SMP-18-008

SMP-18-008

# Search for anomalous triple gauge couplings in $\PW\PW$ and $\PW\PZ$
production in lepton + jet events in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

A search is presented for three additional operators that would lead to
anomalous $\PW\PW\gamma$ or $\PW\PW\PZ$ couplings with respect to those in the
standard model. They are constrained by studying events with two vector
bosons; a $\PW$ boson decaying to $\Pe\Pgn$ or $\Pgm\Pgn$, and a $\PW$ or
$\PZ$ boson decaying hadronically, reconstructed as a single, massive, large-
radius jet. The search uses a data set of proton-proton collisions at a
centre-of-mass energy of 13, recorded by the CMS experiment at the CERN LHC in
2016, and corresponding to an integrated luminosity of 35.9. Using the
reconstructed diboson invariant mass, 95% confidence intervals are obtained
for the anomalous coupling parameters of
$-1.58<c_{\PW\PW\PW}/\Lambda^{2}<1.59\TeV^{-2}$,
$-2.00<c_{\PW}/\Lambda^{2}<2.65\TeV^{-2}$, and
$-8.78<c_{\PB}/\Lambda^{2}<8.54\TeV^{-2}$, in agreement with standard model
expectations of zero for each parameter. These are the strictest bounds on
these parameters to date.

##  0.1 Introduction

The standard model (SM) of particle physics provides a thoroughly tested
description of the known elementary particles and their interactions. Its
theoretical and observational shortcomings may be explained by the existence
of further inner structure at shorter distances or, equivalently, higher
energies. One of the goals of the LHC and its detectors is to reveal such
structure if it exists.

If the physics beyond the SM does not contain new low-mass particles and is
consistent with the symmetries of the SM, its effects can be parametrized in
terms of an effective field theory (EFT). In this approach, the new-physics
model is constructed by expanding around the SM and integrating over degrees
of freedom at higher energies. This leads to additional terms in the
Lagrangian, proportional to inverse powers of the mass scale of the new
particles, up to numerical factors that depend on the new couplings. We refer
to the overall energy scale suppressing these terms as $\Lambda$. In this
paper we focus on possible additional contributions to the production of
$\PW\PW$ and $\PW\PZ$ final states parametrized in such an EFT model by
dimension-six operators [1, 2], with the following CP-conserving modification
to the SM Lagrangian:

$\delta\mathcal{L}=\frac{c_{\PW\PW\PW}}{\Lambda^{2}}\text{Tr}\left[\PW_{\mu\nu}\PW^{\nu\rho}\PW^{\mu}_{\rho}\right]+\frac{c_{\PW}}{\Lambda^{2}}\left(\text{D}_{\mu}\Phi\right)^{{\dagger}}\PW^{\mu\nu}\left(\text{D}_{\nu}\Phi\right)+\frac{c_{\PB}}{\Lambda^{2}}\left(\text{D}_{\mu}\Phi\right)^{{\dagger}}\PB^{\mu\nu}\left(\text{D}_{\nu}\Phi\right),$
(1)

where $\Phi$ is the SM Higgs boson field doublet and

$\displaystyle\text{D}_{\mu}$
$\displaystyle=\partial_{\mu}+\frac{i}{2}g\tau_{I}\PW^{I}_{\mu}+\frac{i}{2}g^{\prime}\PB_{\mu}$
(2) $\displaystyle\PW_{\mu\nu}$
$\displaystyle=\frac{i}{2}g\tau_{I}\left(\partial_{\mu}\PW^{I}_{\nu}-\partial_{\nu}\PW^{I}_{\mu}+g\epsilon^{I}_{JK}\PW^{J}_{\mu}\PW^{K}_{\nu}\right)$
$\displaystyle\PB_{\mu\nu}$
$\displaystyle=\frac{i}{2}g^{\prime}\left(\partial_{\mu}\PB_{\nu}-\partial_{\nu}\PB_{\mu}\right).$

The parameters $\\{c_{\PW\PW\PW},c_{\PW},c_{\PB}\\}$ control the size of each
new contribution. These additional contributions induce triple gauge couplings
(TGCs) beyond those present in the SM, and are referred to as anomalous TGCs
(aTGCs). The SM behaviour is therefore recovered when
$c_{\PW\PW\PW}=c_{\PW}=c_{\PB}=0$. Nonzero aTGCs would lead to increased
$\PW\PW$ and $\PW\PZ$ production cross sections at high vector boson pair
invariant masses. The search for nonzero aTGCs is performed in the
semileptonic final state, with one $\PW$ boson decaying to a lepton ($\Pe$ or
$\Pgm$) and a neutrino, and the other $\PW$ or $\PZ$ boson decaying
hadronically. The leading-order (LO) Feynman diagram for this process
involving triple gauge couplings is shown in Fig. 1.

Figure 1: The LO Feynman diagram for the diboson process involving triple
gauge couplings studied in this analysis. One $\PW$ boson decays to a lepton
and a neutrino, and the other $\PW/\PZ$ boson decays to a quark-antiquark
pair.

Although the hadronic decay channel of a gauge boson has a larger branching
fraction than the leptonic decay channel, it suffers from the presence of
background processes with significantly larger cross sections, especially
those producing multiple hadronic jets. The semileptonic final state therefore
offers a good balance between efficiency and purity. It also allows a full
kinematic reconstruction of the diboson system, using the $\PW$ mass to
constrain the combined four-momentum of the lepton and neutrino. Since the
effects of the aTGCs are most dramatic at high boson momenta, we consider only
hadronic decays from highly Lorentz-boosted vector bosons where the
hadronization products of the two final state quarks overlap in the detector
to form a single, large-radius jet. This analysis distinguishes $\PW\PW$ and
$\PW\PZ$ production using the invariant mass of the jet created as the result
of the hadronic decay of the $\PW/\PZ$ boson, thereby providing some
discrimination between the different aTGC contributions. However, the
relatively poor jet mass resolution significantly limits this separation
power. Further discrimination between the different aTGC parameters is only
possible by studying angular variables that characterize the diboson
production and decay products [3, 4]. Such analysis is outside the scope of
this search. To reduce contributions from the significant $\PW\text{+jets}$ SM
background processes, jet substructure techniques are used for the boson
identification [5].

Previous searches for such signatures by the ATLAS and CMS experiments have
focused on leptonic decays [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 16, 18, 19, 20, 21, 22, 23, 24]. Earlier studies in the semileptonic final
states [25, 26, 27, 28] were performed using data taken at centre-of-mass
energies of 7 and 8. Similar boosted-boson reconstruction techniques were also
used at a centre-of-mass energy of 13, in the context of search for a narrow
resonance decaying to $\PW\PW$ or $\PW\PZ$ in the semileptonic final state
[29].

In this paper, the detector is described in Section 0.2; the data and
simulated samples are described in Section 0.3; the object reconstruction and
the event selection are described in Section 0.4; the signal and background
modelling are described in Sections 0.5 and 0.6, respectively; and the
systematic uncertainties affecting this analysis are described in Section 0.7.
The results are shown in Section 0.8, and a summary is presented in Section
0.9.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are 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 [30]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 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.

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. [31].

##  0.3 Data and simulated samples

The analysis is performed on proton-proton ($\Pp\Pp$) collision data recorded
by the CMS detector in 2016 at a centre-of-mass energy of 13, corresponding to
an integrated luminosity of 35.9.

The signal is simulated using v2.4.2 [32] at next-to-leading order (NLO) in
the strong coupling $\alpS$, using the “EWDim6” model, which implements the
aforementioned EFT [2]. The simulated signal processes include decays of the
$\PW$ boson to a tau lepton and neutrino, with the subsequent decay of the tau
lepton to a muon or electron and the accompanying neutrino. The simulated
signal events are first generated with all three aTGC parameters set to
nonzero positive values, and then reweighted to different permutations of zero
and nonzero aTGCs using the matrix-element event weights computed by . This
includes the scenario where all aTGCs parameters are zero, corresponding to SM
diboson production. The signal sample is rescaled such that the cross sections
for diboson production in this scenario are normalized to the corresponding SM
next-to-next-to-leading order (NNLO) cross sections [33, 34] as described in
Section 0.5.

For the simulation of background SM processes, a variety of event generators
are used. The v1.0 [35, 36, 37, 38] generator is used for the generation of
$\cPqt\PW$ events, whilst v2.0 [39, 40, 41, 42, 43, 44, 45] is used for the
generation of and $t$-channel single top quark events, all at NLO. The v2.2.2
generator is used to generate $\PW\text{+jets}$ and $s$-channel single top
quark processes at NLO. The parton showering and hadronization for all samples
are performed with [46], using v8.205 for the $s$-channel single top quark
samples, and v8.212 for all other samples. The FxFx merging scheme [47, 32] is
used for samples generated at NLO, and the MLM merging scheme [48] for those
generated at LO. The CUETP8M2T4 underlying event tune [49] is used for the
sample, whilst the CUETP8M1 underlying event tune [50] is used for all other
samples.

The $\PW\text{+jets}$ samples are normalized using inclusive cross sections
calculated at NNLO using v6.6 [51]. The Top++2.0 [52] program is used to
calculate the cross section at NNLO in quantum chromodynamics (QCD), including
resummation of next-to-next-to-leading logarithmic soft gluon terms.

All events are generated with the NNPDF 3.0 parton distribution functions
(PDFs) [53]. Detector response in the Monte Carlo (MC) samples is simulated
using a detailed description of the CMS detector implemented with the [54]
package, and processed using the same software chain used for collision data.
Residual differences between data and simulation with respect to jet energy
scale, jet energy resolution, jet tagging efficiency, lepton identification
efficiency, lepton energy scale, trigger efficiency, and jet substructure
selection efficiency are corrected by corresponding scale factors. Minimum-
bias events are superimposed on the simulated events to emulate the effects of
additional $\Pp\Pp$ interactions within the same or nearby bunch crossings
(pileup), with an average number of 23 $\Pp\Pp$ collisions per bunch crossing.
All simulated samples are reweighted to match the distribution of the number
of $\Pp\Pp$ interactions per bunch crossing as measured in the data.

##  0.4 Object reconstruction and event selection

Events targeting the electronic decay of the $\PW$ boson are selected by a
single-electron trigger that requires the event to contain either (i) at least
one electron candidate satisfying “loose” isolation criteria with transverse
momentum $\pt>45\GeV$ and $\abs{\eta}<2.5$, or (ii) at least one electron
candidate with $\pt>115\GeV$ and $\abs{\eta}<2.5$ without any additional
electron isolation criteria [55, 29]. For the muonic $\PW$ boson decay
channel, data are selected by a single-muon trigger [56] that requires an
event to contain at least one muon candidate with $\pt>50\GeV$ and
$\abs{\eta}<2.4$.

Events accepted for analysis must pass a number of quality criteria designed
to reject events containing significant noise in any of the subdetectors, and
are also required to have at least one well-reconstructed collision vertex.
The reconstructed vertex with the largest value of summed object $\pt^{2}$ is
the primary $\Pp\Pp$ interaction vertex. The objects considered are (i) jets
clustered using the anti-jet algorithm [57, 58], with the tracks assigned to
the vertex as the input, and (ii) the associated missing transverse momentum,
taken as the negative vector sum of the of those jets, to account for neutral
particles. More details are given in Section 9.4.1 of Ref. [59].

The particle-flow (PF) algorithm [60] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the primary interaction vertex as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The momentum of muons is obtained from
the curvature of the corresponding track. The energy of charged hadrons is
determined from a combination of their momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for zero-suppression
effects and for the response of the calorimeters to hadronic showers. Finally,
the energy of neutral hadrons is obtained from the corresponding corrected
ECAL and HCAL energies.

Electrons are reconstructed by combining information from the central tracking
detector and ECAL [61, 55]. Electron candidates are required to exceed a
transverse momentum threshold of 50, and to lie within $\abs{\eta}<2.5$, but
outside of the transition region between ECAL barrel and endcaps
($1.44<\abs{\eta}<1.57$) to avoid low-quality reconstruction due to a gap
between the barrel and endcap calorimeters, which is filled with services and
cables. Electron candidates must pass a number of identification and isolation
requirements optimized for high-electrons [62, 55, 63]. These criteria include
requirements on the geometrical matching between the ECAL deposit and the
reconstructed track, the ratio of energies deposited in HCAL and ECAL
calorimeters, the shape of the ECAL deposit, the impact parameters of the
track, and the number of missing hits in the silicon tracker. A requirement on
the electron isolation is also applied, which considers tracks originating
from the same vertex as the electron, within $\Delta
R(\text{electron},\text{track})=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}<0.3$
of the electron, where $\Delta\eta$ and $\Delta\phi$ are the separations in
pseudorapidity and azimuthal angle (in radians), respectively, between the
electron and a track. The scalar sum of the of these tracks is required to be
less than 5.

Muons are reconstructed combining tracks in the CMS muon system and inner
tracker [64, 56]. They are required to have $\pt>53\GeV$ and $\abs{\eta}<2.4$.
Muons must satisfy various reconstruction and identification requirements on
the impact parameters of the track, the number of hits in the pixel tracker,
the number of tracker layers with hits, the relative uncertainty, the number
of muon chambers included in the muon track fit, and the number of segments
reconstructed in the muon detector planes. Muons are considered isolated if
the scalar sum of the of tracks from the primary vertex within $\Delta R<0.3$
of the muon is less than 10% of the of the muon.

Events are required to contain a single lepton (electron or muon). To reject
backgrounds from Drell–Yan and fully leptonic events, we reject events that
contain additional leptons, where the threshold for the additional leptons is
lowered to 35 (20)for the electron (muon) channel, respectively.

Jets are reconstructed from PF particles, clustered by the anti-algorithm [57,
58] with distance parameters of 0.4 and 0.8, denoted as AK4 and AK8 jets,
respectively. The momentum of a jet is determined as the vectorial sum of all
particle momenta in the jet, and is found from simulation to be, on average,
within 5 to 10% of the true momentum of the generated particles in the jet
over the whole distribution and detector acceptance. Additional $\Pp\Pp$
interactions within the same or nearby bunch crossings can contribute
additional tracks and calorimetric energy depositions, increasing the apparent
jet momentum. To mitigate this effect, tracks identified to be originating
from pileup vertices are discarded prior to the clustering, and an offset
correction is applied to correct for remaining contributions [65]. Jet energy
corrections are derived from simulation so that the average measured response
of jets becomes identical to that of particle level jets. In situ measurements
of the energy balance in dijet, photon+jet, $\PZ$+jet, and multijet events are
used to determine any residual differences between the jet energy scale in
data and in simulation, and appropriate corrections are made [65]. Additional
selection criteria are applied to each jet to remove jets potentially
dominated by instrumental effects or reconstruction failures.

The missing transverse momentum vector is computed as the negative vector sum
of the transverse momenta of all the PF candidates in an event, and its
magnitude is denoted as [66]. The is modified to account for corrections to
the energy scale of the reconstructed jets in the event. The is required to be
larger than 110 (40)in the electron (muon) channel to reject QCD multijet
background events. The higher threshold for the electron channel is necessary
to reduce the contribution of QCD multijet events with mismeasured from jets
misidentified as electrons, since the electron identification criteria are
optimized for greater efficiency at the expense of lower purity.

The leptonic $\PW$ boson candidate is constructed from the lepton and the .
The longitudinal momentum of the neutrino can be reconstructed from the $\PW$
boson mass constraint, assuming that the neutrino is the sole contributor to
the . The $x$ and $y$ components of the neutrino momentum therefore come
directly from the . Fixing the mass of the $\PW$ boson candidate to its pole
mass value, one can relate the four-momentum of the $\PW$ boson to those of
the lepton and neutrino via a quadratic equation, which can have two real or
complex solutions. In the case of two real solutions, the solution with the
smaller absolute value is assigned as the neutrino longitudinal momentum,
whereas in the case of two complex solutions, the real part common to both is
instead assigned. In simulated SM diboson samples, this method assigns the
correct solution in approximately 90% of events. Although
$\PW\to\PGt\PGnGt\to\Pe\PGne/\PGm\PGnGm+\PGnGt$ decays are included in the
simulated signals, they are not efficiently reconstructed because of the
presence of the second neutrino. The reconstructed leptonic $\PW$ boson
candidate is then required to have $\pt>200\GeV$.

The AK8 jets with $\pt>200\GeV$ and $\abs{\eta}<2.4$ are used as the basis for
the identification of hadronic boson decays, whereas AK4 jets with
$\pt>30\GeV$ and $\abs{\eta}<2.4$ are used for the rejection of background
processes containing a top quark decay. All jets have to pass basic quality
criteria based on the relative fractions of different PF particle types within
the jets. They are also excluded from the analysis if they are within $\Delta
R<0.3$ of the lepton.

The AK8 jet with the highest serves as the hadronic $\PW$ or $\PZ$ boson
(hereafter denoted by V) candidate. The leptonic and hadronic boson candidates
are combined into a diboson system by adding their four-momenta. The invariant
mass of the reconstructed diboson system, $m_{\PW\text{V}}$, is the chosen
event variable for the signal extraction. Because signal events are expected
to have a back-to-back topology in the detector, we require events in the
signal region to satisfy the following requirements: $\Delta R(\text{AK8
jet},\text{lepton})>\pi/2$, $\Delta\phi(\text{AK8 jet},\ptvecmiss)>2$, and
$\Delta\phi(\text{AK8 jet},\PW)>2$, where $\PW$ denotes the reconstructed
leptonic $\PW$ boson candidate. Additionally, we require
$m_{\PW\text{V}}>900\GeV$ to restrict the phase space to a region where the
background can be described by a monotonically falling parametric function.

Jets originating from the decay of $\cPqb$ quarks are identified using the
combined secondary vertex discriminator [67]. Those AK4 jets fulfilling the
tight working point of the discriminator (${>}0.9535$) are considered as
$\cPqb$ tagged. This working point has an overall efficiency of 41% for
correctly identifying a jet from a bottom quark, with a 0.1% probability of
misidentifying a jet from a light-flavour quark or gluon as $\cPqb$ tagged.
Events that contain one or more -tagged AK4 jets are rejected to reduce the
background from processes containing a top quark decay, especially . However,
only AK4 jets with a separation of $\Delta R>0.8$ with respect to the hadronic
V are included to avoid rejecting $\PW\PZ$ signal events with a $\PZ\to\bbbar$
decay.

To discriminate between AK8 jets originating from heavy-boson decays and jets
originating from the hadronization of quarks and gluons, and to improve the
resolution of the V jet mass and reduce the residual effect of pileup, we
employ a suitable jet grooming algorithm [68, 69]. In this search, we apply a
modified mass-drop algorithm [70, 71], known as the _soft drop_ algorithm
[72], to the AK8 jet, with parameters $\beta=0$, $z_{\text{cut}}=0.1$, and
$R_{0}=0.8$. This removes soft, wide-angle radiation from the jet, reducing
the mass of jets initiated by gluons or single quarks, and improving the jet
mass resolution for jets originating from heavy particles, here the $\PW$ and
$\PZ$ bosons. To further improve the jet mass resolution, prior to grooming
the pileup per particle identification (PUPPI) algorithm [73] is used to
mitigate the effect of pileup at the reconstructed particle level, making use
of local shape information, event pileup properties, and tracking information.
Charged particles identified as originating from pileup vertices are
discarded. For each neutral particle, a local shape variable is computed using
the surrounding charged particles that are compatible with the primary vertex
and within the tracker acceptance ($\abs{\eta}<2.5$), and using both charged
and neutral particles in the region outside of the tracker coverage. The
momenta of the neutral particles are then scaled by the probability that they
originate from the primary interaction vertex deduced from the local shape
variable, superseding the need for jet-based pileup corrections [5]. The
invariant mass of the resulting jet is the PUPPI soft drop mass
$m_{\text{SD}}$, one of the most important variables in this analysis.

To further discriminate against jets from the hadronization of gluons and
single quarks, the $N$-subjettiness [74] variable $\tau_{N}$ is used. The
$N$-subjettiness variable quantifies the compatibility of clustering the jet
constituents into exactly $N$ subjets, with small values representing
configurations more compatible with the $N$-subjet hypothesis. The ratio
between 2- and 1-subjettiness, $\tau_{21}=\tau_{2}/\tau_{1}$, is a powerful
discriminant between jets originating from hadronic V decays and those from
single gluon or quark hadronization.

We require the AK8 jet in the signal region to have $65<m_{\text{SD}}<105\GeV$
and $\tau_{21}<0.55$ to suppress the background processes, especially those
from $\PW\text{+jets}$ events. To better distinguish the $\PW\PW$ and $\PW\PZ$
final states, the signal region is subdivided into the $\PW\PW$-sensitive
region ($65<m_{\text{SD}}<85\GeV$) and the $\PW\PZ$-sensitive region
($85<m_{\text{SD}}<105\GeV$). In addition to the signal region, as defined by
the selection described above, we define several control regions, each of
which is designed to enhance a specific background contribution:

  * •

$\PW\text{+jets}$ control region: also referred to as the sideband, defined
analogously to the signal region but with
$m_{\text{SD}}\in[40,65]\cup[105,150]\GeV$. The two intervals define the lower
and upper sidebands, respectively.

  * •

control region: defined like the signal region, but requiring at least one
-tagged AK4 jet, and $m_{\text{SD}}\in[40,150]\GeV$.

The analysis proceeds simultaneously in the electron and muon channels to take
into account slight differences in efficiency, acceptance, and background
composition. In each of the two channels, the signal is extracted by a two-
dimensional fit to the $m_{\text{SD}}$ and $m_{\PW\text{V}}$ distributions in
data, with each signal and background contribution represented by a parametric
function. Minor background contributions are modelled by directly fitting
parametric functions to the simulated samples and keeping them fixed in the
final fit. In contrast, major backgrounds are modelled by first determining
the function parameters by fitting to the simulation, then using the fit
result uncertainties as priors when fitting these to data in the process of
the signal extraction. The fit range in $m_{\text{SD}}$ includes the signal
region as well as the $\PW\text{+jets}$ control region, to help constrain the
$\PW\text{+jets}$ background. To accurately estimate this dominant background,
the ratio of the $\PW\text{+jets}$ $m_{\PW\text{V}}$ distributions in the
signal and $\PW\text{+jets}$ control regions in data is constrained to match
that predicted by the simulation.

##  0.5 Signal modelling

For diboson processes, with or without additional contributions from anomalous
couplings, the $m_{\PW\text{V}}$ distribution can be modelled to a good
approximation by an exponential decay function. The inclusion of additional
contributions from anomalous couplings leads to an increase of events at
higher $m_{\PW\text{V}}$ values. Therefore, the signal shape is modelled as a
sum of exponential terms, with a combination of terms accounting for pure SM
and aTGC contributions, as well as SM–aTGC and aTGC–aTGC interference effects.
The pure aTGC term also includes the error function to ensure its effect is
only relevant at larger values of $m_{\PW\text{V}}$.

The complete signal diboson mass distribution,
$F_{\text{signal}}(m_{\PW\text{V}})$, is described by:

$\displaystyle F_{\text{signal}}(m_{\PW\text{V}})=$ $\displaystyle
N_{\text{SM}}\left(\re^{a_{0}m_{\PW\text{V}}}+\re^{a_{\text{corr}}m_{\PW\text{V}}}\right)$
(3)
$\displaystyle+\sum_{i}{\left(N_{c_{i},1}c_{i}^{2}\re^{a_{i,1}m_{\PW\text{V}}}\left(\frac{1+\erf[(m_{\PW\text{V}}-a_{0,i})/a_{\mathrm{w},i}]}{2}\right)+N_{c_{i},2}c_{i}\re^{a_{i,2}m_{\PW\text{V}}}\right)}$
$\displaystyle+\sum_{i<j}{\left(N_{c_{i},c_{j}}c_{i}c_{j}\re^{a_{{ij}}m_{\PW\text{V}}}\right)},$

where $c_{i}$ are the various aTGC parameters, and $\erf$ is the error
function. The complete signal distribution can be decomposed into four
contributions: the SM part with no dependence on $c_{i}$, pure aTGC
contributions proportional to $c_{i}^{2}$, aTGC–SM interference terms
proportional to $c_{i}$, and bilinear interference terms between the different
aTGCs proportional to $c_{i}c_{j}$ for $i\neq j$. The parameters
$N_{\text{SM}}$, $N_{c_{i},1}$, $N_{c_{i},2}$, and $N_{c_{i},c_{j}}$ are the
normalization of the SM, pure aTGC, aTGC–SM interference, and aTGC–aTGC
interference terms for the various $c_{i,{j}}$, respectively. Similarly,
$a_{0}$, $a_{i,1}$, $a_{i,2}$, and $a_{{ij}}$ are the exponential decay
constants of each of these contributions. The parameters $a_{0,i}$ and
$a_{\mathrm{w},i}$ govern the turn-on position and steepness of the error
function in the pure aTGC contribution for a given $c_{i}$. The exponential
term with decay constant $a_{\text{corr}}$ is a small correction added to
account for the deviation of the SM contribution from a simple exponential at
higher values of $m_{\PW\text{V}}$.

These parameters are determined empirically from the signal simulation. This
is done to facilitate easier interpolation between aTGC parameter values, and
to avoid large statistical uncertainties from regions with limited numbers of
MC events. The following procedure is used to extract the various slope and
normalization parameters. First, the SM shape and normalization parameters
$a_{0}$ and $N_{\text{SM}}$ are extracted from the simulation by reweighting
the signal simulation (which is generated with aTGCs) to the SM simulation
(without any aTGCs). Then, the aTGC–SM interference parameters $a_{i,2}$ and
$N_{c_{i},2}$ are derived by comparing the shapes when an aTGC is set to equal
values but with opposite signs. The pure aTGC parameters $a_{i,1}$, $a_{0,i}$,
$a_{\mathrm{w},i}$, and $N_{c_{i},1}$ are then extracted in a simultaneous fit
of the SM, aTGC–SM interference, and pure aTGC terms to samples weighted with
only a single, nonzero aTGC. Finally, the aTGC–aTGC interference terms are
derived by comparing samples with pairs of aTGCs set to nonzero values. The
error function in the pure aTGC terms is introduced to accurately model the
turn-on behaviour of the aTGC contributions. To simplify the signal model,
very small contributions from $c_{\PW\PW\PW}$–SM interference,
$c_{\PW\PW\PW}$–$c_{\PB}$ interference, and the error function for $c_{\PB}$
in the $\PW\PZ$ region are neglected.

##  0.6 Background modelling

There are two major contributions to the SM background ($\PW\text{+jets}$, and
), and two minor contributions (single top quark and SM diboson production).
Even with substantial enhancements of the diboson cross section in the event
of nonzero aTGCs, any signal contribution in the control regions is expected
to be small since the control regions are explicitly designed to enrich the
backgrounds whilst rejecting contamination from the signal processes.

The normalizations of the background contributions are determined during the
signal extraction through a two-dimensional fit to the
$(m_{\text{SD}},m_{\PW\text{V}})$ distributions in data. The $m_{\text{SD}}$
and $m_{\PW\text{V}}$ shape parameters of the $\PW\text{+jets}$ background,
along with the $m_{\PW\text{V}}$ shape parameters of the background, are also
extracted from the two-dimensional fit, as described below. The
$m_{\text{SD}}$ shape of the background, as well as the shapes of the single
top quark and SM diboson background contributions, are taken directly from
fits to simulation.

Since the background estimate is largely based on a template derived from
simulation, we validate its accuracy by verifying that data in the control
region are well-modelled by the simulation. Of particular importance are the
$m_{\text{SD}}$ and $m_{\PW\text{V}}$ distributions, since $m_{\PW\text{V}}$
is used to extract limits on anomalous couplings. Figure 2 shows that the
simulation is in agreement with the data for these variables in the control
region, which is verified by a $\chi^{2}$ test (with $p$-value ${>}0.99$ in
all cases).

Figure 2: Comparison between data and simulation for the $m_{\text{SD}}$
(upper) and $m_{\PW\text{V}}$ (lower) distributions in the control region.
Contributions from simulation are normalized to the total integrated
luminosity of the data using their respective SM cross sections. The electron
channel is shown on the left, while the muon channel is shown on the right.
The lower panel in each figure shows the relative difference between data and
simulation. The light grey hashed region in the main panels and dark grey band
in the lower ratio panels represent the combined statistical and systematic
uncertainties, with details of the latter discussed in Section 0.7.

Because of the lack of knowledge of the continuous dependence of the shape
parameters describing the $m_{\PW\text{V}}$ distribution as a function of
$m_{\text{SD}}$, and with no reliable way to continuously model it, the two-
dimensional fit is constructed by defining four separate regions in
$m_{\text{SD}}$ (lower sideband, signal $\PW\PW$, signal $\PW\PZ$, and upper
sideband). All four regions are fitted simultaneously to constrain the shape
parameters. In each region, the shape parameters describing the
$m_{\PW\text{V}}$ distribution are constant with respect to $m_{\text{SD}}$.
In the sideband regions these shape parameters are determined by fitting to
the data. In the signal regions the shape parameters are instead obtained by
assuming that the simulation accurately describes the ratio of the
$m_{\PW\text{V}}$ distributions in the signal and sideband regions. This ratio
function $\alpha^{\text{MC}}(m_{\PW\text{V}})$ is used to transfer the shape
of the $\PW\text{+jets}$ background, which comes from data, from the sideband
to the signal regions, thereby encoding the dependence of $m_{\PW\text{V}}$ on
$m_{\text{SD}}$ (the $\alpha$ ratio method [75, 76]). The total background
contribution in the signal region, $F_{\text{bkg}}^{\text{SR}}$, can therefore
be expressed as:

$\displaystyle F_{\text{bkg}}^{\text{SR}}(m_{\PW\text{V}})$
$\displaystyle=F_{\PW\text{+jets}}^{\text{SB},\
\text{data}}\alpha^{\text{MC}}(m_{\PW\text{V}})+F_{\ttbar}^{\text{SR},\
\text{MC}}+F_{\text{single $\cPqt$}}^{\text{SR},\
\text{MC}}+F_{\text{diboson}}^{\text{SR},\ \text{MC}}$ (4)
$\displaystyle\alpha^{\text{MC}}(m_{\PW\text{V}})$
$\displaystyle=\frac{F_{\PW\text{+jets}}^{\text{SR},\
\text{MC}}}{F_{\PW\text{+jets}}^{\text{SB},\ \text{MC}}},$

where $F$ denotes the parametric functions representing various background
contributions in the signal (SR) and sideband (SB) regions. The statistical
uncertainties from the fits to data and simulation are propagated to the final
prediction of the $\PW\text{+jets}$ and backgrounds, as discussed in Section
0.7.

In the fit, the various background contributions have different constraints
placed upon their normalizations and $m_{\PW\text{V}}$ and $m_{\text{SD}}$
shape parameters, depending on the importance of the contribution, and the
level of certainty in its modelling. The normalization and $m_{\text{SD}}$
shape parameters of the $\PW\text{+jets}$ contribution are allowed to vary
without constraint to account for possible mismodelling. The $m_{\PW\text{V}}$
shape parameters for this contribution are allowed to vary within their
uncertainties, which arise from the uncertainties in the simulation entering
$\alpha^{\text{MC}}$. The normalization and $m_{\PW\text{V}}$ shape parameters
of the contribution are also allowed to vary within their respective
uncertainties; however the $m_{\text{SD}}$ shape parameters are kept fixed.
For the single top quark contribution, the shape parameters for both
$m_{\PW\text{V}}$ and $m_{\text{SD}}$ are kept fixed, whilst the normalization
is allowed to float within the systematic uncertainty. Similarly, the shape
parameters of the SM diboson contribution are also kept fixed. However, the
normalization is constrained with 100% uncertainty to cover its systematic
uncertainty, and to allow for a substantial contribution from aTGC processes,
consistent with the sensitivity of this analysis. Further discussion of the
sources contributing to these uncertainties is provided in Section 0.7.
Normalization values before and after the fit for all contributions are shown
in Table 0.6.

Results of the signal extraction fits. The uncertainties in the pre-fit yields
are their respective pre-fit constraints, whilst the uncertainties in the
post-fit yields are the corresponding total post-fit uncertainties. Since the
normalization of the $\PW\text{+jets}$ contribution is allowed to vary freely
in the fit, it does not have any corresponding pre-fit uncertainties.
Electron channel Muon channel Pre-fit Post-fit Scale factor Pre-fit Post-fit
Scale factor $\PW\text{+jets}$ 2421 $3036\pm 123$ 1.25 4319 $4667\pm 182$ 1.08
$1491\pm 324$ $1127\pm 119$ 0.76 $2632\pm 570$ $1978\pm 202$ 0.75 Single
$\cPqt$ $271\pm 39$ $242\pm 26$ 0.89 $509\pm 69$ $449\pm 43$ 0.88 Diboson
$314\pm 314$ $267\pm 102$ 0.85 $552\pm 552$ $465\pm 162$ 0.84 Total expected
4497 $4672\pm 201$ 1.04 8012 $7559\pm 319$ 0.94 Data 4691 7568

##  0.7 Systematic uncertainties

There are several systematic uncertainties that affect the normalizations of
the , single top quark, and diboson processes that are derived from
simulation. These uncertainties are included in the final fit to the data.

An uncertainty of 2.5% [77] is included to account for the uncertainty in the
integrated luminosity measurement of the 2016 data set. This uncertainty is
treated as correlated between the different processes.

The uncertainty associated with the pileup reweighting of simulated events is
calculated from the uncertainty in the total inelastic cross section that is
used to derive the pileup weights [78].

We include uncertainties in the cross section calculations used to normalize
the contributions from simulation. This is done by utilizing the uncertainties
associated with the PDFs following the recommendations of the PDF4LHC working
group [79]. Uncertainties corresponding to the choice of renormalization and
factorization scales ($\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$, respectively)
are computed by reweighting the simulated samples for all combinations of
nominal scales and scales multiplied/divided by a factor of two, excluding
combinations in which one scale is increased and the other simultaneously
decreased, and using the largest deviation as the uncertainty.

A normalization uncertainty of 14% describing the mismodelling of the
$\tau_{21}$ selection efficiency [76] is applied to all contributions derived
from simulation containing hadronic V boson decays, and is treated as
correlated between the different processes. This uncertainty is not applied to
the $\PW\text{+jets}$ contribution, which is directly estimated from data, nor
to the $t$\- and $s$-channel subprocesses of single top quark production,
where the hadronically decaying V boson candidate is associated with jets
arising from the hadronization of a single light quark or gluon.

For the and $\PW\PZ$ samples, we include the uncertainties in the efficiencies
to identify and misidentify (mistag) quark jets [67]. The uncertainties in the
tagging efficiencies most notably affect the normalization of the background,
whereas the misidentification uncertainties have only a small impact across
the samples.

Uncertainties in the jet energy scale have been measured [65], and are
propagated by varying the jet energy scale within its uncertainty for both AK4
and AK8 jets, simultaneously. Similarly, uncertainties in the jet energy
resolution are applied to both AK4 and AK8 jets simultaneously by varying
their resolutions by ${\pm}1$ standard deviation.

The lepton energy scale is varied within its uncertainty, and its effect is
propagated to the signal extraction fit. Lepton resolution uncertainties are
included in a similar manner.

Uncertainties in the measurement of lepton efficiency and identification scale
factors are also considered. An additional uncertainty is added to account for
additional uncertainty in the scale factors at higher electron energies. In
the barrel region this uncertainty is 1% below 90, 2% between 90and 1, and 3%
above 1; in the endcaps it is 1% below 90, 2% between 90 and 300, and 4% above
300. In the muon channel, an additional 1% uncertainty is added related to the
muon identification criteria, 0.5% related to the isolation requirements, and
0.5% related to the single-muon triggers.

Jet and lepton uncertainties are also propagated to the calculation of . In
addition, the influence of PF candidates not associated to any reconstructed
physics object [80] (“unclustered” energy deposits) on are evaluated and
propagated as normalization uncertainties.

The normalization uncertainties for the contributions derived from simulation
are summarized in Table 0.7. The influence of jet and lepton uncertainties on
are included in the corresponding jet and lepton uncertainty rows, whilst the
uncertainty value is that arising solely from unclustered energy deposits.

Estimated normalization uncertainties (%) for SM background contributions
derived from simulation.  Electron channel Muon channel Uncertainty source
Single $\cPqt$ $\PW\PW$ $\PW\PZ$ Single  $\PW\PW$ $\PW\PZ$ PDF 2.79 0.22 1.93
2.44 2.71 0.25 1.78 2.54 $\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$ 17.99 0.94
5.77 4.82 17.74 1.06 5.99 4.26 Luminosity 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5
Pileup 0.59 0.29 0.90 1.40 0.40 0.41 0.82 0.67 V tag 14 14 14 14 14 14 14 14
tag 1.05 0.85 0.04 0.08 1.04 0.84 0.03 0.08 mistag 0.04 0.05 0.02 0.04 0.05
0.05 0.03 0.04 Jet energy scale 4.41 4.94 4.26 2.44 3.54 2.97 3.75 2.50 Jet
energy resolution 1.79 3.44 1.85 2.69 0.85 0.91 0.62 2.92 Lepton energy scale
0.80 1.45 1.53 0.94 0.68 1.14 1.72 1.19 Lepton energy resolution 0.26 1.22
0.11 0.21 0.02 0.27 0.14 0.33 Lepton ID 2.12 2.22 2.30 2.26 1.81 2.04 2.55
2.42 0.91 1.50 1.01 0.64 0.59 0.99 0.24 0.17 Total 23.74 15.84 16.44 15.91
23.30 14.85 16.31 15.80

Shape uncertainties for the $\PW\text{+jets}$ and contributions, as well as
for the signal model, are also considered. The shape uncertainty in the
$\PW\text{+jets}$ sideband estimate is propagated from the simultaneous fit of
the data sideband, and signal and sideband regions in simulation. The effect
of an alternative fit function is also included by inflating the parameter
uncertainties to cover the estimate from the alternative function.

The shape uncertainty for the contribution is estimated using the
uncertainties in the parameters from the fit of the shape to simulation. These
are included as nuisance parameters in the background model for the final
signal extraction.

For the signal modelling, we include statistical uncertainties from the signal
modelling procedure described in Section 0.5, as well as shape variations from
the PDF, $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ scales, jet energy scale
and resolution, lepton energy scale, , and tagging-related uncertainties.
Uncertainties in the slope parameters of the exponential functions are derived
by extracting the signal model from signal simulation with the relevant
conditions varied, and using the difference between the fitted slope
parameters for the nominal and varied samples. The total uncertainty from
these shape variations is the sum of all individual uncertainties in
quadrature, resulting in a total uncertainty of approximately 5% for all
aTGCs. This is dominated by the PDF and $\mu_{\mathrm{R}}$ and
$\mu_{\mathrm{F}}$ scale uncertainties, with smaller contributions from
experimental sources, particularly jet energy scale and resolution, and lepton
identification, depending on the lepton flavour and signal region under
consideration.

Differential corrections from the consideration of higher order NNLO (QCD)
[81, 82] and NLO (electroweak) [83] contributions have previously been
calculated, and each can be considerable at large $m_{\PW\text{V}}$
(${\gtrsim}20\%$), larger than the scale uncertainty at NLO (QCD). However,
since the two corrections have opposite signs, they partially cancel out,
reducing their overall effect. In addition, the impact and validity of these
higher order corrections on processes with aTGCs has not been fully
investigated. Therefore, they are not considered as an additional source of
uncertainty.

##  0.8 Results

We set limits on the aTGCs using the data in the signal region and the
background estimates. These are shown in Table 0.8, and also in Figs. 3 and 4,
where the $\PW\PW$ and $\PW\PZ$ signal regions are combined into one figure
for each lepton channel. Limits are set at 95% confidence level () using a
simultaneous unbinned maximum likelihood fit of the two-dimensional
($m_{\text{SD}}$, $m_{\PW\text{V}}$) distributions in both the electron and
muon channels. The fit to $m_{\PW\text{V}}$ covers the range
$900<m_{\PW\text{V}}<4500\GeV$, where the lower limit is the minimum
requirement on $m_{\PW\text{V}}$ and the upper limit is chosen based on data
seen in the control regions. The best-fit values of the aTGC parameters, along
with their confidence intervals, are obtained using scans of the profile
likelihood ratio, using the procedure described in Section 3.2 of [84].
Systematic uncertainties are included as nuisance parameters: normalization
uncertainties are treated as multiplicative parameters constrained by a log-
normal distribution, while shape parameter uncertainties are constrained by
Gaussian functions around their nominal values. Limits are derived from the
contours of the negative logarithmic likelihood as a function of the aTGCs.

Summary of background, signal, and data yields in the $\PW\PW$ and $\PW\PZ$
categories for each lepton channel. Uncertainties in the background
contributions are described in Section 0.7. The diboson signal predictions
with anomalous couplings include both standard model and anomalous
contributions, as well as the relevant interference terms.  Electron channel
Muon channel $\PW\PW$ $\PW\PZ$ $\PW\PW$ $\PW\PZ$ $\PW\text{+jets}$ $1618\pm
66$ $1418\pm 57$ $2529\pm 99$ $2138\pm 83$ $\ttbar$ $600\pm 63$ $526\pm 56$
$1040\pm 106$ $938\pm 96$ Single top quark $145\pm 16$ $97\pm 10$ $264\pm 25$
$185\pm 18$ Diboson (SM) $144\pm 52$ $122\pm 52$ $265\pm 88$ $200\pm 79$ Total
expected (SM) $2507\pm 106$ $2163\pm 96$ $4098\pm 172$ $3461\pm 151$ Diboson
$(c_{\PW\PW\PW}/\Lambda^{2}=3.6\TeV^{-2})$ $193\pm 15$ $185\pm 15$ $334\pm 26$
$287\pm 22$ Diboson $(c_{\PW}/\Lambda^{2}=4.5\TeV^{-2})$ $163\pm 14$ $154\pm
15$ $283\pm 23$ $237\pm 21$ Diboson $(c_{\PB}/\Lambda^{2}=20\TeV^{-2})$
$188\pm 21$ $144\pm 14$ $322\pm 33$ $221\pm 20$ Data 2456 2235 3996 3572

Figure 3: Final result of the two-dimensional fit in the electron (left) and
muon (right) channels, showing the $m_{\text{SD}}$ distribution.

Figure 4: Final result of the two-dimensional fit in the electron (left) and
muon (right) channels, showing the $m_{\PW\text{V}}$ distributions. The lower
sideband, signal, and upper sideband regions are shown on the top, middle, and
bottom, respectively. An example of the excluded signal
($c_{\PW\PW\PW}/\Lambda^{2}=1.59\TeV^{-2}$) is represented by the dashed line.

Limits on individual anomalous couplings are derived by setting the other two
couplings to zero. We assume that the EFT parametrization used here is valid
at the energies relevant for this experiment, the true scale associated with
any new particles is much larger than the scale $\Lambda$ to which the
experiment is sensitive. Specifically, this is possible if the underlying
dynamics is strongly coupled [85]. In addition to the EFT parametrization
described in Eq. (1), limits are also computed in terms of the parametrization
derived for $\PW\PW$ searches at LEP [3, 86] (hereafter referred to as the LEP
parametrization), which was also used for searches at the Tevatron [87]. The
signal model is reparametrized in terms of the vertex parameters
$\lambda_{\PZ}$, $\Delta g_{1}^{\PZ}$, and $\Delta\kappa_{\PZ}$ using the
relationships in terms of $c_{\PW}$, $c_{\PB}$, and $c_{\PW\PW\PW}$ given in
Ref. [2], and the likelihood minimization procedure repeated. The resulting
limits from both parametrizations, along with the best-fit values, are shown
in Table 0.8. Limits on the same parameters from $\Pp\Pp$ collision data taken
at a centre-of-mass energy of 8 [26] are also quoted to demonstrate the
improvement in this analysis (where the limit on $\Delta\kappa_{\Pgg}$ has
been converted to a limit on $\Delta\kappa_{\PZ}$ using the relationships in
Ref. [2]).

Two-dimensional expected and observed limits on pairwise combinations of the
couplings, with the remaining coupling set to zero, are also derived, and the
results shown in Fig. 5 for the EFT parametrization, and Fig. 6 for the LEP
parametrization.

Expected and observed limits at 95% on single anomalous couplings, along with
observed best-fit values, for both the EFT and LEP parametrizations. For each
coupling, all other couplings are explicitly set to zero. Observed limits from
collision data taken at a centre-of-mass energy of 8 [26] are also quoted for
comparison.  Parametrization aTGC Expected limit Observed limit Observed best-
fit $8\TeV$ observed limit EFT $c_{\PW\PW\PW}/\Lambda^{2}$ $({\TeVns}^{-2})$
[-1.44, 1.47] [-1.58, 1.59] -0.26 [-2.7, 2.7] $c_{\PW}/\Lambda^{2}$
$({\TeVns}^{-2})$ [-2.45, 2.08] [-2.00, 2.65] 1.21 [-2.0, 5.7]
$c_{\PB}/\Lambda^{2}$ $({\TeVns}^{-2})$ [-8.38, 8.06] [-8.78, 8.54] 1.07 [-14,
17] LEP $\lambda_{\PZ}$ [-0.0060, 0.0061] [-0.0065, 0.0066] -0.0010 [-0.011,
0.011] $\Delta g_{1}^{\PZ}$ [-0.0070, 0.0061] [-0.0061, 0.0074] 0.0027
[-0.009, 0.024 ] $\Delta\kappa_{\PZ}$ [-0.0074, 0.0078] [-0.0079, 0.0082]
-0.0010 [-0.018, 0.013 ]

Figure 5: Two-dimensional limits on the aTGC parameters in the EFT
parametrization, for the combinations
$c_{\PW\PW\PW}/\Lambda^{2}$–$c_{\PW}/\Lambda^{2}$ (left),
$c_{\PW\PW\PW}/\Lambda^{2}$–$c_{\PB}/\Lambda^{2}$ (centre), and
$c_{\PW}/\Lambda^{2}$–$c_{\PB}/\Lambda^{2}$ (right). Contours for the expected
95% are shown in dashed green, with the 68 and 99% contours shown in dotted
blue and dot-dashed red, respectively. Contours for the observed 95% are shown
in solid black. The black square markers represent the SM expectation, while
the black crosses show the observed best-fit points.

Figure 6: Two-dimensional limits on the aTGC parameters in the LEP
parametrization, for the combinations $\lambda_{\PZ}$–$\Delta g_{1}^{\PZ}$
(left), $\lambda_{\PZ}$–$\Delta\kappa_{\PZ}$ (centre), and $\Delta
g_{1}^{\PZ}$–$\Delta\kappa_{\PZ}$ (right). Contours for the expected 95% are
shown in dashed green, with the 68 and 99% contours shown in dotted blue and
dot-dashed red, respectively. Contours for the observed 95% are shown in solid
black. The black square markers represent the SM expectation, while the black
crosses show the observed best-fit points.

While the operators associated with $c_{\PW\PW\PW}$ and $c_{\PW}$ induce
contributions in similar proportions in both the $\PW\PW$ and $\PW\PZ$ signal
regions, we expect the effects of the operator associated with $c_{\PB}$ to be
much greater in the $\PW\PW$ region compared to the $\PW\PZ$ region.
Consequently, there is little separation power in this analysis between
$c_{\PW\PW\PW}$ and $c_{\PW}$, with a similar limit derived on both couplings,
and more separation power between $c_{\PW\PW\PW}$/$c_{\PW}$ and $c_{\PB}$ in
the case of nonzero coupling values.

A comparison of limits derived in this analysis with those obtained by other
analyses performed at the LEP [86], [87], CMS [7, 10, 22, 25, 26, 88, 89], and
ATLAS [16, 18, 24, 15, 19, 27, 28, 90, 91, 23] experiments is shown in Fig. 7.
Limits that were set on $\lambda_{\Pg}$ and $\Delta\kappa_{\Pgg}$ have been
converted to limits on $\lambda_{\PZ}$ and $\Delta\kappa_{\PZ}$, respectively,
using the relationships in Ref. [2].

Figure 7: Comparison of the observed limits on aTGC parameters in the LEP
parametrization from different measurements. The highlighted rows represent
the limits obtained from this measurement.

The limits derived in this analysis are the strictest bounds on all three
parameters to date, improving upon the complementary all-leptonic searches
also performed using collision data recorded at a centre-of-mass energy of
13by the ATLAS [24, 23] and CMS [22] Collaborations. There is an especially
significant improvement in the measured limit on $\Delta\kappa_{\PZ}$ over any
previous measurement.

##  0.9 Summary

A measurement of limits on anomalous triple gauge coupling parameters in terms
of dimension-six effective field theory operators has been presented. It uses
events where two vector bosons are produced, with one decaying leptonically
and the other hadronically to a single, massive, large-radius jet. Results are
based on data recorded in proton-proton collisions at $\sqrt{s}=13\TeV$ with
the CMS detector at the CERN LHC in 2016, corresponding to an integrated
luminosity of 35.9. Limits are presented both in terms of the $c_{\PW\PW\PW}$,
$c_{\PW}$, and $c_{\PB}$ parameters (scaled by an overall new physics energy
scale $\Lambda$) in the effective field theory parametrization, and the
$\lambda_{\PZ}$, $\Delta g_{1}^{\PZ}$, and $\Delta\kappa_{\PZ}$ parameters in
the LEP parametrization. For each parametrization, limits are set at 95%
confidence level on individual parameters, as well as on pairwise combinations
of parameters. Limits on individual parameters in the effective field theory
parametrization are determined to be
$-1.58<c_{\PW\PW\PW}/\Lambda^{2}<1.59\TeV^{-2}$,
$-2.00<c_{\PW}/\Lambda^{2}<2.65\TeV^{-2}$, and
$-8.78<c_{\PB}/\Lambda^{2}<8.54\TeV^{-2}$, in agreement with standard model
expectations of zero for each parameter. These are the strictest bounds on
these parameters 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 centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, 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 and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie programme and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Programme and the János Bolyai Research Scholarship of
the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP,
the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713,
128786, and 129058 (Hungary); the Council of Science and Industrial Research,
India; the HOMING PLUS programme of the Foundation for Polish Science,
cofinanced from European Union, Regional Development Fund, the Mobility Plus
programme of the Ministry of Science and Higher Education, the National
Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Ministry of Science and Education, grant no.
3.2989.2017 (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
programmes 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 Welch Foundation, contract C-1845; and the Weston Havens
Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S.
Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de
Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Tsinghua University, Beijing, China
Z. Hu, Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran11,12, S. Elgammal12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G. Falmagne, R.
Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C.
Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi,
A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras18, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo19, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem18, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann20, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A. Reimers, O. Rieger,
C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I. Zoi Karlsruher Institut
fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, M.A.
Iqbal, S. Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP),
NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity27, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar27, M. Sharan, B. Singh26, S.
Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,29, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, 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,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,30, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,31, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,31,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,17, S. Di Guidaa,b,17,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,17, P. Paoluccia,17, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,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, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi32, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b 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, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns33 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris34, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz35, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
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. Ahmad, Q. Hassan, 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, A. Byszuk36, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavine,
V. Korenkov, A. Lanev, A. Malakhov, V. Matveev37,38, V.V. Mitsyn, P. Moisenz,
V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, B.S. Yuldashev39,
A. Zarubin, V. Zhiltsov Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim40, E. Kuznetsova41, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, 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. Nikitenko42, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, M. Danilov43, S. Polikarpov43, E. Tarkovskii, E. Zhemchugov 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, M. Dubinin44, L. Dudko, A. Ershov, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov45, V. Blinov45, T. Dimova45, L. Kardapoltsev45, Y. Skovpen45
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, 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, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic46, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA)
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo47, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, F. Fallavollita48, D. Fasanella, G. Franzoni, J. Fulcher,
W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M.
Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P.
Janot, O. Karacheban20, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P.
Lecoq, C. Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A.
Merlin, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S.
Nourbakhsh, S. Orfanelli, L. Orsini, F. Pantaleo17, L. Pape, E. Perez, M.
Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters,
D. Rabady, A. Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M.
Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas49, J. Steggemann, V.R.
Tavolaro, D. Treille, A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada50, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini,
M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz
Becerra, M. Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler51, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci52, S. Damarseckin53, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler54, Y. Guler, I. Hos55,
C. Isik, E.E. Kangal56, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir57, S. Ozturk58, A.E. Simsek, D. Sunar Cerci52, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak59, G. Karapinar60, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya61, O. Kaya62, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin63 Istanbul Technical University, Istanbul, Turkey
A. Cakir, Y. Komurcu, S. Sen64 Istanbul University, Istanbul, Turkey
S. Ozkorucuklu 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
F. Ball, E. Bhal, S. Bologna, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev65, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL66, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash67, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee17, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan68, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir69, R. Syarif, E. Usai, D. Yu University of California, Davis,
Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Stolp,
D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius70, A. Ryd, S.M.
Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator
Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M. Joshi, M. Kim,
J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki54, W. Clarida, K. Dilsiz71, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili72, A. Moeller, J. Nachtman, H. Ogul73, Y. Onel, F. Ozok74, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
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Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans, R. Rusack,
M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko37, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali75, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon76, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber77, H. He, M. Herndon, A. Hervé, U. Hussain, P.
Klabbers, A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan
Sreekala, T. Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS/CPNA, Federal University of Mato Grosso do Sul/Campus of Nova
Andradina, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Suez University, Suez, Egypt
12: Now at British University in Egypt, Cairo, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Tbilisi State University, Tbilisi, Georgia
16: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Brandenburg University of Technology, Cottbus, Germany
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
22: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
23: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
24: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
25: Also at Institute of Physics, Bhubaneswar, India
26: Also at Shoolini University, Solan, India
27: Also at University of Visva-Bharati, Santiniketan, India
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
30: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
31: Also at Centro Siciliano di Fisica Nucleare e di Struttura della Materia,
Catania, Italy
32: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
33: Also at Riga Technical University, Riga, Latvia
34: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
35: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
36: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
37: Also at Institute for Nuclear Research, Moscow, Russia
38: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
39: Also at Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
40: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
41: Also at University of Florida, Gainesville, USA
42: Also at Imperial College, London, United Kingdom
43: Also at P.N. Lebedev Physical Institute, Moscow, Russia
44: Also at California Institute of Technology, Pasadena, USA
45: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
46: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
47: Also at Università degli Studi di Siena, Siena, Italy
48: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
49: Also at National and Kapodistrian University of Athens, Athens, Greece
50: Also at Universität Zürich, Zurich, Switzerland
51: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
52: Also at Adiyaman University, Adiyaman, Turkey
53: Also at Sirnak University, Şırnak, Turkey
54: Also at Beykent University, Istanbul, Turkey
55: Also at Istanbul Aydin University, Istanbul, Turkey
56: Also at Mersin University, Mersin, Turkey
57: Also at Piri Reis University, Istanbul, Turkey
58: Also at Gaziosmanpasa University, Tokat, Turkey
59: Also at Ozyegin University, Istanbul, Turkey
60: Also at Izmir Institute of Technology, Izmir, Turkey
61: Also at Marmara University, Istanbul, Turkey
62: Also at Kafkas University, Kars, Turkey
63: Also at Istanbul Bilgi University, Istanbul, Turkey
64: Also at Hacettepe University, Ankara, Turkey
65: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
66: Also at Institute for Particle Physics Phenomenology Durham University,
Durham, United Kingdom
67: Also at Monash University, Faculty of Science, Clayton, Australia
68: Also at Bethel University, St. Paul, USA
69: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
70: Also at Vilnius University, Vilnius, Lithuania
71: Also at Bingol University, Bingol, Turkey
72: Also at Georgian Technical University, Tbilisi, Georgia
73: Also at Sinop University, Sinop, Turkey
74: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
75: Also at Texas A&M University at Qatar, Doha, Qatar
76: Also at Kyungpook National University, Daegu, Korea
77: Also at University of Hyderabad, Hyderabad, India