EXO-19-003

EXO-19-003

[inst1]CMS experiment

# Search for dark matter produced in association with a leptonically decaying
$\PZ$ boson in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for dark matter particles is performed using events with a boson
candidate and large missing transverse momentum. The analysis is based on
proton-proton collision data at a center-of-mass energy of 13, collected by
the CMS experiment at the LHC in 2016–2018, corresponding to an integrated
luminosity of 137. The search uses the decay channels $\PZ\to\Pe\Pe$ and
$\PZ\to\PGm\PGm$. No significant excess of events is observed over the
background expected from the standard model. Limits are set on dark matter
particle production in the context of simplified models with vector, axial-
vector, scalar, and pseudoscalar mediators, as well as on a two-Higgs-doublet
model with an additional pseudoscalar mediator. In addition, limits are
provided for spin-dependent and spin-independent scattering cross sections and
are compared to those from direct-detection experiments. The results are also
interpreted in the context of models of invisible Higgs boson decays,
unparticles, and large extra dimensions.

##  0.1 Introduction

The existence of dark matter (DM) is well established from astrophysical
observations [1], where the evidence relies entirely on gravitational
interactions. According to fits based on the Lambda cold dark matter model of
cosmology [2] to observational data, DM comprises 26.4% of the current matter-
energy density of the universe, while baryonic matter accounts for only 4.8%
[3]. In spite of the abundance of DM, its nature remains unknown. This mystery
is the subject of an active experimental program to search for dark matter
particles, including direct-detection experiments that search for interactions
of ambient DM with ordinary matter, indirect-detection experiments that search
for the products of self-annihilation of DM in outer space, and searches at
accelerators and colliders that attempt to create DM in the laboratory.

The search presented here considers a “mono-” scenario where a boson, produced
in proton-proton ($\Pp\Pp$) collisions, recoils against DM or other beyond the
standard model (BSM) invisible particles. The boson subsequently decays into
two charged leptons ($\ell^{+}\ell^{-}$, where $\ell=\Pe$ or $\PGm$) yielding
a dilepton signature, and the accompanying undetected particles contribute to
missing transverse momentum. The analysis is based on a data set of $\Pp\Pp$
collisions at a center-of-mass energy of 13produced at the CERN LHC. The data
were recorded with the CMS detector in the years 2016–2018, and correspond to
an integrated luminosity of $137\fbinv$. The results are interpreted in the
context of several models for DM production, as well as for two other
scenarios of BSM physics that also predict invisible particles.

These results extend and supersede a previous search by CMS in the mono-
channel based on a data set collected at $\sqrt{s}=13\TeV$ corresponding to an
integrated luminosity of 36 [4]. The ATLAS experiment has published searches
in this channel as well with the latest result based on a data set
corresponding to an integrated luminosity of 36 [5]. Similar searches for DM
use other “mono-X” signatures with missing transverse momentum recoiling
against a hadronic jet [6, 7], a photon [8], a heavy-flavor (bottom or top)
quark [9, 10, 11], a $\PW$ or $\PZ$ boson decaying to hadrons [7, 5, 12], or a
Higgs boson [13, 14, 15, 16, 17, 18]. An additional DM interpretation is
explored in searches for Higgs boson decays to invisible particles [19, 20].

The paper is organized as follows. The DM and other BSM models explored are
introduced along with their relevant parameters in Section 0.2. Section 0.3
gives a brief description of the CMS detector. The data and simulated samples
are described in Section 0.4, along with the event reconstruction. The event
selection procedures and background estimation methods are described in
Sections 0.5 and 0.6, respectively. Section 0.7 details the fitting method
implemented for the different models presented, while Section 0.8 discusses
the systematic uncertainties. The results are given in Section 0.9, and the
paper is summarized in Section 0.10.

##  0.2 Signal models

Several models of BSM physics can lead to a signature of a boson subsequently
decaying into a lepton pair and missing transverse momentum. The goal of this
paper is to explore a set of benchmark models for the production of DM that
can contribute to this final state. In all DM models we consider, the DM
particles are produced in pairs, $\PGc{\HepAntiParticle{\PGc}{}{}}\Xspace$,
where $\PGc$ is assumed to be a Dirac fermion.

First, we consider a set of simplified models for DM production [21, 22].
These models describe the phenomenology of DM production at the LHC with a
small number of parameters and provide a standard for comparing and combining
results from different search channels. Each model contains a massive mediator
exchanged in the $s$-channel, where the mediator (either a vector, axial-
vector, scalar, or pseudoscalar particle) couples directly to quarks and to
the DM particle . An example tree-level diagram is shown in Fig. 1 (upper
left). The free parameters of each model are the mass of the DM particle
$m_{\PGc}$, the mass of the mediator $m_{\text{med}}$, the mediator-quark
coupling $g_{\Pq}$, and the mediator-DM coupling $g_{\PGc}$. Following the
suggestions in Ref. [22], for the vector and axial-vector studies, we fix the
couplings to values of $g_{\Pq}=0.25$ and $g_{\PGc}=1$ and vary the values of
$m_{\PGc}$ and $m_{\text{med}}$, and for the scalar and pseudoscalar studies,
we fix the couplings $g_{\Pq}=1$ and $g_{\PGc}=1$, set the dark matter
particle mass to $m_{\PGc}=1\GeV$, and vary the values of $m_{\text{med}}$.
The comparison with data is carried out separately for each of the four spin-
parity choices for the mediator.

Figure 1: Feynman diagrams illustrative of the BSM processes that produce a
final state of a boson that decays into a pair of leptons and missing
transverse momentum: (upper left) simplified dark matter model for a spin-1
mediator, (upper right) 2HDM+a model, (lower left) invisible Higgs boson
decays, and (lower right) graviton () production in a model with large extra
dimensions or unparticle (U) production. Here represents the DM mediator,
represents a DM particle, while (, ) and a represent the scalar and
pseudoscalar Higgs bosons, respectively. Here is identified with the 125scalar
boson. The dotted line represents either an unparticle or a graviton.

We also explore a two-Higgs-doublet model (2HDM) with an additional
pseudoscalar boson, a, that serves as the mediator between DM and ordinary
matter. This “2HDM+a” model [23, 24] is a gauge-invariant and renormalizable
model that contains a Higgs scalar (), which we take to be the observed 125
GeV Higgs boson, a heavy neutral Higgs scalar (), a charged Higgs scalar (),
and two pseudoscalars (, a), where the pseudoscalar bosons couple to the DM
particles. For the process studied in this paper, the boson is produced via
gluon fusion and decays into a standard model (SM) boson and the pseudoscalar
a. These subsequently decay into a pair of leptons and a pair of DM particles,
respectively, as shown in Fig. 1 (upper right). The sizable couplings of the
boson to the Higgs bosons makes the mono-channel more sensitive to this model
than the mono-jet or mono-photon channels. Among the parameters of this model
are the Higgs boson masses, the ratio $\tan\beta$ of the vacuum expectation
values of the two Higgs doublets, and the mixing angle $\theta$ of the
pseudoscalars. We consider only configurations in which
$m_{\PH}=m_{\Hpm}=m_{\PSA}$, and fix the values $\tan\beta=1$ and
$\sin\theta=0.35$, following the recommendations of Ref. [24].

We also examine the case where the boson acts as a mediator for DM production,
as discussed in “Higgs portal” models [25, 26, 27, 28]. If
$m_{\PGc}<m_{\Ph}/2$, the Higgs boson could decay invisibly into a pair of DM
particles. The mechanism for such decays can be found, for example, in many
supersymmetric theoretical models that contain a stable neutral lightest
supersymmetric particle, , a neutralino [29], that is sufficiently light. An
illustrative Feynman diagram for such a case is shown in Fig. 1 (lower left),
while additional gluon-induced diagrams are also considered.

In addition to the DM paradigm, we consider a model where unparticles are
responsible for the missing transverse momentum in the final state. The
unparticle physics concept [30, 31] is based on scale invariance, which is
anticipated in many BSM physics scenarios [32, 33, 34]. The effects of the
scale-invariant sector (“unparticles”) appear as a non-integral number of
invisible massless particles. In this scenario, the SM is extended by
introducing a scale-invariant Banks–Zaks field, which has a nontrivial
infrared fixed point [35]. This field can interact with the SM particles by
exchanging heavy particles with a high mass scale $M_{\textsf{U}}$ [36]. Below
this mass scale, where the coupling is nonrenormalizable, the interaction is
suppressed by powers of $M_{\textsf{U}}$ and can be treated within an
effective field theory (EFT). The parameters that characterize the unparticle
model are the possible noninteger scaling dimension of the unparticle operator
$d_{\textsf{U}}$, the coupling of the unparticles to SM fields $\lambda$, and
the cutoff scale of the EFT $\Lambda_{\textsf{U}}$. In order to remain in the
EFT regime, the cutoff scale is set to $\Lambda_{\textsf{U}}=15\TeV$ and to
maintain unitarity, only $d_{\textsf{U}}>1$ is considered. Figure 1 (lower
right) shows the tree-level diagram considered in this paper for the
production of unparticles associated with a boson.

The final SM extension considered in this paper is the Arkani-
Hamed–Dimopoulos–Dvali (ADD) model of large extra dimensions [37, 38], which
is motivated by the disparity between the electroweak (EW) unification scale
($M_{\text{EW}}\sim 100\GeV$) and the Planck scale ($M_{\text{Pl}}\sim
10^{19}\GeV$). This model predicts graviton () production via the process
$\PQq\PAQq\to\PZ+\cPG$, as shown in Fig. 1 (lower right). The graviton escapes
detection, leading to a mono-signature. In the ADD model, the apparent Planck
scale in four spacetime dimensions is given by $M_{\text{Pl}}^{2}\approx
M_{\mathrm{D}}^{n+2}R^{n}$, where $M_{\mathrm{D}}$ is the fundamental Planck
scale in the full ($n$+4)-dimensional spacetime and $R$ is the
compactification length scale of the extra dimensions. Assuming
$M_{\mathrm{D}}$ is of the same order as $M_{\text{EW}}$, the observed large
value of $M_{\text{Pl}}$ suggests values of $R$ much larger than the Planck
length. These values are on the order of nm for $n=3$, decreasing with larger
values of $n$. The consequence of the large compactification scale is that the
mass spectrum of the Kaluza–Klein graviton states becomes nearly
continuous[37, 38], resulting in a broadened spectrum for the transverse
momentum () of the boson.

##  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 ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid.

Events of interest are selected using a two-tiered trigger system [39]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a time interval of less than 4. The second level, known as the
high-level trigger (HLT), consists of a farm of processors running a version
of the full event reconstruction software optimized for fast processing, and
reduces the event rate to around 1kHz before data storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [40].

##  0.4 Data samples and event reconstruction

This search uses $\Pp\Pp$ collision events collected with the CMS detector
during 2016, 2017, and 2018 corresponding to a total integrated luminosity of
137. The data sets from the three different years are analyzed independently
with appropriate calibrations and corrections to take into account the
different LHC running conditions and CMS detector performance.

Several SM processes can contribute to the mono-signature. The most important
backgrounds come from diboson processes: $\PW\PZ\to\ell\PGn\ell\ell$ where one
lepton escapes detection, $\PZ\PZ\to\ell\ell\PGn\PGn$, and
$\PW\PW\to\ell\ell\PGn\PGn$. There can also be contributions where energetic
leptons are produced by decays of top quarks in $\ttbar$ or $\PQt\PW$ events.
Smaller contributions may come from triple vector boson processes
($\PW\PW\PZ$, $\PW\PZ\PZ$, and $\PZ\PZ\PZ$), $\ttbar\PW\to\PW\PW\bbbar\PW$,
$\ttbar\PZ\to\PW\PW\bbbar\PZ$, and $\ttbar\PGg\to\PW\PW\bbbar\PGg$, referred
to collectively as due to the similar decay products. Drell–Yan (DY)
production of lepton pairs, $\PZ/\PGg^{*}\to\ell\ell$, has no intrinsic source
of missing transverse momentum but can still mimic a mono-signature when the
momentum of the recoiling system is poorly measured. A minor source of
background is from events with a vector boson and a misreconstructed photon,
referred to as $\PV\PGg$.

Monte Carlo simulated events are used to model the expected signal and
background yields. Three sets of simulated events for each process are used in
order to match the different data taking conditions. The samples for DM
production are generated using the dmsimp package [41, 42] interfaced with
$\MGvATNLO$ 2.4.2 [43, 44, 45, 46]. The pseudoscalar and scalar model samples
are generated at leading order (LO) in quantum chromodynamics (QCD), while the
vector and axial-vector model samples are generated at next-to-leading-order
(NLO) in QCD. The $\POWHEG$v2 [47, 48, 49, 50, 51] generator is used to
simulate the $\PZ\Ph$ signal process of the invisible Higgs boson at NLO in
QCD, as well as the $\ttbar$, $\PQt\PW$, and diboson processes. The BSM Higgs
boson production cross sections, as a function of the Higgs boson mass for the
$\PZ\Ph$ process are taken from Refs. [52]. Samples for the 2HDM+a model are
generated at NLO with $\MGvATNLO$ 2.6.0. Events for both the ADD and
unparticle models are generated at LO using an EFT implementation in $\PYTHIA$
8.205 in 2016 and 8.230 in 2017 and 2018 [53, 54]. In order to ensure the
validity of the effective theory used in the ADD model, a truncation method,
described in Ref. [55], is applied. Perturbative calculations are only valid
in cases where the square of the center-of-mass energy ($\hat{s}$) of the
incoming partons is smaller than the fundamental scale of the theory
($M_{\mathrm{D}}^{2}$). As such, this truncation method suppresses the cross
section for events with $\hat{s}>M_{\text{D}}^{2}$ by a factor of
$M_{\text{D}}^{4}/\hat{s}^{2}$. The effect of this truncation is largest for
small values of $M_{\mathrm{D}}$, but also increases with the number of
dimensions $n$ as more energy is lost in extra dimensions. The $\MGvATNLO$
2.2.2 (2.4.2) generator in 2016 (2017 and 2018) is used for the simulation of
the , $\PV\PGg$, and DY samples, at NLO accuracy in QCD.

The set of parton distribution functions (PDFs) used for simulating the 2016
sample is NNPDF 3.0 NLO [56] and for the 2017 and 2018 samples it is NNPDF 3.1
NNLO. For all processes, the parton showering and hadronization are simulated
using 8.226 in 2016 and 8.230 in 2017 and 2018. The modeling of the underlying
event is generated using the CUETP8M1 [57] (CP5 [58]) for simulated samples
corresponding to the 2016 (2017 and 2018) data sets. The only exceptions to
this are the 2016 top quark sample, which uses CUETP8M2 [57] and the
simplified DM (2HDM+a) samples, which uses CP3 [58] (CP5) tunes for all years.
All events are processed through a simulation of the CMS detector based on
[59] and are reconstructed with the same algorithms as used for data.
Simultaneous $\Pp\Pp$ collisions in the same or nearby bunch crossings,
referred to as pileup, are also simulated. The distribution of the number of
such interactions in the simulation is chosen to match the data, with periodic
adjustments to take account of changes in LHC operating conditions [60]. The
average number of pileup interactions was 23 for the 2016 data and 32 for the
2017 and 2018 data.

Information from all subdetectors is combined and used by the CMS particle-
flow (PF) algorithm [61] for particle reconstruction and identification. The
PF algorithm aims to reconstruct and identify each individual particle in an
event, with an optimized combination of information from the various elements
of the CMS detector. The energies of photons are obtained from the ECAL
measurement. The energies of electrons are determined from a combination of
the electron momentum at the primary interaction vertex as determined by the
tracker, the energy from the corresponding ECAL cluster, and the energy sum
from all bremsstrahlung photons spatially compatible with originating from the
electron track. The momentum of muons is obtained from the curvature of the
corresponding track in the tracker detector in combination with information
from the muon stations. The energies of charged hadrons are determined from a
combination of their momentum measured in the tracker and the matching ECAL
and HCAL energy deposits, corrected for the response function of the
calorimeters to hadronic showers. Finally, the energies of neutral hadrons are
obtained from the corresponding corrected ECAL and HCAL energies.

The candidate vertex with the largest value of summed physics-object $\pt^{2}$
is taken to be the primary $\Pp\Pp$ interaction vertex. The physics objects
are the jets, clustered using the jet finding algorithm [62, 63] with the
tracks assigned to candidate vertices as inputs, and the associated missing
transverse momentum, taken as the negative vector sum of the of those jets.

Both electron and muon candidates must pass certain identification criteria to
be further selected in the analysis. They must satisfy requirements on the
transverse momentum and pseudorapidity: $\pt>10\GeV$ and
$\abs{\eta}<2.5~{}(2.4)$ for electrons (muons). At the final level, a medium
working point [64, 65] is chosen for the identification criteria, including
requirements on the impact parameter of the candidates with respect to the
primary vertex and their isolation with respect to other particles in the
event. The efficiencies for these selections are about 85 and 90% for each
electron and muon, respectively.

In the signal models considered in this paper, the amount of hadronic activity
tends to be small, so events with multiple clustered jets are vetoed. For each
event, hadronic jets are clustered from reconstructed particle candidates
using the infrared and collinear safe anti-algorithm [62, 63] with a distance
parameter of 0.4. Jet momentum is determined as the vectorial sum of all
particle momenta in the jet, and is found from simulation to be, on average,
within 5 to 10% of the true momentum over the entire spectrum and detector
acceptance. Pileup interactions can contribute additional tracks and
calorimetric energy depositions to the jet momentum. To mitigate this effect,
charged particles identified to be originating from pileup vertices are
discarded and an offset is applied to correct for remaining contributions
[66]. Jet energy corrections are derived from simulation to bring the measured
response of jets to the average of simulated jets clustered from the generated
final-state particles. In situ measurements of the momentum balance in dijet,
photon+jet, $\PZ$+jet, and multijet events are used to determine corrections
for residual differences between jet energy scale in data and simulation [66].
The jet energy resolution amounts typically to 15% at 10, 8% at 100, and 4% at
1. Additional selection criteria are applied to each jet to remove jets
potentially dominated by anomalous contributions from some subdetector
components or reconstruction failures [67]. Jets with $\pt>30\GeV$ and
$\abs{\eta}<4.7$ are considered for the analysis.

To identify jets that originated from quarks, we use the medium working point
of the DeepCSV algorithm [68]. This selection was chosen to remove events from
top quark decays originating specifically from $\ttbar$ production, without
causing a significant loss of signal. For this working point, the efficiency
to select b quark jets is about 70% and the probability for mistagging jets
originating from the hadronization of gluons or $\cPqu/\cPqd/\cPqs$ quarks is
about 1% in simulated $\ttbar$ events.

To identify hadronically decaying leptons (), we use the hadron-plus-strips
algorithm [69]. This algorithm constructs candidates seeded by PF jets that
are consistent with either a single or triple charged pion decay of the
lepton. In the single charged pion decay mode, the presence of neutral pions
is detected by reconstructing their photonic decays. Mistagged jets
originating from non-decays are rejected by a discriminator that takes into
account the pileup contribution to the neutral component of the decay [69].
The efficiency to select real hadronically decaying leptons is about 75% and
the probability for mistagging jets is about 1%.

The missing transverse momentum vector is computed as the negative vector sum
of the transverse momenta of all the PF candidates in an event, and its
magnitude is denoted as [70]. The is modified to account for corrections to
the energy scale of the reconstructed jets in the event. Events with
anomalously high can originate from a variety of reconstruction failures,
detector malfunctions, or noncollision backgrounds. Such events are rejected
by event filters that are designed to identify more than 85–90% of the
spurious high-events with a misidentification rate of less than 0.1% [70].

##  0.5 Event selection

Events with electrons (muons) are collected using dielectron (dimuon)
triggers, with thresholds of $\pt>23$ (17)and $\pt>12$ (8)for the electron
(muon) with the highest and second-highest measured , respectively. Single-
electron and single-muon triggers with thresholds of 25 (27) and 20 (24)for
2016 (2017–2018) are used to recover residual inefficiencies, ensuring a
trigger efficiency above 99% for events passing the offline selection.

In the signal region (SR), events are required to have two ($N_{\ell}=2$)
well-identified, isolated electrons or muons with the same flavor and opposite
charge ($\Pep\Pem$ or $\PGmp\PGmm$). At least one electron or muon of the pair
must have $\pt>25\GeV$, while the second must have $\pt>20\GeV$. In order to
reduce nonresonant background, the dilepton invariant mass is required to be
within 15of the world-average boson mass $m_{\PZ}$ [71]. Additionally, we
require the of the dilepton system $\pt^{\ell\ell}$ to be larger than 60to
reject the bulk of the DY background. Since little hadronic activity is
expected for the signal, we reject events having more than one jet with
$\pt>30\GeV$ within $\abs{\eta}<4.7$. The top quark background is further
suppressed by rejecting events containing any -tagged jet with $\pt>30\GeV$
reconstructed within the tracker acceptance of $\abs{\eta}<2.4$. To reduce the
$\PW\PZ$ background in which both bosons decay leptonically, we remove events
containing additional electrons or muons with loose identification and with
$\pt>10\GeV$. Events containing a loosely identified candidate with
$\pt>18\GeV$ and $\abs{\eta}<2.3$ are also rejected. Decays that are
consistent with production of muons or electrons are rejected by an overlap
veto.

In addition to the above criteria, there are several selections designed to
further reduce the SM background. The main discriminating variables are: the
missing transverse momentum, ; the azimuthal angle formed between the dilepton
and the , $\Delta\phi(\ptvec^{\kern 1.0pt\ell\ell},\ptvecmiss)$; and the
balance ratio, $\abs{\ptmiss-\pt^{\ell\ell}}/\pt^{\ell\ell}$. The latter two
variables are especially powerful in rejecting DY and top quark processes.
Selection criteria are optimized to obtain the best signal sensitivity for the
range of DM processes considered. The final selection requirements are:
$\ptmiss>100\GeV$, $\Delta\phi(\ptvec^{\kern
1.0pt\ell\ell},\ptvecmiss)>2.6\unit{radians}$, and
$\abs{\ptmiss-\pt^{\ell\ell}}/\pt^{\ell\ell}<0.4$.

For the 2HDM+a model, the selection differs slightly. We make a less stringent
requirement on the missing transverse momentum, $\ptmiss>80\GeV$, and require
the transverse mass,
$\mT=\sqrt{\smash[b]{2p^{\mathrm{T}}_{\ell\ell}\ptmiss[1-\cos\Delta\phi(\ptvec^{\kern
1.0pt\ell\ell},\ptvecmiss)]}}$ to be greater than 200. The kinematic
properties of the 2HDM+a production yield a peak in the spectrum near the
neutral Higgs scalar (H) mass that is advantageous for background
discrimination.

In order to avoid biases in the calculation due to jet mismeasurement, events
with one jet are required to have the azimuthal angle between this jet and the
missing transverse momentum, $\Delta\phi(\ptvec^{\mathrm{j}},\ptvecmiss)$,
larger than 0.5radians. To reduce the contribution from backgrounds such as
$\PW\PW$ and , we apply a requirement on the distance between the two leptons
in the $(\eta,\phi)$ plane, $\Delta R_{\ell\ell}<1.8$, where $\Delta
R_{\ell\ell}=\sqrt{\smash[b]{(\Delta\phi_{\ell\ell})^{2}+(\Delta\eta_{\ell\ell})^{2}}}$.

A summary of the selection criteria for the SR is given in Table 0.5.

Summary of the kinematic selections for the signal region.  Quantity
Requirement Target backgrounds $N_{\ell}$ ${=}2$ with additional lepton veto
$\PW\PZ$, $\PV\PV\PV$ $\pt^{\ell}$ ${>}$25/20for leading/subleading Multijet
Dilepton mass $\left|m_{\ell\ell}-m_{\PZ}\right|<15\GeV$ $\PW\PW$, top quark
Number of jets ${\leq}1$ jet with $\pt^{\mathrm{j}}>30\GeV$ DY, top quark,
$\PV\PV\PV$ $\pt^{\ell\ell}$ ${>}60\GeV$ DY tagging veto 0 -tagged jet with
$\pt>30\GeV$ Top quark, $\PV\PV\PV$ $\PGt$ lepton veto 0 cand. with
$\pt^{\PGt}>18\GeV$ $\PW\PZ$ $\Delta\phi(\ptvec^{\mathrm{j}},\ptvecmiss)$
${>}0.5\unit{radians}$ DY, $\PW\PZ$ $\Delta\phi(\ptvec^{\kern
1.0pt\ell\ell},\ptvecmiss)$ ${>}2.6\unit{radians}$ DY
$\abs{\ptmiss-\pt^{\ell\ell}}/\pt^{\ell\ell}$ ${<}0.4$ DY $\Delta
R_{\ell\ell}$ ${<}1.8$ $\PW\PW$, top quark (all but 2HDM+a) ${>}100\GeV$ DY,
$\PW\PW$, top quark (2HDM+a only) ${>}80\GeV$ DY, $\PW\PW$, top quark (2HDM+a
only) ${>}200\GeV$ DY, $\PW\PW$, $\PZ\PZ$, top quark

##  0.6 Background estimation

We estimate the background contributions using combined information from
simulation and control regions (CRs) in data. A simultaneous maximum
likelihood fit to the or distributions in the SR and CRs constrains the
background normalizations and their uncertainties. Specific CRs target
different categories of background processes, as described below.

###  0.6.1 The three-lepton control region

The ${\PW\PZ\to\ell^{\prime}\PGn\ell\ell}$ decay mode can contribute to the SR
when the third lepton ($\ell^{\prime}=\Pe$ or $\PGm$) escapes detection, and
this same process can be monitored in an orthogonal CR, where the third lepton
is identified and then removed. The construction of the three-lepton ($3\ell$)
CR is based on events with three well-reconstructed charged leptons. A boson
candidate is selected in the same manner as for the SR , while an additional
electron or muon with identical quality and isolation is required. In cases
where there are multiple boson candidates, the candidate with invariant mass
closest to the boson mass is selected. To enhance the purity of the $\PW\PZ$
selection, of at least 30is required and the invariant mass of three leptons
is required to be larger than 100. The backgrounds in this CR are similar to
those in the SR, with a sizable nonprompt background from DY events where a
jet is misidentified as a lepton [72]. An additional minor source of
background is from events with a vector boson and a misreconstructed photon
($\PV\PGg$). All background estimates for this CR are taken from simulation.

To simulate the consequences of not detecting the third lepton, the “emulated
” is estimated from the vectorial sum of and the transverse momentum () of the
additional lepton. The emulated is then used in place of the reconstructed and
the same selection is applied as for the SR. Since there is negligible
contamination from $\PW\PZ\to\PGt\PGn\ell\ell$ and top quark backgrounds in
this CR, no veto is applied on additional or jet candidates. The resulting
emulated spectrum is shown in Fig. 2 (left). For the 2HDM+a case, the
“emulated ” is used instead of “emulated ” with the same selections.

Figure 2:  Emulated distribution in data and simulation for the $3\ell$ (left)
and $4\ell$ (right) CRs. Uncertainty bands correspond to the postfit combined
statistical and systematic components, where the fitting method is described
in Section 0.7.

###  0.6.2 The four-lepton control region

The $\PZ\PZ$ process contributes to the SR through the
$\PZ\PZ\to\ell\ell\PGn\PGn$ decay mode, and the same production process can be
monitored via the decay mode $\PZ\PZ\to 4\ell$. The $4\ell$ CR is based on
events with two pairs of charged leptons. Each pair comprises two leptons of
opposite charge and the same flavor and corresponds to a candidate. Two of the
four leptons must fulfill the same requirements on the leptons as in the SR,
while, in order to increase the yield, the other two leptons need only pass
relaxed lepton quality requirements. The highest boson candidate is required
to have an invariant mass within 35of the boson mass $m_{\PZ}$ [71].
Additionally, we require the transverse momentum of this boson candidate to be
larger than 60. Additional backgrounds to the $\PZ\PZ$ final state are events
from triboson processes, events with a vector boson and a higgs boson
($\PV\Ph$) and from nonprompt events. These backgrounds are almost negligible.
All background estimates for this CR are taken from simulation.

For these four-lepton events, the emulated is calculated as the vectorial sum
of the $\ptvecmiss$ and the of the boson candidate with the larger absolute
mass difference to $m_{\PZ}$. The choice of which boson to use as a proxy for
an invisibly decaying boson negligibly alters the emulated spectrum. The same
selection as the SR is then applied using the emulated in place of the
reconstructed , with the exception of the and jet candidate vetoes. The
resulting emulated spectrum is shown in Fig. 2 (right). Similarly to the
$3\ell$ CR, the “emulated ” is used instead of “emulated ” for the 2HDM+a case
and the distribution is well described by the SM background estimations.

###  0.6.3 The electron-muon control region

We estimate the contribution of the flavor-symmetric backgrounds from an
$\Pe\PGm$ CR based on events with two leptons of different flavor and opposite
charge ($\Pe^{\pm}\PGm^{\mp}$) that pass all other analysis selections. This
CR is largely populated by nonresonant backgrounds (NRB) consisting mainly of
leptonic boson decays in $\ttbar$, $\cPqt\PW$, 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 leptons decay into light
leptons and neutrinos, are also considered in the NRB estimation.

###  0.6.4 The DY control region

The DY background is dominant in the region of low . This process does not
produce undetectable particles. Therefore, any nonzero arises from
mismeasurement or limitations in the detector acceptance. The estimation of
this background uses simulated DY events, for which the normalization is taken
from data in a sideband CR of $80<\ptmiss<100\GeV$ where the signal
contamination is negligible, with all other selections applied. For the 2HDM+a
analysis, a similar approach is taken with relaxed $\ptmiss$ selection of
$50<\ptmiss<100\GeV$ and an additional selection of $\mT<200\GeV$ applied. The
sideband CR is included in the maximum likelihood fit and a 100% uncertainty
is assigned to the extrapolation from this CR to the SR. This uncertainty has
little effect on the results because of the smallness of the overall
contribution from the DY process in the SR.

##  0.7 Fitting method

After applying the selection, we perform a binned maximum likelihood fit to
discriminate between the potential signal and the remaining background
processes. The data sets for each data-taking year are kept separate in the
fit. This yields a better expected significance than combining them into a
single set because the signal-to-background ratios are different for the three
years due to the different data-taking conditions. The electron and muon
channels have comparable signal-to-background ratios, and are combined in the
fit, while the contributions, corrections and systematic uncertainties are
calculated individually.

The distribution of events passing the selection is used as the discriminating
variable in the fit for all of the signal hypotheses except for the 2HDM+a
model. For this model, the distribution is used since a Jacobian peak around
the pseudoscalar Higgs boson mass is expected. Events in the SR are split into
0-jet and 1-jet categories to take into account the different signal-to-
background ratios. In addition, for the CRs defined in Section 0.6, events
with 0-jet and 1-jet are included as a single category in the fit. The
$\Pe\PGm$ and DY CRs are each included as a single bin corresponding to the
total yield. The or spectra in the $3\ell$ and $4\ell$ CRs are included in the
fit with the same binning as in the SR, where these spectra are based upon the
emulated . To allow for further freedom in the $\PZ\PZ$ and $\PW\PZ$
background estimation, the and emulated distributions are split into three
regions with independent normalization parameters: low (${<}200\GeV$), medium
(200–400), and high (${>}400\GeV$), with uncertainties of 10, 20, and 30%,
respectively. These values are based on the magnitudes of the theoretical
uncertainties as described in Section 0.8. For fits to the 2HDM+a model, three
similar regions are chosen with the same uncertainties: low (${<}400\GeV$),
medium (400–800), and high (${>}800\GeV$). To make the best use of the
statistical power in the CRs and to take advantage of the similarities of the
production processes, we take the normalization factors to be correlated for
the $\PZ\PZ$ and the $\PW\PZ$ backgrounds in each region.

For each individual bin, a Poisson likelihood term describes the fluctuation
of the data around the expected central value, which is given by the sum of
the contributions from signal and background processes. Systematic
uncertainties are represented by nuisance parameters $\theta$ with log-normal
probability density functions used for normalization uncertainties and
Gaussian functions used for shape-based uncertainties, with the functions
centered on their nominal values $\hat{\theta}$. The uncertainties affect the
overall normalizations of the signal and background templates, as well as the
shapes of the predictions across the distributions of observables.
Correlations among systematic uncertainties in different categories are taken
into account as discussed in Section 0.8. The total likelihood is defined as
the product of the likelihoods of the individual bins and the probability
density functions for the nuisance parameters:

$\mathcal{L}=\mathcal{L}_{\text{SR}}\mathcal{L}_{3\ell}\mathcal{L}_{4\ell}\mathcal{L}_{\Pe\PGm}\mathcal{L}_{\text{DY}}\,f_{\text{NP}}\Big{(}\theta\mid\hat{\theta}\Big{)}$
(1)

The factors of the likelihood can be written more explicitly as

$\displaystyle\begin{split}\mathcal{L}_{\text{SR}}=&\prod_{i,j}\mathcal{P}\Big{(}N^{\text{SR}}_{\text{obs},i,j}\mid\mu_{\text{DY}}N^{\text{SR}}_{\text{DY},i,j}(\theta)+\mu_{\text{NRB}}N^{\text{SR}}_{\text{NRB},i,j}(\theta)+N^{\text{SR}}_{\text{other},i,j}(\theta)\\\
&+\mu_{\PV\PV,r(i)}(N^{2\ell}_{\PZ\PZ,i,j}(\theta)+N^{\text{SR}}_{\PW\PZ,i,j}(\theta))+\mu
N^{\text{SR}}_{\text{Sig},i,j}(\theta)\Big{)},\end{split}$ (2)
$\displaystyle\mathcal{L}_{3\ell}=$
$\displaystyle\prod_{i}\mathcal{P}\Big{(}N^{3\ell}_{\text{obs},i}\mid
N^{3\ell}_{\text{other},i}(\theta)+\mu_{\PV\PV,r(i)}N^{3\ell}_{\PW\PZ,i}(\theta)\Big{)},$
(3) $\displaystyle\mathcal{L}_{4\ell}=$
$\displaystyle\prod_{i}\mathcal{P}\Big{(}N^{4\ell}_{\text{obs},i}\mid
N^{4\ell}_{\text{other},i}(\theta)+\mu_{\PV\PV,r(i)}N^{4\ell}_{\PZ\PZ,i}(\theta)\Big{)},$
(4) $\displaystyle\mathcal{L}_{\Pe\PGm}=$
$\displaystyle\mathcal{P}\Big{(}N^{\Pe\PGm}_{\text{obs}}\mid\mu_{\text{NRB}}N^{\Pe\PGm}_{\text{NRB}}(\theta)+N^{\Pe\PGm}_{\text{other}}(\theta)\Big{)},$
(5)
$\displaystyle\begin{split}\mathcal{L}_{\text{DY}}=&\mathcal{P}\Big{(}N^{\text{DY}}_{\text{obs}}\mid\mu_{\text{DY}}N^{\text{DY}}_{\text{DY}}(\theta)+\mu_{\text{NRB}}N^{\text{DY}}_{\text{NRB}}(\theta)\\\
&+N^{\text{DY}}_{\text{other}}(\theta)+N^{\text{DY}}_{\PZ\PZ}(\theta)+N^{\text{DY}}_{\PW\PZ}(\theta)+\mu
N^{\text{DY}}_{\text{Sig}}(\theta)\Big{)}.\end{split}$ (6)

The purpose of the fit is to determine the confidence interval for the signal
strengths $\mu$. Here $\mathcal{P}(N\mid\lambda)$ is the Poisson probability
to observe $N$ events for an expected value of $\lambda$, and
$f_{\text{NP}}(\theta\mid\hat{\theta})$ describes the nuisance parameters with
log-normal probability density functions used for normalization uncertainties
and Gaussian functions used for shape-based uncertainties. The index $i$
indicates the bin of the or distribution, $r(i)$ corresponds to the region
(low, medium, high) of bin $i$, and the index $j$ indicates either the 0-jet
or 1-jet selection. The diboson process normalization in the region $r(i)$ is
$\mu_{\PV\PV,r(i)}$, while $\mu_{\text{DY}}$ is the DY background
normalization and $\mu_{\text{NRB}}$ is the normalization for the nonresonant
background. The yield prediction from simulation for process $x$ in region $y$
is noted as $N^{y}_{x}$. The smaller backgrounds in each region are merged
together and are indicated collectively as ”other”. The method above for
constructing likelihood functions follows that of Ref. [73], where a more
detailed mathematical description may be found.

##  0.8 Systematic uncertainties

In the following, we describe all of the uncertainties that are taken into
account in the maximum likelihood fit. We consider the systematic effects on
both the overall normalization and on the shape of the distribution of or for
all applicable uncertainties. We evaluate the impacts by performing the full
analysis with the value of the relevant parameters shifted up and down by one
standard deviation. The final varied distributions of or are used for signal
extraction and as input to the fit. For each source of uncertainty, variations
in the distributions are thus treated as fully correlated, while independent
sources of uncertainty are treated as uncorrelated. Except where noted
otherwise, the systematic uncertainties for the three different years of data
taking are treated as correlated.

The assigned uncertainties in the integrated luminosity are 2.5, 2.3, and 2.5%
for the 2016, 2017, and 2018 data samples [74, 75, 76], respectively, and are
treated as uncorrelated across the different years.

We apply scale factors to all simulated samples to correct for discrepancies
in the lepton reconstruction and identification efficiencies between data and
simulation. These factors are measured using DY events in the $\PZ$ boson peak
region [65, 77, 78] that are recorded with unbiased triggers. The factors
depend on the lepton and $\eta$ and are within a few percent of unity for
electrons and muons. The uncertainty in the determination of the trigger
efficiency leads to an uncertainty smaller than $1\%$ in the expected signal
yield.

For the kinematic regions used in this analysis, the lepton momentum scale
uncertainty for both electrons and muons is well represented by a constant
value of $0.5\%$. The uncertainty in the calibration of the jet energy scale
(JES) and resolution directly affects the computation and all the selection
requirements related to jets. The estimate of the JES uncertainty is performed
by varying the JES. The variation corresponds to a re-scaling of the jet four-
momentum as $p\to p(1\pm\delta\pt^{\text{JES}}/\pt)$, where
$\delta\pt^{\text{JES}}$ is the absolute uncertainty in the JES, which is
parameterized as function of the and $\eta$ of the jet. In order to account
for the systematic uncertainty from the jet resolution smearing procedure, the
resolution scale factors are varied within their uncertainties. Since the
uncertainties in the JES are derived independently for the three data sets,
they are treated as uncorrelated across the three data sets.

The signal processes are expected to produce very few events containing jets,
and we reject events with any jets that satisfy the tagging algorithm working
point used. In order to account for the tagging efficiencies observed in data,
an event-by-event reweighting using tagging scale factors and efficiencies is
applied to simulated events. The uncertainty is obtained by varying the event-
by-event weight by $\pm$1 standard deviation. Since the uncertainties in the
tagging are derived independently for the three data sets, they are treated as
uncorrelated across the three data sets. The variation of the final yields
induced by this procedure is less than $1\%$.

Simulated samples are reweighted to reproduce the pileup conditions observed
in data. We evaluate the uncertainty related to pileup by recalculating these
weights for variations in the total inelastic cross section by 5% around the
nominal value [79]. The resulting shift in weights is propagated through the
analysis and the corresponding and spectra are used as input to the maximum
likelihood fit. The variation of the final yields induced by this procedure is
less than 1%.

Shape-based uncertainties for the $\PZ\PZ$ and $\PW\PZ$ backgrounds, referred
to jointly as $\PV\PV$, and signal processes are derived from variations of
the renormalization and factorization scales, the strong coupling constant ,
and PDFs [80, 81, 82]. The scales are varied up and down by a factor of two.
Variations of the PDF set and are used to estimate the corresponding
uncertainties in the yields of the signal and background processes following
Ref. [56]. The missing higher-order EW terms in the event generation for the
$\PV\PV$ processes yield another source of theoretical uncertainty [83, 84].
The following additional higher-order corrections are applied: a constant
(approximately $10\%$) correction for the $\PW\PZ$ cross section from NLO to
NNLO in QCD calculations [85]; a constant (approximately $3\%$) correction for
the $\PW\PZ$ cross section from LO to NLO in EW calculations, according to
Ref. [86]; a $\Delta\phi(\PZ,\PZ)$-dependent correction to the $\PZ\PZ$
production cross section from NLO to next-to-next-to-leading order (NNLO) in
QCD calculations [87]; a $p_{\mathrm{T}}$-dependent correction to the $\PZ\PZ$
cross section from LO to NLO in EW calculations, following Refs. [86, 83, 84],
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 [88] as an
estimate of the missing NLO EW$\times$NLO QCD contribution, which is not used
as a correction, but rather assigned as an uncertainty. The resulting
variations in the and distribution are used as a shape uncertainty in the
likelihood fit.

The shapes of the and distributions are needed for each of the background
processes. For the DY and nonresonant processes, we take the shape directly
from simulation. The distributions for the $\PZ\PZ$ and $\PW\PZ$ processes are
obtained by taking the shapes from the simulation and normalizing them to the
yield seen in the data in the CR. The gluon-induced and the quark-induced
$\PZ\PZ$ processes have different acceptances and their uncertainties are
treated separately, while the normalization factors are taken to be
correlated. In all cases, the limited number of simulated events in any given
bin gives rise to a systematic uncertainty. This uncertainty is treated as
fully uncorrelated across the bins and processes.

A summary of the impact on the signal strength of the systematic uncertainties
is shown in Table 0.8. The $\PZ\Ph(\text{invisible})$ model is used as an
example to illustrate the size of the uncertainties, both for the presence
($\mathcal{B}(\Ph\to\text{invisible})=1$) and absence
($\mathcal{B}(\Ph\to\text{invisible})=0$) of a signal. These two paradigms are
used to generate Asimov data sets that are then fit to give the uncertainty
estimates shown in Table 0.8. The systematic uncertainties are dominated by
the theoretical uncertainty in the $\PZ\PZ$ and $\PW\PZ$ background
contributions.

Summary of the uncertainties in the branching fraction arising from the
systematic uncertainties considered in the $\PZ\Ph(\text{invisible})$ model
assuming $\mathcal{B}(\Ph\to\text{invisible})=1$ (signal) and
$\mathcal{B}(\Ph\to\text{invisible})=0$ (no signal). Here, lepton measurement
refers to the combined trigger, lepton reconstruction and identification
efficiencies, and the lepton momentum and electron energy scale systematic
uncertainty. Theory uncertainties include variations of the renormalization
and factorization scales, $\alpha_{s}$, and PDFs as well as the higher-order
EWK corrections.  Source of uncertainty Impact assuming signal Impact assuming
no signal Integrated luminosity 0.013 0.002 Lepton measurement 0.032 0.050 Jet
energy scale and resolution 0.042 0.024 Pileup 0.012 0.009 tagging efficiency
0.004 0.002 Theory 0.088 0.085 Simulation sample size 0.024 0.023 Total
systematic uncertainty 0.11 0.11 Statistical uncertainty 0.089 0.073 Total
uncertainty 0.14 0.13

##  0.9 Results

The number of observed and expected events in the SR after the final selection
is given in Table 0.9, where the values of the expected yields and their
uncertainties are obtained from the maximum likelihood fit. The observed
numbers of events are compatible with the background predictions. The expected
yields and the product of acceptance and efficiency for several signal models
used in the analysis are shown in Table 0.9. The post-fit distributions for
events in the signal region in the 0-jet and 1-jet categories are shown in
Fig. 3. The final $\mT$ distributions used for the 2HDM+a model are shown in
Fig. 4.

Observed number of events and post-fit background estimates in the two jet
multiplicity categories of the SR. The reported uncertainty represents the sum
in quadrature of the statistical and systematic components.  Process 0-jet
category 1-jet category Drell-Yan $502\pm 94$ $1179\pm 64$ $\PW\PZ$ $1479\pm
53$ $389\pm 16$ $\PZ\PZ$ $670\pm 27$ $282\pm 13$ Nonresonant background
$384\pm 31$ $263\pm 22$ Other background $6.3\pm 0.7$ $6.8\pm 0.8$ Total
background $3040\pm 110$ $2120\pm 76$ Data 3053 2142

Expected yields and the product of acceptance and efficiency for several
models probed in the analysis. The quoted values correspond to the
$\PZ\to\ell\ell$ decays. The reported uncertainty represents the sum in
quadrature of the statistical and systematic components.  Model Yields Product
of acceptance and efficiency (%) $\PZ\Ph(125)$ $864\pm 64$ $10.6\pm 0.8$ ADD
$M_{\mathrm{D}}=3\TeV,n=4$ $35.1\pm 2.4$ $18.6\pm 1.3$ Unparticle
$S_{\textsf{U}}=0,d_{\textsf{U}}=1.50$ $221\pm 16$ $8.2\pm 0.6$ 2HDM+a
$m_{\PH}=1000\GeV,m_{\textsf{a}}=400\GeV$ $14.1\pm 4.0$ $12.7\pm 2.7$ DM
Vector $m_{\text{med}}=1000\GeV,m_{\PGc}=1\GeV$ $64.8\pm 6.1$ $17.6\pm 1.7$

Figure 3: The distributions for events in the signal region in the 0-jet
(left) and 1-jet (right) categories. The rightmost bin also includes events
with $\ptmiss>800\GeV$. The uncertainty band includes both statistical and
systematic components. The $\PZ\Ph(\text{invisible})$ signal normalization
assumes SM production rates and the branching fraction
$\mathcal{B}(\Ph\to\text{invisible})=1$. For the ADD model, the signal
normalization assumes the expected values for $n=4$ and
$M_{\mathrm{D}}=2\TeV$.

Figure 4: The distributions for events in the signal region in the 0-jet
(left) and 1-jet (right) categories. The rightmost bin includes all events
with $\mT>1000\GeV$. The uncertainty band includes both statistical and
systematic components. The signal normalization assumes the expected values
for $m_{\PH}=1200\GeV,m_{\textsf{a}}=300\GeV$ within the 2HDM+a framework
where $m_{\PH}=m_{\Hpm}=m_{\PSA}$, $\tan\beta=1$ and $\sin\theta=0.35$

For each of the models considered, simulated signal samples are generated for
relevant sets of model parameters. The observed and $\mT$ spectra are used to
set limits on theories of new physics using the modified frequentist
construction [89, 73, 90] used in the asymptotic approximation [91].

###  0.9.1 Simplified dark matter model interpretation

In the framework of the simplified models of DM, the signal production is
sensitive to the mass, spin, and parity of the mediator as well as the
coupling strengths of the mediator to quarks and to DM. The distribution is
used as an input to the fit. Limits for the vector and axial-vector mediators
are shown as a function of the mediator mass $m_{\text{med}}$ and DM particle
mass $m_{\PGc}$ as shown in Figure 5. Cosmological constraints on the DM
abundance [92] are added to Fig. 5 where the shaded area represents the region
where additional physics would be needed to describe the DM abundance. For
vector mediators, we observe a limit around $m_{\text{med}}>870\GeV$ for most
values of $m_{\PGc}$ less than $m_{\text{med}}/2$. For axial-vector mediators
the highest limit reached in the allowed region is about
$m_{\text{med}}>800\GeV$. In both cases, the previous limits from this channel
are extended by about 150, but the limits are still less restrictive than
those from published mono-jet results [7] because weakly coupled bosons are
radiated from the initial state quarks much less frequently than gluons.
Figure 6 shows the 90% limits on the DM-nucleon cross sections calculated
following the suggestions in Ref. [22]. Limits are shown as a function of the
DM particle mass for both the spin-independent and spin-dependent cases and
compared to selected results from direct-detection experiments.

Figure 5:  The 95% exclusion limits for the vector (left) and the axial-vector
(right) simplified models. The limits are shown as a function of the mediator
and DM particle masses. The coupling to quarks is fixed to $g_{\Pq}=0.25$ and
the coupling to DM is set to $g_{\chi}=1$.

Figure 6: The 90% DM-nucleon upper limits on the cross section for simplified
DM in the spin-independent (left) and spin-dependent (right) cases. The
coupling to quarks is set to $g_{\Pq}=0.25$ and the coupling to DM is set to
$g_{\chi}=1$. Limits from the XENON1T [93], LUX [94], PandaX-ll [95], CRESST-
III [96], and DarkSide-50 [97] experiments are shown for the spin-independent
case with vector couplings. Limits from the PICO-60 [98], PICO-2L [99],
IceCube [100], and Super-Kamiokande [101] experiments are shown for the spin-
dependent case with axial-vector couplings.

In addition to vector and axial-vector mediators, scalar and pseudoscalar
mediators are also tested. For these models, we fix both couplings to quarks
and to DM particles: $g_{\Pq}=1$ and $g_{\chi}=1$ as suggested in Ref. [22].
Since the choice of DM particle mass is shown to have negligible effects on
the kinematic distributions of the detected particles, we set it to the
constant value of $m_{\PGc}=1\GeV$. Figure 7 gives the 95% exclusion limits on
the production cross section over the predicted cross section as a function
the mediator mass $m_{\text{med}}$. The expected limits are about 25% better
than the previous results in this channel [4], but are not yet sensitive
enough to exclude any value of $m_{\text{med}}$. The best limits obtained on
the cross section are about 1.5 times larger than the predicted values for low
values of $m_{\text{med}}$.

Figure 7: The 95% upper limits on the cross section for simplified DM models
with scalar (left) and pseudoscalar (right) mediators. The coupling to quarks
is set to $g_{\Pq}=1$, the coupling to DM is set to $g_{\chi}=1$ and the DM
mass is $m_{\chi}=1\GeV$.

###  0.9.2 Two-Higgs-doublet model interpretation

For the 2HDM+a model, the signal production is sensitive to the heavy Higgs
boson and the pseudoscalar a masses. As discussed in Section 0.7, the
distribution is used in the fit rather than . The limits on both the heavy
Higgs boson and the additional pseudoscalar mediator a are shown in Fig. 8.
The mixing angles are set to $\tan\beta=1$ and $\sin\theta=0.35$ with a DM
particle mass of $m_{\PGc}=10\GeV$. The mediator mass with the most
sensitivity is $m_{\PH}=1000\GeV$, where the observed (expected) limit on
$m_{\textsf{a}}$ is 440 (340). For small values of $m_{\textsf{a}}$, the limit
on $m_{\PH}$ is about 1200. These can be compared with the observed (expected)
limits from ATLAS of $m_{\textsf{a}}>340$ (340)and $m_{\PH}>1050$ (1000)based
on a $\sqrt{s}=13\TeV$ data set corresponding to an integrated luminosity of
36 [102].

Figure 8:  The 95% upper limits on the 2HDM+a model with the mixing angles set
to $\tan\beta=1$ and $\sin\theta=0.35$ and with a DM particle mass of
$m_{\PGc}=10\GeV$. The limits are shown as a function of the heavy Higgs boson
and the pseudoscalar masses.

###  0.9.3 Invisible Higgs boson interpretation

For the search for invisible decays of the Higgs boson, we use the
distribution as input to the fit. We obtain upper limits on the product of the
Higgs boson production cross section and branching fraction to invisible
particles $\sigma_{\PZ\Ph}\mathcal{B}(\Ph\to{\text{invisible}})$. This can be
interpreted as an upper limit on $\mathcal{B}(\Ph\to{\text{invisible}})$ by
assuming the production rate [103, 52, 104] for an SM Higgs boson at
$m_{\Ph}=125\GeV$. The observed (expected) 95% upper limit at
$m_{\Ph}=125\GeV$ on $\mathcal{B}(\Ph\to{\text{invisible}})$ is 29%
($25^{+9}_{-7}$%) as shown in Fig. 9. The observed (expected) limit from the
previous CMS result in this channel was
$\mathcal{B}(\Ph\to{\text{invisible}})<45(44)$%. The combinations of all
earlier results yields an observed (expected) limit of 19 (15)% from CMS [19]
and 26% ($17^{+5}_{-5}$%) from ATLAS [20].

Figure 9:  The value of the negative log-likelihood,
$-2\Delta$ln$\mathcal{L}$, as a function of the branching fraction of the
Higgs boson decaying to invisible particles.

###  0.9.4 Unparticle interpretation

In the unparticle scenario, the same analysis of the spectrum is performed. At
95% , upper limits are set on the cross section with
$\Lambda_{\textsf{U}}=15\TeV$. The limits are shown in Fig. 10 as a function
of the scaling dimension $d_{\textsf{U}}$. The observed (expected) limits are
0.5 (0.7)pb, 0.24 (0.26)pb, and 0.09 (0.07)pb for $d_{\textsf{U}}=1$,
$d_{\textsf{U}}=1.5$, and $d_{\textsf{U}}=2$ respectively, compared to 1.0
(1.0)pb, 0.4 (0.4)pb, and 0.15 (0.15)pb for the earlier result [4]. These
limits depend on the choice of $\lambda$ and $\Lambda_{\textsf{U}}$, as the
cross section scales with the Wilson coefficient
$\lambda/\Lambda_{\textsf{U}}$ [30]. We fix the coupling between the SM and
the unparticle fields to $\lambda=1$.

Figure 10:  The 95% upper limits on unparticle+production cross section, as a
function of the scaling dimension $d_{\textsf{U}}$. These limits apply to
fixed values of the effective cutoff scale $\Lambda_{\textsf{U}}=15\TeV$ and
coupling $\lambda=1$.

###  0.9.5 The ADD Interpretation

In the framework of the ADD model of extra dimensions, we use the fits to the
distribution to calculate limits on the number of extra dimensions $n$ and the
fundamental Planck scale $M_{\mathrm{D}}$. The cross section limit calculated
as a function of $M_{\mathrm{D}}$ for the case where $n=4$ is shown in Fig.
11. The limits on $M_{\mathrm{D}}$ as a function of $n$ are obtained, as shown
in Fig. 12. The observed (expected) 95% exclusion upper limit on the mass
$M_{\mathrm{D}}$ is 2.9–3.0 (2.7–2.8)compared to earlier results of 2.3–2.5
(2.3–2.5) [4].

Figure 11: The 95% cross section limit in the ADD scenario as a function of
$M_{\mathrm{D}}$ for $n=4$.  Figure 12: The 95% expected and observed
exclusion limits on $M_{\mathrm{D}}$ as a function of the number of extra
dimensions $n$.

###  0.9.6 Summary of limits

Table 0.9.6 gives a summary of the limits expected and observed for a
selection of relevant parameters in all of the models considered.

Observed and expected 95% limits on parameters for the simplified DM models,
invisible decays of the Higgs boson, two-Higgs-doublet model, large extra
dimensions in the ADD scenario, and unparticle model. For the scalar and
pseudoscalar mediators, the limits are dependent on the mediator mass, so the
lowest values for the ratio of observed to theoretical cross sections are
presented. For the vector and axial-vector mediators, the limits are dependent
on the DM particle mass, so the limits are shown for $m_{\PGc}<300\GeV$ for
the vector mediator and $m_{\PGc}=240\GeV$ for the axial-vector mediator.
Model Parameter Observed Expected DM - vector $m_{\text{med}}$ 870 870
$g_{\chi}=1$ $g_{\Pq}=0.25$ DM - axial-vector $m_{\text{med}}$ 800 800
$g_{\chi}=1$ $g_{\Pq}=0.25$ DM - scalar
$\sigma_{\text{obs}}/\sigma_{\text{theo}}$ 1.8 1.5 $g_{\chi}=1$ $g_{\Pq}=1$
$m_{\PGc}=1\GeV$ DM - pseudoscalar $\sigma_{\text{obs}}/\sigma_{\text{theo}}$
1.8 1.4 $g_{\chi}=1$ $g_{\Pq}=1$ $m_{\PGc}=1\GeV$ 2HDM+a $m_{\textsf{a}}$ 330
440 $\tan\beta=1$ $m_{\PGc}=1\GeV$ $\sin\theta=0.35$ $m_{\PH}=m_{\PSA}=1\TeV$
2HDM+a $m_{\PH}$ 1200 1200 $\tan\beta=1$ $m_{\PGc}=1\GeV$ $\sin\theta=0.35$
$m_{\textsf{a}}=100\GeV$ Invisible Higgs boson
$\mathcal{B}(\Ph\to{\text{invisible}})$ 0.29 0.25 ADD $M_{\mathrm{D}}$ 2.8–2.9
2.6–2.7 $n=2$–7 Unparticles $\sigma$ 0.26pb 0.24pb Scaling dimension
$d_{\textsf{U}}$=1.5

##  0.10 Summary

Events with a boson recoiling against missing transverse momentum in proton-
proton collisions at the LHC are used to search for physics beyond the
standard model. The results are interpreted in the context of several
different models of the coupling mechanism between dark matter and ordinary
matter: simplified models of dark matter with vector, axial-vector, scalar,
and pseudoscalar mediators; invisible decays of a 125scalar Higgs boson; and a
two-Higgs-doublet model with an extra pseudoscalar. Outside the context of
dark matter, models that invoke large extra dimensions or propose the
production of unparticles could contribute to the same signature and are also
considered. The observed limits on the production cross sections are used to
constrain parameters of each of these models. The search utilizes a data set
collected by the CMS experiment in 2016–2018, corresponding to an integrated
luminosity of 137at $\sqrt{s}=13\TeV$. No evidence of physics beyond the
standard model is observed. Comparing to the previous results in this channel
based on a partial data sample collected at $\sqrt{s}=13\TeV$ in 2016,
corresponding to an integrated luminosity of approximately 36for CMS [4] and
for ATLAS [5], the exclusion limits for simplified dark matter mediators,
gravitons and unparticles are significantly extended. For the case of a
125scalar boson, an upper limit of 29% is set for the branching fraction to
fully invisible decays at 95% confidence level. Results for the two-Higgs-
doublet model with an additional pseudoscalar are presented in this final
state and probe masses of the pseudoscalar mediator up to 440and of the heavy
Higgs boson up to 1200when the other model parameters are set to specific
benchmark values.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306; the Lendület (“Momentum”) Program and
the János Bolyai Research Scholarship of the Hungarian Academy of Sciences,
the New National Excellence Program ÚNKP, the NKFIA research grants 123842,
123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the
Council of Science and Industrial Research, India; the HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Higher Education, project no. 02.a03.21.0005 (Russia); the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation;
the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

## References

  * [1] G. Bertone and D. Hooper, “History of dark matter”,  Rev. Mod. Phys. 90 (2018) 045002, 10.1103/RevModPhys.90.045002, arXiv:1605.04909.
  * [2] S. F. Daniel et al., “Testing general relativity with current cosmological data”,  Phys. Rev. D 81 (2010) 123508, 10.1103/PhysRevD.81.123508, arXiv:1002.1962.
  * [3] Planck Collaboration, “Planck 2018 results. I. overview and the cosmological legacy of Planck”, (2018). arXiv:1807.06205.
  * [4] CMS Collaboration, “Search for new physics in events with a leptonically decaying boson and a large transverse momentum imbalance in proton-proton collisions at $\sqrt{s}$ = 13 TeV”,  Eur. Phys. J. C 78 (2018) 291, 10.1140/epjc/s10052-018-5740-1, arXiv:1711.00431.
  * [5] ATLAS Collaboration, “Search for an invisibly decaying Higgs boson or dark matter candidates produced in association with a boson in $\Pp\Pp$ collisions at $\sqrt{s}=$ 13 TeV with the ATLAS detector”,  Phys. Lett. B 776 (2018) 318, 10.1016/j.physletb.2017.11.049, arXiv:1708.09624.
  * [6] ATLAS Collaboration, “Search for dark matter and other new phenomena in events with an energetic jet and large missing transverse momentum using the ATLAS detector”,  JHEP 01 (2018) 126, 10.1007/JHEP01(2018)126, arXiv:1711.03301.
  * [7] CMS Collaboration, “Search for new physics in final states with an energetic jet or a hadronically decaying or boson and transverse momentum imbalance at $\sqrt{s}=13$ TeV”,  Phys. Rev. D 97 (2018) 092005, 10.1103/PhysRevD.97.092005, arXiv:1712.02345.
  * [8] ATLAS Collaboration, “Search for dark matter at $\sqrt{s}=13$ TeV in final states containing an energetic photon and large missing transverse momentum with the ATLAS detector”,  Eur. Phys. J. C 77 (2017) 393, 10.1140/epjc/s10052-017-4965-8, arXiv:1704.03848.
  * [9] ATLAS Collaboration, “Search for dark matter produced in association with bottom or top quarks in $\sqrt{s}=13$ TeV $\Pp\Pp$ collisions with the ATLAS detector”,  Eur. Phys. J. C 78 (2018) 18, 10.1140/epjc/s10052-017-5486-1, arXiv:1710.11412.
  * [10] ATLAS Collaboration, “Search for a scalar partner of the top quark in the all-hadronic $t{\bar{t}}$ plus missing transverse momentum final state at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Eur. Phys. J. C 80 (2020), no. 8, 737, 10.1140/epjc/s10052-020-8102-8, arXiv:2004.14060.
  * [11] CMS Collaboration, “Search for dark matter in events with energetic, hadronically decaying top quarks and missing transverse momentum at $\sqrt{s}=13$ TeV”,  JHEP 06 (2018) 027, 10.1007/JHEP06(2018)027, arXiv:1801.08427.
  * [12] ATLAS Collaboration, “Search for dark matter in events with a hadronically decaying vector boson and missing transverse momentum in $\Pp\Pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  JHEP 10 (2018) 180, 10.1007/JHEP10(2018)180, arXiv:1807.11471.
  * [13] ATLAS Collaboration, “Search for dark matter in association with a Higgs boson decaying to two photons at $\sqrt{s}$ = 13 TeV with the atlas detector”,  Phys. Rev. D 96 (2017) 112004, 10.1103/PhysRevD.96.112004, arXiv:1706.03948.
  * [14] ATLAS Collaboration, “Search for dark matter produced in association with a Higgs boson decaying to $b\bar{b}$ using 36 fb-1 of $\Pp\Pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Rev. Lett. 119 (2017) 181804, 10.1103/PhysRevLett.119.181804, arXiv:1707.01302.
  * [15] CMS Collaboration, “Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos and b quarks at $\sqrt{s}=13$ TeV”,  JHEP 11 (2018) 172, 10.1007/JHEP11(2018)172, arXiv:1807.02826.
  * [16] CMS Collaboration, “Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton-proton collisions at $\sqrt{s}=13$ TeV”,  Eur. Phys. J. C 79 (2019) 280, 10.1140/epjc/s10052-019-6730-7, arXiv:1811.06562.
  * [17] CMS Collaboration, “Search for dark matter produced in association with a Higgs boson decaying to $\gamma\gamma$ or $\tau^{+}\tau^{-}$ at $\sqrt{s}=$ 13 TeV”,  JHEP 09 (2018) 046, 10.1007/JHEP09(2018)046, arXiv:1806.04771.
  * [18] CMS Collaboration, “Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2020) 025, 10.1007/JHEP03(2020)025, arXiv:1908.01713.
  * [19] CMS Collaboration, “Search for invisible decays of a Higgs boson produced through vector boson fusion in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Lett. B 793 (2019) 520, 10.1016/j.physletb.2019.04.025, arXiv:1809.05937.
  * [20] ATLAS Collaboration, “Combination of searches for invisible Higgs boson decays with the ATLAS experiment”,  Phys. Rev. Lett. 122 (2019) 231801, 10.1103/PhysRevLett.122.231801, arXiv:1904.05105.
  * [21] LHC Dark Matter Forum (Abercrombie, D. et al.), “Dark matter benchmark models for early LHC Run-2 searches: report of the ATLAS/CMS dark matter forum”,  Phys. Dark Univ. 27 (2020) 100371, 10.1016/j.dark.2019.100371, arXiv:1507.00966.
  * [22] LHC Dark Matter Working Group, “Recommendations on presenting LHC searches for missing transverse energy signals using simplified $s$-channel models of dark matter”,  Phys. Dark Univ. 27 (2020) 100365, 10.1016/j.dark.2019.100365, arXiv:1603.04156.
  * [23] M. Bauer, U. Haisch, and F. Kahlhoefer, “Simplified dark matter models with two Higgs doublets: I. pseudoscalar mediators”,  JHEP 05 (2017) 138, 10.1007/JHEP05(2017)138, arXiv:1701.07427.
  * [24] LHC Dark Matter Working Group, “LHC Dark Matter Working Group: next-generation spin-0 dark matter models”,  Phys. Dark Univ. 27 (2020) 100351, 10.1016/j.dark.2019.100351, arXiv:1810.09420.
  * [25] S. Baek, P. Ko, W.-I. Park, and E. Senaha, “Higgs portal vector dark matter : revisited”,  JHEP 05 (2013) 036, 10.1007/JHEP05(2013)036, arXiv:1212.2131.
  * [26] A. Djouadi, O. Lebedev, Y. Mambrini, and J. Quevillon, “Implications of LHC searches for Higgs–portal dark matter”,  Phys. Lett. B 709 (2012) 65, 10.1016/j.physletb.2012.01.062, arXiv:1112.3299.
  * [27] A. Djouadi, A. Falkowski, Y. Mambrini, and J. Quevillon, “Direct detection of Higgs-portal dark matter at the LHC”,  Eur. Phys. J. C 73 (2013) 2455, 10.1140/epjc/s10052-013-2455-1, arXiv:1205.3169.
  * [28] G. Arcadi, A. Djouadi, and M. Raidal, “Dark matter through the Higgs portal”,  Phys. Rept. 842 (2020) 1, 10.1016/j.physrep.2019.11.003, arXiv:1903.03616.
  * [29] G. Belanger et al., “The MSSM invisible Higgs in the light of dark matter and g-2”,  Phys. Lett. B 519 (2001) 93, 10.1016/S0370-2693(01)00976-5, arXiv:hep-ph/0106275.
  * [30] H. Georgi, “Unparticle physics”,  Phys. Rev. Lett. 98 (2007) 221601, 10.1103/PhysRevLett.98.221601, arXiv:hep-ph/0703260.
  * [31] H. Georgi, “Another odd thing about unparticle physics”,  Phys. Lett. B 650 (2007) 275, 10.1016/j.physletb.2007.05.037, arXiv:0704.2457.
  * [32] Z. Kang, “Upgrading sterile neutrino dark matter to FI$m$P using scale invariance”,  Eur. Phys. J. C 75 (2015) 471, 10.1140/epjc/s10052-015-3702-4, arXiv:1411.2773.
  * [33] M. Rinaldi, G. Cognola, L. Vanzo, and S. Zerbini, “Inflation in scale-invariant theories of gravity”,  Phys. Rev. D 91 (2015) 123527, 10.1103/PhysRevD.91.123527, arXiv:1410.0631.
  * [34] H. Cheng, “The possible existence of Weyl’s vector meson”,  Phys. Rev. Lett. 61 (1988) 2182, 10.1103/PhysRevLett.61.2182.
  * [35] T. Banks and A. Zaks, “On the phase structure of vector-like gauge theories with massless fermions”,  Nucl. Phys. B 196 (1982) 189, 10.1016/0550-3213(82)90035-9.
  * [36] K. Cheung, W.-Y. Keung, and T.-C. Yuan, “Collider signals of unparticle physics”,  Phys. Rev. Lett. 99 (2007) 051803, 10.1103/PhysRevLett.99.051803, arXiv:0704.2588.
  * [37] N. Arkani-Hamed, S. Dimopoulos, and G. R. Dvali, “The hierarchy problem and new dimensions at a millimeter”,  Phys. Lett. B 429 (1998) 263, 10.1016/S0370-2693(98)00466-3, arXiv:hep-ph/9803315.
  * [38] T. Han, J. D. Lykken, and R. Zhang, “On Kaluza-Klein states from large extra dimensions”,  Phys. Rev. D 59 (1999) 105006, 10.1103/PhysRevD.59.105006, arXiv:hep-ph/9811350.
  * [39] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [40] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [41] O. Mattelaer and E. Vryonidou, “Dark matter production through loop-induced processes at the LHC: the s-channel mediator case”,  Eur. Phys. J. C 75 (2015) 436, 10.1140/epjc/s10052-015-3665-5, arXiv:1508.00564.
  * [42] M. Neubert, J. Wang, and C. Zhang, “Higher-order QCD predictions for dark matter production in mono- searches at the LHC”,  JHEP 02 (2016) 082, 10.1007/JHEP02(2016)082, arXiv:1509.05785.
  * [43] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [44] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [45] P. Artoisenet, R. Frederix, O. Mattelaer, and R. Rietkerk, “Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations”,  JHEP 03 (2013) 015, 10.1007/JHEP03(2013)015, arXiv:1212.3460.
  * [46] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [47] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [48] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [49] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [50] S. Frixione, P. Nason, and G. Ridolfi, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,  JHEP 09 (2007) 126, 10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  * [51] E. Bagnaschi, G. Degrassi, P. Slavich, and A. Vicini, “Higgs production via gluon fusion in the POWHEG approach in the SM and in the MSSM”,  JHEP 02 (2012) 088, 10.1007/JHEP02(2012)088, arXiv:1111.2854.
  * [52] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 4. deciphering the nature of the Higgs sector”,  CERN (2016) 10.23731/CYRM-2017-002, arXiv:1610.07922.
  * [53] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [54] S. Ask et al., “Real emission and virtual exchange of gravitons and unparticles in Pythia8”,  Comput. Phys. Commun. 181 (2010) 1593, 10.1016/j.cpc.2010.05.013, arXiv:0912.4233.
  * [55] S. Ask, “Simulation of plus graviton/unparticle production at the LHC”,  Eur. Phys. J. C 60 (2009) 509, 10.1140/epjc/s10052-009-0949-7, arXiv:0809.4750.
  * [56] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [57] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [58] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [59] Collaboration, “$\GEANTfour$ — a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [60] CMS Collaboration, “Pileup mitigation at CMS in 13 TeV data”, (3, 2020). arXiv:2003.00503. Submitted to JINST.
  * [61] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [62] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [63] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [64] CMS Collaboration, “Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at sqrt(s) = 8 TeV”,  JINST 10 (2015) P08010, 10.1088/1748-0221/10/08/P08010, arXiv:1502.02702.
  * [65] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13\TeV$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [66] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [67] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2016.
  * [68] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [69] CMS Collaboration, “Performance of reconstruction and identification of $\tau$ leptons decaying to hadrons and $\nu_{\tau}$ in pp collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P10005, 10.1088/1748-0221/13/10/P10005, arXiv:1809.02816.
  * [70] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=13\TeV$ using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [71] Particle Data Group, M. Tanabashi et al., “Review of particle physics”,  Phys. Rev. D 98 (2018) 030001, 10.1103/PhysRevD.98.030001.
  * [72] CMS Collaboration, “Measurements of the pp $\to$ WZ inclusive and differential production cross section and constraints on charged anomalous triple gauge couplings at $\sqrt{s}=$ 13 TeV”,  JHEP 04 (2019) 122, 10.1007/JHEP04(2019)122, arXiv:1901.03428.
  * [73] The ATLAS Collaboration, the CMS Collaboration, and the LHC Higgs Combination Group, “Procedure for the LHC Higgs boson search combination in Summer 2011”, Technical Report CMS-NOTE-2011-005. ATL-PHYS-PUB-2011-11, CERN, Geneva, Aug, 2011.
  * [74] CMS Collaboration, “CMS luminosity measurement for the 2016 data-taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [75] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13\TeV$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2017.
  * [76] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}=13\TeV$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2018.
  * [77] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8\TeV$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [78] CMS Collaboration, “Measurements of differential boson production cross sections in proton-proton collisions at $\sqrt{s}$ = 13 TeV”,  JHEP 12 (2019) 061, 10.1007/JHEP12(2019)061, arXiv:1909.04133.
  * [79] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$ TeV”,  JHEP 07 (2018) 161, 10.1007/JHEP07(2018)161, arXiv:1802.02613.
  * [80] J. Rojo et al., “The PDF4LHC report on PDFs and LHC data: Results from Run I and preparation for Run II”,  J. Phys. G 42 (2015) 103103, 10.1088/0954-3899/42/10/103103, arXiv:1507.00556.
  * [81] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”,  J. Phys. G 43 (2016) 023001, 10.1088/0954-3899/43/2/023001, arXiv:1510.03865.
  * [82] A. Accardi et al., “A critical appraisal and evaluation of modern PDFs”,  Eur. Phys. J. C 76 (2016) 471, 10.1140/epjc/s10052-016-4285-4, arXiv:1603.08906.
  * [83] A. Bierweiler, T. Kasprzik, and J. H. Kuhn, “Vector-boson pair production at the LHC to $\mathcal{O}(\alpha^{3})$ accuracy”,  JHEP 12 (2013) 071, 10.1007/JHEP12(2013)071, arXiv:1305.5402.
  * [84] S. Gieseke, T. Kasprzik, and J. H. Kahn, “Vector-boson pair production and electroweak corrections in HERWIG++”,  Eur. Phys. J. C 74 (2014) 2988, 10.1140/epjc/s10052-014-2988-y, arXiv:1401.3964.
  * [85] M. Grazzini, S. Kallweit, D. Rathlev, and M. Wiesemann, “$W^{\pm}Z$ production at hadron colliders in NNLO QCD”,  Phys. Lett. B 761 (2016) 179–183, 10.1016/j.physletb.2016.08.017, arXiv:1604.08576.
  * [86] J. Baglio, L. D. Ninh, and M. M. Weber, “Massive gauge boson pair production at the LHC: a next-to-leading order story”,  Phys. Rev. D 88 (2013) 113005, 10.1103/PhysRevD.94.099902, arXiv:1307.4331. [Erratum: Phys.Rev.D 94, 099902 (2016)].
  * [87] M. Grazzini, S. Kallweit, and D. Rathlev, “ZZ production at the LHC: fiducial cross sections and distributions in NNLO QCD”,  Phys. Lett. B 750 (2015) 407–410, 10.1016/j.physletb.2015.09.055, arXiv:1507.06257.
  * [88] J. M. Campbell, R. Ellis, and C. Williams, “Vector boson pair production at the LHC”,  JHEP 07 (2011) 018, 10.1007/JHEP07(2011)018, arXiv:1105.0020.
  * [89] A. L. Read, “Presentation of search results: the CLs technique”,  J. Phys. G: Nucl. Part. Phys. 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [90] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2.
  * [91] L. Demortier, “P values and nuisance parameters”, in  Statistical issues for LHC physics. Proceedings, Workshop, PHYSTAT-LHC, Geneva, Switzerland, June 27-29, 2007, p. 23.  2008\.  10.5170/CERN-2008-001.
  * [92] LHC Dark Matter Working Group, “Recommendations of the LHC Dark Matter Working Group: Comparing LHC searches for dark matter mediators in visible and invisible decay channels and calculations of the thermal relic density”,  Phys. Dark Univ. 26 (2019) 100377, 10.1016/j.dark.2019.100377, arXiv:1703.05703.
  * [93] XENON Collaboration, “Dark matter search results from a one ton-year exposure of XENON1T”,  Phys. Rev. Lett. 121 (2018) 111302, 10.1103/PhysRevLett.121.111302, arXiv:1805.12562.
  * [94] LUX Collaboration, “Results from a search for dark matter in the complete LUX exposure”,  Phys. Rev. Lett. 118 (2017) 021303, 10.1103/PhysRevLett.118.021303, arXiv:1608.07648.
  * [95] PandaX-II Collaboration, “Dark matter results from 54-ton-day exposure of PandaX-II experiment”,  Phys. Rev. Lett. 119 (2017) 181302, 10.1103/PhysRevLett.119.181302, arXiv:1708.06917.
  * [96] CRESST Collaboration, “First results from the CRESST-III low-mass dark matter program”,  Phys. Rev. D 100 (2019) 102002, 10.1103/PhysRevD.100.102002, arXiv:1904.00498.
  * [97] DarkSide Collaboration, “Low-mass dark matter search with the DarkSide-50 experiment”,  Phys. Rev. Lett. 121 (2018) 081307, 10.1103/PhysRevLett.121.081307, arXiv:1802.06994.
  * [98] PICO Collaboration, “Dark matter search results from the complete exposure of the PICO-60 C3F8 bubble chamber”,  Phys. Rev. D 100 (2019) 022001, 10.1103/PhysRevD.100.022001, arXiv:1902.04031.
  * [99] PICO Collaboration, “Improved dark matter search results from PICO-2L Run 2”,  Phys. Rev. D 93 (2016) 061101, 10.1103/PhysRevD.93.061101, arXiv:1601.03729.
  * [100] IceCube Collaboration, “Improved limits on dark matter annihilation in the sun with the 79-string IceCube detector and implications for supersymmetry”,  JCAP 04 (2016) 022, 10.1088/1475-7516/2016/04/022, arXiv:1601.00653.
  * [101] Super-Kamiokande Collaboration, “Search for neutrinos from annihilation of captured low-mass dark matter particles in the Sun by Super-Kamiokande”,  Phys. Rev. Lett. 114 (2015) 141301, 10.1103/PhysRevLett.114.141301, arXiv:1503.04858.
  * [102] ATLAS Collaboration, “Constraints on mediator-based dark matter and scalar dark energy models using $\sqrt{s}=13$ TeV $\Pp\Pp$ collision data collected by the ATLAS detector”,  JHEP 05 (2019) 142, 10.1007/JHEP05(2019)142, arXiv:1903.01400.
  * [103] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs Cross Sections: 3. Higgs Properties”,  CERN (2013) 10.5170/CERN-2013-004, arXiv:1307.1347.
  * [104] R. V. Harlander, J. Klappert, S. Liebler, and L. Simon, “vh@nnlo-v2: new physics in Higgs strahlung”,  JHEP 05 (2018) 089, 10.1007/JHEP05(2018)089, arXiv:1802.04817.

##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, J. Erö, A. Escalante Del Valle, R.
Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D. Liko, T. Madlener, I.
Mikulec, F.M. Pitters, N. Rad, J. Schieck1, R. Schöfbeck, M. Spanring, S.
Templ, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez Universiteit
Antwerpen, Antwerpen, Belgium
M.R. Darwish2, E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello3, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, A. Morton, Q. Python, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A.
Grebenyuk, A.K. Kalsi, I. Makarenko, L. Moureaux, L. Pétré, A. Popov, N.
Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, L.
Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, M. Niedziela, C. Roskas,
K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S.
Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio,
M. Teklishyn, P. Vischia, S. Wuyckens, J. Zobec Centro Brasileiro de Pesquisas
Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, M. Medina
Jaime8, C. Mora Herrera, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez
Rosas, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da
Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulista a,
Universidade Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
W. Fang3, Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen9, H.S. Chen9, M. Chen, A. Kapoor, D. Leggat, H. Liao, Z.
Liu, R. Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S.
Zhang9, J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3 Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov10, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A.
Razis, H. Rykaczewski, H. Saka, D. Tsiakkouri Charles University, Prague,
Czech Republic
M. Finger11, M. Finger Jr.11, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Elgammal12, A. Ellithi Kamel13, A. Mohamed14 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, M.A. Mahmoud National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, H. Siikonen, E.
Tuominen, J. Tuominiemi Lappeenranta University of Technology, Lappeenranta,
Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro15, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Paris, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram16, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine16, D. Gelé, U. Goerlach, C. Grimault, A.-C. Le Bihan,
P. Van Hove Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret Georgian
Technical University, Tbilisi, Georgia
A. Khvedelidze11, Z. Tsamalaidze11 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch,
J. Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin,
S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast,
Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S.
Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B,
Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad17, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl18, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras19, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G.
Eckerlin, D. Eckstein, T. Eichhorn, L.I. Estevez Banos, E. Gallo20, A. Geiser,
A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari21, N.Z. Jomhari, H.
Jung, A. Kasem19, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker,
W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann22, R. Mankel, I.-A.
Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, J. Mnich, A.
Mussgiller, V. Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D. Pitzl,
A. Raspereza, A. Saggio, A. Saibel, M. Savitskyi, V. Scheurer, C.
Schwanenberger, A. Singh, R.E. Sosa Ricardo, N. Tonon, O. Turkot, A.
Vagnerini, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C.
Wissing, S. Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg,
Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A.
Ebrahimi, M. Eich, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, V. Kutzner, J. Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A.
Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S. Schumann, J.
Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbrück, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
S. Baur, J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer,
A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M. Giffels, A.
Gottmann, F. Hartmann18, C. Heidecker, U. Husemann, M.A. Iqbal, I. Katkov23,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, D. Müller, Th.
Müller, M. Musich, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M.
Schnepf, M. Schröder, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, R. Wolf, S. Wozniewski Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, K. Vellidis, E. Vourliotis National Technical University of
Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE Lendület CMS
Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest,
Hungary
M. Bartók24, R. Chudasama, M. Csanad, M.M.A. Gadallah25, S. Lökös26, P. Major,
K. Mandal, A. Mehta, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath27, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi24, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo, F. Nemes, T. Novak Indian Institute of Science (IISc), Bangalore,
India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati28, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu, A. Nayak29, D.K. Sahoo28, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra30, R. Gupta, A.
Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, S. Sharma, J.B. Singh, A.K.
Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti31, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber32, M. Maity33, S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha,
S. Sarkar, M. Sharan, B. Singh31, S. Thakur31 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Kumar, K. Naskar34, P.K. Netrakanti, L.M. Pant, P. Shukla Tata
Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, G.B. Mohanty, U. Sarkar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, M. Guchait, S. Karmakar, S.
Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy Indian Institute of
Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi35 Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani36, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,37, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A. Fanfania,b,
E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,38, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,39, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,39,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,18,
F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,18, D. Zuoloa,b INFN Sezione di Napoli a, Università
di Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c,
Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,18, P. Paoluccia,18, B. Rossia, C.
Sciaccaa,b, E. Voevodinaa,b INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera,40, M. Margonia,b, A.T. Meneguzzoa,b,
M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Stronga, A.
Tikoa, M. Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G.
Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b,
A. Rossia,b, A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa
a, Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di Domenicoa,b, S.
Donatoa, L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Leggera,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R. Salvaticoa,b, F.
Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, D. Trocinoa,b
INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic
of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, K. Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I.
Yoon University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns41 Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis National Centre for Particle
Physics, Universiti Malaya, Kuala Lumpur, Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz42,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A. Shah, M.
Shoaib, M. Waqas AGH University of Science and Technology Faculty of Computer
Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk43, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, M. Walczak Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, P. Faccioli, M. Gallinaro, J. Hollar, N.
Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev44,45, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim46, E. Kuznetsova47, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko48, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov49, M. Danilov50, P. Parygin, D. Philippov, S. Polikarpov49 P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin51, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov52, T. Dimova52, L. Kardapoltsev52, I. Ovtin52, Y. Skovpen52
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic53, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo
Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris,
J.P. Fernández Ramos, J. Flix, M.C. Fouz, A. García Alonso, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León Holgado, D. Moran, Á. Navarro
Tobar, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S.
Sánchez Navas, M.S. Soares, A. Triossi, L. Urda Gómez, C. Willmott Universidad
Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, S. Sanchez Cruz, A. Trapote Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, G. Gomez, C. Martinez Rivero, P. Martinez
Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T.
Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, I. Vila, J.M. Vizan Garcia University
of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy54, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P.
Baillon, A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, P.
Bortignon, E. Bossini, E. Brondolin, T. Camporesi, G. Cerminara, L. Cristella,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, M.
Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita55, D. Fasanella, S. Fiorendi, A. Florent, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L.
Gouskos, M. Guilbaud, D. Gulhan, M. Haranko, J. Hegeman, Y. Iiyama, V.
Innocente, T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil,
C. Lange, P. Lecoq, K. Long, C. Lourenço, L. Malgeri, M. Mannelli, A.
Massironi, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J.
Ngadiuba, J. Niedziela, S. Orfanelli, L. Orsini, F. Pantaleo18, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, D.
Rabady, A. Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S.
Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys,
P. Sphicas56, J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou,
G.P. Van Onsem, A. Vartak, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada57, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D.
Kotlinski, U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics
and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De Cosa, G.
Dissertori, M. Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa,
C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F.
Micheli, F. Nessi-Tedaldi, F. Pauss, V. Perovic, G. Perrin, L. Perrozzi, S.
Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,
D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V. Stampf, M.L. Vesterbacka
Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler58, C. Botta, D. Brzhechko, M.F. Canelli, R. Del Burgo, J.K.
Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A. Macchiolo,
P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A. Reimers, P.
Robmann, K. Schweiger, Y. Takahashi, S. Wertz National Central University,
Chung-Li, Taiwan
C. Adloff59, C.M. Kuo, W. Lin, A. Roy, T. Sarkar33, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan Chulalongkorn University,
Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin60, Z.S. Demiroglu, F. Dolek, C. Dozen61, I.
Dumanoglu62, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler63, I. Hos64,
C. Isik, E.E. Kangal65, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir66, A. Polatoz, A.E. Simsek, B. Tali67, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak68, G. Karapinar69, K. Ocalan70, M. Yalvac71 Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya72, O. Kaya73, Ö. Özçelik, S. Tekten74, E.A.
Yetkin75 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak62, Y. Komurcu, S. Sen76 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci67, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci67
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
E. Bhal, S. Bologna, J.J. Brooke, E. Clement, D. Cussans, H. Flacher, J.
Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S. Paramesvaran, T.
Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J. Taylor, A. Titterton Rutherford
Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev77, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, V. Cepaitis, G.S. Chahal78, D. Colling, P. Dauncey, G. Davies, M.
Della Negra, G. Fedi, G. Hall, G. Iles, J. Langford, L. Lyons, A.-M. Magnan,
S. Malik, A. Martelli, V. Milosevic, J. Nash79, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, M.
Stoye, A. Tapper, K. Uchida, T. Virdee18, N. Wardle, S.N. Webb, D.
Winterbottom, A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith, J. Wilson
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio,
C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J.
Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou Brown
University, Providence, USA
G. Benelli, B. Burkle, X. Coubez19, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan80, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir81, R. Syarif, E. Usai, W.Y. Wong, D. Yu, W. Zhang University
of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko${}^{\textrm{\textdagger}}$, O. Kukral, R. Lander, M. Mulhearn, D. Pellett,
J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, D. Hamilton, J. Hauser, M. Ignatenko, T.
Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B. Stone,
V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, O.R. Long, N. Manganelli, M. Olmedo Negrete, M.I. Paneva, W. Si,
S. Wimpenny, Y. Zhang University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, D. Gilbert, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S.
Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil University of
California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, B.
Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U. Sarica, D.
Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I.
Vorobiev University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K.
Mcdermott, J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z.
Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator Laboratory,
Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, R.M. Harris, S. Hasegawa, R. Heller, T.C.
Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P.
Klabbers, T. Klijnsma, B. Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R.
Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena51,
O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E.
Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J.
Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A.
Weber, A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S.
Wang, X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy82, M. Hohlmann, D. Noonan, M. Rahmani,
M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz83, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili84, A. Moeller, J.
Nachtman, H. Ogul85, Y. Onel, F. Ozok86, A. Penzo, C. Snyder, E. Tiras, J.
Wetzel, K. Yi87 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T.Á. Vámi The
University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, A.
Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, G.Y. Jeng, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M.
Seidel, A. Skuja, S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini,
C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G.
Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, Z. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R.
Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud University of
Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R.
González Fernández, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, B. Stieger, W. Tabb, F. Yan State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, C. Harrington, L. Hay, I. Iashvili, A.
Kharchilava, C. McLean, D. Nguyen, J. Pekkanen, S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K.
Mohrman, Y. Musienko44, R. Ruchti, P. Siddireddy, S. Taroni, M. Wayne, A.
Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A.
Lefeld, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
P. Das, G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, 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
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, B. Mahakud, G.
Negro, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, M.
Stojanovic15, N. Trevisani, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A.
Kumar, W. Li, B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$,
J. Rorie, W. Shi, A.G. Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban22, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali88, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon89, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perniè, D.
Rathjens, A. Safonov, J. Sturdy Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H.
Ni, K. Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij
University of Virginia, Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A.
Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, D. Pinna, T.
Ruggles, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, W. Vetens †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Department of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5: Also at Universidade Estadual de Campinas, Campinas, Brazil
6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
7: Also at UFMS, Nova Andradina, Brazil
8: Also at Universidade Federal de Pelotas, Pelotas, Brazil
9: Also at University of Chinese Academy of Sciences, Beijing, China
10: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia