carnevalia,b, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,19, P. Paoluccia,19, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U.
Gasparinia,b, S.Y. Hoha,b, L. Layera,44, M. Margonia,b, A.T. Meneguzzoa,b, J.
Pazzinia,b, M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G.
Stronga, M. Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
P. Asenova,45, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, M.
Magherinib, G. Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia,45,
A. Piccinellia,b, A. Rossia,b, A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b
INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore di Pisa
c, Pisa Italy, Università di Siena d, Siena, Italy
P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T. Boccalia, E.
Bossinia,b, R. Castaldia, M.A. Cioccia,b, V. D’Amantea,d, R. Dell’Orsoa, M.R.
Di Domenicoa,d, S. Donatoa, A. Giassia, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, S. Paroliaa,b, G. Ramirez-Sancheza,c,
A. Rizzia,b, G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b,
P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
M. Campanaa,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M.
Diemoza, E. Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia, R. Tramontanoa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, N.
Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B.
Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b, E.
Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N.
Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, K.
Shchelinaa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa,
M. Tornagoa,b, D. Trocinoa,b, A. Vagnerini INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, G. Sorrentinoa,b, F. Vazzolera,b Kyungpook National
University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Kim, J. Lee, S.W. Lee, C.S.
Moon, Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam
National University, Institute for Universe and Elementary Particles, Kwangju,
Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon University of
Seoul, Seoul, Korea
W. Jang, D. Jeon, D.Y. Kang, Y. Kang, J.H. Kim, S. Kim, B. Ko, J.S.H. Lee, Y.
Lee, I.C. Park, Y. Roh, M.S. Ryu, D. Song, I.J. Watson, 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. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait
T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga, Latvia
T. Torims, V. Veckalns46 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
N. Bin Norjoharuddeen, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, M. León Coello, J.A. Murillo Quijada, A.
Sehrawat, L. Valencia Palomo Centro de Investigacion y de Estudios Avanzados
del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz47,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
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. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, M. Pisano, J. Seixas, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov, V. Karjavine, V.
Korenkov, 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. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim52, E. Kuznetsova53, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, D.
Kirpichnikov, M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko54, V. Popov, A.
Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow Institute of
Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, R. Chistov55, M. Danilov56, P. Parygin, S. Polikarpov55 P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin57, L. Dudko, A. Gribushin, V.
Klyukhin, N. Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov58, T. Dimova58, L. Kardapoltsev58, A. Kozyrev58, I. Ovtin58, Y.
Skovpen58 Institute for High Energy Physics of National Research Centre
‘Kurchatov Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, D. Elumakhov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, 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. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic59, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones
Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, L. Urda Gómez, C. Willmott
Universidad Autónoma de Madrid, Madrid, Spain
J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo, Instituto
Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA),
Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, C. Ramón
Álvarez, J. Ripoll Sau, V. Rodríguez Bouza, A. Trapote, N. Trevisani Instituto
de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, J. Duarte Campderros, M.
Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca, A. García Alonso, G.
Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, P.
Matorras Cuevas, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno, L.
Scodellaro, I. Vila, J.M. Vizan Garcia University of Colombo, Colombo, Sri
Lanka
MK Jayananda, B. Kailasapathy60, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, J. Alimena, E. Auffray, G. Auzinger, J. Baechler,
P. Baillon${}^{\textrm{\textdagger}}$, D. Barney, J. Bendavid, M. Bianco, A.
Bocci, T. Camporesi, M. Capeans Garrido, G. Cerminara, S.S. Chhibra, L.
Cristella, D. d’Enterria, A. Dabrowski, N. Daci, A. David, A. De Roeck, M.M.
Defranchis, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita61, D. Fasanella, S. Fiorendi, A. Florent, G.
Franzoni, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar,
J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S. Laurila, P. Lecoq, K. Long,
C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli, A.C. Marini, F. Meijers, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F.
Pantaleo, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A.
Pfeiffer, M. Pierini, D. Piparo, M. Pitt, H. Qu, T. Quast, D. Rabady, A. Racz,
G. Reales Gutiérrez, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S.
Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys,
P. Sphicas62, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van
Onsem, M. Verzetti, J. Wanczyk63, K.A. Wozniak, W.D. Zeuner Paul Scherrer
Institut, Villigen, Switzerland
L. Caminada64, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
K. Androsov63, M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De
Cosa, G. Dissertori, M. Dittmar, M. Donegà, C. Dorfer, F. Eble, K. Gedia, F.
Glessgen, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon,
R.A. Manzoni, C. Martin Perez, M.T. Meinhard, F. Nessi-Tedaldi, J. Niedziela,
F. Pauss, V. Perovic, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V.
Stampf, J. Steggemann63, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
C. Amsler65, P. Bärtschi, C. Botta, D. Brzhechko, M.F. Canelli, K. Cormier, A.
De Wit, R. Del Burgo, J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster,
S. Leontsinis, A. Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I.
Neutelings, A. Reimers, P. Robmann, S. Sanchez Cruz, K. Schweiger, Y.
Takahashi National Central University, Chung-Li, Taiwan
C. Adloff66, C.M. Kuo, W. Lin, A. Roy, T. Sarkar36, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen, H.y. Wu, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin67, Z.S. Demiroglu, F. Dolek, I. Dumanoglu68, E.
Eskut, Y. Guler, E. Gurpinar Guler69, I. Hos70, C. Isik, O. Kara, A. Kayis
Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir71, A. Polatoz, A.E. Simsek, B.
Tali72, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East
Technical University, Physics Department, Ankara, Turkey
B. Isildak73, G. Karapinar74, K. Ocalan75, M. Yalvac76 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya77, O. Kaya78, Ö. Özçelik, S.
Tekten79, E.A. Yetkin80 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak68, Y. Komurcu, S. Sen81 Istanbul University, Istanbul,
Turkey
S. Cerci72, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci72 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
D. Anthony, E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D.
Cussans, H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, M.l. Holmberg82, L.
Kreczko, B. Krikler, S. Paramesvaran, S. Seif El Nasr-Storey, V.J. Smith, N.
Stylianou83, K. Walkingshaw Pass, R. White Rutherford Appleton Laboratory,
Didcot, United Kingdom
K.W. Bell, A. Belyaev84, C. Brew, R.M. Brown, D.J.A. Cockerill, C. Cooke, K.V.
Ellis, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, I.R.
Tomalin, T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal85, D. Colling, P. Dauncey, G. Davies, M. Della Negra, S.
Fayer, G. Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, D.G. Monk, J. Nash86, M. Pesaresi, D.M.
Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A. Tapper,
K. Uchida, T. Virdee19, M. Vojinovic, N. Wardle, S.N. Webb, D. Winterbottom,
A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
K. Coldham, J.E. Cole, A. Khan, P. Kyberd, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, M. Saunders, S. Sawant, C. Sutantawibul,
J. Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson, C.U. Perez,
P. Rumerio87, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, E. Fontanesi, D.
Gastler, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, A. Tsatsos,
S. Yuan, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez20, D. Cutts, M. Hadley, U. Heintz, J.M.
Hogan88, G. Landsberg, K.T. Lau, M. Lukasik, J. Luo, M. Narain, S. Sagir89, E.
Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University of California, Davis,
Davis, USA
J. Bonilla, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M.
Chertok, J. Conway, P.T. Cox, R. Erbacher, G. Haza, F. Jensen, O. Kukral, R.
Lander, M. Mulhearn, D. Pellett, B. Regnery, D. Taylor, Y. Yao, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Datta, D. Hamilton, J. Hauser, M. Ignatenko, M.A.
Iqbal, T. Lam, W.A. Nash, S. Regnard, D. Saltzberg, B. Stone, V. Valuev
University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, M. Gordon, G. Hanson, G. Karapostoli,
O.R. Long, N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang