HIG-17-023

$HeadURL$ $Id$

HIG-17-023

# Search for invisible decays of a Higgs boson produced through vector boson
fusion in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for invisible decays of a Higgs boson is performed using proton-
proton collision data collected with the CMS detector at the LHC in 2016 at a
center-of-mass energy $\sqrt{s}=13\TeV$, corresponding to an integrated
luminosity of 35.9. The search targets the production of a Higgs boson via
vector boson fusion. The data are found to be in agreement with the background
contributions from standard model processes. An observed (expected) upper
limit of 0.33 (0.25), at 95% confidence level, is placed on the branching
fraction of the Higgs boson decay to invisible particles, assuming standard
model production rates and a Higgs boson mass of 125.09. Results from a
combination of this analysis and other direct searches for invisible decays of
the Higgs boson, performed using data collected at $\sqrt{s}=7$, 8, and 13,
are presented. An observed (expected) upper limit of 0.19 (0.15), at 95%
confidence level, is set on the branching fraction of invisible decays of the
Higgs boson. The combined limit represents the most stringent bound on the
invisible branching fraction of the Higgs boson reported to date. This result
is also interpreted in the context of Higgs-portal dark matter models, in
which upper bounds are placed on the spin-independent dark-matter-nucleon
scattering cross section.

##  0.1 Introduction

Since the discovery of the Higgs boson at the CERN LHC [1, 2, 3], the ATLAS
and CMS Collaborations have pursued a wide-ranging program to study its
properties and interactions. Precise measurements of the couplings of the
Higgs boson to standard model (SM) particles indicate that the properties of
the new particle are consistent with the SM predictions [4]. These
measurements also provide indirect constraints on additional contributions to
the Higgs boson width from beyond the SM (BSM) decays. Based on the results
presented in Ref. [4], an indirect upper limit on the Higgs boson branching
fraction to BSM particles of 0.34 is set at 95% confidence level (CL).

In the SM, the Higgs boson decays invisibly ($\PH\to\text{inv}$) only through
the $\PH\to\PZ\PZ\to 4\nu$ process, with a branching fraction,
${\mathcal{B}(\PH\to\text{inv})}$, of about $10^{-3}$. The rate for invisible
decays of the Higgs boson may be significantly enhanced in the context of
several BSM scenarios [5, 6, 7, 8], including those in which the Higgs boson
acts as a portal to dark matter (DM) [9, 10, 11, 12]. Direct searches for
$\PH\to\text{inv}$ decays increase the sensitivity to
${\mathcal{B}(\PH\to\text{inv})}$ beyond the indirect constraints. The ATLAS
Collaboration [13] presented a combination of direct searches using
$\sqrt{s}=7$ and 8data from proton-proton ($\Pp\Pp$) collisions, yielding an
observed (expected) upper limit of 0.25 (0.27) on
${\mathcal{B}(\PH\to\text{inv})}$ at 95% CL [14]. The CMS Collaboration [15]
performed a similar combination based on $\sqrt{s}=7$, 8, and 13$\Pp\Pp$
collision data collected up to the end of 2015, setting an observed (expected)
upper limit of 0.24 (0.23) on ${\mathcal{B}(\PH\to\text{inv})}$ at 95% CL
[16].

This Letter presents a search for invisible decays of a Higgs boson, using
$\Pp\Pp$ collision data at $\sqrt{s}=13\TeV$ collected with the CMS detector
in 2016, corresponding to an integrated luminosity of 35.9. The search targets
events in which a Higgs boson is produced in association with jets from vector
boson fusion (VBF), as illustrated by the Feynman diagram in Fig. 1 (left). In
these events, a Higgs boson is produced along with two jets that exhibit a
large separation in pseudorapidity ($\abs{\Delta\eta_{\mathrm{jj}}}$) and a
large dijet invariant mass ($m_{\mathrm{jj}}$). This characteristic signature
allows for the suppression of SM backgrounds, making the VBF channel the most
sensitive mode for invisible decays of a Higgs boson at hadron colliders [17,
16]. The invisible particles produced by the Higgs boson decay can recoil with
high transverse momentum () against the visible VBF-jet system, resulting in
an event with large imbalance, which can be used to select signal enriched
regions. In this phase space, the main expected backgrounds originate from
$\PZ(\PGn\PAGn)\text{+jets}$ and $\PW(\ell\nu)\text{+jets}$ processes. They
are estimated from data using dedicated control regions (CRs), which consist
of high purity samples of $\PZ$ or $\PW$ bosons decaying leptonically
(${\ell=\mu,\Pe}$). While earlier searches probing this final state at the LHC
were based on counting experiments, the analysis presented in this Letter more
optimally exploits the distinctive kinematic features of the VBF topology by
fitting the shape of the $m_{\mathrm{jj}}$ distribution. This approach is
referred to as the “shape analysis”. The shape analysis has been designed to
provide a substantially improved sensitivity to invisible decays of the SM
Higgs boson, resulting in the most sensitive VBF $\PH\to\text{inv}$ search
reported to date. In addition, a simple but less sensitive counting approach,
referred to as the “cut-and-count analysis”, allows for an easier
interpretation of the results of this search in the context of other
phenomenological models predicting the same final-state signature. Upper
limits on the product of the cross section and branching fraction for an
additional Higgs boson with SM-like couplings, which does not mix with the
125Higgs boson, are also reported.

To further improve the sensitivity, results from a combination of searches for
invisible decays of the Higgs boson, using data collected at $\sqrt{s}=7,$ 8,
and 13, are also presented. The searches considered in this combination target
the VBF, the associated production (denoted by $\cmsSymbolFace{V}\PH$, where
$\cmsSymbolFace{V}$ denotes a $\PW$ or $\PZ$ boson), and the gluon fusion
modes, whose representative Feynman diagrams are shown in Fig. 1. The
$\cmsSymbolFace{V}\PH$-tag includes both a search for $\PZ\PH$ production, in
which the $\PZ$ boson decays to a pair of leptons ($\Pe,\mu$) or b quarks, and
one where a Lorentz-boosted $\PW$ or $\PZ$ boson decays to light-flavor
quarks, whose corresponding hadronization products are reconstructed as a
single large-radius jet. Additional sensitivity is achieved by including a
search for ${\Pg\Pg\to\Pg\PH}$ production (hereafter referred to as
$\Pg\Pg\PH$), where a high-Higgs boson candidate is produced in association
with jets from initial-state radiation. When these searches are combined to
set an upper limit on ${\mathcal{B}(\PH\to\text{inv})}$, SM production cross
sections are assumed. The result of this combination is also interpreted in
the context of Higgs-portal models of DM interactions [9, 10, 11, 12], in
which the 125Higgs boson plays the role of a mediator between the SM and DM
particles, thereby allowing for the possibility of producing DM candidates.

Figure 1: Leading order Feynman diagrams for the main production processes
targeted in the combination: VBF (left), ${\cmsSymbolFace{V}\PH}$ (middle),
and ${\Pg\Pg\PH}$ (right).

This Letter is organized as follows: after a brief description of the CMS
detector in Section 0.2, the event reconstruction in Section 0.3, and the
simulated signal and background processes in Section 0.4, Section 0.5 is
dedicated to the event selection requirements followed by a detailed
description of the analysis strategy in Section 0.6. Section 0.7 reports the
results of the VBF search in terms of upper limits on
${\mathcal{B}(\PH\to\text{inv})}$. Section 0.8 reports the upper limit on
${\mathcal{B}(\PH\to\text{inv})}$ from a combination of the aforementioned
searches for invisible decays of the Higgs boson based on 13data collected in
2016 while, in Section 0.9, results from a more complete combination,
involving also similar analyses performed on the 7 and 8data sets, are
presented. The Letter is summarized in Section 0.10.

##  0.2 The CMS detector

The CMS detector is a multi-purpose apparatus designed to study a wide range
of physics processes in both ${\Pp\Pp}$ and heavy ion collisions. The central
feature of the experiment is a superconducting solenoid of 6m internal
diameter, providing a magnetic field of 3.8T parallel to the beam direction.
Within the solenoid volume a silicon pixel and strip tracker, a lead tungstate
crystal electromagnetic calorimeter (ECAL), and a brass and scintillator
hadron calorimeter (HCAL) are installed, each composed of a barrel and two
endcap sections. The tracker system measures the momentum of charged particles
up to $\abs{\eta}=2.5$, while the ECAL and HCAL provide coverage up to
$\abs{\eta}=3.0$. In addition, the steel and quartz-fiber Cherenkov hadron
forward calorimeter extends the coverage to $\abs{\eta}=5.0$. Muons are
detected in gas-ionization chambers embedded in the steel flux-return yoke
outside the solenoid, which cover up to $\abs{\eta}=2.4$.

Events of interest are selected using a two-tiered trigger system [18]. The
first level (L1) is composed of custom hardware processors, which use
information from the calorimeters and muon detectors to select events at a
rate of about 100kHz. The second level, known as high-level trigger (HLT), is
a software-based system which runs a version of the CMS full event
reconstruction optimized for fast processing, reducing the event rate to about
1kHz.

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. [15].

##  0.3 Event reconstruction

The particle-flow (PF) algorithm [19] aims to reconstruct and identify each
particle in an event with an optimized combination of information from the
various elements of the CMS detector. The energy of photons is obtained from
the ECAL measurement. The energy of electrons is determined from a combination
of the momentum of the associated track at the primary interaction vertex, the
energy of the corresponding ECAL cluster, and the energy sum of all
bremsstrahlung photons spatially compatible with originating from the electron
track. The momentum of muons is obtained from the curvature of the
corresponding tracks. The energy of charged hadrons is determined from a
combination of their momentum measured in the tracker and the matched ECAL and
HCAL energy deposits, corrected for the response function of the calorimeters
to hadronic showers. Finally, the energy of neutral hadrons is obtained from
the corresponding corrected ECAL and HCAL energy.

The missing transverse momentum vector () is computed as the negative vector
sum of all the PF candidates in an event, and its magnitude is denoted as .
Hadronic jets are reconstructed by clustering PF candidates using the anti-
algorithm [20, 21], with a distance parameter of 0.4. The reconstructed vertex
with the largest value of summed physics object $\pt^{2}$ is taken to be the
primary ${\Pp\Pp}$ interaction vertex, where physics objects correspond to the
jets and the measured in the event. The charged PF candidates originating from
any other vertex are ignored during the jet finding procedure. Jet momentum is
determined as the vector sum of all particle momenta inside the jet, and is
found from simulation to vary, on average, between 5 and 10% of the true
momentum over the whole spectrum and detector acceptance. An offset correction
is applied to jet energies to take into account the contribution from
additional ${\Pp\Pp}$ interactions within the same or adjacent bunch crossings
(pileup) [22]. Jet energy corrections are derived from simulation and are
confirmed with in situ measurements of the energy balance in dijet, multijet,
$\gamma\text{+jets}$, and leptonically decaying $\PZ\text{+jets}$ events [23].
These energy corrections are also propagated to the calculation [24].

Muon candidates, within the geometrical acceptance of the silicon tracker and
muon subdetectors (${\abs{\eta}<2.4}$), are reconstructed by combining the
information from the tracker and the muon chambers [25]. These candidates are
required to satisfy a set of quality criteria based on the number of hits
measured in the tracker and the muon system, the properties of the fitted muon
track, as well as the impact parameters of the track with respect to the
primary vertex of the event.

Electron candidates within $\abs{\eta}<2.5$ are reconstructed using an
algorithm that associates fitted tracks in the silicon tracker with
electromagnetic energy clusters in the ECAL [26]. To reduce the
misidentification rate, these candidates are required to satisfy
identification criteria based on the shower shape of the energy deposit, the
matching of the electron track to the ECAL energy cluster, the relative amount
of energy deposited in the HCAL detector, and the consistency of the electron
track with the primary vertex. Because of non-optimal reconstruction
performance, electron candidates in the transition region between the ECAL
barrel and endcap, ${1.44<\abs{\eta}<1.57}$, are not considered in the
analysis. Electrons identified as coming from photon conversions in the
detector are discarded [27].

Identified electrons or muons are required to be isolated from hadronic
activity in the event. The isolation is defined by summing the of all the PF
candidates within a cone of radius
${R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.4\,(0.3)}$ around
the muon (electron) track, and is corrected for the contribution of neutral
hadrons from pileup interactions [25, 26].

Hadronically decaying $\tau$ leptons () are identified from reconstructed jets
via the hadron-plus-strip algorithm [28], that requires a subset of particles
inside the jet to be consistent with the decay products of a $\tau$ lepton. In
addition, the candidate must be isolated from other activity in the detector.
The isolation is computed by summing the of all the charged PF candidates and
PF photons within a cone of radius $R=0.3$ around the jet axis. Hadronic
$\tau$ leptons are selected with an average efficiency between 60 and 65%.

##  0.4 Simulated samples

The signal and background processes are simulated using several Monte Carlo
(MC) generators. Higgs boson signal events, produced through $\Pg\Pg\PH$ and
VBF, are generated with v2.0 [29, 30, 31, 32, 33] at next-to-leading order
(NLO) approximation in perturbative quantum chromodynamics (QCD). Signal
events are normalized to the inclusive Higgs boson production cross sections
taken from the recommendations of Ref. [34]. The $\Pg\Pg\PH$ production cross
section is computed at next-to-next-to-NLO ($\mathrm{N}^{3}\mathrm{LO}$)
precision in QCD, and at NLO in electroweak (EW) theory [35]. The cross
section for Higgs boson production through VBF is calculated at next-to-NLO
(NNLO) in QCD, including also NLO EW corrections. The $\Pg\Pg\PH$ process is
simulated using calculations in which the top quark loop is fully resolved.
The distribution of the Higgs boson produced via $\Pg\Pg\PH$ is reweighted to
match the NNLO plus next-to-next-to-leading-logarithmic (NNLL) prediction from
hres v2.1 [36, 37]. When upper limits are set on
${\mathcal{B}(\PH\to\text{inv})}$ for the SM Higgs boson, both $\Pg\Pg\PH$ and
VBF signal events are generated assuming a Higgs boson mass of 125.09, which
is consistent with the combined ATLAS and CMS measurement [38] based on
7+8data, as well as the recent CMS measurement at 13in the ${\PH\to\PZ\PZ\to
4\ell}$ channel [39].

The $\PZ/\gamma^{*}(\ell^{+}\ell^{-})$+jets, $\PZ(\PGn\PAGn)\text{+jets}$, and
$\PW(\ell\nu)\text{+jets}$ backgrounds are simulated at leading order (LO)
using v2.2.2 [40], where up to four partons in the final state are included in
the matrix element calculation. The background processes involving the
production of a vector boson ($\cmsSymbolFace{V}$) in association with two
jets exclusively through EW interactions, of order $\alpha^{4}$, are simulated
at LO via . In addition, the QCD multijet background is also simulated at LO
using . The and single top quark background samples are produced at NLO QCD
using v2.0 and v1.0, respectively [41, 42, 43]. Finally, the $\PW\PZ$ and
$\PZ\PZ$ diboson productions are simulated at LO with v8.205 [44], while the
$\cmsSymbolFace{V}\gamma$ and $\PW\PW$ processes are simulated at NLO QCD
using and [45], respectively.

In all cases, generated events are interfaced with v8.205 or higher for the
simulation of fragmentation, parton showering, and the underlying event
description, using the parameters from the cuetp8m1 tune [46]. In the case of
LO (NLO) samples, partons from the matrix elements are matched to the parton
shower description via the MLM [47] (FxFx [48]) scheme. The nnpdf v3.0 [49]
parton distribution functions (PDFs) are used for all the matrix element
calculations. Interactions of the final-state particles with the CMS detector
are simulated with [50]. Simulated events include the effects of pileup, and
are weighted to reproduce the observed pileup distribution.

##  0.5 Event selection

Events in the signal region (SR) are selected initially by the L1 trigger
exploting the information, whose threshold varies between 60 and 90depending
on the instantaneous luminosity. The at the L1 trigger is computed from the
vector sum of all the energy depositions in the calorimeters with
${\abs{\eta}<3}$. Partial mistiming of signals in the forward region of the
ECAL endcaps (${2.5<\abs{\eta}<3.0}$) led to a reduction in the L1 trigger
efficiency. A correction for this effect was determined using an unbiased data
sample. This correction was found to be about 1% for $m_{\mathrm{jj}}$ of
200and it increases to about 20% for $m_{\mathrm{jj}}$ larger than 3.5.

At the HLT level, events of interest are collected using triggers with
thresholds of 110 or 120, depending on the data taking period, applied equally
to both the missing transverse momentum computed at the trigger level
($p_{\text{T, trig}}^{\text{miss}}$) and the $H_{\text{T,
trig}}^{\text{miss}}$ variable. The $H_{\text{T, trig}}^{\text{miss}}$ is
defined as the magnitude of the vector sum of the reconstructed jets at the
trigger level in the event with ${\pt>20\GeV}$ and ${\abs{\eta}<5}$. The
energy fraction attributed to neutral hadrons in jets with ${\abs{\eta}<3.0}$
is required to be less than 90%, in order to remove spurious jets originating
from detector noise. Both $p_{\text{T, trig}}^{\text{miss}}$ and $H_{\text{T,
trig}}^{\text{miss}}$ are calculated without including muon candidates,
allowing the same triggers to be used also for selecting events in the muon
CRs, which are used in the background estimation procedure described in
Section 0.6.

Offline, events considered in the VBF search are required to have at least two
jets with larger than 80 (40)for the leading (subleading) jet. Since the L1
trigger decision does not use information from the hadronic activity in the
forward region, at least one of the two leading jets in the event is required
to have ${\abs{\eta}<3}$. To ensure a high and stable trigger efficiency,
events are further required to have ${\ptmiss>250\GeV}$. The trigger
efficiency is measured as a function of , computed from jets with
${\pt>30\GeV}$ and ${\abs{\eta}<3}$. After correcting for the L1 mistiming
inefficiency, these triggers are found to be fully efficient for events
passing the analysis selection with ${\mht>250\GeV}$. In addition, if the
leading jet is within the geometrical acceptance of the tracker
(${\abs{\eta}<2.4}$), its energy fraction attributed to charged hadrons is
required to be greater than 10%, while the energy fraction attributed to
neutral hadrons is required to be smaller than 80%. These requirements, along
with quality filters applied to tracks, muon candidates, and other physics
objects, reduce the contamination arising from large misreconstructed from
noncollision backgrounds [51]. To further suppress the contamination from QCD
multijet events, in which a large may arise from a severe mismeasurement of
the jet momentum, the jets in the event, with ${\pt>30\GeV}$ and
${\abs{\eta}<4.7}$, are required to not be aligned with the . The minimum
value of the azimuthal angle between the vector and each jet
($\text{min}\Delta\phi(\ptvecmiss,\vec{p}_{\mathrm{T}}^{\kern
1.0pt\text{jet}})$) is required to be larger than 0.5rad, where only the first
four leading jets are included in the
$\text{min}\Delta\phi(\ptvecmiss,\vec{p}_{\mathrm{T}}^{\kern
1.0pt\text{jet}})$ definition. This selection reduces the QCD multijet
contamination to less than 1% of the total background.

The two leading jets in VBF signal events typically show a large separation in
$\eta$, large $m_{\mathrm{jj}}$ and a small azimuthal separation
($\abs{\Delta\phi_{\mathrm{jj}}}$). The discriminatory power of
$\abs{\Delta\phi_{\mathrm{jj}}}$ results from a combination of the spin-parity
properties of the Higgs boson and the high-regime explored by this search
[17], in which the two VBF jets tend to recoil against the invisible system.
The $\PZ(\PGn\PAGn)\text{+jets}$ and $\PW(\ell\nu)\text{+jets}$ processes
constitute the largest backgrounds in this search. The shape analysis
primarily employs the large separation power of $m_{\mathrm{jj}}$ to
discriminate between VBF signal and $\cmsSymbolFace{V}\text{+jets}$
backgrounds. Therefore, in this scenario, a set of loose requirements is
applied on both $m_{\mathrm{jj}}$ and $\abs{\Delta\eta_{\mathrm{jj}}}$,
$\abs{\Delta\eta_{\mathrm{jj}}}>1.0$ and $m_{\mathrm{jj}}>200\GeV$. To further
reduce the $\cmsSymbolFace{V}\text{+jets}$ contamination,
$\abs{\Delta\phi_{\mathrm{jj}}}$ is required to be smaller than 1.5rad and the
two jets must lie in opposite hemispheres,
$\eta_{\mathrm{j1}}\,\eta_{\mathrm{j}2}<0$. In the cut-and-count approach,
$\PZ(\PGn\PAGn)\text{+jets}$ and $\PW(\ell\nu)\text{+jets}$ processes are
suppressed by a more stringent event selection requiring
${\abs{\Delta\eta_{\mathrm{jj}}}>4.0}$ and ${m_{\mathrm{jj}}>1.3\TeV}$, while
the requirement applied on $\abs{\Delta\phi_{\mathrm{jj}}}$ remains unchanged.

The $\PW(\ell\nu)\text{+jets}$ background is further suppressed by rejecting
events that contain at least one isolated electron or muon with $\pt>10\GeV$,
or a candidate with $\pt>18\GeV$ and $\abs{\eta}<2.3$, where the isolation is
required to be less than 25 (16)% of the muon (electron) . With this strategy,
prompt muons (electrons) are selected with an average efficiency of about 98
(95)%. In order to further reduce the contribution from $\gamma\text{+jets}$
and $\cmsSymbolFace{V}\gamma$ processes, events containing an isolated photon
with $\pt>15\GeV$ and $\abs{\eta}<2.5$, passing identification criteria based
on its ECAL shower shape [27], are vetoed.

Top quark backgrounds (and single top quark processes) are suppressed by
rejecting events in which at least one jet, with ${\pt>20\GeV}$ and
${\abs{\eta}<2.4}$, is identified as a quark jet using the combined secondary
vertex (CSVv2) algorithm [52]. A working point that yields a 60% efficiency
for tagging a quark jet and a 1 (10)% probability of misidentifying a light-
flavor ( quark) jet as a quark jet is used.

A summary of the selection criteria for the SR for both the shape and the cut-
and-count analyses is shown in Table 0.5.

Summary of the kinematic selections used to define the SR for both the shape
and the cut-and-count analyses.  Observable Shape analysis Cut-and-count
analysis Target background Leading (subleading) jet $\pt>80\,(40)\GeV$,
$\abs{\eta}<4.7$ All $>$250 QCD multijet, , $\gamma\text{+jets}$,
$\cmsSymbolFace{V}\text{+jets}$
$\Delta\phi(\ptvecmiss,\vec{p}_{\mathrm{T}}^{\kern 1.0pt\text{jet}})$
$>$0.5rad QCD multijet, $\gamma\text{+jets}$ Muons (electrons)
$\text{N}_{\mu,\Pe}=0$ with $\pt>10$, $\abs{\eta}<2.4\,(2.5)$
$\PW(\ell\nu)\text{+jets}$ candidates $\text{N}_{\tauh}=0$ with $\pt>18$,
$\abs{\eta}<2.3$ $\PW(\ell\nu)\text{+jets}$ Photons $\text{N}_{\gamma}=0$ with
$\pt>15$, $\abs{\eta}<2.5$ $\gamma\text{+jets}$, $\mathrm{V}\gamma$ quark jet
$\text{N}_{\text{jet}}=0$ with $\pt>20$, $\text{CSVv2}>0.848$ , single top
quark $\eta_{\mathrm{j1}}\,\eta_{\mathrm{j2}}$ $<$0
$\PZ(\PGn\PAGn)\text{+jets}$, $\PW(\ell\nu)\text{+jets}$
$\abs{\Delta\phi_{\mathrm{jj}}}$ $<$1.5rad $\PZ(\PGn\PAGn)\text{+jets}$,
$\PW(\ell\nu)\text{+jets}$ $\abs{\Delta\eta_{\mathrm{jj}}}$ $>$1 $>$4
$\PZ(\PGn\PAGn)\text{+jets}$, $\PW(\ell\nu)\text{+jets}$ $m_{\mathrm{jj}}$
$>$200 $>$1.3 $\PZ(\PGn\PAGn)\text{+jets}$, $\PW(\ell\nu)\text{+jets}$

##  0.6 Analysis strategy

The search exploits the large $m_{\mathrm{jj}}$ and
$\abs{\Delta\eta_{\mathrm{jj}}}$ that characterize events from VBF Higgs boson
production. In the shape analysis case, the signal is extracted by fitting the
sum of the signal and background shapes to the binned $m_{\mathrm{jj}}$
distribution observed in data. The signal is expected to accumulate as an
excess of events over the background at large values of $m_{\mathrm{jj}}$.
This strategy necessitates a precise estimation of the shape of the background
$m_{\mathrm{jj}}$ distribution.

About 95% of the total expected background in this search is due to the
$\cmsSymbolFace{V}\text{+jets}$ processes, namely $\PZ(\PGn\PAGn)\text{+jets}$
and $\PW(\ell\nu)\text{+jets}$. A fraction of the
$\cmsSymbolFace{V}\text{+jets}$ background, referred to as
$\cmsSymbolFace{V}\text{+jets}$ (EW), can be attributed to the EW production
of a $\PZ$ or a $\PW$ boson in association with two jets. A representative
Feynman diagram contributing to $\cmsSymbolFace{V}$+jets (EW) production is
shown in Fig. 2 (left). The remaining $\cmsSymbolFace{V}\text{+jets}$
contribution arises from the production of a vector boson in association with
QCD radiation, as shown in Fig. 2 (right). This is referred to as the
$\cmsSymbolFace{V}\text{+jets}$ (QCD) background. For both EW and QCD
productions, the expected $\PZ(\PGn\PAGn)\text{+jets}$ rate in the SR is about
two times larger than the $\PW(\ell\nu)\text{+jets}$ contribution.

Figure 2: Representative leading order Feynman diagrams for the production of
a $\PZ$ boson in association with two partons arising from EW (left) and QCD
(right) interactions. The left diagram contributes to the
$\PZ(\PGn\PAGn)\text{+jets}$ (EW) production cross section, while the diagram
on the right to the $\PZ(\PGn\PAGn)\text{+jets}$ (QCD) one. Diagrams for EW
and QCD production of a $\PW$ boson in association with two jets are similar
to those reported above for the $\PZ(\PGn\PAGn)\text{+jets}$ process.

A comparison of the shapes of the key discriminating observables used in this
analysis, obtained after applying the requirements listed in Table 0.5 except
for those on $m_{\mathrm{jj}}$, $\abs{\Delta\eta_{\mathrm{jj}}}$ and
$\abs{\Delta\phi_{\mathrm{jj}}}$, is shown in Fig. 3 for simulated signal and
$\cmsSymbolFace{V}\text{+jets}$ background events. From these distributions,
it can be seen that the $\cmsSymbolFace{V}\text{+jets}$ (EW) background is
kinematically similar to the VBF Higgs boson signal. Therefore, its
contribution to the total $\cmsSymbolFace{V}\text{+jets}$ background rate
increases when the two leading jets have large $m_{\mathrm{jj}}$ and
$\abs{\Delta\eta_{\mathrm{jj}}}$. The $\cmsSymbolFace{V}\text{+jets}$ (EW)
process constitutes about $2\%$ of the total $\cmsSymbolFace{V}\text{+jets}$
background for $m_{\mathrm{jj}}$ around 200. Its contribution increases to
about 20% for $m_{\mathrm{jj}}\approx 1.5\TeV$, and is more than 50% for
$m_{\mathrm{jj}}>3\TeV$.

Figure 3: Comparison between the shapes of the $m_{\mathrm{jj}}$ (left),
$\abs{\Delta\eta_{\mathrm{jj}}}$ (middle) and $\abs{\Delta\phi_{\mathrm{jj}}}$
(right) distributions of signal events, produced by VBF (solid black) and
$\Pg\Pg\PH$ (dashed black) mechanisms, and $\cmsSymbolFace{V}\text{+jets}$
backgrounds from both QCD (solid red) and EW (solid blue) production. Both
signal and background distributions are scaled in order to have unit area.
Distributions are obtained from simulated events passed through the CMS event
reconstruction.

###  0.6.1 Overview of the $\cmsSymbolFace{V}$+jets background estimation

The $\PZ\text{+jets}$ and $\PW\text{+jets}$ backgrounds are estimated using
four mutually exclusive CRs. These include a dimuon and a dielectron CR
consisting mostly of $\PZ(\ell\ell)\text{+jets}$ events that are kinematically
similar to $\PZ(\PGn\PAGn)\text{+jets}$ background if the presence of the two
leptons in the event is ignored. The $\PW\text{+jets}$ background is estimated
using CRs consisting of single-muon and single-electron events stemming mainly
from leptonic decays of a $\PW$ boson. In contrast to the $\PW\text{+jets}$
background in the SR, the single-lepton CRs consist of leptons that fall
within the detector acceptance and pass the identification requirements. The
in all the CRs is calculated by excluding the contribution of the identified
leptons. Therefore, it corresponds to the of the hadronic recoil system, which
resembles the expected from the $\cmsSymbolFace{V}\text{+jets}$ backgrounds in
the SR.

The event yield in the dilepton CRs is considerably smaller than the
$\PZ(\PGn\PAGn)\text{+jets}$ contribution in the SR because the
$\PZ(\ell\ell)$ branching fraction, where $\ell=\mu$ or , is six times smaller
than the $\PZ(\nu\overline{\nu})$ branching fraction. Consequently, the
dilepton CRs have a limited statistical power to constrain the
$\PZ(\PGn\PAGn)\text{+jets}$ background by themselves. In contrast, the yield
of the single-lepton CRs is comparable to the $\PZ(\PGn\PAGn)\text{+jets}$
background. Furthermore, the $\PZ(\PGn\PAGn)\text{+jets}$ and
$\PW(\ell\nu)\text{+jets}$ processes are kinematically similar if the presence
of the charged lepton is ignored. The theoretical uncertainties involved in
the prediction of the $\PZ\text{+jets}$ and $\PW\text{+jets}$ cross sections
largely cancel out in their ratio. Therefore, this ratio is predicted very
reliably by the simulation and can be used as a constraint to connect the
statistically rich single-lepton CRs to the $\PZ(\PGn\PAGn)\text{+jets}$
background in the SR.

The predictions for the $\cmsSymbolFace{V}\text{+jets}$ processes obtained
from simulation are referred to as “pre-fit” expectations, and are considered
to be the initial estimates for the $\cmsSymbolFace{V}\text{+jets}$ yields in
the CRs and SR. These $\cmsSymbolFace{V}\text{+jets}$ yields are then treated
as freely floating parameters, and are fit to the data in all CRs and the SR.
The $\cmsSymbolFace{V}\text{+jets}$ yields obtained from this fit are referred
to as “post-fit” estimates, and serve as the final
$\cmsSymbolFace{V}\text{+jets}$ background predictions in the analysis.

###  0.6.2 Definition of control regions

Dimuon and single-muon CRs are selected using the same L1 and HLT -based
triggers that are used to collect events in the SR. Dimuon events are required
to contain exactly two oppositely charged muons with $\pt>10\GeV$ that form an
invariant mass ($m_{\mu\mu}$) between 60 and 120, which is compatible with a
$\PZ$ boson decay. Events with additional electrons or photons are rejected.
At least one of the two muons must have $\pt>20\GeV$, and is required to pass
tighter identification criteria based on the number of measurements in the
tracker and the muon systems, the quality of the muon track fit, and the
consistency of the muon track with the primary vertex. The isolation, as
defined in Section 0.3, is required to be smaller than 15% of the muon . These
tightly identified muons are selected with an average efficiency of 90%.

In the single-muon CR, events are required to contain exactly one muon with
${\pt>20\GeV}$, passing both tight identification and isolation requirements.
The transverse mass () of the muon-system is computed as
${\mT=\sqrt{\smash[b]{2\ptmiss\pt^{\mu}(1-\cos\Delta\phi)}}}$, where
$\pt^{\mu}$ is the of the muon, and $\Delta\phi$ is the angle between
$\ptvec^{\mu}$ and in the transverse plane. The is required to be smaller than
160, and no additional electrons or photons are allowed in the event.

Events in the dielectron and single-electron CRs are collected mainly using a
single-electron trigger with a threshold of 27. In the case of dielectron
events where the $\PZ$ boson has $\pt>600\GeV$, the two electrons have a small
angular separation, and are likely to get included in each other’s isolation
cones. This results in an inefficiency for the chosen trigger that imposes
isolation requirements on electron candidates. This inefficiency is mitigated
by including events collected by a single-electron trigger with a threshold of
105and no isolation requirements on the electron candidate.

The dielectron events are required to contain exactly two oppositely charged
electrons with $\pt>10\GeV$ and no additional muons or photons. As in the case
of the dimuon events, the invariant mass of the dielectron system is required
to be between 60 and 120. At least one of the two electrons must have
${\pt>40\GeV}$, and is required to pass a tight identification criterion based
on the shower shape of its ECAL energy deposit, the matching of the electron
track to the ECAL energy cluster, and the consistency of the electron track
with the primary vertex. Furthermore, the isolation is required to be smaller
than 6% of the electron . These selection requirements for electrons have an
average efficiency of 70%.

Events in the single-electron CR are required to contain exactly one tightly
identified and isolated electron with $\pt>40\GeV$; no additional muons or
photons are allowed. The contamination from QCD multijet events is reduced by
requiring $\ptmiss>60\GeV$ and $\mT<160\GeV$.

Events in the CRs must also satisfy the requirements imposed on events in the
SR. When doing so, the negative of the hadronic recoil system is used instead
of the in the event.

###  0.6.3 Estimation of $\cmsSymbolFace{V}$+jets backgrounds

The $\cmsSymbolFace{V}\text{+jets}$ yields in the CRs are translated to the
background estimates in the SR using transfer factors that are derived from
simulation. The transfer factors are defined as the ratio of the yields of a
given $\cmsSymbolFace{V}\text{+jets}$ background in the SR and the
corresponding process measured in each CR.

The transfer factors for the dilepton CRs account for the difference in the
branching fractions of the $\PZ(\nu\overline{\nu})$ and $\PZ(\ell\ell)$
decays, and the $\gamma^{*}(\ell\ell)$ contribution, as well as the impact of
lepton acceptance and selection efficiencies. In the case of dielectron
events, the transfer factors also account for the difference in trigger
efficiencies. Transfer factors between the $\PW(\ell\nu)\text{+jets}$ event
yields in the single-lepton CRs and the $\PW\text{+jets}$ background estimate
in the SR take into account the effect of lepton acceptance, selection
efficiencies, and lepton and veto efficiencies, as well as the difference in
trigger efficiencies in the case of the single-electron CR.

The constraint on the ratio of the cross sections of the $\PZ\text{+jets}$ and
$\PW\text{+jets}$ processes, which is used to connect the single-lepton CRs to
the $\PZ(\PGn\PAGn)\text{+jets}$ in the SR, is also implemented as a transfer
factor, and is computed as the ratio of the $\PZ(\PGn\PAGn)\text{+jets}$ and
$\PW(\ell\nu)\text{+jets}$ yields in the SR. In order to have the most precise
estimate of this constraint, the LO simulations for the $\PZ\text{+jets}$
(QCD) and the $\PW\text{+jets}$ (QCD) processes are corrected using boson and
${m_{\mathrm{jj}}\text{--\,dependent}}$ NLO QCD $K$-factors derived with . The
$\PZ\text{+jets}$ and $\PW\text{+jets}$ simulations are also corrected as a
function of boson with NLO EW $K$-factors derived from theoretical
calculations [53]. Similarly, $\PZ\text{+jets}$ (EW) and $\PW\text{+jets}$
(EW) processes are corrected with NLO QCD $K$-factors derived using the vbfnlo
event generator [54, 55] as a function of boson and $m_{\mathrm{jj}}$.

The $\cmsSymbolFace{V}\text{+jets}$ background yields are determined using a
maximum-likelihood fit, performed simultaneously across all CRs and the SR.
The likelihood function is defined as:

$\displaystyle\mathcal{L}(\mu,\kappa\smash[b]{{}^{\PGn\PAGn}},\boldsymbol{\theta})=$
$\displaystyle\prod_{i}\mathrm{P}\left(d_{i}\Big{|}B_{i}(\boldsymbol{\theta})+(1+f_{i}(\boldsymbol{\theta})_{\mathrm{Q}})\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}+R^{\PZ}_{i}(1+f_{i}(\boldsymbol{\theta})_{\mathrm{E}})\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}+\mu
S_{i}(\boldsymbol{\theta})\right)$ (1)
$\displaystyle\prod_{i}\mathrm{P}\left(d^{\mu\mu}_{i}\Big{|}B^{\mu\mu}_{i}(\boldsymbol{\theta})+\frac{\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\mu\mu}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}}+\frac{R^{\PZ}_{i}\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\mu\mu}_{i}(\boldsymbol{\theta})_{\mathrm{E}}}\right)$
$\displaystyle\prod_{i}\mathrm{P}\left(d^{\Pe\Pe}_{i}\Big{|}B^{\Pe\Pe}_{i}(\boldsymbol{\theta})+\frac{\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\Pe\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}}+\frac{R^{\PZ}_{i}\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\Pe\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{E}}}\right)$
$\displaystyle\prod_{i}\mathrm{P}\left(d^{\mu}_{i}\Big{|}B^{\mu}_{i}(\boldsymbol{\theta})+\frac{f_{i}(\boldsymbol{\theta})_{\mathrm{Q}}\,\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\mu}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}}+\frac{R^{\PZ}_{i}f_{i}(\boldsymbol{\theta})_{\mathrm{E}}\,\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\mu}_{i}(\boldsymbol{\theta})_{\mathrm{E}}}\right)$
$\displaystyle\prod_{i}\mathrm{P}\left(d^{\Pe}_{i}\Big{|}B^{\Pe}_{i}(\boldsymbol{\theta})+\frac{f_{i}(\boldsymbol{\theta})_{\mathrm{Q}}\,\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}}+\frac{R^{\PZ}_{i}f_{i}(\boldsymbol{\theta})_{\mathrm{E}}\,\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}}{R^{\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{E}}}\right)\,\prod_{j}\mathrm{P}(\theta_{j})$

where ${\mathrm{P}(x|y)=y^{x}\re^{-y}/x!}$. The symbol $i$ denotes each bin of
the $m_{\mathrm{jj}}$ distribution in the shape analysis, while, in the cut-
and-count case, $i$ stands for a single bin that represents the event yields
obtained at the end of the event selection. The symbols $d^{\mu\mu}_{i}$,
$d^{\Pe\Pe}_{i}$, $d^{\mu}_{i}$, $d^{\Pe}_{i}$, and $d_{i}$ denote the
observed number of events in each bin $i$ of the dimuon, dielectron, single-
muon, single-electron CRs, and the SR, respectively. The symbols
$f_{i}(\boldsymbol{\theta})_{\mathrm{Q}}$ and
$f_{i}(\boldsymbol{\theta})_{\mathrm{E}}$ indicate the ratios between the
$\PW(\ell\nu)\text{+jets}$ and $\PZ(\PGn\PAGn)\text{+jets}$ backgrounds in the
SR from QCD and EW production, respectively. The symbols
$R^{\mu\mu}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}$,
$R^{\Pe\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}$,
$R^{\mu}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}$, and
$R^{\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{Q}}$ are the transfer factors
relating the dimuon, dielectron, single-muon, and single-electron CRs,
respectively, to the SR for the $\cmsSymbolFace{V}\text{+jets}$ (QCD)
processes. Similarly, $R^{\mu\mu}_{i}(\boldsymbol{\theta})_{\mathrm{E}}$,
$R^{\Pe\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{E}}$,
$R^{\mu}_{i}(\boldsymbol{\theta})_{\mathrm{E}}$, and
$R^{\Pe}_{i}(\boldsymbol{\theta})_{\mathrm{E}}$ indicate the transfer factors
for the $\cmsSymbolFace{V}\text{+jets}$ (EW) processes. The parameters
$\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}$ represent the yield of the
$\PZ(\PGn\PAGn)\text{+jets}$ (QCD) background in each bin $i$ of the SR, and
are left to float freely in the fit. In a given bin, the
$\PZ(\PGn\PAGn)\text{+jets}$ (EW) background yield is obtained from
$\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}$ through the transfer factor
$R_{i}^{\PZ}$ that represents the ratio between the
$\PZ(\PGn\PAGn)\text{+jets}$ (QCD) and $\PZ(\PGn\PAGn)\text{+jets}$ (EW)
processes. The contributions from subleading backgrounds in each region are
estimated directly from simulation and they are denoted by $B^{\mu\mu}_{i}$,
$B^{\Pe\Pe}_{i}$, $B^{\mu}_{i}$, $B^{\Pe}_{i}$ and $B_{i}$. Finally, the
likelihood also includes a signal term in which $S_{i}$ represents the
expected signal prediction, while
$\mu=(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}$ denotes
the signal strength parameter.

Systematic uncertainties are modeled as constrained nuisance parameters
($\boldsymbol{\theta}$), for which log-normal or Gaussian priors, indicated by
$\mathrm{P}_{j}(\theta)$ in previous equation, are considered. The systematic
uncertainties in the $\cmsSymbolFace{V}\text{+jets}$ background estimates are
introduced in the likelihood as variations of the transfer factors. These
include theoretical uncertainties in the $\PZ\text{+jets}$ to
$\PW\text{+jets}$ differential cross section ratio for both the QCD and EW
processes due to the choice of the renormalization and the factorization
scales, as well as the choice of the PDFs. The QCD scale variations are
assumed to be uncorrelated between the $\PZ\text{+jets}$ and $\PW\text{+jets}$
processes, and therefore they do not cancel in the $\PZ\text{+jets}$ to
$\PW\text{+jets}$ cross section ratio. This results in larger uncertainties
compared to those from NLO calculations recommended in Ref. [53]. The
uncertainty due to the choice of the renormalization scale varies between 8
and 12% as a function of $m_{\mathrm{jj}}$ for both
$\PZ\text{+jets}$/$\PW\text{+jets}$ (QCD) and (EW) ratios. Similarly, the
uncertainty due to the choice of the factorization scale varies between 2 and
7%. This also covers the uncertainty in the
$\PZ\text{+jets}$/$\PW\text{+jets}$ cross section ratio due to the
interference between the $\cmsSymbolFace{V}\text{+jets}$ (QCD) and
$\cmsSymbolFace{V}\text{+jets}$ (EW) processes, which is not included in the
simulation. The PDF uncertainties are assumed to be correlated across
$\cmsSymbolFace{V}\text{+jets}$ processes, resulting in a residual uncertainty
smaller than 1% on the $\PZ\text{+jets}$/$\PW\text{+jets}$ cross section
ratio. The uncertainties related to NLO EW corrections to the
$\cmsSymbolFace{V}\text{+jets}$ (QCD) processes are estimated according to the
recommendations in Ref. [53], and are found to be about 1–2% across the entire
$m_{\mathrm{jj}}$ spectrum. Additional uncertainties included in the transfer
factors include uncertainties in the reconstruction efficiencies of leptons
(around 1% per muon or electron), the selection efficiencies of leptons (about
1% per muon, 1.5% per electron), the veto efficiency of leptons (around 2% per
muon, 1% per electron) and candidates (about 3.5% per ), the knowledge of the
jet energy scale (1–2%), and the efficiency of the electron (around 1%) and
triggers (about 2%).

The full set of systematic uncertainties related to the
$\cmsSymbolFace{V}\text{+jets}$ transfer factors are listed in Table 0.6.3.
Before any fit is performed, the total uncertainty in the expected background
in the SR ranges between 4.5 and 6% as a function of $m_{\mathrm{jj}}$,
dominated by the theoretical uncertainties in the $\PZ\text{+jets}$ to
$\PW\text{+jets}$ cross section ratio for both QCD and EW production. The
impact of each source of systematic uncertainty, as reported in Table 0.6.3 in
the context of the shape analysis, is defined as the maximum difference in the
fitted value of the signal strength,
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$, obtained
by varying the associated nuisance parameter within one standard deviation of
its maximum likelihood estimate. In this procedure, the per-bin
$\kappa_{i}\smash[b]{{}^{\PGn\PAGn}}$ parameters are profiled when a given
nuisance parameter is shifted from its best fit estimate.

Experimental and theoretical sources of systematic uncertainties on the
$\cmsSymbolFace{V}\text{+jets}$ transfer factors, which enter in the
simultaneous fit, used to estimate the $\cmsSymbolFace{V}\text{+jets}$
backgrounds, as constrained nuisance parameters. In addition, the impact on
the fitted signal strength,
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$, is
reported in the last column estimated after performing the $m_{\mathrm{jj}}$
shape fit to the observed data across signal and control regions.  Source of
uncertainty Ratios Uncertainty vs. $m_{\mathrm{jj}}$ Impact on
${\mathcal{B}(\PH\to\text{inv})}$ Theoretical uncertainties Ren. scale
$\cmsSymbolFace{V}\text{+jets}$ (EW) $\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$
(EW) 9–12% 48% Ren. scale $\cmsSymbolFace{V}\text{+jets}$ (QCD)
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$ (QCD) 9–12% 25% Fac. scale
$\cmsSymbolFace{V}\text{+jets}$ (EW) $\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$
(EW) 2–7% 4% Fac. scale $\cmsSymbolFace{V}\text{+jets}$ (QCD)
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$ (QCD) 2–7% 2% PDF
$\cmsSymbolFace{V}\text{+jets}$ (QCD) $\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$
(QCD) 0.5–1% $<$1% PDF $\cmsSymbolFace{V}\text{+jets}$ (EW)
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$ (EW) 0.5–1% $<$1% NLO EW corr.
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$ (QCD) 1–2% $<$1% Experimental
uncertainties Muon reco. eff. $\PZ(\mu\mu)$/$\PZ(\nu\overline{\nu})$,
$\PW(\mu\nu)$/$\PW(\ell\nu)$ $\approx$1%(per lepton) 8% Electron reco. eff.
$\PZ(\Pe\Pe)$/$\PZ(\nu\overline{\nu})$, $\PW(\Pe\nu)$/$\PW(\ell\nu)$
$\approx$1%(per lepton) 3% Muon id. eff.
$\PZ(\mu\mu)$/$\PZ(\nu\overline{\nu})$, $\PW(\mu\nu)$/$\PW(\ell\nu)$
$\approx$1%(per lepton) 8% Electron id. eff.
$\PZ(\Pe\Pe)$/$\PZ(\nu\overline{\nu})$, $\PW(\Pe\nu)$/$\PW(\ell\nu)$
$\approx$1.5%(per lepton) 4% Muon veto
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$, (CRs)/$\PW(\ell\nu)$ $\approx$2.5
(2)% for EW (QCD) 7% Electron veto $\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$,
(CRs)/$\PW(\ell\nu)$ $\approx$1.5 (1)% for EW (QCD) 5% $\tau$ veto
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$, (CRs)/$\PW(\ell\nu)$ $\approx$3.5
(3)% for EW (QCD) 13% Jet energy scale (CRs)/$\PZ(\nu\overline{\nu})$,
(CRs)/$\PW(\ell\nu)$ $\approx$1 (2)% for /(/) 4% Electron trigger
$\PZ(\Pe\Pe)$/$\PZ(\nu\overline{\nu})$, $\PW(\Pe\nu)$/$\PW(\ell\nu)$
$\approx$1% $<$1% trigger All ratios $\approx$2% 18%

The $m_{\mathrm{jj}}$ distributions in the dilepton and single-lepton CRs are
shown in Fig. 4. The pre-fit predictions for the
$\cmsSymbolFace{V}\text{+jets}$ processes are shown in red. These indicate the
level of agreement between data and simulation before a fit is performed. An
estimate of the $\cmsSymbolFace{V}\text{+jets}$ backgrounds is then obtained
by fitting the data across all the CRs. This is depicted by the blue line in
Fig. 4. This fit is referred to as the “CR-only” fit, since it does not impose
any constraint on the $\cmsSymbolFace{V}\text{+jets}$ yields due to the data
in the SR.

Figure 4:  The $m_{\mathrm{jj}}$ distributions in the dimuon (top left),
dielectron (top right), single-muon (bottom left), and single-electron (bottom
right) CRs as computed in the shape analysis. Prediction from simulation (pre-
fit estimate) is shown by the dashed red line. The solid blue line shows the
$\cmsSymbolFace{V}\text{+jets}$ expectation after fitting the data in all the
CRs. The filled histograms indicate all processes other than
$\cmsSymbolFace{V}\text{+jets}$ (QCD). The last bin includes all events with
${m_{\mathrm{jj}}>3.5\TeV}$. Ratios of data and the pre-fit background (red
points) and the post-fit background prediction (blue points) are shown. The
gray band in the ratio panel indicates the total uncertainty after performing
the fit. The lowest panel shows the difference between data and the post-fit
background estimate relative to the post-fit background uncertainty.

To assess the level of agreement between data and simulation obtained through
the application of –$m_{\mathrm{jj}}$ dependent NLO corrections to both the
$\cmsSymbolFace{V}\text{+jets}$ (QCD) and $\cmsSymbolFace{V}\text{+jets}$ (EW)
processes, the ratio between the number of $\PZ\text{+jets}$ and
$\PW\text{+jets}$ events in the CRs in bins of $m_{\mathrm{jj}}$ is used as a
benchmark. Figure 5 shows the ratio of the $\PZ\text{+jets}$ and
$\PW\text{+jets}$ event yields in the muon (left) and electron (right) CRs,
respectively. A good agreement is observed between data and simulation and
local differences are covered by the systematic uncertainties listed in Table
0.6.3.

Figure 5: Comparison between data and simulation of the
$\PZ(\mu\mu)\text{+jets}$/$\PW(\mu\nu)\text{+jets}$ (left) and
$\PZ(\Pe\Pe)\text{+jets}$/$\PW(\Pe\nu)\text{+jets}$ (right) ratios as
functions of $m_{\mathrm{jj}}$, computed in the shape analysis phase-space. In
the bottom panels, ratios of data with the pre-fit background prediction are
reported. The gray bands include both the theoretical and experimental
systematic uncertainties listed in Table 0.6.3, as well as the statistical
uncertainty in the simulation.

###  0.6.4 Other backgrounds

In addition to the $\cmsSymbolFace{V}\text{+jets}$ processes, several other
minor sources of background contribute to the total event yield in the SR.
These include QCD multijet events that typically have small genuine . However,
jet momentum mismeasurements and instrumental effects may give rise to large
tails. A $\text{min}\Delta\phi$ extrapolation method [56] is used to estimate
this background from data, where a QCD multijet enriched CR is defined by
selecting events that fail the $\text{min}\Delta\phi$ requirement between the
jets and the vector, but still fulfill the remaining event selection criteria.
A transfer factor, derived from simulated QCD multijet events, is used to
estimate the background in the SR from the event rate measured in the
low-$\Delta\phi(\ptvecmiss,\vec{p}_{\mathrm{T}}^{\kern 1.0pt\text{jet}})$
sample. The low-$\Delta\phi(\ptvecmiss,\vec{p}_{\mathrm{T}}^{\kern
1.0pt\text{jet}})$ region contains a significant contamination from
$\cmsSymbolFace{V}\text{+jets}$ production, which have genuine . They
contribute about 40% of the total event yield for $m_{\mathrm{jj}}$ smaller
than 500, and about 80% for ${m_{\mathrm{jj}}>3\TeV}$. This contamination is
estimated from simulation and subtracted from the event yield measured in the
low-$\Delta\phi(\ptvecmiss,\vec{p}_{\mathrm{T}}^{\kern 1.0pt\text{jet}})$
sample. An uncertainty of 20% is assigned while performing the subtraction,
which results in an uncertainty of about 30% in the estimated QCD multijet
background in the SR. The MC statistical uncertainty of the QCD multijet
samples, which affects the transfer factor prediction, is also considered and
is found to vary between 40 and 100% as a function of $m_{\mathrm{jj}}$.
Lastly, a validation of the $\Delta\phi$ method is performed using a purer
sample of QCD multijet events that pass the analysis requirements, but have in
the range of 100–175. In this validation region, the predicted QCD background
is found to agree with the observation within 50%, which is taken as a
conservative estimate of an additional uncertainty.

The remaining background sources include top quark production and diboson
processes, which are estimated from simulation. The distribution of the top
quark in simulation is corrected to match the observed distribution in data
[57]. An uncertainty of about 10% is assigned to the overall top quark
background normalization, while an additional 10% uncertainty is added to
account for the modeling of the top quark distribution in simulation. The
overall normalization of the diboson background has an uncertainty of about
15% [58, 59]. The uncertainties in the top quark and diboson backgrounds are
correlated across the SR and the CRs. Several experimental sources of
uncertainty are also assigned to these backgrounds. An uncertainty of 2.5% in
the integrated luminosity measurement [60] is propagated to the background
yields. The uncertainty in the efficiency of the quark jet veto is estimated
to be around 3% for the top quark background and of about 1% for the other
simulated processes. The uncertainty related to the jet energy scale varies
between 8 and 15%, depending on both the process and the CR.

##  0.7 Results

This section presents the results obtained from the shape and the cut-and-
count analyses. These include 95% CL upper limits on
${\mathcal{B}(\PH\to\text{inv})}$, and an interpretation of the search in the
context of a BSM model which allows for the presence of a SM-like Higgs boson
with a mass between 110 and 1000.

###  0.7.1 The shape analysis

The observed and the expected $m_{\mathrm{jj}}$ distributions in the SR,
obtained after applying the full event selection, are shown in Fig. 6. The
background prediction shown in Fig. 6 (left) is obtained from a fit to the
data in the CRs. Signal distributions for the SM Higgs boson produced via the
$\Pg\Pg\PH$ and VBF modes are overlaid, assuming
${\mathcal{B}(\PH\to\text{inv})}=1$. The estimated background yields from the
CR-only fit are listed in Table 0.7.1, along with the observed event yield in
the SR. The large contamination from $\Pg\Pg\PH$ production arises from the
low-$m_{\mathrm{jj}}$ bins, which represent the least sensitive region to
$\PH\to\text{inv}$ decays. Systematic uncertainties in the
$\cmsSymbolFace{V}\text{+jets}$ transfer factors and in the minor backgrounds
introduce correlations across the $m_{\mathrm{jj}}$ bins used in the fit. The
correlations in the predicted background yields in each $m_{\mathrm{jj}}$ bin
are reported in Table 0.7.1.

Figure 6: The observed $m_{\mathrm{jj}}$ distribution of the shape analysis SR
compared to the post-fit backgrounds from various SM processes. On the left,
the predicted backgrounds are obtained from a combined fit to the data in all
the CRs, but excluding the SR. On the right, the predicted backgrounds are
obtained from a combined fit to the data in all the CRs, as well as in the SR,
assuming the absence of any signal. Expected signal distributions for a
125Higgs boson produced through $\Pg\Pg\PH$ and VBF modes, and decaying to
invisible particles with a branching fraction
${\mathcal{B}(\PH\to\text{inv})}=1$, are overlaid. The last bin includes all
events with ${m_{\mathrm{jj}}>3.5\TeV}$. The description of the ratio panels
is the same as in Fig. 4.

Expected event yields in each $m_{\mathrm{jj}}$ bin for various background
processes in the SR of the shape analysis. The background yields and the
corresponding uncertainties are obtained after performing a combined fit
across all the CRs, but excluding data in the SR. The “other backgrounds”
includes QCD multijet and $\PZ(\ell\ell)\text{+jets}$ processes. The expected
total signal contribution for the 125Higgs boson, decaying to invisible
particles with a branching fraction ${\mathcal{B}(\PH\to\text{inv})}=1$, and
the observed event yields are also reported.  Process $m_{\mathrm{jj}}$ range
in  0.2–0.4 0.4–0.6 0.6–0.9 0.9–1.2 1.2–1.5 1.5–2.0 2.0–2.75 2.75–3.5 $>$ 3.5
$\PZ(\nu\nu)$ (QCD) 9311 $\pm$ 388 5669 $\pm$ 257 3884 $\pm$ 179 1648 $\pm$ 88
677 $\pm$ 42 405 $\pm$ 28 153 $\pm$ 14 22.8 $\pm$ 3.5 8.1 $\pm$ 2.2
$\PZ(\nu\nu)$ (EW) 201 $\pm$ 8 228 $\pm$ 10 273 $\pm$ 13 198 $\pm$ 11 129
$\pm$ 8 112 $\pm$ 8 70.6 $\pm$ 6.6 20.2 $\pm$ 3.1 10.8 $\pm$ 2.9
$\PW(\ell\nu)$ (QCD) 4755 $\pm$ 267 3017 $\pm$ 180 2090 $\pm$ 130 928 $\pm$ 63
361 $\pm$ 28 227 $\pm$ 19 80.4 $\pm$ 9.1 13.7 $\pm$ 2.7 4.5 $\pm$ 1.9
$\PW(\ell\nu)$ (EW) 102 $\pm$ 14 118 $\pm$ 16 133 $\pm$ 18 100 $\pm$ 13 61.2
$\pm$ 8.1 61.4 $\pm$ 7.6 39.4 $\pm$ 4.9 12.6 $\pm$ 1.9 5.6 $\pm$ 1.4 Top quark
208 $\pm$ 37 159 $\pm$ 28 119 $\pm$ 21 57.6 $\pm$ 10.2 28.7 $\pm$ 5.1 16.1
$\pm$ 2.9 8.9 $\pm$ 1.6 2.2 $\pm$ 0.4 0.7 $\pm$ 0.1 Dibosons 222 $\pm$ 39 157
$\pm$ 28 116 $\pm$ 21 48.2 $\pm$ 8.5 19.0 $\pm$ 3.4 9.3 $\pm$ 1.6 2.6 $\pm$
0.5 1.4 $\pm$ 0.3 0.4 $\pm$ 0.1 Others 78.6 $\pm$ 19.5 51.0 $\pm$ 11.6 42.8
$\pm$ 11.5 13.6 $\pm$ 2.9 6.5 $\pm$ 1.5 3.3 $\pm$ 0.8 2.4 $\pm$ 0.6 0.7 $\pm$
0.2 0.3 $\pm$ 0.4 Total bkg. 14878 $\pm$ 566 9401 $\pm$ 387 6658 $\pm$ 271
2994 $\pm$ 144 1283 $\pm$ 69 834 $\pm$ 51 358 $\pm$ 29 73.8 $\pm$ 9.4 30.3
$\pm$ 7.4 Signal 590 $\pm$ 244 559 $\pm$ 199 547 $\pm$ 151 447 $\pm$ 109 276
$\pm$ 58 304 $\pm$ 66 201 $\pm$ 36 68.6 $\pm$ 11.7 30.0 $\pm$ 6.4 Data 16177
10008 7277 3138 1439 911 408 87 29

An excess of about 4–10% is observed in the SR data when compared to the
estimated backgrounds. The discrepancy resides mainly in the bulk of the
$m_{\mathrm{jj}}$ distribution. The shape of the excess is inconsistent with
the characteristic features of a VBF signal, whose presence is expected to
produce an increasing discrepancy between data and backgrounds as
$m_{\mathrm{jj}}$ increases. A goodness of fit test, based on a saturated
$\chi^{2}$ test statistic [61, 62], yields a p-value of about 6% indicating
that the data are compatible with the SM prediction.

Figure 6 (right) shows the background prediction obtained after including
events from the SR in the fit, but assuming the absence of a signal. Such a
fit is referred to as the “b-only fit”. The comparison between the results of
the b-only fit with that allowing for the presence of the signal is used to
set an upper limit on ${\mathcal{B}(\PH\to\text{inv})}$. In the b-only fit,
the $\cmsSymbolFace{V}\text{+jets}$ estimate in the SR can vary with respect
to the prediction from the CRs within the systematic uncertainties assigned to
the transfer factors. Therefore, the additional constraint due to the data in
the SR mitigates the excess shown in Fig. 6 (left), yielding a p-value for the
b-only fit of about 65%.

The results of this search are interpreted in terms of an upper limit on the
product of the Higgs boson production cross section and its branching fraction
to invisible particles, $\sigma{\mathcal{B}(\PH\to\text{inv})}$, relative to
the predicted cross section assuming SM interactions, $\sigma_{\text{SM}}$.
Observed and expected 95% CL upper limits are computed using an asymptotic
approximation of the method [63, 64] with a profile likelihood ratio test
statistic [65] in which systematic uncertainties are modeled as nuisance
parameters following a frequentist approach [66]. The profile likelihood ratio
is defined as:

$q=-2\Delta\ln\mathcal{L}=-2\ln\frac{\mathcal{L}(\text{data}|(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})},\boldsymbol{\hat{\theta}_{a}},\boldsymbol{\hat{\kappa}_{a}})}{\mathcal{L}(\text{data}|(\sigma/\sigma_{\mathrm{SM}})\,\hat{\mathcal{B}}(\PH\to\text{inv}),\boldsymbol{\hat{\theta}},\boldsymbol{\hat{\kappa}})}$
(2)

where ${(\sigma/\sigma_{\mathrm{SM}})\,\hat{\mathcal{B}}(\PH\to\text{inv})}$
represents the value of the signal strength that maximizes the likelihood
$\mathcal{L}$ for the data, while
$\boldsymbol{\hat{\theta}}~{}(\boldsymbol{\hat{\kappa}})$ and
$\boldsymbol{\hat{\theta}_{a}}~{}(\boldsymbol{\hat{\kappa}_{a}})$ denote the
best fit estimates for the nuisance parameters ($\PZ(\PGn\PAGn)\text{+jets}$
rate in each bin) and the estimates for a given fixed value of
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$,
respectively.

The relative contributions of the VBF and $\Pg\Pg\PH$ production modes are
fixed to the SM prediction within their uncertainties. The uncertainties in
the predictions of the inclusive VBF and $\Pg\Pg\PH$ production cross sections
due to PDF uncertainties, renormalization and factorization scale variations
are taken from Ref. [34]. An additional uncertainty of 40% is assigned to the
expected $\Pg\Pg\PH$ contribution. This accounts for both the limited
knowledge of the $\Pg\Pg\PH$ cross section in association with two or more
jets, as well as the uncertainty in the prediction of the $\Pg\Pg\PH$
differential cross section for large Higgs boson transverse momentum,
$\pt^{\PH}>250\GeV$. The former contribution is obtained by following the
recipe outlined in Ref. [34] and is found to be about 30%, while the latter
uncertainty is estimated by comparing the prediction from powheg+minlo [67]
with the one from and ranges between 20 and 25%. Furthermore, the
uncertainties in the signal acceptance due to the choice of the PDF set are
also evaluated independently for the different signal processes, and are
treated as independent nuisance parameters in the fit.

The observed (expected) 95% CL upper limit on
${\mathcal{B}(\PH\to\text{inv})}$ is measured to be 0.33 (0.25), and the
regions containing 68% and 95% of the distribution of upper limits, expected
in absence of a signal, are found to be 0.18–0.35 and 0.14–0.47, respectively.

The background estimates reported in Table 0.7.1, along with the correlation
matrix presented in Table 0.7.1, can be used in the simplified likelihood
approach detailed in Ref. [68] to reinterpret these results in theoretical
models different from those presented in this Letter.

Correlation between the uncertainties in predicted background yields across
the $m_{\mathrm{jj}}$ bins of the shape analysis SR. The backgrounds are
estimated by fitting the data in the CRs. Bin ranges are expressed in.
Correlation coefficients 0.2–0.4 0.4–0.6 0.6–0.9 0.9–1.2 1.2–1.5 1.5–2.0
2.0–2.75 2.75–3.5 $>3.5$ 0.2–0.4 1.00 — — — — — — — — 0.4–0.6 0.88 1.00 — — —
— — — — 0.6–0.9 0.85 0.84 1.00 — — — — — — 0.9–1.2 0.78 0.76 0.75 1.00 — — — —
— 1.2–1.5 0.70 0.72 0.71 0.60 1.00 — — — — 1.5–2.0 0.62 0.57 0.63 0.59 0.54
1.00 — — — 2.0–2.75 0.38 0.40 0.43 0.43 0.45 0.43 1.00 — — 2.75–3.5 0.28 0.28
0.33 0.26 0.34 0.27 0.22 1.00 — $>$3.5 0.22 0.21 0.22 0.23 0.24 0.20 0.23 0.19
1.00

###  0.7.2 The cut-and-count analysis

The cut-and-count analysis is presented because it allows for an easier
reinterpretation of this search in the context of other theoretical models
that predict plus VBF dijet signatures. The observed event yield after the
cut-and-count selection is reported in Table 0.7.2, along with the predicted
backgrounds in the SR. The backgrounds are estimated by fitting the data in
the CRs. An excess, characterized by a significance of about 2.5 standard
deviations, is observed in the SR compared to the background prediction
obtained from the CRs. As for the shape analysis, this excess is mostly due to
low $m_{\mathrm{jj}}$ events. The excess is incompatible with a VBF Higgs
boson signal and, upon detailed scrutiny, is ascribed to a statistical
fluctuation.

The results of the cut-and-count analysis are presented in terms of a 95% CL
upper limit on ${\mathcal{B}(\PH\to\text{inv})}$ using the statistical
procedure outlined in Section 0.7.1. The observed (expected) upper limit is
found to be 0.58 (0.30), and the regions containing 68% and 95% of the
distribution of upper limits, expected in absence of a signal, are found to be
0.22–0.43 and 0.17–0.58, respectively.

Expected event yields in the SR and in the CRs of the cut-and-count analysis
for various SM processes. The background yields and the corresponding
uncertainties are obtained from a combined fit to data in all the CRs, but
excluding data in the SR. The expected total signal contribution for the
125Higgs boson, decaying to invisible particles with a branching fraction
${\mathcal{B}(\PH\to\text{inv})}=1$, and the observed event yields are also
reported.  Process Signal region Dimuon CR Dielectron CR Single-muon CR
Single-electron CR $\PZ(\nu\nu)$ (QCD) $810\pm 71$ — — — — $\PZ(\nu\nu)$ (EW)
$269\pm 33$ — — — — $\PZ(\ell\ell)$ (QCD) — $91.5\pm 7.6$ $66.5\pm 6.0$
$27.1\pm 1.2$ $5.2\pm 0.3$ $\PZ(\ell\ell)$ (EW) — $32.5\pm 4.1$ $24.1\pm 3.2$
$5.7\pm 0.3$ $2.4\pm 0.2$ $\PW(\ell\nu)$ (QCD) $499\pm 33$ $0.2\pm 0.2$
$0.9\pm 0.6$ $907\pm 30$ $544\pm 21$ $\PW(\ell\nu)$ (EW) $141\pm 11$ $0.1\pm
0.1$ — $406\pm 15$ $254\pm 11$ Top quark $37.8\pm 8.8$ $4.8\pm 1.4$ $2.9\pm
1.0$ $112\pm 22$ $74.2\pm 13.6$ Dibosons $18.6\pm 6.2$ $2.3\pm 1.1$ $0.7\pm
0.4$ $21.3\pm 4.4$ $14.4\pm 3.7$ Others $3.3\pm 2.3$ — — $22.9\pm 13.9$
$2.1\pm 1.9$ Total bkg. $1779\pm 96$ $131\pm 8$ $95.0\pm 6.3$ $1502\pm 34$
$896\pm 24$ Signal $m_{\PH}=125.09\GeV$ $743\pm 129$ — — — — Data 2035 114 104
1504 902

###  0.7.3 Constraints on a SM-like Higgs boson

The results presented are also interpreted in the context of an additional SM-
like Higgs boson that does not mix with the 125boson and decays to invisible
particles [69]. Such a boson may be produced via both the $\Pg\Pg\PH$ and VBF
mechanisms. This model has already been studied in earlier CMS publications
[70, 71, 72]. Upper limits, computed at 95% CL on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$, are shown
in Fig. 7 as a function of the SM-like Higgs boson mass hypothesis ($m_{\PH}$)
for both the shape and the cut-and-count analyses.

Figure 7: Expected and observed 95% CL upper limits on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$ for an SM-
like Higgs boson as a function of its mass ($m_{\PH}$). On the left, observed
(solid black) and expected (dashed black) upper limits are obtained from the
shape analysis while, on the right, results from the cut-and-count analysis
are reported. The 68% (green) and 95% (yellow) CL intervals around the
expected upper limits are also shown for both the shape and the cut-and-count
analyses.

##  0.8 Combined limits on $\PH\to\text{inv}$ from 2016 data

The common feature of all the searches included in this combination is a large
, where at least one high-jet or a weak boson recoils against the invisible
particles produced by the Higgs boson decay. Specific topological selections
are designed to reduce the contamination from large SM backgrounds, targeting
a particular Higgs boson production mode. The analyses included in this
combination are listed in Table 0.8, together with their expected signal
composition and their individual upper limits on
${\mathcal{B}(\PH\to\text{inv})}$. The results quoted for the VBF channel come
from the shape analysis described earlier in this Letter. The
$\PZ(\ell^{+}\ell^{-})\PH$ analysis is identical to the one described in Ref.
[72], where the expected signal comes entirely from invisible decays of the SM
Higgs boson produced in association with a leptonically decaying boson, via
either ${\cPq\cPq\to\PZ\PH}$ or ${\Pg\Pg\to\PZ\PH}$ production. In contrast,
the $\cmsSymbolFace{V}(\cPq\cPq\textsf{'})\PH$ and the $\Pg\Pg\PH$-tagged
searches are similar to those described in Ref. [73], but events which overlap
with the VBF analysis have been removed to avoid double counting. In both the
$\Pg\Pg\PH$ and $\cmsSymbolFace{V}(\cPq\cPq\textsf{'})\PH$ searches,
overlapping events represent about 6 (15)% of the total background for a of
about 250 (1000). The overlap removal introduces a 5% loss in the expected
exclusion sensitivity compared to that of Ref. [73]. Both the
$\cmsSymbolFace{V}(\cPq\cPq\textsf{'})\PH$ and the $\Pg\Pg\PH$ searches target
events with at least one high-central jet, and their SRs contain a mixture of
different production modes. This mixture results from the limited
discrimination power of the substructure observables exploited to select
boosted $\cmsSymbolFace{V}(\cPq\cPq\textsf{'})\PH$ candidates.

Signal composition and upper limits (observed and expected) on the invisible
Higgs boson branching fraction classified according to the final state
considered in each analysis. The relative contributions from the different
Higgs production mechanisms are derived from simulation, fixing the Higgs
boson mass to 125.09and assuming SM production cross sections.  Analysis Final
state Signal composition Observed limit Expected limit VBF-tag $\text{VBF-
jet}+\ptmiss$ 52% VBF, 48% $\Pg\Pg\PH$ 0.33 0.25 $\cmsSymbolFace{V}\PH$-tag
$\PZ(\ell\ell)+\ptmiss$ [72] 79% $\cPq\cPq\PZ\PH$, 21% $\Pg\Pg\PZ\PH$ 0.40
0.42 $\cmsSymbolFace{V}(\cPq\cPq\textsf{'})+\ptmiss$ [73] 39% $\Pg\Pg\PH$, 6%
VBF, 33% $\PW\PH$, 22% $\PZ\PH$ 0.50 0.48 $\Pg\Pg\PH$-tag
$\text{jets}+\ptmiss$ [73] 80% $\Pg\Pg\PH$, 12% VBF, 5% $\PW\PH$, 3% $\PZ\PH$
0.66 0.59

No significant deviations from the SM expectations are observed in any of the
searches. The results are interpreted as an upper limit on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$. These
limits are calculated following the same approach described in Section 0.7.1.
The combined likelihood fit accounts for correlations between the nuisance
parameters in each search. The uncertainties in the diboson backgrounds
(except for those considered in the ${\PZ(\ell\ell)\PH}$ channel), and single
top quark cross sections, lepton efficiencies, momentum scales, integrated
luminosity, quark jet and vetoes are correlated among all the searches. In
addition, the uncertainties in the inclusive signal production cross sections,
due to renormalization and factorization scale variations, and the PDF
uncertainties are also correlated across the channels. In contrast, since the
jet kinematics in the VBF search differ from that in the other analyses, jet
energy scale and resolution uncertainties are correlated only across the
$\Pg\Pg\PH$ and $\cmsSymbolFace{V}\PH$-tagged categories. The theoretical
uncertainties applied to the $\cmsSymbolFace{V}\text{+jets}$ (QCD) ratios are
assumed to be uncorrelated between the VBF analysis and the other searches.

Observed and expected upper limits on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$ are
computed at 95% CL and are presented in Fig. 8 (left). Assuming SM cross
sections for each production mode, the combination yields an observed
(expected) upper limit of ${{\mathcal{B}(\PH\to\text{inv})}<0.26\,(0.20)}$.
The profile likelihood ratios as a function of
${\mathcal{B}(\PH\to\text{inv})}$, for both the combined fit and each
individual search channel, are reported in Fig. 8 (right). Results are shown
for both data and an Asimov dataset [65], defined by fixing the nuisances
parameters to their maximum likelihood estimate obtained from a fit to the
data in which ${{\mathcal{B}(\PH\to\text{inv})}=0}$ is assumed.

Figure 8: On the left, observed and expected 95% CL upper limits on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$ for both
individual categories targeting VBF, ${\PZ(\ell\ell)\PH}$,
$\cmsSymbolFace{V}(\cPq\cPq\textsf{'})$, and $\Pg\Pg\PH$ production mode, as
well as their combination, assuming an SM Higgs boson with a mass of 125.09.
On the right, profile likelihood ratios as a function of
${\mathcal{B}(\PH\to\text{inv})}$. The solid curves represent the observations
in data, while the dashed lines represent the expected result from a b-only
fit. The observed and expected likelihood scans are reported for the full
combination, as well as for the individual VBF, $\PZ(\ell\ell)\PH$,
$\cmsSymbolFace{V}(\cPq\cPq^{\prime})\PH$ and $\Pg\Pg\PH$-tagged analyses.

##  0.9 Combination of 7, 8, and 13searches for $\PH\to\text{inv}$ decays

The analyses previously described and listed in Table 0.8 are further combined
with earlier searches performed using data collected at $\sqrt{s}=7$, 8, and
13up to the end of 2015, as reported in Refs. [16, 70, 74]. The 7 and 8data,
collected in 2011 and 2012, correspond to integrated luminosities of up to 4.9
and 19.7 [75, 76], respectively. The 13data collected in 2015 correspond to an
integrated luminosity of 2.3 [77]. Systematic uncertainties in the inclusive
$\Pg\Pg\PH$, VBF, and $\cmsSymbolFace{V}\PH$ production cross sections are
fully correlated across the 7, 8, and 13analyses. The uncertainty in the
prediction of the Higgs boson distribution in $\Pg\Pg\PH$ production, included
in both the $\Pg\Pg\PH$ and $\cmsSymbolFace{V}\PH$ channels, and those arising
from the limited knowledge of the PDFs, are also correlated among all
searches. The uncertainties in the lepton and photon reconstruction and
identification efficiencies, in the lepton momentum scales, and in the veto
efficiency of leptons, candidates, and b quark jets are uncorrelated between
7+8 and 13searches. Similarly, uncertainties in the jet energy scale and
resolution, mistag rate of leptons, and modeling of the unclustered particles
are also uncorrelated between 7+8 and 13searches. The b jet energy scale and
resolution uncertainties for the $\PZ(\cPqb\cPqb)\PH$ analysis are estimated
using different techniques, and therefore are treated as uncorrelated with
other channels. Theoretical uncertainties affecting the ratio of
$\PZ(\nu\overline{\nu})$ and $\PW(\ell\nu)$ predictions in the VBF searches,
for both QCD and EW $\cmsSymbolFace{V}\text{+jets}$ processes, are
uncorrelated across data sets because different strategies are followed to
quantify and assign these uncertainties. In contrast, those affecting the
$\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$ and
$\PZ(\nu\overline{\nu})$/$\gamma\text{+jets}$ ratios in the $\Pg\Pg\PH$ and
$\cmsSymbolFace{V}(\cPq\cPq)\PH$ channels are correlated across the 7+8 and
the 13(2015 data) searches, as described in Ref. [16]. The uncertainties in
the tune of the underlying event simulation and in the pileup modeling are
uncorrelated between 7+8 and 13searches. Finally, theoretical uncertainties
affecting diboson and top quark cross sections, except for those considered on
the $\PZ\PZ/\PW\PZ$ ratio in the $\PZ(\ell\ell)\PH$ channel, are correlated
across all data sets.

Observed and expected upper limits on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$ at 95% CL
are presented in Fig. 9 (left). Limits are computed for the combination of all
data sets, as well as for partial combinations based either on 7+8 or 13data.
The relative contributions from different Higgs production mechanisms are
constrained to their SM values within the theoretical uncertainties. The
combination yields an observed (expected) upper limit of
${{\mathcal{B}(\PH\to\text{inv})}<0.19\,(0.15)}$ at 95% CL. The corresponding
profile likelihood ratios as a function of ${\mathcal{B}(\PH\to\text{inv})}$
are shown in Fig. 9 (right). The measured value of the invisible branching
fraction and an approximate 68% CL interval, obtained from the profile
likelihood, are ${\mathcal{B}(\PH\to\text{inv})}=0.05\pm 0.03\stat\pm
0.07\syst$. The systematic uncertainties with the highest impact in the
${\mathcal{B}(\PH\to\text{inv})}$ measurement are the theoretical
uncertainties affecting the $\PZ(\nu\overline{\nu})$/$\PW(\ell\nu)$ and
$\PZ\PZ/\PW\PW$ ratios in the VBF and $\PZ(\ell\ell)\PH$ channels,
respectively, as well as the uncertainties in the lepton and photon
reconstruction and identification efficiencies, jet energy scale, and veto
efficiency of candidates.

Figure 9: On the left, observed and expected 95% CL upper limits on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$ for partial
combinations based either on 7+8 or 13data as well as their combination,
assuming SM production cross sections for the Higgs boson with mass of 125.09.
On the right, the corresponding profile likelihood ratios as a function of
${\mathcal{B}(\PH\to\text{inv})}$ are presented. The solid curves represent
the observations in data, while the dashed lines represent the expected result
obtained from the background-only hypothesis.

The relative sensitivity of each search considered in the combination depends
on the assumed SM production rates. The cross sections for the $\Pg\Pg\PH$,
VBF and $\cmsSymbolFace{V}\PH$ production modes are parametrized in terms of
coupling strength modifiers $\kappa_{\mathrm{V}}$ and $\kappa_{\mathrm{F}}$,
which directly scale the coupling of the Higgs boson to vector bosons and
fermions, respectively [69]. The contribution from the ${\Pg\Pg\to\PZ\PH}$
production is scaled to account for the interference between the $\cPqt\PH$
and $\PZ\PH$ diagrams, as described in Ref. [34]. In this context, SM
production rates are obtained for
${\kappa_{\mathrm{V}}=\kappa_{\mathrm{F}}=1}$. Figure 10 (left) shows the
observed 95% CL upper limits on
${(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}}$ evaluated
as a function of $\kappa_{\mathrm{V}}$ and $\kappa_{\mathrm{F}}$. The LHC best
estimates for $\kappa_{\mathrm{V}}$ and $\kappa_{\mathrm{F}}$ from Ref. [4]
are superimposed, along with the 68% and 95% CL limit contours. Within the 95%
CL region, the observed (expected) upper limit on
${\mathcal{B}(\PH\to\text{inv})}$ varies between 0.14 (0.11) and 0.24 (0.19).

The upper limit on ${\mathcal{B}(\PH\to\text{inv})}$, obtained from the
combination of $\sqrt{s}=7,$ 8, and 13searches, is interpreted in the context
of Higgs-portal models of DM interactions, in which a stable DM particle
couples to the SM Higgs boson. Direct-detection experiments are sensitive to
the interaction between a DM particle and an atomic nucleus, which may be
mediated by the exchange of a Higgs boson, producing nuclear recoil signatures
that can be interpreted in terms of the DM-nucleon scattering cross section.
The sensitivity of these experiments depends mainly on the DM particle mass
($m_{\chi}$). If $m_{\chi}$ is smaller than half of the Higgs boson mass, the
Higgs boson invisible width ($\Gamma_{\text{inv}}$) can be translated, within
an effective field theory approach, into a spin-independent DM-nucleon elastic
scattering cross section, as outlined in Ref. [9]. This translation is
performed assuming that the DM candidate is either a scalar or a Majorana
fermion, and both the central value and the uncertainty of the dimensionless
nuclear form-factor $f_{N}$ are taken from the recommendations of Ref. [78].
The conversion from ${\mathcal{B}(\PH\to\text{inv})}$ to $\Gamma_{\text{inv}}$
uses the relation
${{\mathcal{B}(\PH\to\text{inv})}=\Gamma_{\text{inv}}/(\Gamma_{\mathrm{SM}}+\Gamma_{\text{inv}})}$,
where $\Gamma_{\mathrm{SM}}$ is set to 4.07 [69]. Since renormalizable models
predicting a vectorial DM candidate require an extended dark Higgs sector,
which may lead to modifications of kinematic distributions assumed for the
invisible Higgs boson signal, such interpretation is not provided in the
context of this Letter. Figure 10 (right) shows the 90% CL upper limits on the
spin-independent DM-nucleon scattering cross section as a function of
$m_{\chi}$, for both the scalar and the fermion DM scenarios. These limits are
computed at 90% CL so that they can be compared with those from direct
detection experiments such as XENON1T [79], LUX [80], PandaX-II [81], CDMSlite
[82], CRESST-II [83], and CDEX-10 [84] which provide the strongest constraints
in the $m_{\chi}$ range probed by this search. In the context of Higgs-portal
models, the result presented in this Letter provides the most stringent limits
for $m_{\chi}$ smaller than 18 (7), assuming a fermion (scalar) DM candidate.

Figure 10: On the left, observed 95% CL upper limits on
$(\sigma/\sigma_{\mathrm{SM}})\,{\mathcal{B}(\PH\to\text{inv})}$ for a Higgs
boson with a mass of 125.09, whose production cross section varies as a
function of the coupling modifiers $\kappa_{\mathrm{V}}$ and
$\kappa_{\mathrm{F}}$. Their best estimate, along with the 68% and 95% CL
contours from Ref. [4], are also reported. The SM prediction corresponds to
${\kappa_{\mathrm{V}}=\kappa_{\mathrm{F}}=1}$. On the right, 90% CL upper
limits on the spin-independent DM-nucleon scattering cross section in Higgs-
portal models, assuming a scalar (solid orange) or fermion (dashed red) DM
candidate. Limits are computed as a function of $m_{\chi}$ and are compared to
those from the XENON1T [79], LUX [80], PandaX-II [81], CDMSlite [82], CRESST-
II [83], and CDEX-10 [84] experiments.

##  0.10 Summary

A search for invisible decays of a Higgs boson is presented using proton-
proton ($\Pp\Pp$) collision data at a center-of-mass energy $\sqrt{s}=13\TeV$,
collected by the CMS experiment in 2016 and corresponding to an integrated
luminosity of 35.9. The search targets events in which a Higgs boson is
produced through vector boson fusion (VBF). The data are found to be
consistent with the predicted standard model (SM) backgrounds. An observed
(expected) upper limit of 0.33 (0.25) is set, at 95% confidence level (CL), on
the branching fraction of the Higgs boson decay to invisible particles,
${\mathcal{B}(\PH\to\text{inv})}$, by means of a binned likelihood fit to the
dijet mass distribution. In addition, upper limits are set on the product of
the cross section and branching fraction of an SM-like Higgs boson, with mass
ranging between 110 and 1000.

A combination of CMS searches for the Higgs boson decaying to invisible
particles, using $\Pp\Pp$ collision data collected at $\sqrt{s}=7,$ 8, and
13(2015 and 2016), is also presented. The combination includes searches
targeting Higgs boson production via VBF, in association with a vector boson
(with hadronic decays of the $\PW$ boson and hadronic or leptonic decays of
the $\PZ$ boson) and via gluon fusion with initial state radiation. The VBF
search is the most sensitive channel involved in the combination. No
significant deviations from the SM predictions are observed in any of these
searches. The combination yields an observed (expected) upper limit on
${\mathcal{B}(\PH\to\text{inv})}$ of 0.19 (0.15) at 95% CL, assuming SM
production rates for the Higgs boson and a Higgs boson mass of 125.09. The
observed 90% CL upper limit of ${\mathcal{B}(\PH\to\text{inv})}<0.16$ is
interpreted in terms of Higgs-portal models of dark matter (DM) interactions.
Constraints are placed on the spin-independent DM-nucleon interaction cross
section. When compared to the upper bounds from direct detection experiments,
this limit provides the strongest constraints on fermion (scalar) DM particles
with masses smaller than about 18 (7).

###### Acknowledgements.

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

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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, J. Wittmann, C.-E.
Wulz1, M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. 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, G. Flouris, D.
Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K.
Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs
Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, Q. Wang
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, C. Roskas, D. Trocino, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, G. Krintiras, V. Lemaitre, A. Magitteri, K. Piotrzkowski, J.
Prisciandaro, A. Saggio, M. Vidal Marono, P. Vischia, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins, D. Matos
Figueiredo, M. Medina Jaime5, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, D. De Souza Lemos, T.R. Fernandez
Perez Tomeia, E.M. Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen7, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang7, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J.D. Ruiz Alvarez University of Split, Faculty of Electrical Engineering,
Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanoviç, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov8,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger9, M. Finger Jr.9, J. Tomsa Escuela Politecnica Nacional, Quito,
Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran10,11, S. Elgammal11 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, H. Kirschenmann, K. Osterberg, J. Pekkanen, M. Voutilainen Helsinki
Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro12, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, R. Granier de
Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze9 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze9 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, M. Teroerde, B. Wittmer RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, A.
Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH Aachen
University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl14 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, C. Asawatangtrakuldee, P. Asmuss, I. Babounikau, H.
Bakhshiansohi, K. Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D.
Bertsche, A.A. Bin Anuar, K. Borras15, V. Botta, A. Campbell, A. Cardini, P.
Connor, S. Consuegra Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit,
M.M. Defranchis, C. Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D.
Eckstein, T. Eichhorn, A. Elwood, E. Eren, E. Gallo16, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, N.Z. Jomhari, H. Jung,
A. Kasem15, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann17, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, M. Niedziela, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A.
Reimers, O. Rieger, C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J.
Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, M.A. Harrendorf, F. Hartmann14, U. Husemann, S. Kudella, S.
Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast, K. Rabbertz,
M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Weber, C. Wöhrmann, R.
Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia
Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, K. Theofilatos, K. Vellidis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A.
Triantis, D. Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Bartók18, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi18, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T.F. Csorgo, F. Nemes, T. Novak Indian Institute of Science (IISc), Bangalore,
India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati21, C. Kar, P. Mal, V.K. Muraleedharan Nair Bindhu, A. Nayak22,
S. Roy Chowdhury, D.K. Sahoo21, S.K. Swain Panjab University, Chandigarh,
India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj23, M. Bharti23, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep23,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity24, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, G. Saha, S. Sarkar, T. Sarkar24, M. Sharan, B.
Singh23, S. Thakur23 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani25, E. Eskandari Tadavani, S.M. Etesami25, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh26
University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea, A. Gelmia,b, G.
Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b,
S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, A.
Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P. Verwilligena INFN
Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,27, 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,28, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,28,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,29, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,14, S. Di Guidaa,b,14,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,14, P. Paoluccia,14, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa,
U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P.
Lujan, M. Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P.
Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M.
Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a, Università di
Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi30, A.
Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini, A. Venturia,
P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome,
Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, S.
Gellia,b, E. Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F.
Pandolfia, R. Paramattia,b, F. Preiatoa,b, C. Quarantaa,b, S. Rahatloua,b, C.
Rovellia, F. Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, F. Cennaa,b, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, K.
Shchelinaa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di
Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, J. Park, S.K. Park, Y. Roh Kyung Hee University, Department of
Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, S.B. Oh, B.C. Radburn-Smith, S.h. Seo, U.K. Yang, H.D. Yoo, I.
Yoon, G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns31 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris32, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz33, R. Lopez-Fernandez, R.I. Rabadan-Trejo, G. Ramirez-Sanchez, R. Reyes-
Almanza, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk34, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, G. Strong, O. Toldaiev, J.
Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev35,36, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim37, E. Kuznetsova38, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko8, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva39, P. Parygin, E. Popova, V. Rusinov P.N. Lebedev Physical
Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin36, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin40, L. Dudko, A.
Ershov, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov41, V. Blinov41, T. Dimova41, L. Kardapoltsev41, Y. Skovpen41
Institute for High Energy Physics of National Research Centre ’Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic42, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic43, J.
Milosevic, M. Stojanovic Centro de Investigaciones Energéticas
Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, V. Rodríguez Bouza,
S. Sanchez Cruz, J.M. Vizan Garcia Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila University of
Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, E. Bossini, C. Botta, E.
Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G. Cucciati,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, N.
Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, F.
Fallavollita44, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, S. Giani, D.
Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D. Gulhan, J.
Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, A. Jafari, P. Janot, O.
Karacheban17, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo14, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, M. Quinto, D.
Rabady, A. Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi,
A. Sharma, P. Silva, W. Snoeys, P. Sphicas45, A. Stakia, J. Steggemann, V.R.
Tavolaro, D. Treille, A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada46, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.