SMP-17-011

$Revision$ $HeadURL$ $Id$

SMP-17-011

# Measurement of electroweak production of a $\PW$ boson in association with
two jets in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A measurement is presented of electroweak (EW) production of a $\PW$ boson in
association with two jets in proton-proton collisions at $\sqrt{s}=13\TeV$.
The data sample was recorded by the CMS Collaboration at the LHC and
corresponds to an integrated luminosity of 35.9. The measurement is performed
for the $\ell\nu$jj final state (with $\ell\nu$ indicating a lepton-neutrino
pair, and j representing the quarks produced in the hard interaction) in a
kinematic region defined by invariant mass $m_{\mathrm{jj}}>120\GeV$ and
transverse momenta $p_{\mathrm{Tj}}>25\GeV$. The cross section of the process
is measured in the electron and muon channels yielding
$\sigma_{\mathrm{EW}}(\PW\mathrm{jj})=6.23\pm 0.12\stat\pm 0.61\syst\unit{pb}$
per channel, in agreement with leading-order standard model predictions. The
additional hadronic activity of events in a signal-enriched region is studied,
and the measurements are compared with predictions. The final state is also
used to perform a search for anomalous trilinear gauge couplings. Limits on
anomalous trilinear gauge couplings associated with dimension-six operators
are given in the framework of an effective field theory. The corresponding 95%
confidence level intervals are
$-2.3<c_{{\PW\PW\PW}}/\Lambda^{2}<2.5\TeV^{-2}$,
$-8.8<c_{\PW}/\Lambda^{2}<16\TeV^{-2}$, and
$-45<c_{\PB}/\Lambda^{2}<46\TeV^{-2}$. These results are combined with the CMS
EW $\cPZ\mathrm{jj}$ analysis, yielding the constraint on the
$c_{{\PW\PW\PW}}$ coupling : $-1.8<c_{{\PW\PW\PW}}/\Lambda^{2}<2.0\TeV^{-2}$.

##  0.1 Introduction

In proton-proton (pp) collisions at the CERN LHC, the pure electroweak (EW)
production of a lepton-neutrino pair ($\ell\nu$) in association with two jets
($\mathrm{jj}$) includes production via vector boson fusion (VBF). This
process has a distinctive signature of two jets with large energy and
separation in pseudorapidity ($\eta$), produced in association with a lepton-
neutrino pair. This EW process is referred to as EW $\PW\mathrm{jj}$, and the
two jets produced through the fragmentation of the outgoing quarks are
referred to as “tagging jets”.

Figure 1 shows representative Feynman diagrams for the EW $\PW\mathrm{jj}$
signal processes, namely VBF (Fig. 1, left), bremsstrahlung-like (Fig. 1,
center), and multiperipheral (Fig. 1, right) production. Gauge cancellations
lead to a large negative interference between the VBF diagram and the other
two diagrams, with the larger interference coming from bremsstrahlung-like
production. Interference with multiperipheral production is limited to cases
where the lepton-neutrino pair mass is close to the $\PW$ boson mass.

Figure 1:  Representative Feynman diagrams for lepton-neutrino production in
association with two jets from purely electroweak amplitudes: vector boson
fusion (left), bremsstrahlung-like (center), and multiperipheral (right)
production.

In addition to the purely EW signal diagrams described above, there are other,
not purely EW processes, that lead to the same $\ell\nu\mathrm{jj}$ final
states and can interfere with the signal diagrams in Fig. 1. This interference
effect between the signal production and the main Drell-Yan (DY) background
processes ($\mathrm{DY}\,\PW\mathrm{jj}$) is small compared to the
interference effects among the EW production amplitudes, but needs to be
included when measuring the signal contribution. Figure 2 (left) shows one
example of $\PW$ boson production in association with two jets that has the
same initial and final states as those in Fig. 1. A process that does not
interfere with the EW signal is shown in Fig. 2 (right).

Figure 2: Representative diagrams for $\PW$ boson production in association
with two jets ($\mathrm{DY}\,\PW\mathrm{jj}$) that constitute the main
background for the measurement.

The study of EW $\PW\mathrm{jj}$ processes is part of a more general
investigation of standard model (SM) VBF and scattering processes that
includes the measurements of EW $\cPZ\mathrm{jj}$ processes, Higgs boson
production [1, 2, 3], and searches for physics beyond the SM [4]. The
properties of EW $\PW\mathrm{jj}$ events that are isolated from the
backgrounds can be compared with SM predictions. Probing the additional
hadronic activity in selected events can shed light on the modeling of the
additional parton radiation [5, 6], which is important for signal selection
and the vetoing of background events.

Higher-dimensional operators outside the SM can generate anomalous trilinear
gauge couplings (ATGCs) [7, 8], so the measurement of the coupling strengths
provides an indirect search for beyond-the-SM physics at mass scales not
directly accessible at the LHC.

At the LHC, the EW $\PW\mathrm{jj}$ process was first measured by the CMS
Collaboration using pp collisions at $\sqrt{s}=8\TeV$ [9] and then by the
ATLAS Collaboration at both $\sqrt{s}=8\TeV$ and $\sqrt{s}=7\TeV$ [10]. The
closely related EW $\cPZ\mathrm{jj}$ process was first measured during Run 1
by the CMS Collaboration using pp collisions at $\sqrt{s}=7\TeV$ [11], and
then at $\sqrt{s}=8\TeV$ by both the CMS [12] and ATLAS [13] Collaborations.
The EW $\cPZ\mathrm{jj}$ measurements using data samples of pp collisions at
$\sqrt{s}=13\TeV$ have been performed by ATLAS [14] and by CMS [15].
Considering leptonic final states in the same kinematic region the EW
$\PW\mathrm{jj}$ cross section is about a factor 10 larger than the EW
$\cPZ\mathrm{jj}$ cross section. All results so far agree with the
expectations of the SM within a precision of 10–20%.

This paper presents measurements of the EW $\PW\mathrm{jj}$ process with the
CMS detector using pp collisions collected at $\sqrt{s}=$13during 2016,
corresponding to an integrated luminosity of 35.9. A multivariate analysis
(BDT), based on the methods developed for the EW Zjj measurement [11, 12], is
used to separate signal events from the large +jets background. The analysis
of the 13data offers the opportunity to measure the cross section at a higher
energy than previously done and to reduce the uncertainties obtained with
previous measurements, given both the larger integrated luminosity and the
larger predicted total cross section.

This paper is organized as follows: Section 0.2 describes the experimental
apparatus and Section 0.3 the event simulations. Event selection procedures
are described in Section 0.4, together with the selection efficiencies and
background estimations using control regions (CRs). Section 0.5 describes an
estimation of the multijet background from quantum chromodynamics (QCD), based
on CRs in data. Section 0.6 discusses a correction applied to the simulation
as a function of the invariant mass $m_{\mathrm{jj}}$. Section 0.7 presents
distributions of the main discriminating variables in data. Section 0.8
details the strategy adopted to extract the signal from the data, and the
corresponding systematic uncertainties are summarized in Section 0.9. The
cross section and anomalous coupling results are presented in Sections 0.10
and 0.11, respectively. Section 0.12 presents a study of the additional
hadronic activity in an EW $\PW\mathrm{jj}$ enriched region. Finally, a brief
summary of the results is given in Section 0.13.

##  0.2 The CMS detector and physics objects

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter, each composed of a barrel and two endcap sections. Forward
calorimeters extend the $\eta$ coverage provided by the barrel and endcap
detectors to $\abs{\eta}$ = 5.2. Muons are measured in gas-ionization
detectors embedded in the steel flux-return yoke outside the solenoid.

The tracker measures charged particles within the range $\abs{\eta}<2.5$. It
consists of 1440 pixel and 15 148 strip detector modules. For nonisolated
particles with transverse momenta $1<\pt<10\GeV$ and $\abs{\eta}<1.4$, the
track resolutions are typically 1.5% in and 25–90 (45–150)in the transverse
(longitudinal) impact parameter [16].

The energy of electrons is measured after combining the information from the
ECAL and the tracker, whereas their direction is measured by the tracker. The
momentum resolution for electrons with $\pt\approx$ 45from $\Z\to\Pe\Pe$
decays ranges from 1.7% to 4.5%. It is generally better in the barrel region
than in the endcaps, and also depends on the bremsstrahlung energy emitted by
the electron as it traverses the material in front of the ECAL [17].

Muons are measured in the range $\abs{\eta}<2.4$, with detection planes made
using three technologies: drift tubes, cathode strip chambers, and resistive-
plate chambers. Matching muons to tracks measured in the silicon tracker
results in a relative transverse momentum resolution for muons with
$20<\pt<100\GeV$ of 1.3–2.0% in the barrel and better than 6% in the endcaps.
The resolution in the barrel is better than 10% for muons with up to 1 [18].

Events of interest are selected using a two-tiered trigger system [19]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a time interval of less than 4. The second level, known as the
high-level trigger (HLT), 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. [20].

##  0.3 Simulation of signal and background events

Signal events are simulated at leading order (LO) using the (v2.3.3) Monte
Carlo (MC) generator [21], interfaced with (v8.212) [22] for parton showering
(PS) and hadronization. The NNPDF30 [23] parton distribution functions (PDFs)
are used to generate the events. The underlying event is modeled using the
CUETP8M1 tune [24]. The simulation does not include extra partons at matrix
element (ME) level. The signal is defined in the kinematic region with parton
transverse momentum $p_{\mathrm{Tj}}>25\GeV$, and diparton invariant mass
$m_{\mathrm{jj}}>120\GeV$. The simulated cross section for the $\ell\nu$jj
final state (with $\ell$ = e, $\mu$ or $\tau$), applying the above
requirements, is
$\sigma_{\mathrm{LO}}(\mathrm{EW}~{}\ell\nu\mathrm{jj})=6.81^{+0.03}_{-0.06}\,\text{(scale)}\pm
0.26\,\text{(PDFs)}\unit{pb}$, where the first uncertainty is obtained by
changing simultaneously the factorization ($\mu_{\mathrm{F}}$) and
renormalization ($\mu_{\mathrm{R}}$) scales by factors of $2$ and $1/2$, and
the second one reflects the uncertainties in the NNPDF30 PDFs. The LO signal
cross section and relevant kinematic distributions estimated with are in
agreement within 2–5% with the next-to-leading-order (NLO) predictions of the
vbfnlo generator (v2.6.3) [25, 26, 27], which include QCD NLO corrections to
the LO ME-level diagrams evaluated with . For additional comparisons, signal
events produced with are also processed with the (v2.7.1) [28] PS, using the
EE5C [29] tune.

An additional signal sample that includes NLO QCD corrections but does not
include the s-channel contributions to the final state has been generated with
(v2.0) [30, 31, 32], based on the vbfnlo ME calculations [33, 34]. In the
sample the $m_{\mathrm{jj}}>120\GeV$ condition is applied on the two -leading
parton-level jets, after clustering the ME final state partons with the
-algorithm [35, 36, 37], with a distance parameter $D=0.8$, as done in Ref.
[33]. The sample has also been processed alternatively with and parton
showering (PS) and hadronization programs, as done for the samples. In the
following, results obtained with the signal samples are given as a cross check
of the main results obtained with the signal samples.

Events coming from processes including ATGCs are generated with the same
settings as the SM sample, but include additional information for reweighting
in the three-dimensional effective field theory (EFT) parameter space, which
is described in more detail in Section 0.11. The ’EWdim6NLO’ model [21, 8] is
used for the generation of anomalous couplings.

Background $\PW$ boson events are also simulated with using (i) an NLO ME
calculation with up to three final-state partons generated from QCD
interactions, and (ii) an LO ME calculation with up to four partons from QCD
interactions. The ME-PS matching is performed following the FxFx prescription
[38] for the NLO case, and the MLM prescription [39, 40] for the LO case. The
NLO background simulation is used to extract the final results, while the
independent LO samples are used to perform the multivariate discriminant
training. The inclusive $\PW$ boson production is normalized to
$\sigma_{\text{th}}(\PW)=61.5\unit{nb}$, as computed at next-to-next-to-
leading order (NNLO) with (v3.1) [41].

The evaluation of the interference between $\mathrm{EW}\,\PW\mathrm{jj}$ and
$\mathrm{DY}\,\PW\mathrm{jj}$ processes relies on the predictions obtained
with . A dedicated sample of events arising from the interference terms is
generated directly by selecting the contributions of order
$\alpha_{\mathrm{s}}\alpha_{\mathrm{EW}}^{3}$, and passed through the full
detector simulation to estimate the expected interference contribution.

Other backgrounds are expected from events with one electron or muon and
missing transverse momentum together with jets in the final state. Events from
top quark pair production are generated with (v2.0) [30, 31, 32], and
normalized to the inclusive cross section calculated at NNLO, including next-
to-next-to-leading logarithmic corrections, of 832pb [42, 43]. Single top
quark processes are modeled at NLO with [32, 30, 31, 44] and normalized to
cross sections of $71.7\pm 2.0\unit{pb}$, $217\pm 3\unit{pb}$, and $10.32\pm
0.20\unit{pb}$, respectively, for the tW (v1) [45], $t$-, and $s$-channel
production [42, 46]. The diboson (VV) production processes ($\PW\PW$,
$\PW\cPZ$, and $\cPZ\cPZ$) are generated with and normalized to NNLO cross
section computations obtained with (v8.0) [47].

The contribution from QCD multijet processes is derived via an extrapolation
from a QCD data CR with the lepton relative isolation selection inverted. All
background simulations make use of the PS model with the CUETP8M1 tune.

A detector simulation based on (v9.4p03) [48, 49] is applied to all the
generated signal and background samples. The presence of multiple pp
interactions is incorporated by simulating additional interactions (pileup),
both in-time and out-of-time with respect to the hard interaction, with a
multiplicity that matches the distribution observed in data. The average
pileup is measured to be about 23 additional interactions per bunch crossing.

##  0.4 Reconstruction and selection of events

Events containing exactly one isolated, high-lepton and at least two high-jets
are selected. Isolated single-lepton triggers are used to acquire the data,
where the lepton is required to have $\pt>27\GeV$ for the electron trigger and
$\pt>24\GeV$ for the muon trigger.

The offline analysis uses candidates reconstructed by the particle-flow (PF)
algorithm [50]. In the PF event reconstruction, all stable particles in the
event — , electrons, muons, photons, charged and neutral hadrons — are
reconstructed as PF candidates using information from all subdetectors to
obtain an optimal determination of their direction, energy, and type. The PF
candidates are used to reconstruct the jets and the missing transverse
momentum.

The reconstructed primary vertex (PV) with the largest value of summed
physics-object $\pt^{2}$ is the primary $\Pp\Pp$ interaction vertex. The
physics objects are the objects returned by a jet finding algorithm [51, 52]
applied to all charged particle tracks associated with the vertex, along with
the corresponding associated missing transverse momentum. Charged tracks
identified as hadrons from pileup vertices are omitted in the subsequent PF
event reconstruction [50].

Offline electrons are reconstructed from clusters of energy deposits in the
ECAL that match tracks extrapolated from the silicon tracker [17]. Offline
muons are reconstructed by fitting trajectories based on hits in the silicon
tracker and in the muon system [53]. Reconstructed electron or muon candidates
are required to have $\pt>20\GeV$. Electron candidates are required to be
reconstructed within $\abs{\eta}\leq 2.4$, excluding the barrel-to-endcap
transitional region $1.444<\abs{\eta}<1.566$ of the ECAL [20]. Muon candidates
are required to be reconstructed in the fiducial region $\abs{\eta}\leq 2.4$.
The track associated with a lepton candidate is required to have both its
transverse and longitudinal impact parameters compatible with the position of
the PV of the event.

The leptons are required to be isolated; the isolation ($I$) variable is
calculated from PF candidates and is corrected for pileup on an event-by-event
basis [54]. The scalar sum of all PF candidates reconstructed in an isolation
cone with radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.4$ around the lepton’s
momentum vector, excluding the lepton itself, is required to be less than 6%
of the electron or muon value. For additional offline analysis, the isolated
lepton is required to have $\pt>25$for the muon channel and $\pt>30$for the
electron channel. Events with more than one lepton satisfying the above
requirements are rejected. The lepton flavor samples are exclusive and
precedence is given to the selection of muons.

The missing transverse momentum vector, $\ptvec^{\text{miss}}$, is calculated
offline as the negative of the vector sum of transverse momenta of all PF
objects identified in the event [55], and the magnitude of this vector is
denoted $\pt^{\text{miss}}$. Events are required to have $\pt^{\text{miss}}$
in excess of 20in the muon channel and 40in the electron channel. The tighter
requirement for the electron channel reduces the corresponding higher
background of QCD multijet events. The transverse mass (${m}_{\mathrm{T}}$) of
the lepton and $\ptvec^{\text{miss}}$ four-vector sum is then required to
exceed 40in both channels.

Jets are reconstructed by clustering PF candidates with the anti-algorithm
[56, 51] using a distance parameter of 0.4. The jet momentum is the vector sum
of all particle momenta in the jet and is typically within 5 to 10% of the
true momentum over the whole spectrum and detector acceptance.

An offset correction is applied to jet energies because of the contribution
from pileup. Jet energy corrections are derived from simulation, and are
confirmed with in situ measurements of the energy balance in dijet, multijet,
photon+jet, and Z+jets events with leptonic Z boson decays [57]. Loose jet
identification criteria are applied to reject misreconstructed jets resulting
from detector noise [58]. Loose criteria are also applied to remove jets
heavily contaminated with pileup energy (clustering of energy deposits not
associated with a parton from the primary $\Pp\Pp$ interaction) [59, 58]. The
efficiency of the jet identification is greater than 99%, with a rejection of
90% of background pileup jets with $\pt\simeq 50\GeV$ and $\abs{\eta}\leq
2.5$. For jets with $\abs{\eta}>2.5$ and $30<\pt<50\GeV$, the efficiency is
approximately 90% and the pileup jet rejection is approximately 50%. The jet
energy resolution (JER) is typically ${\approx}$15% at 10, 8% at 100, and 4%
at 1for jets with $\abs{\eta}\leq 1$ [57]. Jets reconstructed with $\pt\geq
15$and $\abs{\eta}\leq 4.7$ are used in the analysis.

The two highest jets are defined as the tagging jets, and are required to have
$\pt>50$and $\pt>30$for the leading and subleading (in ) jet, respectively.
The invariant mass of the two tagging jets is required to satisfy
$m_{\mathrm{jj}}>200\GeV$.

The transverse momentum of the $\PW$ boson ($\vec{p}_{\mathrm{T}\PW}$) is
evaluated as the vector sum of the lepton and $\ptvec^{\text{miss}}$. The
event $\pt$ balance ($R(\pt$)) is then defined as

$R(\pt)=\frac{\abs{\vec{p}_{\mathrm{T}\mathrm{j}_{1}}+\vec{p}_{\mathrm{T}\mathrm{j}_{2}}+\vec{p}_{\mathrm{T}\PW}}}{\abs{\vec{p}_{\mathrm{T}\mathrm{j}_{1}}}+\abs{\vec{p}_{\mathrm{T}\mathrm{j}_{2}}}+\abs{\vec{p}_{\mathrm{T}\PW}}}$
(1)

where $\vec{p}_{\mathrm{T}\mathrm{j}_{1}}$ and
$\vec{p}_{\mathrm{T}\mathrm{j}_{2}}$ are the transverse momenta of the two
tagging jets.

Finally, events are required to have $R(\pt)<0.2$. This has a negligible
effect on the analysis sensitivity and allows the definition of a
nonoverlapping control sample with $R(\pt)>0.2$ that is used to derive a
correction to the invariant mass based on a CR in data, as described in
Section 0.6.

A multivariate analysis technique, described in Section 0.8, is used to
provide an optimal separation of the $\mathrm{DY}\,\PW\mathrm{jj}$ and
$\mathrm{EW}\,\PW\mathrm{jj}$ components of the inclusive $\ell\nu\mathrm{jj}$
spectrum. The main discriminating variables are the dijet invariant mass
$m_{\mathrm{jj}}$ and pseudorapidity separation $\Delta\eta_{\mathrm{jj}}$.

Angular variables useful for signal discrimination include the $y^{*}$
Zeppenfeld variable [6], defined as the difference between the rapidity of the
$\PW$ boson $y_{\PW}$ and the average rapidity of the two tagging jets, ,

$y^{*}=y_{\PW}-\frac{1}{2}(y_{\mathrm{j}_{1}}+y_{\mathrm{j}_{2}}),$ (2)

and the $z^{*}$ Zeppenfeld variable [6] defined as

$z^{*}=\frac{y^{*}}{\Delta y_{\mathrm{jj}}},$ (3)

where $\Delta y_{\mathrm{jj}}$ is the dijet rapidity separation.

Table 0.4 reports the expected and observed event yields after the initial
selection and after imposing a minimum value for the final multivariate
discriminant output applied to define the signal-enriched region used for the
studies of additional hadronic activity described in Section 0.12.

Event yields expected for background and signal processes using the initial
selections and with a selection on the multivariate analysis output (BDT) that
provides similar signal and background yields. The yields are compared to the
data observed in the different channels and categories. The total
uncertainties quoted for signal, $\mathrm{DY}\,\PW\mathrm{jj}$ and diboson
backgrounds, and processes with top quarks (and single top quarks) include the
systematic uncertainties.  Sample Initial $\mathrm{BDT}>0.95$ $\mu$ $\Pe$
$\mu$ $\Pe$ VV 20300 $\pm$ 2000 9820 $\pm$ 980 11.0 $\pm$ 2.5 9.6 $\pm$ 2.8
$\mathrm{DY}\,\PZ\mathrm{jj}$ 102000 $\pm$ 10000 29900 $\pm$ 3000 9.4 $\pm$
5.9 7.7 $\pm$ 3.0 $\ttbar$ 298000 $\pm$ 28000 164000 $\pm$ 15000 146 $\pm$ 17
102 $\pm$ 12 Single top quark 96000 $\pm$ 14000 45800 $\pm$ 6900 35.5 $\pm$
5.6 25.7 $\pm$ 4.2 QCD multijet 100000 $\pm$ 39000 65000 $\pm$ 21000 98 $\pm$
39 17.0 $\pm$ 5.6 $\mathrm{DY}\,\PW\mathrm{jj}$ 1720000 $\pm$ 120000 715000
$\pm$ 51000 356 $\pm$ 65 240 $\pm$ 41 Interference 7000 $\pm$ 2100 3400 $\pm$
1000 18.2 $\pm$ 8.1 9.8 $\pm$ 5.5 Total backgrounds 2340000 $\pm$ 170000
1032000 $\pm$ 58000 674 $\pm$ 78 412 $\pm$ 44 $\mathrm{EW}\,\PW\mathrm{jj}$
signal 43100 $\pm$ 4300 20700 $\pm$ 2100 503 $\pm$ 54 308 $\pm$ 34
$\mathrm{EW}\,\PZ\mathrm{jj}$ signal 1330 $\pm$ 130 407 $\pm$ 41 11.2 $\pm$
1.3 6.6 $\pm$ 0.9 Total prediction 2390000 $\pm$ 170000 1054000 $\pm$ 58000
1186 $\pm$ 95 726 $\pm$ 56 Data $2381901$ $1051285$ $1138$ $686$

###  0.4.1 Discriminating quarks from gluons

Jets in signal events are expected to originate from quarks, whereas for
background events it is more probable that jets are initiated by a gluon. A
quark-gluon likelihood (QGL) discriminant [11] is evaluated for the two
tagging jets with the intent of distinguishing the nature of each jet.

The QGL discriminant exploits differences in the showering and fragmentation
of quarks and gluons, making use of the following internal jet composition
observables: (i) the particle multiplicity of the jet, (ii) the minor root-
mean-square of distance between the jet constituents in the $\eta$–$\phi$
plane, and (iii) the distribution function of the jet constituents, as defined
in Ref. [60].

The variables are used as inputs to a likelihood discriminant on gluon and
quark jets constructed from simulated dijet events. The performance of the QGL
discriminant is evaluated and validated using independent, exclusive samples
of +jet and dijet data [60]. Corrections to the simulated QGL distributions
and related systematic uncertainties are derived from a comparison of
simulation and data distributions.

##  0.5 The QCD multijet background

The QCD multijet contribution is estimated by defining a multijet-enriched CR
with inverted lepton isolation criteria for both the muon and electron
channels. In the nominal selection both lepton types are required to pass the
relative isolation requirement $I<0.06$, whereas the multijet-enriched CRs are
defined with the same event selection but with isolation requirements
$0.06<I<0.12$ and $0.06<I<0.15$, for the muon and electron channel
respectively. It is then assumed that the $\pt^{\text{miss}}$ distribution of
QCD events has the same shape in both the nominal and the multijet-enriched
CR.

The various components, with floating +jets and QCD multijet background scale
factors, are simultaneously fitted to the $\pt^{\text{miss}}$ data
distributions, independently in the muon and electron channels, and the
expected QCD multijet yields in the nominal regions are derived.

The contribution of QCD multijet processes in any other observable ($x$) used
in the analysis is then normalized to the yields obtained above from the fit
to the $\pt^{\text{miss}}$ distribution, and the shape for the distribution
$x$ is taken as the difference between data and all simulated background
contributions in the $x$ distribution in the multijet-enriched CR.

The estimation of the QCD multijet contribution based on a CR in data is
validated by checking the modeling of other variables that discriminate QCD
multijets from +jets such as the W transverse mass and the minimum difference
in $\phi$ between the missing transerse energy and the jets. Good agreement
with the data is observed in all distributions. The stability of the +jets
fitted normalization is checked by varying the selection requirements for the
fitted region and repeating the QCD extraction fit. The observed variations in
fitted normalization when varying the $m_{\mathrm{T}}$(W) and
$\pt^{\text{miss}}$ selection requirements with respect to the fit region
definition are much smaller than systematic uncertainties.

Although tagging is not used in this analysis, a -tagging discriminant output
[61] is used to check the fitted +jets background normalization as well as the
$\ttbar$ normalization from simulation, and they agree with data within the
uncertainties. Finally, the selections on $m_{\mathrm{jj}}$,
$\pt^{\text{miss}}$, and $m_{\mathrm{T}}$(W) are also loosened in order to
verify that the +jets background scale factor is not biased by these
requirements.

##  0.6 The $m_{\mathrm{jj}}$ correction

A systematic overestimation of the simulation yields is caused by a partial
mistiming of the signals in the forward region of the ECAL endcaps
($2.5<|\eta|<3.0$). This effect, which increases with increasing
$m_{\mathrm{jj}}$, is observed in both electron and muon channels. A
correction for this effect is derived in the nonoverlapping signal-depleted CR
obtained by requiring that the event transverse momentum balance $R(\pt)$,
defined in Section 0.4, exceeds 0.2.

A third-order polynomial correction is first applied to the +jets simulation
separately in the muon and electron channels in order to match the $R(\pt)$
distribution in data. The magnitude of the applied $R(\pt)$ corrections is
about 10%. The uncertainty in this correction due to the limited statistical
precision of the simulation as well as data is propagated to the fitted +jets
templates.

A correction to the $m_{\mathrm{jj}}$ prediction from simulation is derived in
the signal-depleted $R(\pt)>0.2$ CR via a third-order polynomial fit to the
ratio of data to the overall prediction from simulation for signal and
background as a function of $\ln(m_{\mathrm{jj}}/\GeV)$. The electron and muon
channels are combined when deriving the $m_{\mathrm{jj}}$ correction. The
uncertainty in the correction includes the data statistical component as well
as the systematic uncertainty due to the limited statistical precision of the
simulation.

Figure 3 shows the fitted correction including the uncertainty. This
correction is applied to all simulated results, including the signal, and the
corresponding uncertainty is propagated to the signal extraction fits.

Figure 3:  Data divided by simulation as a function of
$\ln(m_{\mathrm{jj}}/\GeV)$ in a signal-depleted control sample with
$R(\pt)>0.2$. This distribution is fit by a third-order polynomial (solid
black line) in order to derive a correction on the simulation
$m_{\mathrm{jj}}$ prediction. The points are varied by the uncertainty,
including the effect of the limited number of simulated events and refitted in
order to derive the systematic variations on the correction (dashed lines)
corresponding to a standard deviation (s.d.).

##  0.7 Distributions of discriminating variables

Figure 4 shows the $\pt^{\text{miss}}$ and ${m}_{\mathrm{T}}$(W) distributions
after the event preselection. The dijet invariant mass and pseudorapidity
difference ($\Delta\eta_{\mathrm{jj}}$) after preselection are presented in
Fig. 5, and Fig. 6 shows the $y^{\star}$ and $z^{\star}$ distributions after
the event preselection. The distributions of the QGL likelihood output values
in data and simulation for the two tagging jets are shown in Fig. 7. The
prediction from simulated events and the data agree within total uncertainties
for all discriminating variables.

Figure 4: Distribution of the missing transverse momentum (upper) and the
lepton-$\pt^{\text{miss}}$system transverse mass (lower) after the event
preselection for the selected leading lepton in the event, in the muon (left)
and electron (right) channels. In all plots the last bin contains overflow
events.

Figure 5: Dijet invariant mass (upper) and pseudorapidity difference (lower)
distributions after the event preselection, in the muon (left) and electron
(right) channels. In all plots the last bin contains overflow events.

Figure 6: Distributions of the “Zeppenfeld” variables $y^{\star}$(W) (upper)
and $z^{\star}$(W) (lower) after event preselection in the muon (left) and
electron (right) channels. In all plots the first and last bins contain
overflow events.

Figure 7: The QGL output for the leading (upper) and subleading (lower) quark
jet candidates in the preselected muon (left) and electron (right) samples.

##  0.8 Signal discriminants and extraction procedure

The $\mathrm{EW}\,\PW\mathrm{jj}$ signal is characterized by a large
pseudorapidity separation between the tagging jets, due to the small-angle
scattering of the two initial partons. Because of both the topological
configuration and the large energy of the outgoing partons, $m_{\mathrm{jj}}$
is also expected to be large, and can be used to distinguish the
$\mathrm{EW}\,\PW\mathrm{jj}$ and $\mathrm{DY}\,\PW\mathrm{jj}$ processes. The
correlation between $\Delta\eta_{\mathrm{jj}}$ and $m_{\mathrm{jj}}$ is
expected to be different in signal and background events, therefore these
characteristics are expected to yield a high separation power between
$\mathrm{EW}\,\PW\mathrm{jj}$ and $\mathrm{DY}\,\PW\mathrm{jj}$ production. In
addition, in signal events it is expected that the $\PW$ boson candidate is
produced centrally in the rapidity region defined by the two tagging jets. As
a consequence, signal events are expected to yield lower values of $z^{*}$
compared to the DY background. Other variables that are used to enhance the
signal-to-background separation are related to the kinematics of the event or
to the properties of the jets that are expected to be initiated by quarks. The
variables that are used in the multivariate analysis are: (i)
$m_{\mathrm{jj}}$, (ii) $\Delta\eta_{\mathrm{jj}}$, (iii) $z^{*}$, and (iv)
the QGL values of the two tagging jets.

The output is built by training a boosted decision tree (BDT) discriminator
with the tmva package [62] to achieve an optimal separation between the
$\mathrm{EW}\,\PW\mathrm{jj}$ and $\mathrm{DY}\,\PW\mathrm{jj}$ processes. The
simulated events that are used for the BDT training are not used for the
signal extraction.

To improve the sensitivity for the extraction of the signal component, the
transformation that originally projects the BDT output value in the
[$-$1,$+$1] interval is changed to
$\mathrm{BDT^{\prime}}=\tanh^{-1}((\mathrm{BDT}+1)/2)$. This allows the purest
signal region of the BDT output to be better sampled while keeping an equal-
width binning of the BDT variable.

Figure 8 shows the distributions of the discriminants for the two leptonic
channels. Good overall agreement between simulation and data is observed in
all distributions, and the signal presence is visible at high BDT’ values.

Figure 8: Data and MC simulation BDT’ output distributions for the muon (left)
and electron (right) channels, using the BDT output transformed with the
$\tanh^{-1}$ function to enhance the purest signal region. The ratio panel
shows the statistical uncertainty from the simulation as well as the
independent systematic uncertainties front the leading sources.

A binned maximum likelihood is built from the expected rates for each process,
as a function of the value of the discriminant, which is fit to extract the
strength modifiers for the $\mathrm{EW}\,\PW\mathrm{jj}$ and
$\mathrm{DY}\,\PW\mathrm{jj}$ processes,
$\mu=\sigma({\mathrm{EW}~{}\PW\mathrm{jj}})/\sigma_{\mathrm{LO}}({\mathrm{EW}~{}\ell\nu\mathrm{jj}})$
and $\upsilon=\sigma({\PW})/\sigma_{\text{NNLO}}({\PW})$. Nuisance parameters
are added to modify the expected rates and shapes according to the estimate of
the systematic uncertainties affecting the measurement.

The interference between the $\mathrm{EW}\,\PW\mathrm{jj}$ and
$\mathrm{DY}\,\PW\mathrm{jj}$ processes is included in the fit procedure, and
its strength scales as $\sqrt{\mu\upsilon}$. The interference model is derived
from the simulation described in Section 0.3.

The parameters of the model ($\mu$ and $\upsilon$) are determined by
maximizing the likelihood. The statistical methodology follows the one used in
other analyses [63] using asymptotic formulas [64]. In this procedure the
systematic uncertainties affecting the measurement of the signal and
background strengths ($\mu$ and $\upsilon$) are constrained with log-normal
probability distributions.

##  0.9 Systematic uncertainties

The main systematic uncertainties affecting the measurement are classified
into experimental and theoretical according to their sources. Some
uncertainties affect only normalizations, whereas others affect both the
normalization and shape of the BDT output distribution.

###  0.9.1 Experimental uncertainties

The following experimental uncertainties are considered.

Integrated luminosity.

A 2.5% uncertainty is assigned to the value of the integrated luminosity [65].

Trigger and selection efficiencies.

Uncertainties in the efficiency corrections based on control samples in data
for the leptonic trigger and offline selections are included and amount to a
total of 2–3% depending on the lepton $\pt$ and $\eta$, for both the e and
$\mu$ channels. These uncertainties are estimated by comparing the lepton
efficiencies expected in simulation and measured in data with a “tag-and-
probe” method [66].

Jet energy scale and resolution.

The uncertainty in the energy of the jets affects the event selection and the
computation of the kinematic variables used to calculate the discriminants.
Therefore, the uncertainty in the jet energy scale (JES) affects both the
expected event yields and the final shapes. The effect of the JES uncertainty
is studied by rescaling up and down the reconstructed jet energy by - and
$\eta$-dependent scale factors [57]. An analogous approach is used for the
JER.

QGL discriminator.

The uncertainty in the performance of the QGL discriminator is measured using
independent +jet and dijet data, after comparing with the corresponding
simulation predictions [60]. Shape variations corresponding to the full
differences between the data and the simulation are used as estimates of the
uncertainty.

Pileup.

Pileup can affect the identification and isolation of the leptons or the
corrected energy of the jets. When the jet clustering algorithm is run, pileup
can distort the reconstructed dijet system because of the contamination of
tracks and calorimetric deposits. This uncertainty is evaluated by generating
alternative distributions of the number of pileup interactions, corresponding
to a 4.6% uncertainty in the total inelastic pp cross section at
$\sqrt{s}=13\TeV$ [67].

Limited number of simulated events.

For each signal and background simulation, shape variations for the
distributions are considered by shifting the content of each bin up or down by
its statistical uncertainty [68]. This generates alternatives to the nominal
shape that are used to describe the uncertainty from the limited number of
simulated events.

$m_{\mathrm{jj}}$ correction.

As described in Section 0.6, the $m_{\mathrm{jj}}$ prediction from simulation
is corrected to match the distribution in data in a signal-depleted
$R(\pt)>0.2$ control region. The uncertainty in this correction is derived by
varying the fitted points within the statistical uncertainty from data and
simulation combined and refitting the correction.

QCD multijet background template.

As described in Section 0.5, the QCD multijet prediction is extrapolated from
the data in a nonoverlapping CR. The uncertainty in the QCD multijet
background template shape is derived by taking the envelope of the shape
obtained when varying the lepton isolation requirement used to define the
multijet-enriched CR. A 50% uncertainty in the QCD multijet background
normalization is also included.

###  0.9.2 Theoretical uncertainties

The following theoretical uncertainties are considered in the analysis.

PDF.

The PDF uncertainties are evaluated by comparing the nominal distributions to
those obtained when using the alternative PDFs of the NNPDF set, including
$\alpha_{\mathrm{s}}$ variations.

Factorization and renormalization scales.

To account for theoretical uncertainties, signal and background shape
variations are built by changing the values of $\mu_{\mathrm{F}}$ and
$\mu_{\mathrm{R}}$ from their defaults by factors of 2 or 1/2 in the ME
calculation, simultaneously for $\mu_{\mathrm{F}}$ and $\mu_{\mathrm{R}}$, but
independently for each simulated sample.

Signal acceptance.

A 5% uncertainty on the signal yield is assigned to account for differences
between the prediction for the LO signal with respect to the NLO predictions
of the vbfnlo generator (v2.6.3).

Normalization of top quark and diboson backgrounds.

Diboson and top quark production processes are modeled with MC simulations. An
uncertainty in the normalization of these backgrounds is assigned based on the
PDF and $\mu_{\mathrm{F}}$, $\mu_{\mathrm{R}}$ uncertainties, following
calculations in Refs. [47, 43, 42].

Interference between $\mathrm{EW}\,\PW\mathrm{jj}$ and
$\mathrm{DY}\,\PW\mathrm{jj}$.

An overall normalization and a shape uncertainty are assigned to the
interference term in the fit, based on an envelope of predictions with
different $\mu_{\mathrm{F}}$, $\mu_{\mathrm{R}}$ scales.

Parton showering model.

The uncertainty in the PS model and the event tune is assessed as the full
difference of the acceptance and shape predictions using and .

$R(\pt)$ correction.

As described in Section 0.6, the $R(\pt)$ prediction from +jets simulation is
corrected to match the distribution in data with all expected contributions
other than +jets subtracted. The uncertainty in this correction is derived by
varying the fitted points within the statistical uncertainty from data and
simulation combined and refitting the correction.

##  0.10 Measurement of the $\mathrm{EW}\,\PW\mathrm{jj}$ production cross
section

The signal strength, defined with the $\ell\nu$jj final state in the kinematic
region described in Section 0.3, is extracted from the fit to the BDT output
distribution as discussed in Section 0.8. Figure 9 shows the BDT distribution
in the muon and electron channels for data and simulation after the fit, where
the grey uncertainty band includes all systematic uncertainties. Good
agreement is observed between the data and simulation within the
uncertainties.

Figure 9: Data compared with simulation for the BDT’ output distribution for
the muon (left) and electron (right) channels, after the fit. The grey
uncertainty band in the ratio panel includes all systematic uncertainties.

In the muon channel, the signal strength is measured to be

$\mu=0.91\pm 0.02\stat\pm 0.12\syst=0.91\pm 0.12\,\text{(total)},$

corresponding to a measured signal cross section

$\sigma({\mathrm{EW}~{}\ell\nu\mathrm{jj}})=6.22\pm 0.12\stat\pm
0.74\syst\unit{pb}=6.22\pm 0.75\,\text{(total)}\unit{pb}.$

In the electron channel, the signal strength is measured to be

$\mu=0.92\pm 0.03\stat\pm 0.13\syst=0.92\pm 0.13\,\text{(total)},$

corresponding to a measured signal cross section

$\sigma({\mathrm{EW}~{}\ell\nu\mathrm{jj}})=6.27\pm 0.19\stat\pm
0.80\syst\unit{pb}=6.27\pm 0.82\,\text{(total)}\unit{pb}.$

The results obtained for the different lepton channels are compatible with
each other, and in agreement with the SM predictions.

From the combined fit of the two channels, the signal strength is measured to
be

$\mu=0.91\pm 0.02\stat\pm 0.10\syst=0.91\pm 0.10\,\text{(total)},$

corresponding to a measured signal cross section

$\sigma({\mathrm{EW}~{}\ell\nu\mathrm{jj}})=6.23\pm 0.12\stat\pm
0.61\syst\unit{pb}=6.23\pm 0.62\,\text{(total)}\unit{pb},$

in agreement with the LO prediction
$\sigma_{\mathrm{LO}}(\mathrm{EW}\,\ell\nu\mathrm{jj})=6.81^{+0.03}_{-0.06}\,\text{(scale)}\pm
0.26\,\text{(PDF)}\unit{pb}$. In the combined fit, the DY strength is
$\nu=0.88\pm 0.07$. Using the statistical methodology described in Section
0.8, the background-only hypotheses in the electron, muon, and combined
channels are all excluded with significance above five standard deviations.
Table 1 lists the major sources of uncertainty and their impact on the
measured precision of $\mu$. The largest sources of experimental uncertainty
are the $m_{\mathrm{jj}}$ correction, the JES, and the limited number of
simulated events, while the largest sources of theoretical uncertainty are the
$\mu_{\mathrm{F}}$, $\mu_{\mathrm{R}}$ scale uncertainties and the uncertainty
in the signal acceptance, derived by comparing the LO signal prediction with
the prediction from the vbfnlo generator.

Table 1: Major sources of uncertainty in the measurement of the signal strength $\mu$, and their impact. The total uncertainty is separated into four components: statistical, number of simulated events, experimental, and theory. The experimental and theory components are further decomposed into their primary individual uncertainty sources.  Uncertainty source | $\Delta\mu$
---|---
Statistical | $+0.02$ | $-0.02$
Size of simulated samples | $+0.05$ | $-0.05$
Experimental | $+0.07$ | $-0.07$
Jet energy scale and resolution | $+0.03$ | $-0.01$
QCD multijet estimation | $+0.03$ | $-0.03$
$m_{jj}$ correction | $+0.05$ | $-0.05$
Background normalization | $+0.02$ | $-0.02$
Other experimental uncertainties | $<0.01$ |
Theory | $+0.07$ | $-0.07$
QCD scale and PDF | $+0.05$ | $-0.05$
Interference | $+0.02$ | $-0.02$
Signal acceptance | $+0.05$ | $-0.05$
Other theory uncertainties | $+0.01$ | $-0.01$
Total | $+0.10$ | $-0.10$

The signal strength is also measured with respect to the NLO signal
prediction, as described in Section 0.3. In the muon channel, the signal
strength is measured to be

$\mu_{\mathrm{NLO}}=0.91\pm 0.02\stat\pm 0.12\syst=0.91\pm
0.12\,\text{(total)}.$

In the electron channel, the signal strength is measured to be

$\mu_{\mathrm{NLO}}=0.89\pm 0.03\stat\pm 0.12\syst=0.89\pm
0.12\,\text{(total)}.$

From the combined fit of the two channels, the signal strength is measured to
be

$\mu_{\mathrm{NLO}}=0.90\pm 0.02\stat\pm 0.10\syst=0.90\pm
0.10\,\text{(total)},$

corresponding to a measured signal cross section

$\sigma({\mathrm{EW}~{}\ell\nu\mathrm{jj}})=6.07\pm 0.12\stat\pm
0.57\syst\unit{pb}=6.07\pm 0.58\,\text{(total)}\unit{pb},$

in agreement with the NLO prediction
$\sigma_{\mathrm{NLO}}(\mathrm{EW}\,\ell\nu\mathrm{jj})=6.74^{+0.02}_{-0.04}\,\text{(scale)}\pm
0.26\,\text{(PDF)}\unit{pb}$.

##  0.11 Limits on anomalous gauge couplings

It is useful to look for signs of new physics via a model-independent EFT
framework. In the framework of EFT, new physics can be described as an
infinite series of new interaction terms organized as an expansion in the mass
dimension of the operators.

In the EW sector of the SM, the first higher-dimensional operators containing
bosons are six-dimensional [8]:

$\begin{split}\mathcal{O}_{WWW}&=\frac{c_{WWW}}{\Lambda^{2}}W_{\mu\nu}W^{\nu\rho}W_{\rho}^{\mu},\\\
\mathcal{O}_{W}&=\frac{c_{W}}{\Lambda^{2}}(D^{\mu}\Phi)^{\dagger}W_{\mu\nu}(D^{\nu}\Phi),\\\
\mathcal{O}_{B}&=\frac{c_{B}}{\Lambda^{2}}(D^{\mu}\Phi)^{\dagger}B_{\mu\nu}(D^{\nu}\Phi),\\\
\widetilde{\mathcal{O}}_{WWW}&=\frac{\widetilde{c}_{WWW}}{\Lambda^{2}}\widetilde{W}_{\mu\nu}W^{\nu\rho}W_{\rho}^{\mu},\\\
\widetilde{\mathcal{O}}_{W}&=\frac{\widetilde{c}_{W}}{\Lambda^{2}}(D^{\mu}\Phi)^{\dagger}\widetilde{W}_{\mu\nu}(D^{\nu}\Phi),\end{split}$
(4)

where, as is customary, group indices are suppressed and the mass scale
$\Lambda$ is factorized from the coupling constants $c$. In Eq. (4),
$W_{\mu\nu}$ is the SU(2) field strength, $B_{\mu\nu}$ is the U(1) field
strength, $\Phi$ is the Higgs doublet, and operators with a tilde are the
magnetic duals of the field strengths. The first three operators are charge
and parity conserving, whereas the last two are not. Models with operators
that preserve charge conjugation and parity symmetries can be included in the
calculation either individually or in pairs. With these assumptions, the
values of coupling constants divided by the mass scale $c/\Lambda^{2}$ are
measured.

These operators have a rich phenomenology since they contribute to many
multiboson scattering processes at tree level. The operator
$\mathcal{O}_{WWW}$ modifies vertices with three or six vector bosons, whereas
the operators $\mathcal{O}_{W}$ and $\mathcal{O}_{B}$ modify both the HVV
vertices and vertices with three or four vector bosons. A more detailed
description of the phenomenology of these operators can be found in Ref. [69].
Modifications to the ZWW and $\gamma$WW vertices are investigated in this
analysis, since these modify the $\Pp\Pp\to\PW\mathrm{jj}$ cross section.

Previously, modifications to these vertices have been studied using anomalous
trilinear gauge couplings [70]. The relationship between the dimension-six
operators in Eq. (4) and ATGCs can be found in Ref. [8]. Most stringent limits
on ATGC parameters were previously set by LEP [71], CDF [72], D0 [73], ATLAS
[74, 75], and CMS [76, 77].

###  0.11.1 Statistical analysis

The measurement of the coupling constants uses templates in the of the lepton
from the $W\to\ell\nu$ decay. Because this is well measured and longitudinally
Lorentz invariant, this variable is robust against mismodeling and ideal for
this purpose. An additional requirement of $\mathrm{BDT}>0.5$ has been
applied, which is optimized based on the expected sensitivity to the ATGC
signal. The expected limits are subsequently improved by 20–25% with respect
to the expected limits without a BDT selection. In each channel, four bins
from $0<\pt^{\ell}<1.2\TeV$ are used, where the last bin contains overflow and
its lower bin edge boundary has been optimized separately for each channel.

For each signal MC event, 125 weights are assigned that correspond to a
$5{\times}5{\times}5$ grid in
$(c_{WWW}/\Lambda^{2})\,(c_{W}/\Lambda^{2})\,(c_{B}/\Lambda^{2})$. Equal bins
are used in the interval $[-15,15]\TeV^{-2}$ for $c_{WWW}/\Lambda^{2}$,
$[-40,40]\TeV^{-2}$ for $c_{W}/\Lambda^{2}$, and equal bins in the interval
$[-175,175]\TeV^{-2}$ for $c_{B}/\Lambda^{2}$.

To construct the $\pt^{\ell}$ templates, the associated weights calculated for
each event are used to construct a parametrized model of the expected yield in
each bin as a function of the values of the dimension-six operators’ coupling
constants. For each bin, the ratios of the expected signal yield with
dimension-six operators to the one without (leaving only the SM contribution)
are fitted at each point of the grid to a quadratic polynomial. The highest
$\pt^{\ell}$ bin has the largest statistical power to detect the presence of
higher-dimensional operators. Figure 10 shows examples of the final templates,
with the expected signal overlaid on the background expectation, for three
different hypotheses of dimension-six operators. The SM distribution is
normalized to the expected cross section.

A simultaneous binned fit for the values of the ATGCs is performed in the two
lepton channels. A profile likelihood method, the Wald Gaussian approximation,
and Wilks’ theorem [78] are used to derive confidence intervals at 95%
confidence level (CL). One-dimensional and two-dimensional limits are derived
on each of the three ATGC parameters and each combination of two ATGC
parameters while all other parameters are set to their SM values. Systematic
and theoretical uncertainties are represented by the individual nuisance
parameters with log-normal distributions and are profiled in the fit.

###  0.11.2 Results

No significant deviation from the SM expectation is observed. Limits on the
EFT parameters are reported and also translated into the equivalent parameters
defined in an effective Lagrangian (LEP parametrization) in Ref. [79], without
form factors: $\lambda^{\gamma}=\lambda^{\PZ}=\lambda$,
$\Delta{\kappa^{\PZ}}=\Delta{g_{1}^{\PZ}}-\Delta{\kappa^{\gamma}}\,\tan^{2}\theta_{{\PW}}$.
The parameters $\lambda$, $\Delta{\kappa^{Z}}$, and $\Delta{g_{1}^{\PZ}}$ are
considered, where the $\Delta$ symbols represent deviations from their
respective SM values.

Results for the one-dimensional limits are listed in Table 0.11.2 for
$c_{WWW}$, $c_{W}$ and $c_{B}$, and in Table 0.11.2 for $\lambda$, $\Delta
g_{1}^{\PZ}$ and $\Delta\kappa_{1}^{\PZ}$; two-dimensions limits are shown in
Figs. 11 and 12. The results are dominated by the sensitivity in the muon
channel due to the larger acceptance for muons. An ATGC signal is not included
in the interference between EW and DY production. The effect on the limits is
small (${<}$3%). The LHC semileptonic WZ analysis using 13data currently sets
the most stringent limits on $c_{WWW}/\Lambda^{2}$ and $c_{W}/\Lambda^{2}$,
while the WW analysis using 8data currently sets the tightest limits on
$c_{B}/\Lambda^{2}$. This analysis is most sensitive to $c_{WWW}/\Lambda^{2}$,
where the limit is slightly less restrictive but comparable.

Figure 10:  Distributions of $\pt^{\ell}$ in data and SM backgrounds, and
various ATGC scenarios in the muon (left) and electron (right) channels,
before the fit. For each ATGC scenario plotted a particular parameter is
varied while the other ATGC parameters are fixed to zero. The lower panels
show the ratio between data and prediction minus one with the statistical
uncertainty from simulation (grey hatched band) as well as the leading
systematic uncertainties in the shape of the $\pt^{\ell}$ distribution.

One-dimensional limits on the ATGC EFT parameters at 95% CL.  Coupling
constant Expected 95% CL interval ($\TeVns{}^{-2}$) Observed 95% CL interval
($\TeVns{}^{-2}$) $c_{WWW}/\Lambda^{2}$ $[-2.5,2.5]$ $[-2.3,2.5]$
$c_{W}/\Lambda^{2}$ $[-16,19]$ $[-8.8,16]$ $c_{B}/\Lambda^{2}$ $[-62,61]$
$[-45,46]$

One-dimensional limits on the ATGC effective Lagrangian (LEP parametrization)
parameters at 95% CL.  Coupling constant Expected 95% CL interval Observed 95%
CL interval $\lambda^{Z}$ $[-0.0094,0.0097]$ $[-0.0088,0.0095]$ $\Delta
g_{1}^{Z}$ $[-0.046,0.053]$ $[-0.029,0.044]$ $\Delta\kappa_{1}^{Z}$
$[-0.059,0.059]$ $[-0.044,0.044]$

Figure 11:  Expected and observed two-dimensional limits on the EFT parameters
at 95% CL.

Figure 12:  Expected and observed two-dimensional limits on the ATGC effective
Lagrangian (LEP parametrization) parameters at 95% CL.

###  0.11.3 Combination with the VBF Z boson production analysis

As mentioned in Section 0.1, the closely-related EW $\cPZ\mathrm{jj}$ process
has been measured by CMS at $\sqrt{s}=13\TeV$ [15]. This result included
constraints on ATGC EFT parameters obtained via a fit to the $\pt$(Z)
distribution, an experimentally clean observable sensitive to deviations from
zero in the ATGC parameters. Both the EW $\cPZ\mathrm{jj}$ and EW
$\PW\mathrm{jj}$ analyses are sensitive to anomalous couplings related to the
WWZ vertex. A simultaneous binned likelihood fit for the ATGC parameters is
performed to the $\pt$(Z) distribution in the EW Zjj production and and
$\pt^{\ell}$ in the EW $\PW\mathrm{jj}$ production. In the combined fit, the
primary uncertainty sources are correlated including the JES and JER
uncertainties. Results for the one-dimensional limits are listed in Table
0.11.3 for $c_{WWW}$, $c_{W}$ and $c_{B}$, and in Table 0.11.3 for $\lambda$,
$\Delta g_{1}^{\PZ}$, and $\Delta\kappa_{1}^{\PZ}$; two-dimensions limits are
shown in Figs. 13 and 14.

One-dimensional limits on the ATGC EFT parameters at 95% CL from the
combination of EW $\PW\mathrm{jj}$ and EW $\cPZ\mathrm{jj}$ analyses.
Coupling constant Expected 95% CL interval ($\TeVns{}^{-2}$) Observed 95% CL
interval ($\TeVns{}^{-2}$) $c_{WWW}/\Lambda^{2}$ $[-2.3,2.4]$ $[-1.8,2.0]$
$c_{W}/\Lambda^{2}$ $[-11,14]$ $[-5.8,10.0]$ $c_{B}/\Lambda^{2}$ $[-61,61]$
$[-43,45]$

One-dimensional limits on the ATGC effective Lagrangian (LEP parametrization)
parameters at 95% CL from the combination of EW $\PW\mathrm{jj}$ and EW
$\cPZ\mathrm{jj}$ analyses.  Coupling constant Expected 95% CL interval
Observed 95% CL interval $\lambda^{Z}$ $[-0.0089,0.0091]$ $[-0.0071,0.0076]$
$\Delta g_{1}^{Z}$ $[-0.040,0.047]$ $[-0.021,0.034]$ $\Delta\kappa_{1}^{Z}$
$[-0.058,0.059]$ $[-0.043,0.042]$

Figure 13:  Expected and observed two-dimensional limits on the EFT parameters
at 95% CL from the combination of EW $\PW\mathrm{jj}$ and EW $\cPZ\mathrm{jj}$
analyses.

Figure 14:  Expected and observed two-dimensional limits on the ATGC effective
Lagrangian (LEP parametrization) parameters at 95% CL from the combination of
EW $\PW\mathrm{jj}$ and EW $\cPZ\mathrm{jj}$ analyses.

##  0.12 Study of the hadronic and jet activity in +jet events

Having established the presence of the SM signal, the properties of the
hadronic activity in the selected events can be examined, in particular in the
the region in rapidity between the two tagging jets, with low expected hadron
activity (rapidity gap). The production of additional jets in the rapidity
gap, in a region with a larger contribution of $\mathrm{EW}\,\PW\mathrm{jj}$
processes is explored in Section 0.12.1. Studies of the rapidity gap hadronic
activity using track-only observables, are presented in Section 0.12.2.
Finally, a study of hadronic activity vetoes, using both PF jets and track-
only observables, is presented in Section 0.12.3. A significant suppression of
the hadronic activity in signal events is expected because the final-state
objects originate from EW interactions, in contrast with the radiative QCD
production of jets in $\mathrm{DY}\,\PW\mathrm{jj}$ events.

In all these studies, event distributions are shown with a selection on the
output value at BDT $>0.95$, which allows a signal-enriched region to be
selected with a similar fraction of signal and background events. None of the
BDT input observables listed in Section 0.8 are related to additional hadronic
activity observables, as a consequence there is no bias on the additional
hadronic activity observables due to the BDT output cut. The reconstructed
distributions are compared directly to the prediction obtained with a full
simulation of the CMS detector. In the BDT $>0.95$ region, the dominant
uncertainty on the prediction from simulation is due to the limited number of
generated events.

###  0.12.1 Jet activity studies in a high-purity region

For this study, aside from the two tagging jets used in the preselection, all
PF jets with $\pt>15\GeV$ found within the pseudorapidity gap of the tagging
jets, $\eta^{\text{tag jet}}_{\text{min}}<\eta<\eta^{\text{tag
jet}}_{\text{max}}$, are used. For the estimation of the background
contributions, the normalizations obtained from the fit discussed in Section
0.10 are used.

The of the leading additional jet in $\PW\mathrm{jj}$ events, as well as the
scalar sum ($\HT$) of all additional jets, are shown in Figs. 15 and 16,
comparing data and simulations including the signal prediction from interfaced
with either or parton showering. The comparison reveals a deficit in the
simulation predictions with parton showering for the rate of events with lower
additional jet activity, whereas the tail of higher additional activity is
generally in better agreement.

A suppression of additional jets is observed in data compared with the
background-only simulation shapes. In the simulation of the signal, the
additional jets are produced by the PS (see Section 0.3), so studying these
distributions provides insight on the PS model in the rapidity gap region.

Figure 15: Leading additional jet ((j3)) for $\mathrm{BDT}>0.95$ in the muon
(left) and electron (right) channels including the signal prediction from
interfaced with parton showering (upper) and parton showering (lower). In all
plots the last bin contains overflow events, and the first bin contains events
where no additional jet with $>15$is present.

Figure 16: Total of the additional jets for $\mathrm{BDT}>0.95$ in the muon
(left) and electron (right) channels including the signal prediction from
interfaced with parton showering (upper) and parton showering (lower). In all
plots the last bin contains overflow events, and the first bin contains events
where no additional jet with $>15$is present.

###  0.12.2 Study of charged hadron activity

For this study, a collection is formed of high-purity tracks [80] with
$\pt>0.3\GeV$, uniquely associated with the main PV in the event. Tracks
associated with the lepton or with the tagging jets are excluded from the
selection. The association between the selected tracks and the reconstructed
PVs is carried out by minimizing the longitudinal impact parameter, which is
defined as the $z$-distance between the PV and the point of closest approach
of the track helix to the PV, labeled $d_{z}^{\mathrm{PV}}$. The association
is required to satisfy the conditions $d_{z}^{\mathrm{PV}}<2\unit{mm}$ and
$d_{z}^{\mathrm{PV}}<3\delta d_{z}^{\mathrm{PV}}$, where $\delta
d_{z}^{\mathrm{PV}}$ is the uncertainty in $d_{z}^{\mathrm{PV}}$.

A collection of “soft-track” jets is defined by clustering the selected tracks
using the anti-clustering algorithm [51] with a distance parameter of $R=0.4$.
The use of track jets represents a clean and well-understood method [81] to
reconstruct jets with energy as low as a few . These jets are not affected by
pileup because of the association of the constituent tracks with the hard
scattering vertex [82].

Track jets of low and within $\eta^{\text{tag
jet}}_{\text{min}}<\eta<\eta^{\text{tag jet}}_{\text{max}}$ are considered for
the study of the hadronic activity between the tagging jets, and referred to
as “soft activity” (SA). For each event, the scalar sum of the soft-track jets
with $>1$is computed, and referred to as the “soft $\HT$” variable. Figures 17
and 18 show the distribution of the leading soft-track jet and soft in the
signal-enriched region (BDT $>0.95$), for the electron and muon channels,
compared to predictions from and PS models. The plots show some disagreement
between the data and the predictions, in particular in the regions of small
additional activity, when compared with the predictions.

Figure 17: Leading additional soft-activity (SA) jet for BDT $>0.95$ in the
muon (left) and electron (right) channels including the signal prediction from
interfaced with parton showering (upper) and parton showering (lower).

Figure 18: Total soft activity (SA) jet for BDT $>0.95$ in the muon (left) and
electron (right) channels including the signal prediction from interfaced with
parton showering (upper) and parton showering (lower). In all plots the last
bin contains overflow events.

###  0.12.3 Study of hadronic activity vetoes

The efficiency of a hadronic activity veto corresponds to the fraction of
events with a measured gap activity below a given threshold. This efficiency
is studied as a function of the applied threshold for various gap activity
observables. The veto thresholds studied here start at 15for gap activities
measured with standard PF jets, while they go down to 1for gap activities
measured with soft-track jets.

Figure 19 shows the gap activity veto efficiency of combined muon and electron
events in the signal-enriched region when placing an upper threshold on the of
the additional third jet, on the of all additional jets, on the leading soft-
activity jet , or on the soft-activity . The observed efficiency in data is
compared to expected efficiencies for background-only events, and efficiencies
for background plus signal events where the signal is modeled with or . Data
points clearly disfavor the background-only predictions and are in reasonable
agreement with the presence of the signal with the PS predictions for gap
activities above 20, while the signal with PS seems to generally overestimate
the gap activity. In the events with very low gap activity, in particular
below 10as measured with the soft track jets, the data indicates gap
activities also below the PS predictions. In addition, the expected
efficiencies are included for background plus signal events where the signal
is modeled with (Sec. 0.3) with PS. The plus prediction is in good agreement
with the LO plus prediction.

Figure 19: Hadronic activity veto efficiencies in the signal-enriched
$\mathrm{BDT}>0.95$ region for the muon and electron channels combined, as a
function of the leading additional jet (upper left), additional jet (upper
right), leading soft-activity jet (lower left), and soft-activity jet (lower
right). The data are compared with the background-only prediction as well as
background+signal with parton showering and background+signal with parton
showering. In addition, the background+signal prediction from plus parton
showering is included. The uncertainty bands include only the statistical
uncertainty in the prediction from simulation, and the data points include
only the statistical uncertainty in data.

##  0.13 Summary

The cross section of the electroweak production of a boson in association with
two jets is measured in the kinematic region defined as invariant mass
$m_{\mathrm{jj}}>120\GeV$ and transverse momenta $p_{\mathrm{Tj}}>25\GeV$. The
data sample corresponds to an integrated luminosity of
$35.9~{}\mathrm{fb}^{-1}$ of proton-proton collisions at centre-of-mass energy
$\sqrt{s}=13\TeV$ recorded by the CMS Collaboration at the LHC. The measured
cross section $\sigma_{\mathrm{EW}}(\PW\mathrm{jj})=6.23\pm 0.12\stat\pm
0.61\syst\unit{pb}$ agrees with the leading order standard model prediction.
This is the first observation of this process at $\sqrt{s}=13\TeV$.

A search is performed for anomalous trilinear gauge couplings associated with
dimension-six operators as given in the framework of an effective field
theory. No evidence for ATGCs is found, and the corresponding 95% confidence
level intervals on the dimension-six operators are
$-2.3<c_{{\PW\PW\PW}}/\Lambda^{2}<2.5\TeV^{-2}$,
$-8.8<c_{\PW}/\Lambda^{2}<16\TeV^{-2}$, and
$-45<c_{\mathrm{B}}/\Lambda^{2}<46\TeV^{-2}$. These results are combined with
previous results on the electroweak production of a Z boson in association
with two jets, yielding the limit on the $c_{{\PW\PW\PW}}$ coupling
$-1.8<c_{{\PW\PW\PW}}/\Lambda^{2}<2.0\TeV^{-2}$.

The additional hadronic activity, as well as the efficiencies for gap activity
vetos, are studied in a signal-enriched region. Generally reasonable agreement
is found between the data and the quantum chromodynamics predictions with the
parton shower and hadronization model, while the model predictions typically
show greater activity in the rapidity gap between the two tagging jets.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid 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, 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 program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440 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”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Welch Foundation,
contract C-1845; and the Weston Havens Foundation (USA).

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##  .14 Additional rapidity gap observables

A set of rapidity gap observables in the high signal purity region
$\mathrm{BDT}>0.95$ is studied in addition to the results described in Section
0.12. The number of soft activity jets, defined in Section 0.12.2, in the
rapidity gap between the two tag jets is shown for soft activity jet $\pt>10$,
5, and 2in Figures 20, 21, and 22, respectively. These distributions are
consistent with the general underestimation of the simulation with respect to
data at low activity values, particularly for the parton showering.

Figure 20: Number of soft activity jets with $\pt>10\GeV$ in the rapidity gap
for $\mathrm{BDT}>0.95$ in the muon (left) and electron (right) channels
including signal with parton showering (upper) and parton showering (lower).
In all plots the last bin contains overflow events.

Figure 21: Number of soft activity jets with $\pt>5\GeV$ in the rapidity gap
for $\mathrm{BDT}>0.95$ in the muon (left) and electron (right) channels
including signal with parton showering (upper) and parton showering (lower).

Figure 22: Number of soft activity jets with $\pt>2\GeV$ in the rapidity gap
for $\mathrm{BDT}>0.95$ in the muon (left) and electron (right) channels
including signal with parton showering (upper) and parton showering (lower).

##  .15 Jet activity in signal-depleted region

Section 0.12 shows a comparison of the data with simulation with and parton
showering separately in a high purity signal region with $\mathrm{BDT}>0.95.$
The agreement of the simulation with data for the background prediction is
validated for the rapidity gap observables in the signal-depleted region BDT
$<$ 0.95, where the signal purity is less than 2%. Figures 23, 24, 25, and 26
show the leading additional jet , the total of the additional jets, the
leading soft activity jet , and the total soft activity jet , respectively, in
the region BDT $<$ 0.95. Good agreement is observed between the background
prediction and the data for all observables.

Figure 23: Leading additional jet ((j3)) for BDT $<$ 0.95 in the muon (left)
and electron (right) channels. In all plots the first bin contains events
where no additional jet with $\pt>15\GeV$ is present. Figure 24: Total of the
additional jets for BDT $<$ 0.95 in the muon (left) and electron (right)
channels. In all plots the fist bin contains events where no additional jet
with $\pt>15\GeV$ is present. Figure 25: Leading additional soft-activity (SA)
jet for BDT $<$ 0.95 in the muon (left) and electron (right)  Figure 26: Total
soft activity (SA) jet for BDT $<$ 0.95 in the muon (left) and electron
(right) channels.

###  .15.1 Hadronic activity vetoes

The efficiency of a hadronic activity veto, as described in Section 0.12.3, is
studied in the signal-depleted $\mathrm{BDT}<0.95$ region. Figure 27 shows the
gap activity veto efficiency of combined muon and electron events in the
signal-depleted region when placing an upper threshold on the of the
additional third jet, on the of all additional jets, on the leading soft-
activity jet , or on the soft-activity . There is very little difference
between the background-only prediction and the predictions including signal
with either or parton showering due to the very small fraction of signal in
this region. Good agreement is observed between the data and the simulation,
giving further confidence in the modelling of the background observables for
the rapidity gap studies.

Figure 27: Hadronic activity veto efficiencies in the signal-depleted BDT $<$
0.95 region for the muon and electron channels combined, as a function of the
leading additional jet (upper left), additional jet (upper right), leading
soft-activity jet (lower left), and soft-activity jet (lower right). The data
are compared with the background-only prediction as well as background+signal
with parton showering and background+signal with parton showering. The
uncertainty bands include only the statistical uncertainty in the prediction
from simulation. There is very little difference between the predictions due
to the small fraction of signal in this region.

##  .16 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D.
Spitzbart, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for
Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M. Pieters, H.
Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, J. D’Hondt, J. De Clercq, K. Deroover, G. Flouris, 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. Popov2, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, Q. Wang
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov3, 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, G. Krintiras, V. Lemaitre, A. Magitteri, K. Piotrzkowski, 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, M.E. Pol, P.
Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato4, E. Coelho, E.M.
Da Costa, G.G. Da Silveira5, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, D. Matos Figueiredo,
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
Manganote4, 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, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, S.M. Shaheen7, A. Spiezia, J. Tao, E. Yazgan, H.
Zhang, S. Zhang7, J. Zhao State Key Laboratory of Nuclear Physics and
Technology, Peking University, Beijing, China
Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado Universidad de Antioquia,
Medellin, Colombia
J.D. Ruiz Alvarez University of Split, Faculty of Electrical Engineering,
Mechanical Engineering and Naval Architecture, Split, Croatia
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, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov8,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, S. Konstantinou, G. Mavromanolakis, J.
Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski Charles University,
Prague, Czech Republic
M. Finger9, M. Finger Jr.9 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim10,11, A. Ellithi Kamel12, E. Salama13,14 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, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
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, 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-Navarro15,
M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique,
Institut Polytechnique de Paris
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, R. Granier de
Cassagnac, I. Kucher, A. Lobanov, J. Martin Blanco, C. Martin Perez, M.
Nguyen, C. Ochando, G. Ortona, P. Paganini, J. Rembser, R. Salerno, J.B.
Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC
UMR 7178, Strasbourg, France
J.-L. Agram16, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte16, J.-C. Fontaine16, 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, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille,
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. Toriashvili17 Tbilisi State University, Tbilisi, Georgia
I. Bagaturia18 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, R. Fischer, S. Ghosh, T. Hebbeker,
C. Heidemann, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, S. Mukherjee, A. Novak, T. Pook, A. Pozdnyakov, M. Radziej,
H. Reithler, M. Rieger, A. Schmidt, A. Sharma, D. Teyssier, S. Thüer RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl19 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, H.
Bakhshiansohi, K. Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D.
Bertsche, A.A. Bin Anuar, K. Borras20, V. Botta, A. Campbell, P. Connor, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo21,
A. Geiser, J.M. Grados Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb,
N.Z. Jomhari, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D.
Krücker, W. Lange, T. Lenz, J. Leonard, K. Lipka, W. Lohmann22, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, V. Myronenko, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M.
Savitskyi, P. Saxena, 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
University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, 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, D. Marconi, J. Multhaup, M. Niedziela, 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,
M.A. Harrendorf, F. Hartmann19, U. Husemann, I. Katkov2, S. Kudella, S. Mitra,
M.U. Mozer, Th. Müller, M. Musich, 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, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki
National and Kapodistrian University of Athens, Athens, Greece
A. Agapitos, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I. Papavergou, N.
Saoulidou, 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, E. Paradas, J. Strologas, F.A.
Triantis, D. Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Bartók23, M. Csanad, N. Filipovic, 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. Horvath24, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi23, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari 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. Bahinipati26, C. Kar, P. Mal, A. Nayak27, S. Roy Chowdhury, D.K. Sahoo26,
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. Bhardwaj28, M. Bharti28, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep28,
D. Bhowmik, S. Dey, S. Dutt28, S. Dutta, S. Ghosh, M. Maity29, K. Mondal, S.
Nandan, A. Purohit, P.K. Rout, A. Roy, G. Saha, S. Sarkar, T. Sarkar29, M.
Sharan, B. Singh28, S. Thakur28 Indian Institute of Technology Madras, Madras,
India
P.K. Behera, A. Muhammad 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, P. Suggisetti 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, Sa. Jain, 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. Chenarani30, E. Eskandari Tadavani, S.M. Etesami30, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh31, M.
Zeinali 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, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,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,
S.S. Chhibraa,b, 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,32, S. Marcellinia, G. Masettia, A.
Montanaria, 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,33, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,33, C. Tuvea,b
INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,34, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
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. Beschib, F. Brivioa,b, V. Cirioloa,b,19, S. Di Guidaa,b,19,
M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, D. Menascea, F. Monti, L. Moronia, 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,19, P. Paoluccia,19, 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, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa,
U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, S.
Lacapraraa, 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, E.
Torassaa, 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, A. Magnania, 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, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
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. Rolandi35, A.
Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M.
Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P. Meridiania, G.
Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, C. Quarantaa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,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, F. Cennaa,b, 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, K.
Shchelinaa,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
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, J. Goh36, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, S. Lee, K. Nam, S.B. Oh, B.C.
Radburn-Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul,
Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Riga Technical University, Riga, Latvia
V. Veckalns37 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, M.A.B. Md Ali38, F. Mohamad Idris39, 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 Centro de
Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz40, R. Lopez-Fernandez, R.I. Rabadan-Trejo, G. Ramirez-Sanchez, R. Reyes-
Almanza, 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, M.I. Asghar, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk41, 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, C. Beirão Da Cruz E Silva, A. Di Francesco,
P. Faccioli, B. Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev42,43, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
V. Golovtsov, Y. Ivanov, V. Kim44, E. Kuznetsova45, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, A. Shabanov, 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, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, 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
M. Chadeeva46, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev Physical
Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin43, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, M. Dubinin47, 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. Barnyakov48, V. Blinov48, T. Dimova48, L. Kardapoltsev48, Y. Skovpen48
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, A. Kalinin, 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, S. Baidali, A. Iuzhakov, V. Okhotnikov University of Belgrade:
Faculty of Physics and VINCA Institute of Nuclear Sciences
P. Adzic49, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic50, J. Milosevic
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, J.A.
Brochero Cifuentes, 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, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi 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), Oviedo, Spain
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, J.M. Vizan Garcia 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, A. Lopez Virto,
C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez,
C. Prieels, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila
University of Ruhuna, Department of Physics, Matara, Sri Lanka
N. Wickramage CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, P.
Baillon${}^{\textrm{\textdagger}}$, A.H. Ball, D. Barney, J. Bendavid, M.
Bianco, A. Bocci, C. Botta, E. Brondolin, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, G. Cucciati, D. d’Enterria, A. Dabrowski, N.
Daci, V. Daponte, A. David, A. De Roeck, N. Deelen, M. Dobson, M. Dünser, N.
Dupont, A. Elliott-Peisert, F. Fallavollita51, D. Fasanella, G. Franzoni, J.
Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M.
Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, G.M.
Innocenti, A. Jafari, P. Janot, O. Karacheban22, J. Kieseler, A. Kornmayer, 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. Pantaleo19,
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, P. Sphicas52, A. Stakia, J.
Steggemann, V.R. Tavolaro, D. Treille, A. Tsirou, A. Vartak, M. Verzetti, W.D.
Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada53, 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.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, K. Theofilatos, M.L. Vesterbacka Olsson, R.
Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler54, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, 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, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.F. Liu, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, N. Srimanobhas, N. Suwonjandee Çukurova University, Physics
Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci55, S. Damarseckin56, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler57, Y. Guler, I. Hos58,
C. Isik, E.E. Kangal59, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir60, S. Ozturk61, D. Sunar Cerci55, B. Tali55, U.G. Tok, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak62, G. Karapinar63, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya64, O. Kaya65, B. Kaynak, Ö. Özçelik, S.
Ozkorucuklu66, S. Tekten, E.A. Yetkin67 Istanbul Technical University,
Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen68 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, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold69, S.
Paramesvaran, B. Penning, T. Sakuma, D. Smith, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev70, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, 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 CHAHAL71, 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. Nash72, A.
Nikitenko8, 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. Virdee19, N. Wardle, D. Winterbottom, J. Wright, 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 The University of Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, 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, M. Hadley, J. Hakala, U. Heintz,
J.M. Hogan73, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain,
S. Sagir74, R. Syarif, E. Usai, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
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, S. Regnard, D. Saltzberg, C. Schnaible, 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 University of California, San Diego, La
Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, D. Olivito, S. Padhi, M. Pieri, V. Sharma, M. Tadel, J.
Wood, 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, J.M. Lawhorn, N. Lu, H.B. Newman, T.Q. Nguyen, J.
Pata, M. Spiropulu, J.R. Vlimant, R. Wilkinson, 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, F. Jensen, 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. Monroy, J.R. Patterson, D. Quach, A. Rinkevicius, A.
Ryd, L. Skinnari, L. Soffi, S.M. Tan, Z. Tao, J. Thom, J. Tucker, 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, J. Hanlon, R.M. Harris, S. Hasegawa, R.
Heller, J. Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U.
Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, 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, K. Pedro, C. Pena, O. Prokofyev, G.
Rakness, F. Ravera, A. Reinsvold, 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, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, V. Cherepanov, D. Curry, 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, S. Linn 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, A. Saha, C. Schiber, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M.
Rahmani, T. Roy, 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, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu, J. Zhang The
University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki57, W. Clarida, K. Dilsiz75, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul76, Y. Onel, F. Ozok77, 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
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J.
Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C.
Rogan, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang 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, C. Ferraioli, 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, V. Azzolini, 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, C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G.
Roland, Z. 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, P.
Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, M. Krohn, 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
E. Avdeeva, 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
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, 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, 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, F. Meng, C. Mueller,
Y. Musienko42, 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, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C.
Hill, W. Ji, A. Lefeld, T.Y. Ling, W. Luo, 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
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W.
Li, B.P. Padley, 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, Y.t. Duh, J.L. Dulemba, C. Fallon, T.
Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A.