EXO-16-052

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/EXO-16-052/trunk/EXO-16-052.tex$
$Id:EXO-16-052.tex4549592018-04-1114:18:26Zncsmith$

EXO-16-052

# Search for new physics in events with a leptonically decaying Z boson and a
large transverse momentum imbalance in proton-proton collisions at $\sqrt{s}$
= 13

###### Abstract

A search for new physics in events with a Z boson produced in association with
large missing transverse momentum at the LHC is presented. The search is based
on the 2016 data sample of proton-proton collisions recorded with the CMS
experiment at $\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of
35.9. The results of this search are interpreted in terms of a simplified
model of dark matter production via spin-0 or spin-1 mediators, a scenario
with a standard-model-like Higgs boson produced in association with the Z
boson and decaying invisibly, a model of unparticle production, and a model
with large extra spatial dimensions. No significant deviations from the
background expectations are found, and limits are set on relevant model
parameters, significantly extending the results previously achieved in this
channel.

##  0.1 Introduction

In the pursuit of new physics at the CERN LHC, many scenarios have been
proposed in which production of particles that leave no trace in collider
detectors is accompanied also by production of a standard model (SM) particle,
which balances the transverse momentum in an event. The final state considered
in this analysis is the production of a pair of leptons ($\ell^{+}\ell^{-}$,
where $\ell=\Pe$ or $\Pgm$), consistent with originating from a $\PZ$ boson,
together with large missing transverse momentum ($\ptmiss$). This final state
is well-suited to probe such beyond the SM (BSM) scenarios, as it has
relatively small and precisely known SM backgrounds.

One of the most significant puzzles in modern physics is the nature of dark
matter (DM). In the culmination of over a century of observations, the
“$\Lambda_{\mathrm{CDM}}$” standard model of cosmology has established that,
in the total cosmic energy budget, known matter only accounts for about 5%, DM
corresponds to 27%, and the rest is dark energy [1]. Although several
astrophysical observations indicate that DM exists and interacts
gravitationally with known matter, there is no evidence yet for
nongravitational interactions between DM and SM particles. While the nature of
DM remains a mystery, there are a number of models that predict a particle
physics origin. If DM particles exist, they can possibly be produced directly
from, annihilate into, or scatter off SM particles. Recent DM searches have
exploited various methods including direct [2] and indirect [3] detection. If
DM can be observed in direct detection experiments, it must have substantial
couplings to quarks and/or gluons, and could also be produced at the LHC [4,
5, 6, 7, 8, 9].

A promising possibility is that DM may take the form of weakly interacting
massive particles. The study presented here considers one possible mechanism
for producing such particles at the LHC [10]. In this scenario, a $\PZ$ boson,
produced in proton-proton (pp) collisions, recoils against a pair of DM
particles, $\chi\overline{\chi}$. The $\PZ$ boson subsequently decays into two
charged leptons, producing a low-background dilepton signature, together with
$\ptmiss$ due to the undetected DM particles. In this analysis, the DM
particle $\chi$ is assumed to be a Dirac fermion. Four simplified models of DM
production via an $s$-channel mediator exchange are considered. In these
models, the mediator has a spin of 1 (0) and vector or axial-vector (scalar or
pseudoscalar) couplings to quarks and DM particles. The free parameters of
each model are the masses $m_{\text{med}}$ and $m_{\mathrm{DM}}$ of the
mediator and DM particle, respectively, as well as the coupling constant
$g_{\Pq}$ ($g_{\mathrm{DM}}$) between the mediator and the quarks (DM
particles). The vector coupling model can be described with the following
Lagrangian:

$\mathcal{L}_{\text{vector}}=g_{\mathrm{DM}}{Z^{\prime}}_{\mu}\overline{\chi}\gamma^{\mu}\chi+g_{\Pq}\sum_{\Pq}{Z^{\prime}}_{\mu}\overline{\Pq}\gamma^{\mu}\Pq,$

where the spin-1 mediator is denoted as $\PZ^{\prime}$ and the SM quark fields
are referred to as and $\overline{\PQq}$. The Lagrangian for an axial-vector
coupling is obtained by making the replacement
$\gamma^{\mu}\to\gamma^{5}\gamma^{\mu}$. In the case of a spin-0 mediator
$\phi$, the couplings between mediator and quarks are assumed to be Yukawa-
like, with $g_{\Pq}$ acting as a multiplicative modifier for the SM Yukawa
coupling ${y_{\Pq}=\sqrt{2}m_{\Pq}/v}$ (where $v=246\GeV$ is the SM Higgs
field vacuum expectation value), leading to the Lagrangian:

$\mathcal{L}_{\text{scalar}}=g_{\mathrm{DM}}{\phi}\overline{\chi}\chi+g_{\Pq}\frac{\phi}{\sqrt{2}}\sum_{\Pq}y_{\Pq}\overline{\Pq}\Pq.$

The Lagrangian with pseudoscalar couplings is obtained by inserting a factor
of $i\gamma^{5}$ into each of the two terms (i.e., $\bar{\chi}\chi\to
i\bar{\chi}\gamma^{5}\chi$ and $\bar{\Pq}\Pq\to i\bar{\Pq}\gamma^{5}\Pq$).
Example diagrams of DM production via spin-1 and spin-0 mediators are shown in
Fig. 1 (upper left and right, respectively).

Figure 1: Feynman diagrams illustrative of the processes beyond the SM
considered in this paper: (upper left) DM production in a simplified model
with a spin-1 mediator $\PZ^{\prime}$; (upper right) DM production in a
simplified model with a spin-0 mediator $\phi$; (lower left) production of a
Higgs boson in association with Z boson with subsequent decay of the Higgs
boson into invisible particles; (lower right) unparticle or graviton
production. The diagrams were drawn using the TikZ-Feynman package [11].

A primary focus of the LHC physics program after the discovery of a Higgs
boson (H) [12, 13, 14] by the ATLAS and CMS Collaborations is the study of the
properties of this new particle. The observation of a sizable branching
fraction of the Higgs boson to invisible states [15, 16, 17] would be a strong
sign of BSM physics. Supersymmetric (SUSY) models embodying R-parity
conservation contain a stable neutral lightest SUSY particle (LSP), e.g., the
lightest neutralino [18], leading to the possibility of decays of the Higgs
boson into pairs of LSPs. Certain models with extra spatial dimensions predict
graviscalars that could mix with the Higgs boson [19]. As a consequence, the
Higgs boson could oscillate to a graviscalar and disappear from the SM brane.
The signature would be equivalent to an invisible decay of the Higgs boson.
There could also be contributions from Higgs boson decays into graviscalars
[20]. With the same effect as the simplified DM models presented earlier,
“Higgs portal” models [21, 22, 23] construct a generic connection between SM
and DM particles via a Higgs boson mediator. This analysis considers decays
into invisible particles of an SM-like Higgs boson produced in association
with a $\PZ$ boson, as shown in Fig. 1 (lower left).

Another popular BSM paradigm considered here is the Arkani-
Hamed–Dimopoulos–Dvali (ADD) model with large extra spatial dimensions [24,
25, 26], which is motivated by the hierarchy problem, i.e., the disparity
between the electroweak unification scale ($M_{\mathrm{EW}}\sim 1\TeV$) and
the Planck scale ($M_{\mathrm{Pl}}\sim 10^{16}\TeV$). This model predicts
graviton () production via the process $\PQq\PAQq\to\PZ+\cPG$. The graviton
escapes detection, leading to a mono-$\PZ$ signature (Fig. 1, lower right). In
the ADD model, the apparent Planck scale in four space-time dimensions is
given by $M_{\mathrm{Pl}}^{2}\approx M_{\mathrm{D}}^{n+2}R^{n}$, where
$M_{\mathrm{D}}$ is the true Planck scale of the full $n$+4 dimensional space-
time and $R$ is the compactification radius of the extra dimensions. Assuming
$M_{\mathrm{D}}$ is of the same order as $M_{\mathrm{EW}}$, the observed large
value of $M_{\mathrm{Pl}}$ points to an $R$ of order 1 mm to 1 fm for 2 to 7
extra dimensions. The consequence of the large compactification scale is that
the mass spectrum of the Kaluza–Klein graviton states becomes nearly
continuous, resulting in a broad $\PZ$ boson transverse momentum () spectrum.

The final BSM model considered in this analysis is the phenomenologically
interesting concept of unparticles, which appear in the low-energy limit of
conformal field theories. In the high-energy regime, a new, scale invariant
Banks–Zaks field with a nontrivial infrared fixed point is introduced [27].
The interaction between the SM and Banks–Zaks sectors is mediated by particles
of large mass scale $M_{\textsf{U}}$, below which the interaction is
suppressed and can be treated via an effective field theory (EFT). The low-
energy regime will include unparticles, which have phase space factors
equivalent to those of a noninteger number of ordinary particles [28, 29, 30].
In this analysis, the emission of spin-0 unparticles from SM quarks is
considered. Because of the weakness of the unparticle interactions with the SM
fields, the unparticle evades detection. The EFT Lagrangian used to interpret
the results is defined as follows:

$\mathcal{L}_{U}=\frac{\lambda}{\Lambda_{\textsf{U}}^{d_{\textsf{U}}-1}}\mathcal{O}_{\textsf{U}}\overline{\PQq}\PQq,$

where $\lambda$ represents the coupling between the SM and unparticle fields,
$\Lambda_{\textsf{U}}$ is the cutoff scale of the EFT, and $d_{\textsf{U}}$ is
the characteristic scaling dimension of the theory. The unparticle operator is
denoted as $\mathcal{O}_{\textsf{U}}$. A representative Feynman diagram of the
interaction is shown in Fig. 1 (lower right).

The search described in this paper is based on a data set recorded with the
CMS detector in 2016, which corresponds to an integrated luminosity of
$35.9\pm 0.9\fbinv$ of pp collisions at a center-of-mass energy of 13.

The paper is organized as follows: after a brief review of previous work in
Section 0.2, followed by a description of the CMS detector in Section 0.3, we
discuss the background composition in Section 0.4. Simulated samples are
reviewed in Section 0.5, followed by the event reconstruction and event
selection description in Sections 0.6 and 0.7, respectively. The details of
the background estimation are given in Section 0.8. The multivariate analysis
of invisible Higgs boson decays is summarized in Section 0.9, followed by the
discussion of selection efficiencies and systematic uncertainties in Section
0.10. The results are given in Section 0.11, and Section 0.12 summarizes the
paper.

##  0.2 Review of previous work

A search by the CMS Collaboration in the same topology using an earlier data
set corresponding to an integrated luminosity of 2.3of pp collisions collected
in 2015 at $\sqrt{s}=13\TeV$ found no evidence for BSM physics [31]. In
addition to the order of magnitude increase in the integrated luminosity,
significant differences with respect to the previous analysis include: new
techniques for estimating irreducible backgrounds, which were not viable with
the previous data set; improvements in the event selection; and a broader
range of BSM models probed.

In the previous CMS result [31], under the same simplified model assumptions
as used in this paper, DM mediator masses of up to 290 (300)were excluded for
fixed vector (axial-vector) couplings of $g_{\Pq}=0.25$ and
$g_{\mathrm{DM}}=1.0$. Here and in what follows all limits are given at 95%
confidence level (CL), unless explicitly specified otherwise. Similar DM
models have been also probed in the $\gamma$\+ [32] and jet+ [33] topologies
at $\sqrt{s}=13\TeV$ by the ATLAS Collaboration, excluding mediators with
vector (axial-vector) couplings up to masses of 1.2 (1.25) . The most
stringent limits on DM production in this context were obtained in a CMS
analysis of events with a jets+topology performed on a subset of the present
data set, corresponding to an integrated luminosity of 12.9 [34]. In that
analysis, mediator masses of up to 1.95were excluded for both vector and
axial-vector couplings. In the case of a scalar mediator coupled only to
quarks and DM particles with $g_{\Pq}=g_{\mathrm{DM}}=1$, no exclusion was
set. For the pseudoscalar mediator, under the same assumptions, masses below
430were excluded.

Invisible decays of the SM Higgs boson—hereafter H(inv.)—have been targeted by
both ATLAS and CMS. These searches used both the Z+and jets+topologies, the
latter including gluon fusion and vector fusion processes as well as
associated production with a vector boson reconstructed as a single jet. The
most stringent constraints were obtained from a combination of searches in
these final states at $\sqrt{s}=8\TeV$ by ATLAS [35] and at multiple center-
of-mass energies by CMS [36], which, under the assumption of SM production,
exclude a branching fraction for H(inv.) decays larger than $25\%$ for ATLAS
and $24\%$ for CMS.

Real emission of gravitons in the ADD scenario has been most recently probed
in the jet+topology by CMS at $\sqrt{s}=8\TeV$ [37] and by ATLAS at
$\sqrt{s}=13\TeV$ [38]. In these analyses, the fundamental Planck scale
$M_{\mathrm{D}}$ of the $n$+4 dimensional theory has been constrained to be
larger than 3.3–5.6(CMS) and 4.1–6.6(ATLAS), for the number of extra
dimensions between $6$ and $2$. Previous CMS analyses in the same final state
as this analysis have excluded unparticle cutoff scales from $400\GeV$ at
large values of the scaling dimension $d_{\textsf{U}}=2.2$, up to hundreds of
at low values of $d_{\textsf{U}}\approx 1$ [39, 31].

##  0.3 The CMS detector

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

Events of interest are selected using a two-tiered trigger system [40]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [41].

##  0.4 Background composition

Several SM processes can produce the dilepton+$\ptmiss$ final state. Since
none of the BSM physics signals probed in this analysis are expected to
produce a resonance peak in the $\ptmiss$ distribution, adequate modeling of
each SM background process is necessary. The following SM background processes
have been considered in this analysis:

  * $\centerdot$

$\Z\Z\to 2\ell 2\nu$ production, which yields the same final state as the
signal and contributes approximately 60% of the total background.

  * $\centerdot$

$\PW\Z\to\ell\nu\ell\ell$ production, where the lepton from the $\PW$ boson
decay is not identified either because it fails the lepton identification, or
because it falls outside the detector acceptance or kinematic selections. This
process contributes approximately 25% of the total background, and the
kinematic distributions are similar to those for the $\Z\Z\to 2\ell 2\nu$
process.

  * $\centerdot$

$\PW\PW\to\ell\nu\ell\nu$ events, where the dilepton invariant mass falls into
the $\Z$ boson mass window. These events constitute approximately 5% of the
background.

  * $\centerdot$

Events with leptonically decaying top quarks (mostly $\ttbar$ and $\PQt\PW$),
where the dilepton invariant mass falls into the $\Z$ boson mass window, and
which contribute about 5% of the total background.

  * $\centerdot$

Drell–Yan (DY) production, $\PZ/\gamma^{*}\to\ell\ell$, which can produce
events with large $\ptmiss$ caused mainly by jet energy mismeasurement and
detector acceptance effects. It amounts to approximately 5% of the total
background.

  * $\centerdot$

Triboson processes (e.g., $\PW\PW\PW$), which have a small cross section and
contribute less than 1% of the total background.

Processes that were found to have a negligible contribution to the signal
region include: $\PW$+jets, because of the very low probability for a jet to
be reconstructed as a lepton and the dilepton system to be within the $\Z$
boson mass window; the SM process $\Z(\to\ell\ell)\PH(\to\Z\Z\to 4\PGn)$,
which is a subset of the $\PZ\PH(\text{inv.})$ signal and accounts for 0.1% of
SM Higgs boson decays; and ${{\rm gg}\to\PH(\to\PW\PW)}$, which has similar
topology to continuum $\PW\PW$ production but makes a negligible contribution
after the full selection.

##  0.5 Simulation

Simulated Monte Carlo (MC) samples are used to estimate backgrounds, to
validate the background estimation techniques using control samples in data,
to calculate signal efficiency, and to optimize the analysis.

Diboson production (VV, where $\mathrm{V}=$ W or Z) via $\PQq\PAQq$
annihilation, as well as $\Z\PH$ production via $\PQq\PAQq$ annihilation and
gluon fusion, are generated at next-to-leading order (NLO) in quantum
chromodynamics (QCD) with 2.0 [42, 43, 44, 45]. The ${\rm gg\to\PW\PW}$ and
${\rm gg\to\Z\Z}$ processes are simulated at NLO with mcfm v7.01 [46]. The
$\PZ$+jets, $\Z\gamma$, $\ttbar$, $\ttbar\mathrm{V}$, and $\mathrm{VVV}$
samples are generated at NLO with either v2.3.2 [47] or .

Samples of DM particle production in the simplified model framework are
generated using DmSimp [48, 49, 50] interfaced with v2.4.3. Samples are
generated over a range of values for the masses $m_{\text{med}}$ and
$m_{\mathrm{DM}}$. For the vector and axial-vector models, samples are
generated at NLO in QCD with up to one additional parton in the matrix element
calculations, and the mediator couplings to the SM and DM fields are set to
$g_{\Pq}=0.25$ and $g_{\mathrm{DM}}=1$, respectively. For the scalar and
pseudoscalar models, samples are generated at leading order in QCD, and the
couplings are set to $g_{\Pq}=g_{\mathrm{DM}}=1$. This choice of couplings is
recommended by the ATLAS/CMS dark matter forum [10] and by the LHC dark matter
working group [51]. For all DM particle production samples, the central values
of the renormalization and factorization scales are set to the
$m_{\mathrm{T}}^{2}$ scale after $k_{\mathrm{T}}$-clustering of the event.

Events for the ADD scenario of large extra dimensions and for the unparticle
model are generated at leading order (LO) using an EFT implementation in 8.205
[52, 53, 54]. In the ADD case, event samples are produced for
$M_{\mathrm{D}}=1$, 2, and 3 , each with $n=$ 2–7. In order to ensure the
validity of the EFT, the signal is truncated for $\hat{s}>M_{\mathrm{D}}^{2}$,
where $\hat{s}$ is the center-of-mass energy squared of the incoming partons.
Events above this threshold are suppressed by an additional weight of
$M_{\mathrm{D}}^{4}/\hat{s}^{2}$. In general, this procedure has a larger
effect for large values of $n$, for which the distribution of $\hat{s}$ is
shifted towards higher values [53]. For the unparticle case, samples are
generated for scaling dimensions $d_{\textsf{U}}$ between $1.01$ and $2.2$,
with the cutoff scale $\Lambda_{\textsf{U}}$ set to $15\TeV$ and the coupling
$\lambda$ set to 1. Since both $\Lambda_{\textsf{U}}$ and $\lambda$ modify the
cross sections of the signal prediction, but not its kinematic distributions
[54], a simple rescaling of cross sections is performed to obtain signal
predictions for alternative values of these parameters. No truncation is
performed for the unparticle signal so that the results can be compared with
those of previous searches.

In all cases, versions 8.205 or higher is used for parton showering,
hadronization, and the underlying event simulation, using tune CUETP8M1 [55].
The merging of jets from matrix element calculations and parton shower
descriptions is done using the MLM [56] (FxFx [57]) scheme for LO (NLO)
samples. The NNPDF3.0 [58] parton distribution function (PDF) set is used,
with the order corresponding to the one used for the signal or background
simulation.

For all MC samples, the detector response is simulated using a detailed
description of the CMS detector, based on the $\GEANTfour$ package [59].
Minimum bias events are superimposed on the simulated events to emulate the
additional pp interactions per bunch crossing (pileup). All MC samples are
corrected to reproduce the pileup distribution as measured in the data. The
average number of pileup events per bunch crossing is approximately 23 in the
data sample analyzed.

##  0.6 Event reconstruction

In this analysis, the particle-flow (PF) event reconstruction algorithm [60]
is used. The PF algorithm is designed to leverage information from all CMS
detector components to reconstruct and identify individual particles, namely:
electrons, muons, photons, and charged and neutral hadrons. The reconstructed
vertex with the largest value of summed physics-object $\pt^{2}$ is taken to
be the primary $\Pp\Pp$ interaction vertex. The physics objects are the track-
jets, clustered using the jet finding algorithm [61, 62] with the tracks
assigned to the vertex as inputs, and the associated missing transverse
momentum, taken as the negative vector sum of the $\pt$ of those jets.

Electron candidates are reconstructed using an algorithm that combines
information from the ECAL, HCAL, and the tracker [63]. To reduce the electron
misidentification rate, electron candidates are subjected to additional
identification criteria, which are based on the distribution of the
electromagnetic shower in the ECAL, the relative amount of energy deposited in
the HCAL in the cluster, a matching of the trajectory of an electron track
with the cluster in the ECAL, and its consistency with originating from the
selected primary vertex. Candidates that are identified as originating from
photon conversions in the detector material are removed.

Muon candidate reconstruction is based on two main algorithms: in the first,
tracks in the silicon tracker are matched to track stubs (or segments)
reconstructed in the muon detectors; in the second algorithm, a combined fit
is performed to signals in both the silicon tracker and the muon system [64].
The two resulting collections are merged, with the momentum measurement of the
latter algorithm taking precedence. To reduce the muon misidentification rate,
further identification criteria are applied on the basis of the number of
measurements in the tracker and in the muon system, the quality of the muon
track fit, and its consistency with the selected primary vertex location.

Leptons produced in the decay of $\PZ$ bosons are expected to be isolated from
hadronic activity in the event. The isolation is defined from the sum of the
momenta of all PF candidates found in a cone of radius
$R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.4$ built around each
lepton, where $\Delta\phi$ and $\Delta\eta$ are, respectively, the differences
in the azimuthal angle (measured in radians) and in the pseudorapidity between
the lepton and the PF candidate. The contribution to the isolation from the
lepton candidate itself is removed. For muons, the isolation sum is required
to be smaller than 15% of the muon $\pt$. For electrons in the ECAL barrel
(endcap), the limit on this isolation sum is 6.9 (8.2)% of the electron $\pt$.
In order to mitigate the dependence of the isolation variable on the number of
pileup interactions, charged hadrons are included in the sum only if they are
consistent with originating from the selected primary vertex of the event. To
correct for the contribution to the isolation sum of neutral hadrons and
photons from pileup interactions, different strategies are adopted for
electrons and muons. For electrons, a median energy density ($\rho$) is
determined on an event-by-event basis using the method described in Ref. [65].
The contribution of the pileup particles is then estimated as a product of
$\rho$ and the effective area of the isolation cone and is subtracted from the
isolation sum. For muon candidates, the correction is performed instead by
subtracting half the sum of the $\pt$ of the charged-hadron candidates in the
cone of interest, which are not associated with the primary vertex. The factor
of one half corresponds to the average ratio of neutral to charged particles
in pileup interactions.

Jets are constructed from PF candidates using the anti-clustering algorithm
[61] with a distance parameter $R=0.4$, as implemented in the fastjet package
[62, 66]. The jet momentum is defined as the vectorial sum of all PF candidate
momenta assigned to the jet, and is found in the simulation to be within 5 to
10% of the true momentum over the entire range and detector acceptance used in
this analysis. An overall energy subtraction is applied to correct for the
extra energy clustered in jets due to pileup interactions, following the
procedure in Refs. [65, 67]. Corrections to the jet energy scale and
resolution are derived from measurements both in simulation and in data of the
energy balance in dijet, multijet, $\gamma$+jet, and leptonic Z+jet events
[68, 69].

The missing transverse momentum vector, $\ptvecmiss$, is defined as the
projection of the negative vector sum of the momenta of all reconstructed PF
candidates in an event onto the plane perpendicular to the beams. Its
magnitude is referred to as $\ptmiss$. Several event-level filters are applied
to discard events with anomalous $\ptmiss$ arising from specific well-
understood issues with the detector components or event reconstruction [70].
Jet energy corrections are propagated to the missing transverse momentum by
adjusting the momentum of the PF candidate constituents of each reconstructed
jet.

For the purpose of rejecting events involving top quark production, jets
originating from b quark fragmentation (b jets) are identified by “b tagging.”
The b tagging technique employed is based on the “combined secondary vertex”
CSVv2 algorithm [71, 72]. The algorithm is calibrated to provide, on average,
80% efficiency for tagging jets originating from b quarks, and 10% probability
of light-flavor jet misidentification.

For the purpose of rejecting events containing $\tau$ leptons, hadronically
decaying $\tau$ leptons ($\tauh$) are identified using the “hadron-plus-
strips” algorithm [73]. The algorithm identifies a jet as a $\tauh$ candidate
if a subset of the particles assigned to the jet is consistent with the
hadronic decay products of a $\tau$ lepton [73]. In addition, $\tauh$
candidates are required to be isolated from other activity in the event.

##  0.7 Event selection

Events with electrons (muons) are collected using dielectron (dimuon)
triggers, with the thresholds of $\pt>23$ (17)and $\pt>12$ (8)for the leading
and subleading electron (muon), respectively. Single-electron and single-muon
triggers (with $\pt$ thresholds of 27 and 24, respectively) are also used in
order to recover residual trigger inefficiencies.

Events are required to have exactly two ($N_{\ell}=2$) well-identified,
isolated leptons of the same flavor and opposite electric charge ($\Pep\Pem$
or $\Pgmp\Pgmm$). The leading electron (muon) of the pair must have $\pt>25$
(20), while $\pt>20\GeV$ is required for the subleading lepton. The dilepton
invariant mass is required to be within 15of the established $\PZ$ boson mass
$m_{\PZ}$ [74]. The dilepton $\pt$ ($\pt^{\,\ell\ell}$) must be larger than
60to reject the bulk of the $\PZ/\gamma^{*}\to\ell\ell$ background. Since
little hadronic activity is expected in this final state, events having more
than one jet with $\pt>30~{}\GeV$ are rejected. The top quark background is
suppressed by applying a b jet veto: events with at least one b-tagged jet
with $\pt>20\GeV$ reconstructed within the tracker acceptance, $|\eta|<2.4$,
are removed. To reduce the $\PW\PZ$ background in which both bosons decay
leptonically, events containing additional electrons (muons) with $\pt>10$
(5)and events with loosely identified hadronically decaying $\tau$ leptons
($\tauh$) with $p_{\mathrm{T}}>18\GeV$ are removed.

The event selection is optimized using three variables: the $\ptmiss$, the
azimuthal angle formed between the dilepton and the missing transverse
momentum vector, $\Delta\phi(\ptvec^{\,\ell\ell},\ptvecmiss)$, and the
-$\pt^{\,\ell\ell}$ balance ratio,
$|\ptmiss-\pt^{\,\ell\ell}|/\pt^{\,\ell\ell}$. The latter two variables are
powerful in suppressing reducible background processes, such as DY and top
quark production. The selection criteria applied to these variables are
optimized in order to obtain the best expected signal sensitivity for a wide
range of DM parameters that are considered. For each possible set of
selections, the full analysis is repeated, including the estimation of
backgrounds from control samples in data and the systematic uncertainties. The
final selection criteria obtained after optimization are: $\ptmiss>100\GeV$,
$\Delta\phi(\ptvec^{\,\ell\ell},\ptvecmiss)>2.6$ rad, and
$|\ptmiss-\pt^{\,\ell\ell}|/\pt^{\,\ell\ell}<0.4$.

To avoid positive biases in the $\ptmiss$ calculation due to jet
mismeasurement, in events with one jet a threshold is applied on the azimuthal
angle between this jet and the missing transverse momentum,
$\Delta\phi(\ptvec^{\,j},\ptvecmiss)>0.5\unit{rad}$. To reduce the
contribution from backgrounds such as $\PW\PW$ and $\ttbar$, a requirement on
the distance between the two leptons in the $(\eta,\phi)$ plane, $\Delta
R_{\ell\ell}<1.8$, is applied.

There are two types of analyses performed in this paper. The main analysis
method is based on fitting the spectrum in data after applying the above
selection criteria defining the signal region (SR). For the specific
interpretation of this analysis involving invisible decays of the SM (125)
Higgs boson, a multivariate boosted decision tree (BDT) classifier is employed
to increase the sensitivity of the analysis. We use the following set of
twelve variables to train a multiclass BDT classifier:

  * $\centerdot$

$\left|m_{\ell\ell}-m_{\Z}\right|$ (dilepton mass);

  * $\centerdot$

$\pt^{\ell 1}$ (leading lepton transverse momentum);

  * $\centerdot$

$\pt^{\ell 2}$ (subleading lepton transverse momentum);

  * $\centerdot$

$\pt^{\,\ell\ell}$ (dilepton transverse momentum);

  * $\centerdot$

$|\eta^{\ell 1}|$ (leading lepton pseudorapidity);

  * $\centerdot$

$|\eta^{\ell 2}|$ (subleading lepton pseudorapidity);

  * $\centerdot$

$\ptmiss$ (missing transverse momentum);

  * $\centerdot$

$m_{T}(\pt^{\ell 1},$$)$ (leading lepton transverse mass);

  * $\centerdot$

$m_{T}(\pt^{\ell 2},$$)$ (subleading lepton transverse mass);

  * $\centerdot$

$\Delta\phi(\ptvec^{\,\ell\ell},\ptvecmiss)$ (azimuthal separation between
dilepton and missing momentum);

  * $\centerdot$

$\Delta R_{\ell\ell}$ (separation between leptons); and

  * $\centerdot$

$|\cos\theta^{\rm CS}_{\ell 1}|$ (cosine of the polar angle in the
Collins–Soper frame [75] for the leading lepton).

Several classes of event samples are considered for the multiclass BDT:
ZH(inv.) signal; ZZ; WZ; DY; and flavor-symmetric or nonresonant backgrounds.
A BDT is trained targeting each class, and the final discriminator is taken to
be the likelihood assigned to ZH(inv.) production, normalized to the sum of
the likelihoods of all processes. The SR selection for the BDT analysis is
slightly altered from that of the $\ptmiss$-based analysis: the dilepton mass
requirement is relaxed to be within $30\GeV$ of the $\PZ$ boson mass, and the
selections on $\Delta\phi(\ptvec^{\,\ell\ell},\ptvecmiss)$,
$|\ptmiss-\pt^{\,\ell\ell}|/\pt^{\,\ell\ell}$, and $\Delta R_{\ell\ell}$ are
omitted. The selection for training the BDT additionally requires the missing
transverse momentum to be greater than $130\GeV$, where differentiating
between the diboson background and signal is most challenging. The BDT
performance in the untrained region of $100\leq\ptmiss\leq 130\GeV$ is found
to be adequate, whereas a BDT trained on event samples including this region
was found to have significantly degraded performance in the $\ptmiss>130\GeV$
region.

A summary of the selection criteria for the SR of both the -based analysis and
the BDT analysis is given in Table 0.7.

Summary of the kinematic selections for the signal region of both the the
$\ptmiss$-based analysis and the BDT analysis. Where the selections for the
two analyses differ, the BDT requirement is given in parentheses.  Selection
Requirement Reject $N_{\ell}$ ${=}2$ $\PW\Z$, $\mathrm{V}\mathrm{V}\mathrm{V}$
$\pt^{\ell}$ ${>}25/20\GeV$ for electrons QCD ${>}20\GeV$ for muons $\Z$ boson
mass requirement $\left|m_{\ell\ell}-m_{\Z}\right|<15\ (30)\GeV$ $\PW\PW$, top
quark Jet counting ${\leq}1$ jet with $\pt^{\,j}>30\GeV$
$\PZ/\gamma^{*}\to\ell\ell$, top quark, $\mathrm{V}\mathrm{V}\mathrm{V}$
$\pt^{\,\ell\ell}$ ${>}60\GeV$ $\PZ/\gamma^{*}\to\ell\ell$ b tagging veto
CSVv2 $<$ 0.8484 Top quark, $\mathrm{V}\mathrm{V}\mathrm{V}$ $\tau$ lepton
veto 0 $\PGt_{\rm h}$ cand. with $\pt^{\tau}>18\GeV$ $\PW\Z$ $\ptmiss$
${>}100\GeV$ ($130\GeV$, training only) $\PZ/\gamma^{*}\to\ell\ell$, $\PW\PW$,
top quark $\Delta\phi(\ptvec^{\,j},\ptvecmiss)$ ${>}0.5\unit{rad}$
$\PZ/\gamma^{*}\to\ell\ell$, $\PW\Z$
$\Delta\phi(\ptvec^{\,\ell\ell},\ptvecmiss)$ ${>}2.6\unit{rad}$ (omitted)
$\PZ/\gamma^{*}\to\ell\ell$ $|\ptmiss-\pt^{\,\ell\ell}|/\pt^{\,\ell\ell}$
${<}0.4$ (omitted) $\PZ/\gamma^{*}\to\ell\ell$ $\Delta R_{\ell\ell}$ ${<}1.8$
(omitted) $\PW\PW$, top quark

##  0.8 Background estimation

Background contributions are estimated using combined information from
simulation and control regions (CRs) in data. The normalizations of the
dominant background processes are constrained by using a simultaneous maximum
likelihood fit to the SR, as well as to the CRs that are described in this
section. The contributions of minor backgrounds in both SR and CRs are
predicted from simulation.

###  0.8.1 Diboson background

The $\Z\Z$ and $\PW\Z$ processes contribute to the SR via the
${\Z\Z\to\ell\ell\nu\nu}$ and ${\PW\Z\to\ell\nu\ell\ell}$ decay modes,
respectively, where the decay products of one boson are not detected. The
background estimate for these processes is improved by selecting CRs with
alternative decay modes that not only provide a normalization based on CRs in
data, but also probe the lost-boson distribution, which is expected to be
independent of the decay mode. In this way, the $\ptmiss$ spectra of these
processes are constrained with respect to their theoretical predictions.

The ability of the simulation to correctly model the lost-boson rapidity is
important, as the SR rapidity acceptance of the lost boson is necessarily
larger than the rapidity acceptance of the proxy boson in each CR, due to the
fact that the visible decay products of the proxy boson in the CR must be
inside the detector acceptance. The impact of possible data-to-simulation
discrepancies in the high-rapidity portion of diboson background in the SR is
suppressed by the fact that, as measured in simulation, the majority of the
$\PW\Z$ and $\Z\Z$ contamination in the SR is comprised of events where the
lost boson is within the rapidity range of the CRs. In addition, the proxy
boson rapidity distributions in the CRs (or its visible lepton, in the case of
the $\PW\Z$ CR) show a good agreement between data and simulation.

#### The WZ control region

The $\PW\Z$ control region is formed from events with three well-reconstructed
charged leptons. In this case, the CR is populated by events with the same
decay mode as the SR, but no leptons are lost to identification or acceptance
requirements. A $\PZ$ boson candidate is selected in the same manner as for
the SR, and an additional electron or muon, with identical quality
requirements as applied to the leptons in the SR, is required. To enhance the
purity of the $\PW\Z$ selection, $\ptmiss$ of at least 30is required, the
invariant mass of three leptons is required to be larger than $100\GeV$, and
the invariant masses of all opposite-sign, same-flavor lepton pairs are
required to be larger than $4\GeV$. Backgrounds in this CR are similar to
those in the SR, with a sizeable nonprompt background from the DY+jets
process, where a jet is misidentified as a lepton. All background estimates
for this CR are taken from simulation.

The $\PW$ boson $\pt$ (“emulated ”) is estimated by calculating the vectorial
sum of the $\ptvecmiss$ vector and the transverse momentum vector ($\ptvec$)
of the third charged lepton. In simulation, the majority (over $70\%$) of
$\PW\Z$ background contamination in the signal region originates from events
where over $90\%$ of the $\PW$ boson transverse momentum is carried by one or
more neutrinos from the $\PW$ boson decay. Thus, the majority of the W boson
rapidity distribution in the SR is central, although it is less central than
in the WZ CR. Neither the SR nor the $\PW\Z$ CR topology can probe the W boson
rapidity directly. However, for the $\PW\Z$ CR, good agreement between data
and simulation in the third lepton pseudorapidity distributions is observed.

A minor source of WZ background contamination in the SR originates from events
where the visible lepton from a $\PW$ boson decay failed identification
requirements. Data-to-simulation discrepancies in this contribution would also
manifest in the measured $\PW\Z$ CR $\ptmiss$ distribution, for which no such
mismodeling effects are evident.

Using the emulated $\ptmiss$ in place of the reconstructed $\ptmiss$, the same
selection is applied as for the SR. However, since there is no danger of CR
contamination from $\PW\Z\to\tau\nu\ell\ell$ or top quark backgrounds, no veto
on additional $\tauh$ or b jet candidates is applied. The resulting emulated
$\ptmiss$ spectrum is shown in Fig. 2 (upper left).

Figure 2:  Emulated $\ptmiss$ distribution in data and simulation for the
$\PW\PZ\to 3\ell\nu$ (upper left) and $\Z\Z\to 4\ell$ (upper right) CRs, and
the ratio between both distributions (lower). No events are observed with
emulated $\ptmiss>500\GeV$ in either channel. Uncertainty bands correspond to
the combined statistical and systematic components.

#### The ZZ control region

The $\Z\Z$ control region is formed from events with four well-reconstructed
charged leptons. In addition to a signal-like $\PZ$ boson candidate, a second
$\PZ$ boson candidate is required, the constituents of which only need to pass
relaxed lepton quality requirements. This choice reflects the very high purity
of the four-lepton selection. For both candidates, the same $\PZ$ boson mass
constraint as in the SR is applied. Backgrounds, dominated by triboson
processes, are almost negligible in this CR and are taken from simulation.

Similar to the $\PW\Z$ case, the emulated $\ptmiss$ is calculated as the
vectorial sum of the $\ptvecmiss$ and the $\ptvec$ of the $\PZ$ boson with the
larger mass difference to the nominal value of $m_{\PZ}$ of the two identified
in the event. The choice of which $\PZ$ boson to use as a proxy for an
invisibly decaying one does not significantly alter the emulated $\ptmiss$
spectrum. In this CR, the rapidity of the proxy boson is observable, for which
good agreement between data and simulation is found.

The same selection as in the SR is then applied using the emulated $\ptmiss$
in place of the reconstructed $\ptmiss$, with the exception of the $\tau$
lepton and b jet vetoes. The resulting emulated $\ptmiss$ spectrum is shown in
Fig. 2 (upper right).

#### The VV ratio constraints

Due to a limited event count in the $\Z\Z$ control region, the normalizations
of the $\PW\Z$ and $\Z\Z$ processes in the $\PW\Z$ and $\Z\Z$ CRs and the SR
are controlled by a single free parameter in the maximum likelihood fit, with
their relative normalizations fixed by the theoretical predictions for the
$\PW\Z$ and $\Z\Z$ processes in each bin. The predictions for these processes
are obtained from fully reconstructed simulated events generated as described
in Section 0.5 with the following additional higher-order corrections applied:

  * $\centerdot$

a constant (approximately $10\%$) correction for the $\PW\Z$ cross section
from NLO to NNLO in QCD calculations [76];

  * $\centerdot$

a constant (approximately $3\%$) correction for the $\PW\Z$ cross section from
LO to NLO in electroweak (EW) calculations, considering also photon-quark
initial states, according to Ref. [77];

  * $\centerdot$

a $\Delta\phi(\PZ,\PZ)$-dependent correction, varying in magnitude up to
$15\%$, to $\Z\Z$ production cross section from NLO to next-to-next-to-leading
order (NNLO) in QCD calculations [78];

  * $\centerdot$

a $\pt$-dependent correction, varying in magnitude up to $20\%$ at high
$\ptmiss$, to the $\Z\Z$ cross section from LO to NLO in EW calculations,
following Refs. [79, 80, 77], which is the dominant correction in the signal
region.

We use the product of the above NLO EW corrections and the inclusive NLO QCD
corrections [81] as an estimate of the missing $\textrm{NLO
EW}\times\textrm{NLO QCD}$ contribution, which is not used as a correction,
but rather assigned as an uncertainty. The uncertainties in the $\PW\Z$ and
$\Z\Z$ EW corrections are assumed to be anticorrelated as a conservative
measure. The uncertainty associated with the NNLO QCD corrections for both
processes is represented by the QCD scale variation uncertainties evaluated on
the NLO QCD simulation sample for the respective process, as described in
Section 0.10. Figure 2 (lower) shows the ratio of $\Z\Z$ to $\PW\Z$ CR yields
per $\ptmiss$ bin, which probes the validity of taking the relative
normalizations from simulation. Good agreement is observed between data and
simulation.

###  0.8.2 Nonresonant backgrounds

The contribution of the nonresonant flavor-symmetric backgrounds is estimated
from a CR based on events with two leptons of different flavor
($\Pe^{\pm}\Pgm^{\mp}$) that pass all other analysis selections. Nonresonant
background (NRB) consists mainly of leptonic boson decays in $\ttbar$,
$\PQt\PW$, and $\PW\PW$ events, where the dilepton mass happens to fall inside
the $\PZ$ boson mass window. Small contributions from single top quark events
produced via $s$\- and $t$-channel processes, and $\PZ\to\PGt\PGt$ events in
which $\PGt$ leptons decay into light leptons and neutrinos are also
considered in the NRB estimation.

The method assumes lepton flavor symmetry in the final states of these
processes. Since the leptonic decay branching fraction to the $\Pe\Pe$,
$\PGm\PGm$, and $\Pe\PGm$ final states from NRB are 1:1:2, the $\Pe\PGm$
events selected inside the $\PZ$ boson mass window can be extrapolated to the
$\Pe\Pe$ and $\PGm\PGm$ channels. To account for differences in efficiency for
electrons and muons, a correction factor $k_{\Pe\Pe}$ is derived by comparing
the NRB yields for the $\Pe\Pe$ and $\PGm\PGm$ channels:

$k_{\Pe\Pe}=\frac{\epsilon_{\Pe}}{\epsilon_{\PGm}}=\sqrt{\frac{N^{\Pe\Pe}_{\mathrm{NRB}}}{N^{\PGm\PGm}_{\mathrm{NRB}}}}$

under the assumption that there are no efficiency correlations between the two
leptons. In simulation, $k_{\Pe\Pe}$ is found to be about $0.88$ for the final
selection. With this correction factor, the relation between the NRB yields in
the SR and CR is:

$N^{\ell\ell}_{\mathrm{NRB}}=\frac{1}{2}\left(k_{\Pe\Pe}+\frac{1}{k_{\Pe\Pe}}\right)N^{\Pe\PGm}_{\mathrm{NRB}}.$

The ratio of the NRB contributions in the SR and CR is fixed by this relation.
Their normalization is controlled by a common scaling parameter that is left
to float in the maximum likelihood fit. Perturbations in the predicted
transfer factor due to data-to-simulation discrepancies in $k_{\Pe\Pe}$ are
suppressed upon summing the $\Pe\Pe+\PGm\PGm$ channels. The uncertainty in the
transfer factor is set conservatively to 20%.

###  0.8.3 The Drell–Yan background

The DY background is dominant in the region of low $\ptmiss$. This process
does not produce undetectable particles, therefore any nonzero $\ptmiss$
arises from the limited detector acceptance and mismeasurement. The estimation
of this background uses simulated DY events, for which the normalization is
taken from data in a sideband CR of $50\leq\ptmiss\leq 100\GeV$, with all
other selections applied. In two CRs where a larger DY background contribution
is expected, regions with inverted selections on
$\Delta\phi(\ptvec^{\,\ell\ell},\ptvecmiss)$ and on
$|\ptmiss-\pt^{\,\ell\ell}|/\pt^{\,\ell\ell}$, the simulation is found to
model the data well. The sideband CR is included in the maximum likelihood
fit, for which the normalization factor is found to be consistent with unity,
and a 100% uncertainty is assigned to the resulting DY estimate in order to
cover the extrapolation from this CR to the SR. This uncertainty has little
effect on the results owing to the small overall contribution from the DY
process in the high-$\ptmiss$ SR of this analysis.

##  0.9 Multivariate analysis

For the specific interpretation of this analysis involving invisible decays of
the SM (125) Higgs boson, a maximum likelihood fit is performed to the
spectrum of the BDT classifier values for events satisfying the BDT SR
criteria described in Section 0.7, with the classifier value between 0.2 and
1. The CR strategy is identical to that in the $\ptmiss$-based analysis, as
described in Section 0.8. The three- and four-lepton events shown in Fig. 3
are chosen using the same CR selections as in the $\ptmiss$-based analysis.

The multivariate classifier improves the sensitivity of the analysis to the SM
H(inv.) model by 10% compared to the $\ptmiss$-based analysis. Other than the
$\ptmiss$ itself, the variables that provide the most discrimination power are
the transverse masses of each lepton with respect to the $\ptvecmiss$, along
with the azimuthal separation between the $\ptvecmiss$ and the dilepton system
momentum. Utilization of this classifier for the other signal models
considered in this paper was not pursued, as many of the models’ kinematic
distributions can vary considerably over the relevant parameter space.

Figure 3: Distribution of the BDT classifier in the diboson CRs: (left)
$\PW\Z$ CR; (right) $\Z\Z$ CR. Uncertainty bands correspond to the combined
statistical and systematic components.

##  0.10 Efficiencies and systematic uncertainties

The efficiency for all backgrounds is estimated using simulation. The
uncertainties in the yields from missing higher-order corrections in signal as
well as $\Z\Z$ and $\PW\Z$ background cross sections are evaluated by
independently varying up and down the factorization and renormalization scales
by a factor of two. The effect of these variations on the yields is between 5
and 10%. For the $\Z\Z$ and $\PW\Z$ backgrounds, additional uncertainties
related to known higher-order corrections are applied, as discussed in Section
0.8.

For the Higgs boson signal, the PDF and $\alpha_{s}$ uncertainties comprise
the cross section normalization uncertainties computed by the LHC Higgs cross
section working group [82] and the effect on the signal acceptance of varying
the PDFs and $\alpha_{s}$ within their uncertainties [83]. For other signal
models, as well as the $\PW\Z$ and $\Z\Z$ backgrounds, the effects of the PDF
and $\alpha_{s}$ uncertainties in the signal acceptance are taken into account
following the PDF4LHC prescription [83]. The PDF and $\alpha_{s}$
uncertainties on these processes are found to be about 1–2%.

The efficiencies for triggering on, reconstructing, and identifying isolated
leptons are obtained from simulation, and corrected with scale factors
determined via a “tag-and-probe” technique [84] applied to
$\Z\to\ell^{+}\ell^{-}$ events in data. The associated uncertainty is about
1–2% per lepton. An additional 3% uncertainty associated with the
$\PW\Z\to\ell\PGn\ell\ell$ events, where the reconstructed lepton from the
$\PW$ boson decay fails identification, is also included.

In order to reproduce b tagging efficiencies and light-flavor jet mistag rates
observed in data, an event-by-event reweighting using data-to-simulation scale
factors [72] is applied to simulated events. The uncertainty associated with
this procedure is obtained by varying the event-by-event weight by ${\pm}1$
standard deviation (s.d.). The impact on the final yields due to the b tagging
efficiency and mistag rate uncertainties is around 1% for both signal and
background.

The impacts of the jet energy scale and resolution uncertainties are estimated
by shifting reconstructed jet energies in simulation by ${\pm}1$ s.d., and
each is found to have an effect of about 2% on the yields of the simulated
processes after all selections are applied. The impacts of the electron and
muon energy scales are evaluated in the same manner, and have a similar
effect. Uncertainties in the $\ptmiss$ measurement due to the energy
resolution of unclustered PF candidates (i.e., those not associated with an
electron, muon, or jet) amount to about 2%.

The uncertainty in the expected yields due to the finite size of the MC
samples is considered, and is around 1% for the signal and main backgrounds.
The simulated MC samples are reweighted to reproduce the pileup conditions
observed in data. The uncertainty related to this procedure is obtained by
varying the central value of the estimated inelastic cross section by 5% [85],
and is found to be below 1%. The uncertainty assigned to the integrated
luminosity measurement is 2.5% [86].

The effect of the systematic uncertainties on the shape of the distribution of
the discriminating variable ($\ptmiss$ or BDT classifier) is taken into
account by varying the value of the quantity associated with the uncertainty,
and observing the resulting variations in the individual bins of $\ptmiss$.

In addition to all of the sources of systematic uncertainty in the
$\ptmiss$-based analysis, the following systematic uncertainties in the BDT-
based analysis affect the BDT classifier shape. The most important sources of
uncertainty in the BDT classifier shape are the lepton energy scale and
uncertainties; their impact on the signal ($\PW\Z$ and $\Z\Z$ backgrounds)
amounts to about 2 (6)% and translates into an additional 2% uncertainty in
the expected limit on the H(inv.) branching fraction.

All these sources of uncertainty are summarized in Table 0.10. The combined
uncertainty in the signal efficiency and acceptance is estimated to be about
5% and is dominated by the theoretical uncertainty due to missing higher-order
corrections and PDF uncertainties. The total uncertainty in the background
estimations in the signal region is about 15%, dominated by the theoretical
uncertainties in the $\Z\Z$ and $\PW\Z$ process description.

Summary of the systematic uncertainties for the - and BDT-based analyses. Each
uncertainty represents the variation of the relative yields of the processes
in the SR. Each uncertainty is fully correlated across processes to which it
contributes, including those processes that are also present in CRs. The
symbol “—” indicates that the systematic uncertainty does not contribute or is
deemed negligible. For minor backgrounds, systematic uncertainties are omitted
because of the smallness of their contribution. For shape uncertainties
(indicated with a *), the numbers correspond to the overall effect of the
shape variation on the yield or acceptance. The impact on the expected upper
limit for the signal strength, i.e., the relative decrease in the median
expected upper limit for the signal strength upon removing the nuisance term,
is evaluated with respect to the SM H(inv.) signal and presented in the last
column. In this column the number in parentheses shows the impact on the BDT-
based analysis, if different from that for the -based analysis. The last part
of the table provides the additional uncertainties in the BDT-based analysis.
Source of uncertainty Effect (%) Impact on the Signal $\Z\Z$ $\PW\Z$ NRB DY
exp. limit (%) * VV EW corrections — 10 $-4$ — — 14 (12) * Renorm./fact.
scales, VV — 9 4 — — 2 (1) Renorm./fact. scales, ZH 3.5 — — — — Renorm./fact.
scales, DM 5 — — — — PDF, WZ background — — 1.5 — — PDF, ZZ background — 1.5 —
— — PDF, Higgs boson signal 1.5 — — — — PDF, DM signal 1–2 — — — — * MC sample
size, NRB — — — 5 — 1 MC sample size, DY — — — — 30 MC sample size, $\Z\Z$ —
0.1 — — — MC sample size, $\PW\Z$ — — 2 — — MC sample size, ZH 1 — — — — MC
sample size, DM 3 — — — — NRB extrapolation to the SR — — — 20 — $<$1 DY
extrapolation to the SR — — — — 100 $<$1 Lepton efficiency (WZ CR) — — 3 — —
$<$1 Nonprompt bkg. (WZ CR) — — — — 30 $<$1 Integrated luminosity 2.5 $<$1 *
Electron efficiency 1.5 1 ($<$1) Muon efficiency 1 Electron energy scale 1–2
Muon energy scale 1–2 Jet energy scale 1–3 (typically anticorrelated w/ yield)
Jet energy resolution 1 (typically anticorr.) Unclustered energy ($\ptmiss$)
1–4 (typically anticorr.), strong in DY Pileup 1 (typically anticorrelated) b
tagging eff. & mistag rate 1 * BDT: electron energy scale 1.1 2.9 2.6 — — —
(2) BDT: muon energy scale 1.5 4.3 2.7 — — BDT: $\ptmiss$ scale 1.0 3.2 4.1 —
—

##  0.11 Results

The numbers of observed and expected events for the -based analysis are shown
in Table 0.11. There is no significant difference between the dielectron and
dimuon channels in terms of signal-to-background ratio, and hence both are
treated together when obtaining the final results. The observed number of
events in the $\Pe\Pe$ ($\PGm\PGm$) channel is 292 (406), and the number of
events expected from simulation is $301\pm 23$ ($391\pm 26$). Figure 4 shows
the $\ptmiss$ distribution in the $\Pe\Pe+\PGm\PGm$ channel in the SR. The
total background estimates and the observed numbers of events in each
$\ptmiss$ bin are listed in Table 0.11, for both a combined background-only
fit to the SR and the CRs, as well as for a fit to the CRs only. The latter
results can be used in conjunction with the SR bin correlation matrix
presented in Appendix .13 to recast these results in the simplified likelihood
framework [87].

Signal predictions, post-fit background estimates, and observed numbers of
events in the -based analysis. The combined statistical and systematic
uncertainties are reported.  Process $\Pe\Pe+\PGm\PGm$ qqZH(inv.) $158.6\pm
5.4\phantom{0}\phantom{0}$ $\ \ m_{\PH}=125\GeV$,
$\mathcal{B}({\PH\to\text{inv.}})=1$ ggZH(inv.) $42.7\pm 4.9\phantom{0}$ $\ \
m_{\PH}=125\GeV$, $\mathcal{B}({\PH\to\text{inv.}})=1$ DM, vector mediator
$98.8\pm 3.9\phantom{0}$ $\ \ m_{\text{med}}=500\GeV$,
$m_{\mathrm{DM}}=150\GeV$ DM, axial-vector mediator $65.5\pm 2.6\phantom{0}$
$\ \ m_{\text{med}}=500\GeV$, $m_{\mathrm{DM}}=150\GeV$ ZZ $379.8\pm
9.4\phantom{0}\phantom{0}$ WZ $162.5\pm 6.8\phantom{0}\phantom{0}$ Nonresonant
bkg. $75\pm 15$ Drell–Yan $72\pm 29$ Other bkg. $2.6\pm 0.2$ Total bkg.
$692\pm 35\phantom{0}$ Data 698

Expected event yields in each $\ptmiss$ bin for the sum of background
processes in the SR. The background yields and their corresponding
uncertainties are obtained after performing a fit to data. Two sets of
background yields are reported: one from a background-only fit to data in both
the SR and the CRs, and one from a fit to data in all CRs, but excluding data
in the SR. The observed numbers of events in each bin are also included.
$\ptmiss$ bin () Observed events Total background prediction SR+CR fit CR-only
fit $100\leq\ptmiss<125$ 311 $300\,\pm\,18$ $256\,\pm\,32$
$125\leq\ptmiss<150$ 155 $155.0\,\pm\,7.0$ $150\,\pm\,12$ $150\leq\ptmiss<175$
87 $90.8\,\pm\,4.6$ $86.9\,\pm\,8.4$ $175\leq\ptmiss<200$ 50 $54.7\,\pm\,3.1$
$52.7\,\pm\,5.3$ $200\leq\ptmiss<250$ 56 $51.3\,\pm\,2.9$ $50.2\,\pm\,4.9$
$250\leq\ptmiss<300$ 15 $19.7\,\pm\,1.4$ $19.4\,\pm\,2.2$ $300\leq\ptmiss<350$
11 $9.64\,\pm\,0.80$ $9.4\,\pm\,1.2$ $350\leq\ptmiss<400$ 6 $4.73\,\pm\,0.47$
$4.58\,\pm\,0.66$ $400\leq\ptmiss<500$ 6 $3.44\,\pm\,0.39$ $3.31\,\pm\,0.54$
$\ptmiss\geq 500$ 1 $1.63\,\pm\,0.24$ $1.57\,\pm\,0.33$

Figure 4:  Distribution of the $\ptmiss$ in the combination of the $\Pe\Pe$
and $\PGm\PGm$ channels after the full selection. The last bin also includes
any events with $\ptmiss>600\GeV$. The uncertainty band includes both
statistical and systematic components. The $\PZ\PH(\text{inv.})$ signal
normalization assumes SM production rates and the branching fraction
$\mathcal{B}(\PH\to\text{inv.})=1$.

No deviation from the SM background expectation is found. Upper limits on the
contribution of events from new physics are computed by using the modified
frequentist approach $CL_{s}$ [88, 89] based on asymptotic formulas [90, 91],
via a simultaneous maximum likelihood fit to the SR and the CRs. The expected
numbers of background events and signal events, scaled by a signal strength
modifier, are combined in a profile likelihood test statistic, in which the
systematic uncertainties are incorporated as nuisance parameters. For the
dominant backgrounds in the SR, additional parameters are introduced to link
the background expectations in the SR to their respective contributions in the
CRs discussed in Section 0.8. To compute limits in all models, a binned
likelihood test statistic is employed, based on the $\ptmiss$ distribution in
Fig. 4 and also on the BDT classifier distribution in the case of invisible
decays of the SM Higgs boson.

###  0.11.1 Dark matter interpretation

Figure 5 shows the 95% CL expected and observed limits for vector and axial-
vector scenarios with couplings $g_{\Pq}=0.25$, $g_{\mathrm{DM}}=1$. Figure 6
shows the 95% CL expected and observed limits for couplings
$g_{\Pq}=g_{\mathrm{DM}}=1$ in the scalar and pseudoscalar scenarios. In Fig.
7, limits on the DM-nucleon scattering cross section are set at 90% CL as a
function of the DM particle mass and compared to selected results from direct
detection experiments. Both spin-dependent and spin-independent cases are
considered. In both cases, couplings $g_{\Pq}=0.25$ and $g_{\mathrm{DM}}=1$
are used.

Figure 5:  The 95% CL expected and observed limits on
$\sigma_{\text{obs}}/\sigma_{\text{theo}}$ for the vector (left) and axial-
vector (right) mediators with $g_{\Pq}=0.25$ and $g_{\mathrm{DM}}=1$. Limits
are not shown for far off-shell ($2m_{\mathrm{DM}}>1.5m_{\text{med}}$) regions
of the parameter space.

Figure 6:  The 95% CL expected and observed limits on
$\sigma_{\text{obs}}/\sigma_{\text{theo}}$ for the scalar (left) and
pseudoscalar (right) mediated DM scenario with $g_{\Pq}=g_{\mathrm{DM}}=1$.
The limits are parameterized as a function of mediator mass $m_{\text{med}}$
for a fixed dark matter mass $m_{\mathrm{DM}}=1\GeV$.

Figure 7: Observed 90% CL limits on the DM-nucleon scattering cross sections
in both spin-independent (left) and spin-dependent (right) cases, assuming a
mediator-quark coupling constant $g_{\Pq}=0.25$ and mediator-DM coupling
constant $g_{\mathrm{DM}}=1$. Limits from the CRESST-II [92], CDMSLite [93],
PandaX-II [94], LUX [95], and XENON1T [96] experiments are shown for the spin-
independent case (vector couplings). Limits from the PICASSO [97], PICO-60
[98], Super-Kamiokande [99], and IceCube [100, 101] experiments are shown for
the spin-dependent case (axial-vector couplings).

###  0.11.2 Limits on invisible Higgs boson decays

Upper limits are derived for the Higgs boson production cross section using
the same $\ptmiss$-shape analysis as for the DM model. In addition, for
$m_{\PH}=125\GeV$, a shape analysis using the multivariate classifier
distribution, as described in Section 0.9, is performed. The resulting post-
fit signal region is shown in Fig. 8. The 95% CL expected and observed upper
limits on the product of the production cross section and the branching
fraction, $\sigma_{\Z\PH}\,\mathcal{B}(\PH\to\text{inv.})$, computed with the
asymptotic $CL_{s}$ method are shown as a function of the SM-like Higgs boson
mass in Fig. 9 for the $\ptmiss$-shape analysis. For $m_{\PH}=125\GeV$, the
search can be interpreted as an upper limit on
$\mathcal{B}(\PH\to\text{inv.})$ assuming the SM production rate of a Higgs
boson in association with a $\PZ$ boson. Assuming the SM production rate, the
95% observed (expected) CL upper limit on $\mathcal{B}(\PH\to\text{inv.})$ is
0.45 (0.44) using the $\ptmiss$-shape analysis, and 0.40 (0.42) using the
multivariate analysis. The $\mathrm{gg}\to\Z(\ell\ell)\PH$ process is
considered only for the 125 GeV mass point, and only when interpreting the
result as a limit on branching fraction. For SM-like Higgs production,
considering only the $\mathrm{qq}\to\Z(\ell\ell)\PH$ process, upper limits on
$\mathcal{B}(\PH\to\text{inv.})$ are presented as a function of $m_{\PH}$ in
Appendix .13.

Figure 8: Post-fit distribution of the BDT classifier in the multivariate
analysis signal region for the SM H(inv.) decay hypothesis with
$\mathcal{B}(\PH\to\text{inv.})=100\%$. Uncertainty bands correspond to the
combined statistical and systematic components. Figure 9: Expected and
observed 95% CL upper limits on the product of the production cross section
and the branching fraction,
$\sigma_{{\PQq\PQq}\to\Z\PH}\,\mathcal{B}(\PH\to\text{inv.})$, as a function
of the SM-like Higgs boson mass. The limits consider only quark-induced Higgs
boson production. In addition, for the SM (125) Higgs boson, the limit on
branching fraction assuming SM production rate (considering also gluon fusion)
is presented. The vertical gray line indicates that the result at
$m_{\PH}=125\GeV$ should not be read from the plot, as the gluon contribution
is known for that point.

###  0.11.3 Unparticle interpretation

In the unparticle scenario, a shape analysis of the $\ptmiss$ spectrum is
performed. Upper limits are set at 95% CL on the Wilson coefficient
$\lambda/\Lambda_{\textsf{U}}^{d_{\textsf{U}}-1}$ of the unparticle-quark
coupling operator, and are shown in Fig. 10 as a function of the scaling
dimension $d_{\textsf{U}}$.

Figure 10:  The 95% CL upper limits on the Wilson coefficient
$\lambda/\Lambda_{\textsf{U}}^{d_{\textsf{U}}-1}$ of the unparticle-quark
coupling operator. The results from an earlier CMS search in the same final
state [39] are shown for comparison.

###  0.11.4 The ADD interpretation

In the framework of the ADD model of large extra dimensions, we calculate
limits depending on the number of extra dimensions $n$ and the fundamental
Planck scale $M_{\mathrm{D}}$. For each value of $n$, cross section limits are
calculated as a function of $M_{\mathrm{D}}$. By finding the intersection
between the theory cross section line, calculated in the fiducial phase space
of the graviton transverse momentum $\pt^{\rm G}>50\GeV$, with the observed
and expected excluded cross sections, and projecting that point onto the
$M_{\mathrm{D}}$ axis, we find limits on $M_{\mathrm{D}}$ as a function of
$n$, as shown in Fig. 11.

The observed and expected exclusion of $M_{\mathrm{D}}$ ranges between 2.3 and
2.5for $n$ between 2 and 7, at 95% CL.

Figure 11: Expected and observed 95% CL cross section exclusion limits for the
example case $n=4$ in the ADD scenario (left) and exclusion limits on
$M_{\mathrm{D}}$ for different values of $n$ (right). In both plots, the
markers for the expected exclusion are obscured by the close overlap with
those for the observed exclusion. The red solid line in the left plot shows
the theoretical cross section for the case $n=4$. Cross sections are
calculated in the fiducial phase space of $\pt^{\rm G}>50\GeV$. The vertical
line in the left plot shows the projection onto the $M_{\mathrm{D}}$ axis of
the intersection of the theory curve with the expected and observed exclusion
limits.

##  0.12 Summary

A search for new physics in events with a leptonically decaying $\PZ$ boson
and large missing transverse momentum has been presented. The search is based
on a data set of proton-proton collisions collected with the CMS experiment in
2016, corresponding to an integrated luminosity of $35.9\pm 0.9\fbinv$ at
$\sqrt{s}=13\TeV$. No evidence for physics beyond the standard model is found.
Compared to the previous search in the same final state [31], the exclusion
limits on dark matter and mediator masses are significantly extended for
spin-1 mediators in the simplified model interpretation, and exclusion limits
for unparticles are also extended. Results for dark matter production via
spin-0 mediators in the simplified model interpretation, as well as graviton
emission in a model with large extra dimensions, are presented in this final
state for the first time. In the case of invisible decays of a standard-model-
like Higgs boson, the upper limit of 40% on their branching fraction is set at
95% confidence level, using data not included in the previously published
combined analysis [36].

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, 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); OTKA and NIH (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding
Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA
(Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie programme and the European Research Council and Horizon 2020 Grant,
contract No. 675440 (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 Ministry of Education, Youth and
Sports (MEYS) of the Czech Republic; the Council of Science and Industrial
Research, India; the HOMING PLUS programme of the Foundation for Polish
Science, cofinanced from European Union, Regional Development Fund, the
Mobility Plus programme of the Ministry of Science and Higher Education, the
National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Programa Severo Ochoa del Principado de Asturias;
the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF;
the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .13 Supplementary Material

Figure .13.1 shows the correlations between the estimated background yields in
$\ptmiss$ bins in the signal region. These correlations can be used in
conjunction with the total background estimates in the signal region from the
control-region-only fit to re-construct an approximate likelihood function for
this analysis. Using this likelihood function, together with an alternative
signal model, a fit can be performed to the observed data to recast the
results in the simplified likelihood framework [87]. To utilize the simplified
likelihod method, a prediction of the reconstructed event yields in each
$\ptmiss$ bin is required. This is best obtained by using a detector
simulation program such as delphes, however a reasonable prediction can be
obtained by: applying a generator-level selection that parallels the
reconstruction-level selection described in Section 7, omitting tau lepton and
b jet vetoes; smearing the $\ptmiss$ with a Gaussian kernel of 24width; and
scaling by a reconstruction efficiency of 0.70.

13.1.Figure:  Correlations between the estimated background yields in the
signal region $\ptmiss$ bins. The correlations are obtained after performing a
combined fit to data in all control regions, but excluding data in the signal
region. Since the correlation matrix is symmetric by construction, the part
below the diagonal is not shown.

Figure .13.2 shows the 95% CL upper limits on the SM-like Higgs boson
branching fraction to invisible particles, as a function of its mass. The
Higgs production cross section assumed in this figure includes only the
$\PQq\PAQq\to\PZ(\ell\ell)\PH$ process.

13.2.Figure:  Expected and observed 95% CL upper limits on
$\mathcal{B}(\PH\to\text{inv.})$, assuming SM Higgs boson production cross
sections, as a function of the Higgs boson mass.

##  .14 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, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, 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, M. Van De Klundert, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, 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, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Maerschalk, A. Marinov, T. Seva, E. Starling, C. Vander
Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University,
Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
S. Salva, M. Tytgat, W. Verbeke, N. Zaganidis Université Catholique de
Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior,
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. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J.
Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira
Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,
Brazil
S. Ahujaa, C.A. Bernardesa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, 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. Fang5, X. Gao5, 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, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, S. Zhang, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F.
Zhang5 Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, J.D. Ruiz Alvarez, M.A. Segura Delgado University of Split, Faculty
of Electrical Engineering, Mechanical Engineering and Naval Architecture,
Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, 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, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran8,9, S. Elgammal9, A. Mahrous10 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, 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, 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, S. Ghosh, P. Gras, G. Hamel de Monchenault, P. Jarry, I. Kucher, C.
Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P. Busson,
L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, S. Lisniak, A.
Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P.
Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T. Strebler, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram11, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte11, X. Coubez, J.-C. Fontaine11, 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, R. Chierici, D. Contardo, P. Depasse, H.
El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille,
F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov12, V. Sordini, M. Vander Donckt, S. Viret Georgian Technical
University, Tbilisi, Georgia
A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia
D. Lomidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, V. Zhukov12 RWTH Aachen University, III.
Physikalisches Institut A, Aachen, Germany
A. Albert, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg,
T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K.
Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T.
Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, D. Teyssier, S. Thüer
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl13 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras14, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo15, J. Garay Garcia, A.
Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb,
J. Hauk, M. Hempel16, H. Jung, A. Kasem, M. Kasemann, J. Keaveney, C.
Kleinwort, I. Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, J. Leonard, K.
Lipka, W. Lohmann16, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, G. Mittag,
J. Mnich, A. Mussgiller, E. Ntomari, D. Pitzl, A. Raspereza, M. Savitskyi, P.
Saxena, R. Shevchenko, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K.
Wichmann, C. Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, T. Lapsien, D. Marconi, M. Meyer, M. Niedziela,
D. Nowatschin, F. Pantaleo13, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper,
A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Institut für Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann13, S.M. Heindl, U. Husemann, F.
Kassel13, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, 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, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou National Technical
University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, 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. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres17 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath18, Á. Hunyadi, F. Sikler, V. Veszpremi
Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi19, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók17, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati20, S. Bhowmik, P. Mal, K. Mandal, A. Nayak21, D.K. Sahoo20, N.
Sahoo, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur,
S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of
Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep, S. Dey, S. Dutt,
S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S.
Nandan, A. Purohit, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, S. Thakur
Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty13, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity22, G. Majumder, K. Mazumdar, T. Sarkar22, N. Wickramage23
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani24, E. Eskandari Tadavani, S.M. Etesami24, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi25, F. Rezaei
Hosseinabadi, B. Safarzadeh26, 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, F. Erricoa,b, L. Fiorea, G. Iasellia,c, 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, A.
Sharmaa, L. Silvestrisa,13, 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, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria,
A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, S.
Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, 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, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, 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, P. Lenzia,b, M. Meschinia, S. Paolettia, L.
Russoa,27, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera13 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,13, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,28, S. Ragazzia,b, T.
Tabarelli de Fatisa,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, S. Di Guidaa,d,13, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,13, P. Paoluccia,13, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, A.
Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro
Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, N.
Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, E. Torassaa, 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
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,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,13, G. Bagliesia, T. Boccalia, L. Borrello, R.
Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L. Gianninia,c, A.
Giassia, M.T. Grippoa,27, F. Ligabuea,c, T. Lomtadzea, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-Navarroa,29, 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, N. Dacia, D. Del Rea,b,13, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,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, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, R.
Covarellia,b, A. Deganoa,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, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonbuk National University, Jeonju, Korea
A. Lee Chonnam National University, Institute for Universe and Elementary
Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. 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
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali30, F. Mohamad Idris31, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz32, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana,
Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, 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 Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, 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, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk33, 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
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, A. Golunov, I. Golutvin, N. Gorbounov, A. Kamenev, V. Karjavin,
A. Lanev, A. Malakhov, V. Matveev34,35, V. Palichik, V. Perelygin, M. Savina,
S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin, A. Zarubin
Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
Y. Ivanov, V. Kim36, E. Kuznetsova37, 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, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin35 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
M. Chadeeva38, P. Parygin, D. Philippov, S. Polikarpov, E. Popova, V. Rusinov,
E. Zhemchugov P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin35, I. Dremin35, M. Kirakosyan35, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, M. Dubinin39, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov40, Y.Skovpen40, D. Shtol40 State Research Center of Russian
Federation, Institute for High Energy Physics, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic41, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain
J. Alcaraz Maestre, I. Bachiller, M. Barrio Luna, 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, A. Quintario Olmeda, I.
Redondo, L. Romero, M.S. Soares, A. Álvarez Fernández Universidad Autónoma de
Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, M. Missiroli Universidad de Oviedo, Oviedo,
Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R.
González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P. Vischia, J.M.
Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-Universidad de
Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, J. Duarte Campderros,
M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, C.
Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, T.
Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar
Cortabitarte CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, P. Bloch, A. Bocci, C. Botta, T. Camporesi, R. Castello,
M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V.
Daponte, A. David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du
Pree, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita,
G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, P. Harris, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O.
Karacheban16, J. Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M.
Krammer1, C. Lange, P. Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M.
Mannelli, A. Martelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P.
Milenovic42, F. Moortgat, M. Mulders, H. Neugebauer, J. Ngadiuba, S.
Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G.
Petrucciani, A. Pfeiffer, M. Pierini, D. Rabady, A. Racz, T. Reis, G.
Rolandi43, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M.
Selvaggi, A. Sharma, P. Silva, P. Sphicas44, A. Stakia, J. Steggemann, M.
Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns45, M. Verweij,
W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada46, 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, L. Bäni, P. Berger, L. Bianchini, B. Casal, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, G.
Kasieczka, T. Klijnsma, W. Lustermann, B. Mangano, M. Marionneau, M.T.
Meinhard, D. Meister, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi,
J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat, M. Reichmann, D.A.
Sanz Becerra, M. Schönenberger, L. Shchutska, V.R. Tavolaro, K. Theofilatos,
M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
T.K. Aarrestad, C. Amsler47, M.F. Canelli, A. De Cosa, R. Del Burgo, S.
Donato, C. Galloni, T. Hreus, B. Kilminster, D. Pinna, G. Rauco, P. Robmann,
D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, A. Zucchetta National
Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci48, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos49, E.E. Kangal50, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut51, K. Ozdemir52, D. Sunar
Cerci48, B. Tali48, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar53, K. Ocalan54, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
E. Gülmez, M. Kaya55, O. Kaya56, S. Tekten, E.A. Yetkin57 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, I. Köseoglu 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, L. Beck, 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. Newbold58,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev59, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A.
Cockerill, J.A. Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D.
Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams
Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, S.
Casasso, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies, A. De Wit, M.
Della Negra, R. Di Maria, A. Elwood, Y. Haddad, G. Hall, G. Iles, T. James, R.
Lane, C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T.
Matsushita, J. Nash, A. Nikitenko6, V. Palladino, M. Pesaresi, D.M. Raymond,
A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, S. Summers, A.
Tapper, K. Uchida, M. Vazquez Acosta60, T. Virdee13, N. Wardle, D.
Winterbottom, J. Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, A. Garabedian, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J.
Pazzini, S. Piperov, S. Sagir, R. Syarif, 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, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, K. Tos, M. Tripathi, Z. Wang 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
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, J. Heilman, G. Karapostoli, E. Kennedy, F. Lacroix, 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, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, M. Tadel, A. Vartak, S.
Wasserbaech61, 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, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta,
M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A. Ovcharova, H. Qu,
J. Richman, D. Stuart, I. Suarez, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C. Pena, M.
Spiropulu, J.R. Vlimant, S. Xie, Z. Zhang, R.Y. Zhu Carnegie Mellon
University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, T.
Mulholland, K. Stenson, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, J. Chu, S. Dittmer, K. Mcdermott, N. Mirman, 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, G.
Bolla${}^{\textrm{\textdagger}}$, 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, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln,
R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K. Maeshima, N. Magini,
J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V.
O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, B. Schneider, E.
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of Florida, Gainesville, USA
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K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
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Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
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Zhang The University of Iowa, Iowa City, USA
B. Bilki62, W. Clarida, K. Dilsiz63, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya64, A. Mestvirishvili,
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Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
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University, Manhattan, USA
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Lawrence Livermore National Laboratory, Livermore, USA
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Ricci-Tam, Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, R. Bi, S. Brandt,
W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M.
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Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, S.
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J. Turkewitz, 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, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
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Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
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Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, B. Liu, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, D. Stickland, C. Tully University of Puerto Rico,
Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, S. Folgueras, L. Gutay, M.K. Jha, M. Jones,
A.W. Jung, A. Khatiwada, D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F.
Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University Northwest,
Hammond, USA
T. Cheng, N. Parashar, J. Stupak Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
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A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
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University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
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Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Libeiro, T. Mengke, S. Muthumuni, T.
Peltola, S. Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville,
USA