HIG-16-044

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$Id:HIG-16-044.tex4525542018-03-2521:50:16Zjacobo$

HIG-15-006

# Evidence for the Higgs boson decay to a bottom quark-antiquark pair

###### Abstract

A search for the standard model (SM) Higgs boson ($\PH$) decaying to $\bbbar$
when produced in association with an electroweak vector boson is reported for
the following processes: $\cPZ(\nu\nu)\PH$, $\PW(\mu\nu)\PH$,
$\PW(\Pe\nu)\PH$, $\cPZ(\mu\mu)\PH$, and $\cPZ(\Pe\Pe)\PH$. The search is
performed in data samples corresponding to an integrated luminosity of 35.9at
$\sqrt{s}=13\TeV$ recorded by the CMS experiment at the LHC during Run 2 in
2016. An excess of events is observed in data compared to the expectation in
the absence of a $\PH\to\bbbar$ signal. The significance of this excess is 3.3
standard deviations, where the expectation from SM Higgs boson production is
2.8. The signal strength corresponding to this excess, relative to that of the
SM Higgs boson production, is $1.2\pm 0.4$. When combined with the Run 1
measurement of the same processes, the signal significance is 3.8 standard
deviations with 3.8 expected. The corresponding signal strength, relative to
that of the SM Higgs boson, is $1.06^{+0.31}_{-0.29}$.

##  0.1 Introduction

The ATLAS and CMS Collaborations reported in 2012 the discovery of a new boson
with a mass near 125using data from the Large Hadron Collider (LHC) at CERN
[1, 2, 3]. Significant signals have been observed in channels where the boson
decays into $\gamma\gamma$, $\cPZ\cPZ$, $\PW\PW$, or $\Pgt\Pgt$ [4, 5, 6, 7,
8, 9, 10, 11, 12, 13]. The measured production and decay rates and spin-parity
properties of this boson [14, 15, 16, 17, 18, 19, 20] are compatible with
those of the standard model (SM) Higgs boson (H) [21, 22, 23, 24, 25, 26].

The $\PH\to\bbbar$ decay tests directly the Higgs boson coupling to fermions,
and more specifically to down-type quarks, and has not yet been established
experimentally. In the SM, for a Higgs boson mass $m_{\PH}=125$, the branching
fraction is approximately 58% [27], by far the largest. An observation in this
channel is necessary to solidify the Higgs boson as the source of mass
generation in the fermion sector of the SM [28, 29].

At the Tevatron collider the sensitivity of the SM Higgs boson search, for
masses below 130, was dominated by its production in association with a weak
vector boson (${\cmsSymbolFace{V}}\PH$ production) and its decay to [30]. The
combined searches from the CDF and D0 Collaborations resulted in an excess of
events with a local significance, at $m_{\PH}=125$, of 2.8 standard
deviations, with an expected value of 1.6. For the $\PH\to\bbbar$ search at
the LHC, the following Higgs boson production processes have been considered:
in association with a top quark pair [31, 32, 33, 34], through vector boson
fusion [35, 36], through ${\cmsSymbolFace{V}}\PH$ production [37, 38], and,
more recently, through gluon fusion [39]. The process with the largest
sensitivity is ${\cmsSymbolFace{V}}\PH$ production.

The combined searches for $\PH\to\bbbar$ by the ATLAS and CMS Collaborations
in Run 1, at $\sqrt{s}=7$ and 8, evaluated for a Higgs boson mass of 125.09,
resulted in a significance of 2.6 standard deviations, with 3.7 standard
deviations expected [18]. The corresponding signal strength, relative to the
SM expectation, is $\mu=0.7\pm 0.3$. The significance from the individual
search by the ATLAS (CMS) experiment is 1.7 (2.0) standard deviations, with
2.7 (2.5) standard deviations expected, and a signal strength $\mu=0.6\pm 0.4$
($\mu=0.8\pm 0.4$).

Recent results by the ATLAS Collaboration [40] in the search for
$\PH\to\bbbar$ through VH production at $\sqrt{s}=13$, with data corresponding
to an integrated luminosity of 36.1, report a significance of 3.5 standard
deviations, corresponding to a signal strength of $\mu=1.20^{+0.42}_{-0.36}$.
The combination with the results from the same search in Run 1 [37] yields a
significance of 3.6 standard deviations and a signal strength
$\mu=0.90^{+0.28}_{-0.26}$.

This article reports on the search with the CMS experiment for the decay of
the SM Higgs boson to bottom quarks, $\PH\to\bbbar$, when produced through the
$\Pp\Pp\to{\cmsSymbolFace{V}}\PH$ process, where $\cmsSymbolFace{V}$ is either
a or a boson. This search is performed with data samples from Run 2 of the
LHC, recorded during 2016, corresponding to an integrated luminosity of 35.9at
$\sqrt{s}=13\TeV$. The following five processes are considered in the search:
$\cPZ(\nu\nu)\PH$, $\PW(\mu\nu)\PH$, $\PW(\Pe\nu)\PH$, $\cPZ(\mu\mu)\PH$, and
$\cPZ(\Pe\Pe)\PH$. The final states that predominantly correspond to these
processes, respectively, are characterized by the number of leptons required
in the event selection, and are referred to as the 0-, 1-, and 2-lepton
channels.

Throughout this article the term “lepton” (denoted $\ell$) refers solely to
muons and electrons, but not to taus. The leptonic tau decays in $\PW\PH$ and
$\cPZ\PH$ processes are implicitly included in the $\PW(\mu\nu)\PH$,
$\PW(\Pe\nu)\PH$, $\cPZ(\mu\mu)\PH$, and $\cPZ(\Pe\Pe)\PH$ processes.
Background processes originate from the production of and bosons in
association with jets from gluons and from light- or heavy-flavor quarks
(+jets and +jets), from singly and pair-produced top quarks (single top and ),
from diboson production ($\cmsSymbolFace{V}$$\cmsSymbolFace{V}$), and from
quantum chromodynamics multijet events (QCD).

Simulated samples of signal and background events are used to optimize the
search. For each channel, a signal region enriched in ${\cmsSymbolFace{V}}\PH$
events is selected together with several control regions, each enriched in
events from individual background processes. The control regions are used to
test the accuracy of the simulated samples’ modeling for the variables
relevant to the analysis. A simultaneous binned-likelihood fit to the shape
and normalization of specific distributions for the signal and control regions
for all channels combined is used to extract a possible Higgs boson signal.
The distribution used in the signal region is the output of a boosted decision
tree (BDT) event discriminant [41, 42] that helps separate signal from
background. For the control regions, a variable that identifies jets
originating from b quarks, and that discriminates between the different
background processes, is used. To validate the analysis procedure, the same
methodology is used to extract a signal for the ${\cmsSymbolFace{V}}\cPZ$
process, with $\cPZ\to\bbbar$, which has a nearly identical final state to
${\cmsSymbolFace{V}}\PH$ with $\PH\to\bbbar$, but with a production cross
section of 5 to 15 times larger, depending on the kinematic regime considered.
Finally, the results from this search are combined with those of similar
searches performed by the CMS Collaboration during Run 1 [38, 36, 18].

This article is structured as follows: Sections 0.2–0.3 describe the CMS
detector, the simulated samples used for signal and background processes, and
the triggers used to collect the data. Sections 0.4–0.5 describe the
reconstruction of the detector objects used in the analysis and the selection
criteria for events in the signal and control regions. Section 0.6 describes
the sources of uncertainty in the analysis, and Section 0.7 describes the
results, summarized in Section 0.8.

##  0.2 The CMS detector and simulated samples

A detailed description of the CMS detector can be found elsewhere in Ref.
[43]. The momenta of charged particles are measured using a silicon pixel and
strip tracker that covers the range $\abs{\eta}<2.5$ and is immersed in a 3.8T
axial magnetic field. The pseudorapidity is defined as
$\eta=-\ln[\tan(\theta/2)]$, where $\theta$ is the polar angle of the
trajectory of a particle with respect to the direction of the counterclockwise
proton beam. Surrounding the tracker are a crystal electromagnetic calorimeter
(ECAL) and a brass and scintillator hadron calorimeter (HCAL), both used to
measure particle energy deposits and both consisting of a barrel assembly and
two endcaps. The ECAL and HCAL extend to a range of $\abs{\eta}<3.0$. A steel
and quartz-fiber Cherenkov forward detector extends the calorimetric coverage
to $\abs{\eta}<5.0$. The outermost component of the CMS detector is the muon
system, consisting of gas-ionization detectors placed in the steel flux-return
yoke of the magnet to measure the momenta of muons traversing through the
detector. The two-level CMS trigger system selects events of interest for
permanent storage. The first trigger level, composed of custom hardware
processors, uses information from the calorimeters and muon detectors to
select events in less than 3.2. The high-level trigger software algorithms,
executed on a farm of commercial processors, further reduce the event rate
using information from all detector subsystems. The variable $\Delta
R{=}\sqrt{\smash[b]{(\Delta\eta)^{2}{+}(\Delta\phi)^{2}}}$ is used to measure
the separation between reconstructed objects in the detector, where $\phi$ is
the angle (in radians) of the trajectory of the object in the plane transverse
to the direction of the proton beams.

Samples of simulated signal and background events are produced using the Monte
Carlo (MC) event generators listed below. The CMS detector response is modeled
with [44]. The signal samples used have Higgs bosons with
$m_{\PH}=125$produced in association with vector bosons. The quark-induced ZH
and WH processes are generated at next-to-leading order (NLO) using the [45,
46, 47] v2 event generator extended with the MiNLO procedure [48, 49], while
the gluon-induced ZH processes (denoted ggZH) are generated at leading-order
(LO) accuracy with v2. The [50] v2.3.3 generator is used at NLO with the FxFx
merging scheme [51] for the diboson background samples. The same generator is
used at LO accuracy with the MLM matching scheme [52] for the +jets and +jets
in inclusive and b-quark enriched configurations, as well as the QCD multijet
sample. The [53] production process, as well as the single top quark sample
for the $t$-channel [54], are produced with v2. The single top quark samples
for the - [55] and $s$-channel [56] are instead produced with v1. The
production cross sections for the signal samples are rescaled to next-to-next-
to-leading order (NNLO) QCD + NLO electroweak accuracy combining the vhnnlo
[57, 58, 59], vh@nnlo [60, 61] and hawk v2.0 [62] generators as described in
the documentation produced by the LHC Working Group on Higgs boson cross
sections [63], and they are applied as a function of the vector boson
transverse momentum (). The production cross sections for the samples are
rescaled to the NNLO with the next-to-next-to-leading-log (NNLL) prediction
obtained with Top++ v2.0 [64], while the +jets and +jets samples are rescaled
to the NLO cross sections using . The parton distribution functions (PDFs)
used to produce the NLO samples are the NLO NNPDF3.0 set [65], while the LO
NNPDF3.0 set is used for the LO samples. For parton showering and
hadronization the and samples are interfaced with 8.212 [66]. The 8 parameters
for the underlying event description correspond to the CUETP8M1 tune derived
in Ref. [67] based on the work described in Ref. [68].

During the 2016 data-taking period the LHC instantaneous luminosity reached
approximately $1.5\times 10^{34}\percms$ and the average number of $\Pp\Pp$
interactions per bunch crossing was approximately 23. The simulated samples
include these additional $\Pp\Pp$ interactions, referred to as pileup
interactions (or pileup), that overlap with the event of interest in the same
bunch crossing.

##  0.3 Triggers

Several triggers are used to collect events with final-state objects
consistent with the signal processes in the channels under consideration.

For the 0-lepton channel, the quantities used in the trigger are derived from
the reconstructed objects in the detector identified by a particle-flow (PF)
algorithm [69] that combines the online information from all CMS subsystems to
identify and reconstruct individual particles emerging from the proton-proton
collisions: charged hadrons, neutral hadrons, photons, muons, and electrons.
The main trigger used requires that both the missing transverse momentum,
${p}_{\mathrm{T}}^{\text{miss}}$, and the hadronic missing transverse
momentum, $H_{\mathrm{T}}^{\text{miss}}$, in the event be above a threshold of
110. Online ${p}_{\mathrm{T}}^{\text{miss}}$ is defined as the magnitude of
the negative vector sum of the transverse momenta of all reconstructed objects
identified by the PF algorithm, while $H_{\mathrm{T}}^{\text{miss}}$ is
defined as the magnitude of the negative vector sum of the transverse momenta
of all reconstructed jets (with $\pt>20$and $\abs{\eta}<5.2)$ identified by
the same algorithm. For $\cPZ(\cPgn\cPgn)\PH$ events with
${p}_{\mathrm{T}}^{\text{miss}}>170$, evaluated offline, the trigger
efficiency is approximately 92%, and near 100% above 200.

For the 1-lepton channels, single-lepton triggers are used. The muon trigger
threshold is 24and the electron threshold is 27. For the 2-lepton channels,
dilepton triggers are used. The muon thresholds are 17 and 8, and the electron
thresholds are 23 and 12. All leptons in these triggers are required to pass
stringent lepton identification criteria. In addition, to maintain an
acceptable trigger rate, and to be consistent with what is expected from
signal events, leptons are also required to be isolated from other tracks and
calorimeter energy deposits. For $\PW(\Pgm\cPgn)\PH$ events that pass all
offline requirements described in Section 0.5, the single-muon trigger
efficiency is ${\approx}$95%. The corresponding efficiency for recording
$\PW(\Pe\cPgn)\PH$ events with the single-electron trigger is ${\approx}$90%.
For $\cPZ(\ell\ell)\PH$ signal events that pass all offline requirements in
Section 0.5, the dilepton triggers are nearly 100% efficient.

##  0.4 Event reconstruction

The characterization of ${\cmsSymbolFace{V}}\PH$ events in the channels
studied here requires the reconstruction of the following objects in the
detector, using the PF algorithm [69] and originating from the primary
interaction vertex: muons, electrons, neutrinos (reconstructed as
${p}_{\mathrm{T}}^{\text{miss}}$), and jets — including those that originate
from the hadronization of quarks, referred to as “ jets”.

The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex. The physics
objects are the objects reconstructed by a jet finding algorithm [70, 71]
applied to all charged tracks associated with the vertex, plus the
corresponding associated ${p}_{\mathrm{T}}^{\text{miss}}$. The pileup
interactions affect jet momentum reconstruction,
${p}_{\mathrm{T}}^{\text{miss}}$reconstruction, lepton isolation, and tagging
efficiencies. To mitigate these effects, all charged hadrons that do not
originate from the primary interaction vertex are removed from consideration
in the event. In addition, the average neutral energy density from pileup
interactions is evaluated from PF objects and subtracted from the
reconstructed jets in the event and from the summed energy in the isolation
criteria used for leptons [72]. These pileup mitigation procedures are applied
on an object-by-object basis.

Muons are reconstructed using two algorithms [73]: one in which tracks in the
silicon tracker are matched to hits in the muon detectors, and another in
which a track fit is performed using hits in the silicon tracker and in the
muon systems. In the latter algorithm, the muon is seeded by hits in the muon
systems. The muon candidates used in the analysis are required to be
successfully reconstructed by both algorithms. Further identification criteria
are imposed on the muon candidates to reduce the fraction of tracks
misidentified as muons. These include the number of hits in the tracker and in
the muon systems, the fit quality of the global muon track, and its
consistency with the primary vertex. Muon candidates are required to be in the
$\abs{\eta}<2.4$ region.

Electron reconstruction [74] requires the matching of a set of ECAL clusters,
denoted supercluster (SC), to a track in the silicon tracker. Electron
identification [74] relies on a multivariate technique that combines
observables sensitive to the amount of bremsstrahlung along the electron
trajectory, such as the geometrical matching and momentum consistency between
the electron trajectory and the associated calorimeter clusters, as well as
various shower shape observables in the calorimeters. Additional requirements
are imposed to remove electrons that originate from photon conversions.
Electrons are required to be in the range $\abs{\eta}<2.5$, excluding
candidates for which the SC lies in the $1.444<\abs{\eta_{\mathrm{SC}}}<1.566$
transition region between the ECAL barrel and endcap, where electron
reconstruction is not optimal.

Charged leptons from and boson decays are expected to be isolated from other
activity in the event. For each lepton candidate, a cone in
$\eta\text{---}\phi$ is constructed around the track direction at the event
vertex. The scalar sum of the transverse momentum of each reconstructed
particle, including neutral particles, compatible with the primary vertex and
contained within the cone is calculated, excluding the contribution from the
lepton candidate itself. This sum is called isolation. In the presence of
pileup, isolation is contaminated with particles from the other interactions.
A quantity proportional to the pileup is used to correct the isolation on
average to mitigate reductions in signal efficiency at larger values of
pileup. In the 1-lepton channel, if the corrected isolation sum exceeds 6% of
the lepton candidate , the lepton is rejected. In the 2-lepton channel, the
threshold is looser; the isolation of each candidate can be up to 20 (15%) of
the muon (electron) . Including the isolation requirement, the total
efficiency for reconstructing muons is in the range of 85–100%, depending on
and $\eta$. The corresponding efficiency for electrons is in the range of
40–90%.

Jets are reconstructed from PF objects using the anti-clustering algorithm
[70], with a distance parameter of 0.4, as implemented in the package [71,
75]. Each jet is required to lie within $\abs{\eta}<2.4$, to have at least two
tracks associated with it, and to have electromagnetic and hadronic energy
fractions of at least 1%. The last requirement removes jets originating from
instrumental effects. Jet energy corrections are applied as a function of
$\eta$ and of the jet [76]. The missing transverse momentum vector,
$\ptvec^{\text{miss}}$, is calculated offline as the negative of the vectorial
sum of transverse momenta of all PF objects identified in the event [77], and
the magnitude of this vector is denoted ${p}_{\mathrm{T}}^{\text{miss}}$ in
the rest of this article.

The identification of jets is performed using a combined multivariate (CMVA)
tagging algorithm [78, 79]. This algorithm combines, in a likelihood
discriminant, information within jets that helps differentiate between jets
and jets originating from light quarks, gluons, or charm quarks. This
information includes track impact parameters, secondary vertices, and
information related to low-leptons if contained within a jet. The output of
this discriminant has continuous values between $-1.0$ and 1.0. A jet with a
CMVA discriminant value above a certain threshold is labeled as “b-tagged”.
The efficiency for tagging jets and the rate of misidentification of non- jets
depend on the threshold chosen, and are typically parameterized as a function
of the and $\eta$ of the jets. These performance measurements are obtained
directly from data in samples that can be enriched in jets, such as $\ttbar$
and multijet events (where, for example, requiring the presence of a muon in
the jets enhances the heavy-flavor content of the events). Three thresholds
for the CMVA discriminant value are used in this analysis: loose
($\mathrm{CMVA_{L}}$), medium ($\mathrm{CMVA_{M}}$), and tight
($\mathrm{CMVA_{T}}$). Depending on the threshold used, the efficiencies for
tagging jets that originate from quarks, quarks, and light quarks or gluons
are in the 50–75%, 5–25%, and 0.15–3.0% ranges, respectively. The loose
(tight) threshold has the highest (lowest) efficiency and allows most (least)
contamination.

In background events, particularly , there is often additional, low energy,
hadronic activity in the event. Measuring the hadronic activity associated
with the main primary vertex provides additional discriminating variables to
reject background. To measure this hadronic activity only reconstructed
charged-particle tracks are used, excluding those associated with the vector
boson and the two b jets. A collection of “additional tracks” is assembled
using reconstructed tracks that: (i) satisfy the high purity quality
requirements defined in Ref. [80] and $\pt>300\MeV$; (ii) are not associated
with the vector boson, nor with the selected b jets in the event; (iii) have a
minimum longitudinal impact parameter, $\abs{d_{z}(\mathrm{PV})}$, with
respect to the main PV, rather than to other pileup interaction vertices; (iv)
satisfy $\abs{d_{z}(\mathrm{PV})}<2\unit{mm}$; and (v) are not in the region
between the two selected -tagged jets. This region is defined as an ellipse in
the $\eta\text{---}\phi$ plane, centered on the midpoint between the two jets,
with major axis of length $\Delta R({\PQb\PQb}){+}1$, where $\Delta
R({\PQb\PQb}){=}\sqrt{(\Delta\eta_{\PQb\PQb})^{2}{+}(\Delta\phi_{\PQb\PQb})^{2}}$,
oriented along the direction connecting the two $\PQb$ jets, and with minor
axis of length 1. The additional tracks are then clustered into “soft-track
jets” using the anti-clustering algorithm with a distance parameter of 0.4.
The use of track jets represents a clean and validated method [81] to
reconstruct the hadronization of partons with energies down to a few [82]; an
extensive study of the soft-track jet activity can be found in Refs. [83, 84].
The number of soft track jets with $\pt>5$is used in all channels as a
background discriminating variable.

Events from data and from the simulated samples are required to satisfy the
same trigger and event reconstruction requirements. Corrections that account
for the differences in the performance of these algorithms between data and
simulated samples are computed from data and used in the analysis.

##  0.5 Event selection

A signal region enriched in ${\cmsSymbolFace{V}}\PH$ events is determined
separately for each channel. Simulated events in this region are used to train
an event BDT discriminant to help differentiate between signal and background
events. Also for each channel, different control regions, each enriched in
events from individual background processes, are selected. These regions are
used to study the agreement between simulated samples and data, and to provide
a distribution that is combined with the output distribution of the signal
region event BDT discriminant in the $\PH\to\bbbar$ signal-extraction fit.
This control region distribution is obtained from the second-highest value of
the CMVA discriminant among the two jets selected for the reconstruction of
the $\PH\to\bbbar$ decay, denoted $\mathrm{CMVA_{min}}$.

As mentioned in the Introduction, background processes to
${\cmsSymbolFace{V}}\PH$ production with $\PH\to\bbbar$ are the production of
vector bosons in association with one or more jets ($\cmsSymbolFace{V}$+jets),
production, single-top-quark production, diboson production, and QCD multijet
production. These processes have production cross sections that are several
orders of magnitude larger than that of the Higgs boson, with the exception of
the ${\cmsSymbolFace{V}}\cPZ$ process with $\cPZ\to\bbbar$, with an inclusive
cross section only about 15 times larger than the ${\cmsSymbolFace{V}}\PH$
production cross section. Given the nearly identical final state, this process
provides a benchmark against which the Higgs boson search strategy can be
tested. The results of this test are discussed in Section 0.7.1.

Below we describe the selection criteria used to define the signal regions and
the variables used to construct the event BDT discriminant. Also described are
the criteria used to select appropriate background-specific control regions
and the corresponding distributions used in the signal-extraction fit.

###  0.5.1 Signal regions

The signal region requirements are listed in Table 0.5.1. Events are selected
to belong exclusively to only one of the three channels. Signal events are
characterized by the presence of a vector boson recoiling against two jets
with an invariant mass near 125. The event selection therefore relies on the
reconstruction of the decay of the Higgs boson into two -tagged jets and on
the reconstruction of the leptonic decay modes of the vector boson.

The reconstruction of the $\PH\to\bbbar$ decay is based on the selection of
the pair of jets that have the highest values of the CMVA discriminant among
all jets in the event. The highest and second-highest values of the CMVA
discriminant for these two jets are denoted by $\mathrm{CMVA_{max}}$ and
$\mathrm{CMVA_{min}}$, respectively. Both jets are required to be central
(with $\abs{\eta}<2.4$), to satisfy standard requirements to remove jets from
pileup [85], and to have a above a minimum threshold, that can be different
for the highest ($\mathrm{j}_{1}$) and second-highest ($\mathrm{j}_{2}$) jet.
The selected dijet pair is denoted by “jj” in the rest of this article.

The background from $\cmsSymbolFace{V}$+jets and diboson production is reduced
significantly when the tagging requirements are applied. As a result,
processes where the two jets originate from genuine quarks dominate the sample
composition in the signal region. To provide additional suppression of
background events, several other requirements are imposed on each channel
after the reconstruction of the $\PH\to\bbbar$ decay.

#### 0-lepton channel

This channel targets mainly $\cPZ(\cPgn\cPgn)\PH$ events in which the
${p}_{\mathrm{T}}^{\text{miss}}$ is interpreted as the transverse momentum of
the Z boson in the $\cPZ\to\cPgn\cPagn$ decay. In order to overcome large QCD
multijet backgrounds, a relatively high threshold of
${p}_{\mathrm{T}}^{\text{miss}}>170$is required. The QCD multijet background
is further reduced to negligible levels in this channel when requiring that
the ${p}_{\mathrm{T}}^{\text{miss}}$ does not originate from the direction of
(mismeasured) jets. To that end, if there is a jet with $\abs{\eta}<2.5$ and
$\pt>30$, whose azimuthal angle is within 0.5 radians of the
${p}_{\mathrm{T}}^{\text{miss}}$ direction, the event is rejected. The
rejection of multijet events with ${p}_{\mathrm{T}}^{\text{miss}}$ produced by
mismeasured jets is aided by using a different missing transverse momentum
reconstruction, denoted ${p}_{\mathrm{T}}^{\text{miss}}({\text{trk}})$,
obtained by considering only charged-particle tracks with $\pt>0.5\GeV$ and
$\abs{\eta}<2.5$. For an event to be accepted, it is required that
${p}_{\mathrm{T}}^{\text{miss}}({\text{trk}})$ and
${p}_{\mathrm{T}}^{\text{miss}}$ be aligned in azimuth within 0.5 radians. To
reduce background events from and $\PW\cPZ$ production channels, events with
any additional isolated leptons with $\pt>20\GeV$ are rejected. The number of
these additional leptons is denoted by $N_{\mathrm{a}\ell}$.

Selection criteria that define the signal region. Entries marked with “—”
indicate that the variable is not used in the given channel. Where selections
differ for different $\pt(\cmsSymbolFace{V})$ regions, there are comma
separated entries of thresholds or square brackets with a range that indicate
each region’s selection as defined in the first row of the table. The values
listed for kinematic variables are in units of , and for angles in units of
radians. Where selection differs between lepton flavors, the selection is
listed as (muon, electron).  Variable 0-lepton 1-lepton 2-lepton
$\pt(\cmsSymbolFace{V})$ $>$170 $>$100 $[50,150]$, $>$150 $M(\ell\ell)$ — —
$[75,105]$ $p_{\mathrm{T}}^{\ell}$ — $(>25,>30)$ $>$20
${\pt}({\mathrm{j}}_{1})$ $>$60 $>$25 $>$20 ${\pt}({\mathrm{j}}_{2})$ $>$35
$>$25 $>$20 ${\pt\mathrm{(jj)}}$ $>$120 $>$100 — $M\mathrm{(jj)}$ $[60,160]$
$[90,150]$ $[90,150]$ $\Delta\phi(\mathrm{V,jj})$ $>$2.0 $>$2.5 $>$2.5
${\mathrm{CMVA_{max}}}$ $>$$\mathrm{CMVA_{T}}$ $>$$\mathrm{CMVA_{T}}$
$>$$\mathrm{CMVA_{L}}$ ${\mathrm{CMVA_{min}}}$ $>$$\mathrm{CMVA_{L}}$
$>$$\mathrm{CMVA_{L}}$ $>$$\mathrm{CMVA_{L}}$ $N_{\mathrm{aj}}$ $<$2 $<$2 —
$N_{\mathrm{a}\ell}$ $=$0 $=$0 — ${p}_{\mathrm{T}}^{\text{miss}}$ $>$170 — —
$\Delta\phi(\ptvec^{\text{miss}},\mathrm{j})$ $>$0.5 — —
$\Delta\phi(\ptvec^{\text{miss}},\ptvec^{\text{miss}}({\text{trk}}))$ $<$0.5 —
— $\Delta\phi(\ptvec^{\text{miss}},\ell)$ — $<$2.0 — Lepton isolation —
$<$0.06 $(<0.25,<0.15)$ Event BDT $>-0.8$ $>$0.3 $>-0.8$

#### 1-lepton channel

This channel targets mainly $\PW(\ell\cPgn)\PH$ events in which candidate
$\PW\to\ell\cPgn$ decays are identified by the presence of one isolated lepton
as well as missing transverse momentum, which is implicitly required in the
$\pt(\cmsSymbolFace{V})$ selection criteria mentioned below, where
$\pt(\cmsSymbolFace{V})$ is calculated from the vectorial sum of
$\ptvec^{\text{miss}}$and the lepton . Muons (electrons) are required to have
$\pt>25$ $(30)$. It is also required that the azimuthal angle between the
${p}_{\mathrm{T}}^{\text{miss}}$ direction and the lepton be less than 2.0
radians. The lepton isolation for either flavor of lepton is required to be
smaller than 6% of the lepton . These requirements significantly reduce
possible contamination from QCD multijet production. With the same motivation
as in the 0-lepton channel, events with any additional isolated leptons are
rejected. To substantially reject events, the number of additional jets with
$\abs{\eta}<2.9$ and $\pt>25\GeV$, $N_{\mathrm{aj}}$, is allowed to be at most
one.

#### 2-lepton channel

This channel targets $\cPZ\to\ell\ell$ decays, which are reconstructed by
combining isolated, oppositely charged pairs of electrons or muons and
requiring the dilepton invariant mass to satisfy $75<M(\ell\ell)<105$. The for
each lepton is required to be greater than 20. Isolation requirements are
relaxed in this channel as the QCD multijet background is practically
eliminated after requiring compatibility with the Z boson mass [86].

####  $\pt(\cmsSymbolFace{V})$ requirements, $\PH\to\bbbar$ mass
reconstruction, and event BDT discriminant

Background events are substantially reduced by requiring significant large
transverse momentum of the reconstructed vector boson,
$\pt(\cmsSymbolFace{V})$, or of the Higgs boson candidate [87]. In this
kinematic region, the $\cmsSymbolFace{V}$ and $\PH$ bosons recoil from each
other with a large azimuthal opening angle, $\Delta\phi(\mathrm{V,H})$,
between them. Different $\pt(\cmsSymbolFace{V})$ regions are selected for each
channel. Because of different signal and background content, each of these
regions has different sensitivity and the analysis is performed separately in
each region. For the 0-lepton channel, a single region requiring
${p}_{\mathrm{T}}^{\text{miss}}>170$is studied. The 1-lepton channel is also
analyzed in a single region, with $\pt(\cmsSymbolFace{V})>100$. The 2-lepton
channels consider two regions: low- and high-regions defined by
$50<\pt(\cmsSymbolFace{V})<150$and $\pt(\cmsSymbolFace{V})>150$.

After all event selection criteria described in this section are applied, the
dijet invariant mass resolution of the two jets from the Higgs boson decay is
approximately 15%, depending on the of the reconstructed Higgs boson, with a
few percent shift in the value of the mass peak relative to 125. The Higgs
boson mass resolution is further improved by applying multivariate regression
techniques similar to those used at the CDF experiment [88] and used for
several Run 1 $\PH\to\bbbar$ analyses by ATLAS and CMS [38, 37]. The
regression estimates a correction that is applied after the jet energy
corrections discussed in Section 0.4. It is computed for individual jets in an
attempt to improve the accuracy of the measured energy with respect to the
quark energy. To this end, a BDT is trained on jets from simulated events with
inputs that include detailed jet structure information, which differs in jets
from quarks from that of jets from light-flavor quarks or gluons. These inputs
include variables related to several properties of the secondary vertex (when
reconstructed), information about tracks, jet constituents, and other
variables related to the energy reconstruction of the jet. Because of
semileptonic hadron decays, jets from quarks contain, on average, more leptons
and a larger fraction of missing energy than jets from light quarks or gluons.
Therefore, in the cases where a low-lepton is found in the jet or in its
vicinity, the following variables are also included in the regression BDT: the
of the lepton, the $\Delta R$ distance between the lepton and the jet
directions, and the momentum of the lepton transverse to the jet direction.

For the three channels under consideration, the $\PH\to\bbbar$ mass
resolution, measured on simulated signal samples when the regression-corrected
jet energies are used, is in the 10–13% range, and it depends on the of the
reconstructed Higgs boson. Averaging over all channels, the improvement in
mass resolution is approximately 15%, resulting in an increase of about 10% in
the sensitivity of the analysis. The performance of these corrections is shown
in Fig. 1 for simulated samples of $\cPZ(\ell\ell)\PH(\cPqb\cPqb)$ events. The
validation of the technique in data is done using the
${\pt}(\ell\ell)$/${\pt\mathrm{(jj)}}$ distribution in samples of
$\cPZ\to\ell\ell$ events containing two -tagged jets, and using the
reconstructed top quark mass distribution in the lepton+jets final state in
$\ttbar$-enriched samples. After the jets are corrected, the root-mean-square
value of both distributions decreases, the peak value of the
${\pt}(\ell\ell)$/${\pt\mathrm{(jj)}}$ distribution is shifted closer to 1.0,
and the peak value of the reconstructed top quark mass gets closer to the top
quark mass. These distributions show good agreement between data and the
simulated samples before and after the regression correction is applied.
Importantly, the reconstructed dijet invariant mass distributions for
background processes do not develop a peak structure when the regression
correction is applied to the selected b-tagged jets in the event.

Figure 1:  Dijet invariant mass distributions for simulated samples of
$\cPZ(\ell\ell)\PH(\cPqb\cPqb)$ events ($m_{\PH}=125\GeV$), before (red) and
after (blue) the energy correction from the regression procedure is applied. A
sum of a Bernstein polynomial and a Crystal Ball function is used to fit the
distribution. The displayed resolutions are derived from the peak and RMS of
the Gaussian core of the Crystal Ball function.

As mentioned above, to help separate signal from background in the signal
region, an event BDT discriminant is trained using simulated samples for
signal and all background processes. The set of event input variables used,
listed in Table 0.5.1, is chosen by iterative optimization from a larger
number of potentially discriminating variables. Among the most discriminating
variables for all channels are the dijet invariant mass distribution,
$M\mathrm{(jj)}$, the number of additional jets, $N_{\mathrm{aj}}$, the value
of CMVA for the jet with the second-highest CMVA value, CMVA${}_{\text{min}}$,
and the distance, $\Delta R(\mathrm{jj})$, between the two jets.

Variables used in the training of the event BDT discriminant for the different
channels. Jets are counted as additional jets to those selected to reconstruct
the $\PH\to\bbbar$ decay if they satisfy the following: $\pt>30\GeV$ and
$\abs{\eta}<2.4$ for the 0- and 2-lepton channels, and $\pt>25\GeV$ and
$\abs{\eta}<2.9$ for the 1-lepton channel.  Variable Description 0-lepton
1-lepton 2-lepton $M\mathrm{(jj)}$ dijet invariant mass $\checkmark$
$\checkmark$ $\checkmark$ ${\pt\mathrm{(jj)}}$ dijet transverse momentum
$\checkmark$ $\checkmark$ $\checkmark$ $\pt(\mathrm{j_{1}})$,
$\pt(\mathrm{j_{2}})$ transverse momentum of each jet $\checkmark$
$\checkmark$ $\Delta R(\mathrm{jj})$ distance in $\eta$–$\phi$ between jets
$\checkmark$ $\Delta\eta\mathrm{(jj)}$ difference in $\eta$ between jets
$\checkmark$ $\checkmark$ $\Delta\phi(\mathrm{jj})$ azimuthal angle between
jets $\checkmark$ $\pt(\cmsSymbolFace{V})$ vector boson transverse momentum
$\checkmark$ $\checkmark$ $\Delta\phi(\mathrm{V,jj})$ azimuthal angle between
vector boson and dijet directions $\checkmark$ $\checkmark$ $\checkmark$
${\pt\mathrm{(jj)}}/\pt(\cmsSymbolFace{V})$ ratio between dijet and vector
boson $\checkmark$ $M(\ell\ell)$ reconstructed Z boson mass $\checkmark$
CMVAmax value of CMVA discriminant for the jet $\checkmark$ $\checkmark$ with
highest CMVA value CMVAmin value of CMVA discriminant for the jet $\checkmark$
$\checkmark$ $\checkmark$ with second highest CMVA value CMVAadd value of CMVA
for the additional jet $\checkmark$ with highest CMVA value
${p}_{\mathrm{T}}^{\text{miss}}$ missing transverse momentum $\checkmark$
$\checkmark$ $\checkmark$
$\Delta\phi(\ptvec^{\text{miss}}\mathrm{,}\mathrm{j})$ azimuthal angle between
$\ptvec^{\text{miss}}$ and closest jet ($\pt>30\GeV$) $\checkmark$
$\Delta\phi(\ptvec^{\text{miss}}\mathrm{,}\ell)$ azimuthal angle between
$\ptvec^{\text{miss}}$ and lepton $\checkmark$ $m_{\mathrm{T}}$ mass of lepton
+ $\ptvec^{\text{miss}}$ $\checkmark$ $m_{\mathrm{top}}$ reconstructed top
quark mass $\checkmark$ $N_{\mathrm{aj}}$ number of additional jets
$\checkmark$ $\checkmark$ $\pt(\text{add})$ transverse momentum of leading
additional jet $\checkmark$ SA5 number of soft-track jets with $\pt>5$
$\checkmark$ $\checkmark$ $\checkmark$

###  0.5.2 Background control regions

To help determine the normalization of the main background processes, and to
validate how well the simulated samples model the distributions of variables
most relevant to the analysis, several control regions are selected in data.
Tables 0.5.2–0.5.2 list the selection criteria used to define these regions
for the 0-, 1-, and 2-lepton channels, respectively. Separate control regions
are specified for production and for the production of and bosons in
association with either predominantly heavy-flavor (HF) or light-flavor (LF)
jets. While some control regions are very pure in their targeted background
process, others contain more than one process.

Definition of the control regions for the 0-lepton channel. LF and HF refer to
light- and heavy-flavor jets. The values listed for kinematic variables are in
units of , and for angles in units of radians. Entries marked with “—”
indicate that the variable is not used in that region.  Variable Z+LF Z+HF V
decay category $\PW(\ell\cPgn)$ $\cPZ(\cPgn\cPgn)$ $\cPZ(\cPgn\cPgn)$
${\pt}({\mathrm{j}}_{1})$ $>$60 $>$60 $>$60 ${\pt}({\mathrm{j}}_{2})$ $>$35
$>$35 $>$35 ${\pt\mathrm{(jj)}}$ $>$120 $>$120 $>$120
${p}_{\mathrm{T}}^{\text{miss}}$ $>$170 $>$170 $>$170
$\Delta\phi(\mathrm{V,jj})$ $>$2 $>$2 $>$2 $N_{\mathrm{a}\ell}$ $\geq 1$ $=$0
$=$0 $N_{\mathrm{aj}}$ $\geq 2$ $\leq 1$ $<$1 $M\mathrm{(jj)}$ — —
$\notin[60-160]$ CMVAmax $>$$\mathrm{CMVA_{M}}$ $<$$\mathrm{CMVA_{M}}$
$>$$\mathrm{CMVA_{T}}$ CMVAmin $>$$\mathrm{CMVA_{L}}$ $>$$\mathrm{CMVA_{L}}$
$>$$\mathrm{CMVA_{L}}$ $\Delta\phi(\mathrm{j},\ptvec^{\text{miss}})$ — $>$0.5
$>$0.5 $\Delta\phi(\ptvec^{\text{miss}},\ptvec^{\text{miss}}({\text{trk}}))$ —
$<$0.5 $<$0.5 $\min\Delta\phi(\mathrm{j},\ptvec^{\text{miss}})$ $<\pi/2$ — —

Definition of the control regions for the 1-lepton channels. The HF control
region is divided into low- and high-mass ranges as shown in the table. The
significance of ${p}_{\mathrm{T}}^{\text{miss}}$,
$\sigma({p}_{\mathrm{T}}^{\text{miss}})$, is ${p}_{\mathrm{T}}^{\text{miss}}$
divided by the square root of the scalar sum of jet where jet $\pt>30$. The
values listed for kinematic variables are in units of , except for
$\sigma({p}_{\mathrm{T}}^{\text{miss}})$whose units are $\sqrt{\GeV}$. For
angles units are radians. Entries marked with “—” indicate that the variable
is not used in that region.  Variable W+LF W+HF ${\pt}({\mathrm{j}}_{1})$
$>$25 $>$25 $>$25 ${\pt}({\mathrm{j}}_{2})$ $>$25 $>$25 $>$25
${\pt\mathrm{(jj)}}$ $>$100 $>$100 $>$100 $\pt(\cmsSymbolFace{V})$ $>$100
$>$100 $>$100 CMVA${}_{\text{max}}$ $>$$\mathrm{CMVA_{T}}$
[$\mathrm{CMVA_{L}}$,$\mathrm{CMVA_{M}}$] $>$$\mathrm{CMVA_{T}}$
$N_{\mathrm{aj}}$ $>$1 — $=$0 $N_{\mathrm{a}\ell}$ $=$0 $=$0 $=$0
$\sigma({p}_{\mathrm{T}}^{\text{miss}})$ — $>$2.0 $>$2.0
$\Delta\phi(\ptvec^{\text{miss}},\ell)$ $<$2 $<$2 $<$2 $M\mathrm{(jj)}$ $<$250
$<$250 $<$90, $[150,250]$

Definition of the control regions for the 2-lepton channels. The same
selection is used for both the low- and high-$\pt(\cmsSymbolFace{V})$ regions.
The values listed for kinematic variables are in units of and for angles in
units of radians. Entries marked with “—” indicate that the variable is not
used in that region.  Variable Z+LF Z+HF $\pt(\cmsSymbolFace{V})$
$[50,150]$,$>$150 $[50,150]$,$>$150 $[50,150]$,$>$150 CMVA${}_{\text{max}}$
$>$$\mathrm{CMVA_{T}}$ $<$$\mathrm{CMVA_{L}}$ $>$$\mathrm{CMVA_{T}}$
CMVA${}_{\text{min}}$ $>$$\mathrm{CMVA_{L}}$ $<$$\mathrm{CMVA_{L}}$
$>$$\mathrm{CMVA_{L}}$ ${p}_{\mathrm{T}}^{\text{miss}}$ — — $<$60
$\Delta\phi(\mathrm{V,jj})$ — $>$2.5 $>$2.5 $M(\ell\ell)$ $\notin[0,10]$,
$\notin[75,120]$ $[75,105]$ $[85,97]$ $M\mathrm{(jj)}$ — $[90,150]$
$\notin[90,150]$

Different background processes feature specific b jet compositions, e.g. two
genuine b jets for and $\cmsSymbolFace{V}$+bb, one genuine b jet for
$\cmsSymbolFace{V}$+b, no genuine b jet for $\cmsSymbolFace{V}$+udscg. This
characteristic, together with their different kinematic distributions, results
in distinct $\mathrm{CMVA_{min}}$ distributions that serve to extract the
normalization scale factors of the various simulated background samples when
fit to data in conjunction with the BDT distributions in the signal region to
search for a possible ${\cmsSymbolFace{V}}\PH$ signal. In this signal-
extraction fit, discussed further in Section 0.7, the shape and normalization
of these distributions are allowed to vary, for each background component,
within the systematic and statistical uncertainties described in Section 0.6.
These uncertainties are treated as independent nuisance parameters. The
simulated samples for the $\cmsSymbolFace{V}$+jets processes are split into
independent subprocesses according to the number of MC generator-level jets
(with $\pt>20$and $\abs{\eta}<2.4$) containing at least one hadron. Table
0.5.2 lists the scale factors obtained from the fit. These account not only
for possible cross section discrepancies, but also for potential residual
differences in the selection efficiency of the different objects in the
detector. Scale factors obtained from a similar fit to the control regions
alone are consistent with those in Table 0.5.2. Given the significantly
different event selection criteria, each channel probes different kinematic
and topological features of the same background processes and variations in
the value of the scale factors across channels are to be expected.

Figure 2 shows $\pt(\cmsSymbolFace{V})$ distributions together with examples
of distributions for variables in different control regions and for different
channels after the scale factors in Table 0.5.2 have been applied to the
corresponding simulated samples. Figure 3 shows examples of
$\mathrm{CMVA_{min}}$ and event BDT distributions, also for different control
regions and for different channels, where not only the scale factors are
applied but also the shapes of the distributions are allowed to vary according
to the treatment of systematic uncertainties from all nuisances in the signal-
extraction fit. These BDT distributions are from control regions and do not
participate in that fit. The signal region BDT distributions used in the fit
are presented in Section 0.7.

Data/MC scale factors for each of the main background processes in each
channel, as obtained from the combined signal-extraction fit to control and
signal region distributions described in Section 0.7. Electron and muon
samples in the 1- and 2-lepton channels are fit simultaneously to determine
average scale factors. The same scale factors for W+jets processes are used
for the 0- and 1-lepton channels.  Process 0-lepton 1-lepton 2-lepton
low-$\pt(\cmsSymbolFace{V})$ 2-lepton high-$\pt(\cmsSymbolFace{V})$ W0b
$1.14\pm 0.07$ $1.14\pm 0.07$ — — W1b $1.66\pm 0.12$ $1.66\pm 0.12$ — — W2b
$1.49\pm 0.12$ $1.49\pm 0.12$ — — Z0b $1.03\pm 0.07$ — $1.01\pm 0.06$ $1.02\pm
0.06$ Z1b $1.28\pm 0.17$ — $0.98\pm 0.06$ $1.02\pm 0.11$ Z2b $1.61\pm 0.10$ —
$1.09\pm 0.07$ $1.28\pm 0.09$ $0.78\pm 0.05$ $0.91\pm 0.03$ $1.00\pm 0.03$
$1.04\pm 0.05$

Figure 2:  Examples of distributions for variables in the simulated samples
and in data for different control regions and for different channels after
applying the data/MC scale factors in Table 0.5.2. The top row of plots is
from the 0-lepton Z+HF control region. The middle row shows variables in the
1-lepton control region. The bottom row shows variables in the 2-lepton Z+HF
control region. The plots on the left are always $\pt(\cmsSymbolFace{V})$.
Plots on the right show a key variable that is validated in that control
region. These variables are, from top to bottom, the azimuthal angle between
the two jets that comprise the Higgs boson, the reconstructed top quark mass,
and the ratio of $\pt(\cmsSymbolFace{V})$ and ${\pt\mathrm{(jj)}}$.

Figure 3:  Distributions in control regions after simulated samples are fit to
the data in the signal extraction fit. On the left are examples of
$\mathrm{CMVA_{min}}$ distributions, while on the right are corresponding
event BDT distributions of the same control regions as the plots on the left.
Note that these BDT distributions are not part of the fit and are primarily
for validation. The control regions shown from top to bottom are: for the
0-lepton channel, low-mass HF for the single-muon channel, and HF for the
dielectron channel.

In inclusive vector boson samples, selected for this analysis, the
$\pt(\cmsSymbolFace{V})$ spectrum in data is observed to be softer than in
simulated samples, as expected from higher-order electroweak corrections to
the production processes [89]. The events in all three channels are re-
weighted to account for the electroweak corrections to
$\pt(\cmsSymbolFace{V})$. The correction is negligible for low
$\pt(\cmsSymbolFace{V})$ but is sizable at high $\pt(\cmsSymbolFace{V})$,
reaching 10% near 400.

After these corrections, a residual discrepancy in $\pt(\cmsSymbolFace{V})$
between data and simulated samples is observed in some control regions. In the
0-lepton channel, samples are re-weighted as a function of the generated top
quark’s according to the observed discrepancies in data and simulated samples
in differential top quark cross section measurements [90]. This re-weighting
resolves the discrepancy in $\pt(\cmsSymbolFace{V})$ in control regions. In
the 1-lepton channel, additional corrections are needed for W+jets samples,
and corrections are derived from the data in 1-lepton control regions for
these processes: , +udscg, and the sum of +b, +bb, and single top quark
backgrounds. A re-weighting of simulated events in $\pt(\cmsSymbolFace{V})$ is
derived for each, such that the shape of the sum of simulated processes
matches the data. The correction functions are extracted through a
simultaneous fit of linear functions in $\pt(\cmsSymbolFace{V})$. The
uncertainties in the fit parameters are used to assess the systematic
uncertainty. The $\pt(\cmsSymbolFace{V})$ spectra resulting from re-weighting
in either the top quark or $\pt(\cmsSymbolFace{V})$ are equivalent.

The $\cmsSymbolFace{V}$+jets LO simulated samples are used in the analysis
because, due to computing resource limitations, considerably more events are
available than for the NLO samples. A normalization K factor is applied to the
LO samples to account for the difference in cross sections. Kinematic
distributions between the two samples are found to be consistent after
matching the LO distribution of the pseudorapidity separation
$\Delta\eta(\mathrm{jj})$ between the two $\PH\to\bbbar$ jet candidates to the
NLO one. Different corrections are derived depending on whether these two jets
are matched to zero, one, or two b quarks. Both the $\Delta\eta(\mathrm{jj})$
distributions of the NLO samples and the corrected LO samples agree well with
data in control regions.

##  0.6 Systematic uncertainties

Systematic effects affect the $\PH\to\bbbar$ mass resolution, the shapes of
the $\mathrm{CMVA_{min}}$ distributions, the shapes of the event BDT
distributions, and the signal and background yields in the most sensitive
region of the BDT distributions. The uncertainties associated with the
normalization scale factors of the simulated samples for the main background
processes have the largest impact on the uncertainty in the fitted signal
strength $\mu$. The next largest effects result from the size of the simulated
samples and from uncertainties in correcting mismodeling of kinematic
variables, both in signal and in background simulated samples. The next group
of significant systematic uncertainties are related to tagging uncertainties
and uncertainties in jet energy. All systematic uncertainties considered are
listed in Table 0.6 and are described in more detail below, in the same order
as they appear in the Table.

Effect of each source of systematic uncertainty in the expected signal
strength $\mu$. The third column shows the uncertainty in $\mu$ from each
source when only that particular source is considered. The last column shows
the percentage decrease in the uncertainty when removing that specific source
of uncertainty while applying all other systematic uncertainties. Due to
correlations, the total systematic uncertainty is larger than the sum in
quadrature of the individual uncertainties. The second column shows whether
the source affects only the normalization or both the shape and normalization
of the event BDT output distribution. See text for details.  Individual
contribution Effect of removal to Source Type to the $\mu$ uncertainty (%) the
$\mu$ uncertainty (%) Scale factors (, V+jets) norm. 9.4 3.5 Size of simulated
samples shape 8.1 3.1 Simulated samples’ modeling shape 4.1 2.9 tagging
efficiency shape 7.9 1.8 Jet energy scale shape 4.2 1.8 Signal cross sections
norm. 5.3 1.1 Cross section uncertainties norm. 4.7 1.1 (single-top, VV) Jet
energy resolution shape 5.6 0.9 tagging mistag rate shape 4.6 0.9 Integrated
luminosity norm. 2.2 0.9 Unclustered energy shape 1.3 0.2 Lepton efficiency
and trigger norm. 1.9 0.1

The sizes of simulated samples are sometimes limited. If the statistical
uncertainty in the content of certain bins in the BDT distributions for the
simulated samples is large, Poissonian nuisance parameters are used in the
signal extraction binned-likelihood fit. These are required mainly in the
V+jets samples and are a leading source of systematic uncertainty in the
analysis.

The corrections to the $\pt(\cmsSymbolFace{V})$ spectra in the and W+jets
samples are applied per sample according to the uncertainty in the
simultaneous $\pt(\cmsSymbolFace{V})$ fit described in Section 0.5.2. This
uncertainty on the correction is at most 5% on the background yield near
$\pt(\cmsSymbolFace{V})$ of 400. The shape difference in the event BDT and
$\mathrm{CMVA_{min}}$ distributions between simulations of two event
generators are used to account for imperfect modeling in the nominal simulated
samples. For the $\cmsSymbolFace{V}$+jets, the difference between the shapes
for events generated with the MC generator at LO and NLO is considered as a
shape systematic uncertainty. For the process, the differences in the shapes
between the nominal sample generated with and that obtained from the [91]
generator are considered as shape systematic uncertainties. Variations on the
QCD factorization and renormalization scales and on the PDF choice are
considered for the simulated signal and background samples. The scales are
varied by factors of 0.5 and 2.0, independently, while the PDF uncertainty
effect on the shapes of the BDT distributions is evaluated by using the PDF
replicas associated to the NNPDF set [65].

The tagging efficiencies and the probability to tag as a b jet a jet
originating from a different flavor (mistag) are measured in heavy-flavor
enhanced samples of jets that contain muons and are applied consistently to
jets in signal and background events. The measured uncertainties for the
tagging scale factors are: 1.5% per -quark tag, 5% per charm-quark tag, and
10% per mistagged jet (originating from gluons and light , , or quarks) [79].
These uncertainties are propagated to the $\mathrm{CMVA_{min}}$ distributions
by re-weighting events. The shape of the event BDT distribution is also
affected by the shape of the CMVA distributions because $\mathrm{CMVA_{min}}$
is an input to the BDT discriminant. For the 2-lepton channel
$\mathrm{CMVA_{max}}$ is also an input to this discriminant. The signal
strength uncertainty increases by 8% and 5%, respectively, due to tagging
efficiency and mistag scale factor uncertainties propagated through the CMVA
distributions and finally to the event BDT distributions.

The uncertainties in the jet energy scale and resolution have an effect on the
shape of the event BDT output distribution because the dijet invariant mass is
a crucial input variable to the BDT discriminant. The impact of the jet energy
scale uncertainty is determined by recomputing the BDT output distribution
after shifting the energy scale up and down by its uncertainty. Similarly, the
impact of the jet energy resolution is determined by recomputing the BDT
output distribution after increasing or decreasing the jet energy resolution.
The uncertainties in jet energy scale and resolution affect not only the jets
in the event but also the ${p}_{\mathrm{T}}^{\text{miss}}$, which is
recalculated when these variations are applied. The individual contribution to
the increase in signal strength uncertainty is found to be around 6% for the
jet energy scale and 4% for the jet energy resolution uncertainty. The
uncertainty in the jet energy scale and resolution vary as functions of jet
and $\eta$. For the jet energy scale there are several sources of uncertainty
that are derived and applied independently as they are fully uncorrelated
between themselves [92], while for the jet energy resolution a single shape
systematic is evaluated.

The total ${\cmsSymbolFace{V}}\PH$ signal cross section has been calculated to
NNLO+NNLL accuracy in QCD, combined with NLO electroweak corrections, and the
associated systematic uncertainties [63] include the effect of scale
variations and PDF uncertainties. The estimated uncertainties in the NLO
electroweak corrections are 7% for the $\PW\PH$ and 5% for the $\cPZ\PH$
production processes, respectively. The estimate for the NNLO QCD correction
results in an uncertainty of 1% for the $\PW\PH$ and 4% for the $\cPZ\PH$
production processes, which includes the ggZH contribution.

An uncertainty of 15% is assigned to the event yields obtained from simulated
samples for both single top quark and diboson production. These uncertainties
are about 25% larger than those from the CMS measurements of these processes
[93, 94, 95], to account for the different kinematic regime in which those
measurements are performed.

Another source of uncertainty that affects the
${p}_{\mathrm{T}}^{\text{miss}}$ reconstruction is the estimate of the energy
that is not clustered in jets [77]. This affects only the 0- and 1-lepton
channels, with an individual contribution to the signal strength uncertainty
of 1.3%.

Muon and electron trigger, reconstruction, and identification efficiencies in
simulated samples are corrected for differences in data and simulation using
samples of leptonic boson decays. These corrections are affected by
uncertainties coming from the efficiency measurement method, the lepton
selection, and the limited size of the Z boson samples. They are measured and
propagated as functions of lepton and $\eta$. The parameters describing the
turn-on curve that parametrizes the $\cPZ(\cPgn\cPgn)\PH$ trigger efficiency
as a function of ${p}_{\mathrm{T}}^{\text{miss}}$ are varied within their
statistical uncertainties, and are also estimated for different assumptions on
the methods used to derive the efficiency. The total individual impact of
these uncertainties on lepton identification and trigger efficiencies on the
measured signal strength is about 2%.

The uncertainty in the CMS integrated luminosity measurement is estimated to
be 2.5% [96]. Events in simulated samples must be re-weighted such that the
distribution of pileup in the simulated samples matches that estimated in
data. A 5% uncertainty on pileup re-weighting is assigned, but the impact of
this uncertainty is negligible.

The combined effect of the systematic uncertainties results in a 25% reduction
of the expected significance for the SM Higgs boson rate.

##  0.7 Results

Results are obtained from combined signal and background binned-likelihood
fits, simultaneously for all channels, to both the shape of the output
distribution of the event BDT discriminants in the signal region and to the
$\mathrm{CMVA_{min}}$ distributions for the control regions corresponding to
each channel. The BDT discriminants are trained separately for each channel to
search for a Higgs boson with a mass of 125. To remove the background-
dominated portion of the BDT output distribution, only events with a BDT
output value above thresholds listed in Table 0.5.1 are considered. To achieve
a better sensitivity in the search, this threshold is optimized separately for
each channel. In this signal-extraction fit, the shape and normalization of
all distributions for signal and for each background component are allowed to
vary within the systematic and statistical uncertainties described in Section
0.6. These uncertainties are treated as independent nuisance parameters in the
fit. Nuisance parameters, the signal strength, and the scale factors described
in Section 0.5.2 are allowed to float freely and are adjusted by the fit.

In total, seven event BDT output distributions are included in the fit: one
for the 0-lepton channel, one for each lepton flavor for the 1-lepton
channels, and two for each lepton flavor for the 2-lepton channels
(corresponding to the two $\pt(\cmsSymbolFace{V})$ regions). The number of
$\mathrm{CMVA_{min}}$ distributions included is 24, corresponding to the
control regions listed in Tables 0.5.2–0.5.2: three for the 0-lepton channel,
four for each lepton flavor for the 1-lepton channels, and six for each lepton
flavor for the 2-lepton channels (each corresponding to one of two
$\pt(\cmsSymbolFace{V})$ regions). Figure 4 shows the seven BDT distributions
after they have been adjusted by the fit. Figure 5 combines the BDT output
values of all channels where the events are gathered in bins of similar
expected signal-to-background ratio, as given by the value of the output of
their corresponding BDT discriminant. The observed excess of events in the
bins with the largest signal-to-background ratio is consistent with what is
expected from the production of the SM Higgs boson. To detail this excess, the
total numbers of events for all backgrounds, for the SM Higgs boson signal,
and for data are shown in Table 0.7 for each channel, for the rightmost 20%
region of the BDT output distribution, where the sensitivity is large. The
simulation yields are adjusted using the results of fit.

Figure 4: Post-fit event BDT output distributions for the 13data (points with
error bars), for the 0-lepton channel (top), for the 1-lepton channels
(middle), and for the 2-lepton low-$\pt(\cmsSymbolFace{V})$ and
high-$\pt(\cmsSymbolFace{V})$ regions (bottom). The bottom inset shows the
ratio of the number of events observed in data to that of the prediction from
simulated samples for the SM Higgs boson signal and for backgrounds.

The total numbers of events in each channel, for the rightmost 20% region of
the event BDT output distribution, are shown for all background processes, for
the SM Higgs boson ${\cmsSymbolFace{V}}\PH$ signal, and for data. The yields
from simulated samples are computed with adjustments to the shapes and
normalizations of the BDT distributions given by the signal extraction fit.
The signal-to-background ratio (S/B) is also shown.  Process 0-lepton 1-lepton
2-lepton low-$\pt(\cmsSymbolFace{V})$ 2-lepton high-$\pt(\cmsSymbolFace{V})$
Vbb 216.8 102.5 617.5 113.9 Vb 31.8 20.0 141.1 17.2 V+udscg 10.2 9.8 58.4 4.1
34.7 98.0 157.7 3.2 Single top quark 11.8 44.6 2.3 0.0 VV(udscg) 0.5 1.5 6.6
0.5 VZ(bb) 9.9 6.9 22.9 3.8 Total background 315.7 283.3 1006.5 142.7
${\cmsSymbolFace{V}}\PH$ 38.3 33.5 33.7 22.1 Data 334 320 1030 179 S/B 0.12
0.12 0.033 0.15

Figure 5: Combination of all channels into a single event BDT distribution.
Events are sorted in bins of similar expected signal-to-background ratio, as
given by the value of the output of their corresponding BDT discriminant
(trained with a Higgs boson mass hypothesis of 125). The bottom plots show the
ratio of the data to the background-only prediction.

The significance of the observed excess of events in the signal extraction fit
is computed using the standard LHC profile likelihood asymptotic approximation
[97, 98, 99, 100]. For $m_{\PH}=125.09$, it corresponds to a local
significance of 3.3 standard deviations away from the background-only
hypothesis. This excess is consistent with the SM prediction for Higgs boson
production with signal strength
$\mu=1.19^{+0.21}_{-0.20}\stat^{+0.34}_{-0.32}\syst$. The expected
significance is 2.8 standard deviations with $\mu=1.0$. Together with this
result, Table 0.7 also lists the expected and observed significances for the
0-lepton channel, for the 1-lepton channels combined, and for the 2-lepton
channels combined.

The expected and observed significances for ${\cmsSymbolFace{V}}\PH$
production with $\PH\to\bbbar$ are shown, for $m_{\PH}=125.09$, for each
channel fit individually as well as for the combination of all three channels.
Channels Significance Significance expected observed 0-lepton 1.5 0.0 1-lepton
1.5 3.2 2-lepton 1.8 3.1 Combined 2.8 3.3

The observed signal strength $\mu$ is shown in the lower portion of Fig. 6 for
0-, 1- and 2-lepton channels. The observed signal strengths of the three
channels are consistent with the combined best fit signal strength with a
probability of 5%. In the upper portion of Fig. 6 the signal strengths for the
separate $\PW\PH$ and $\cPZ\PH$ production processes are shown. The two
production modes are consistent with the SM expectations within uncertainties.
The fit for the $\PW\PH$ and $\cPZ\PH$ production modes is not fully
correlated to the analysis channels because the analysis channels contain
mixed processes. The $\PW\PH$ process contributes approximately 15% of the
Higgs boson signal event yields in the 0-lepton channel, resulting from events
in which the lepton is outside the detector acceptance, and the ZH process
contributes less than 3% to the 1-lepton channel when one of the leptons is
outside the detector acceptance.

Figure 6:  The best fit value of the signal strength $\mu$, at
$m_{\PH}=125.09$, is shown in black with a green uncertainty band. Also shown
are the results of a separate fit where each channel is assigned an
independent signal strength parameter. Above the dashed line are the $\PW\PH$
and $\cPZ\PH$ signal strengths derived from a fit where each production mode
is assigned an independent signal strength parameter.

Figure 7 shows a dijet invariant mass distribution, combined for all channels,
for data and for the ${\cmsSymbolFace{V}}\PH$ and ${\cmsSymbolFace{V}}\cPZ$
processes, with all other background processes subtracted. The distribution is
constructed from all events that populate the signal region event BDT
distributions shown in Fig. 4. The values of the scale factors and nuisance
parameters from the fit used to extract the ${\cmsSymbolFace{V}}\PH$ signal
are propagated to this distribution. To better visualize the contribution of
events from signal, all events are weighted by $\mathrm{S/(S{+}B)}$, where S
and B are the numbers of expected signal and total post-fit background events
in the bin of the output of the BDT distribution in which each event is
contained. The data are consistent with the production of a standard model
Higgs boson decaying to . In the Figure, aside from the weights, which favor
the ${\cmsSymbolFace{V}}\PH$ process, the event yield from
${\cmsSymbolFace{V}}\cPZ$ processes is reduced significantly due to the
$\pt(\cmsSymbolFace{V})$ and $M\mathrm{(jj)}$ selection requirements for the
${\cmsSymbolFace{V}}\PH$ signal region, and from the training of the BDT that
further discriminates against diboson processes.

Figure 7: Weighted dijet invariant mass distribution for events in all
channels combined. Shown are data and the ${\cmsSymbolFace{V}}\PH$ and
${\cmsSymbolFace{V}}\cPZ$ processes with all other background processes
subtracted. Weights are derived from the event BDT output distribution as
described in the text.

###  0.7.1 Extraction of ${\cmsSymbolFace{V}}\cPZ$ with $\cPZ\to\bbbar$

The ${\cmsSymbolFace{V}}\cPZ$ process with $\cPZ\to\bbbar$, having a nearly
identical final state as ${\cmsSymbolFace{V}}\PH$ with $\PH\to\bbbar$, serves
as a validation of the methodology used in the search for the latter process.
To extract this diboson signal, event BDT discriminants are trained using as
signal the simulated samples for this process. All other processes, including
${\cmsSymbolFace{V}}\PH$ production (at the predicted SM rate), are treated as
background. The only modification made is the requirement that the signal
region $M\mathrm{(jj)}$ be in the $[60,160]$range.

The results from the combined fit for all channels of the control and signal
region distributions, as defined in Sections 0.5.1 and 0.5.2, are summarized
in Table 0.7.1 for the same $\sqrt{s}=13\TeV$ data used in the
${\cmsSymbolFace{V}}\PH$ search described above. The observed excess of events
for the combined $\PW\cPZ$ and $\cPZ\cPZ$ processes has a significance of 5.0
standard deviations from the background-only event yield expectation. The
corresponding signal strength, relative to the prediction of the generator at
NLO mentioned in Section 0.2, is measured to be
$\mu_{\cmsSymbolFace{V}\cmsSymbolFace{V}}={1.02}_{-0.23}^{+0.22}$.

Validation results for ${\cmsSymbolFace{V}}\cPZ$ production with
$\cPZ\to\bbbar$. Expected and observed significances, and the observed signal
strengths. Significance values are given in numbers of standard deviations.
Channels Significance Significance Signal strength expected observed observed
0-lepton 3.1 2.0 $0.57\pm 0.32$ 1-lepton 2.6 3.7 $1.67\pm 0.47$ 2-lepton 3.2
4.5 $1.33\pm 0.34$ Combined 4.9 5.0 $1.02\pm 0.22$

Figure 8 shows the combined event BDT output distribution for all channels,
with the content of each bin, for each channel, weighted by the expected
signal-to-background ratio. The excess of events in data, over background, is
shown to be compatible with the yield expectation from
${\cmsSymbolFace{V}}\cPZ$ production with $\cPZ\to\bbbar$.

Figure 8:  Combination of all channels in the ${\cmsSymbolFace{V}}\cPZ$
search, with $\cPZ\to\bbbar$ into a single event BDT distribution. Events are
sorted in bins of similar expected signal-to-background ratio, as given by the
value of the output of their corresponding BDT discriminant. The bottom inset
shows the ratio of the data to the predicted background, with a red line
overlaying the expected SM contribution from ${\cmsSymbolFace{V}}\cPZ$ with
$\cPZ\to\bbbar$.

###  0.7.2 Combination with Run 1 VH(bb) analysis

The results from the search for ${\cmsSymbolFace{V}}\PH$ with $\PH\to\bbbar$,
presented in this article, are combined with those from the similar searches
performed by the CMS experiment [38, 36, 18] during Run 1 of the LHC, using
proton-proton collisions at $\sqrt{s}=7$ and 8with data samples corresponding
to integrated luminosities of up to 5.1 and 18.9, respectively. The
combination yields an observed signal significance, at $m_{\PH}=125.09$, of
3.8 standard deviations, where 3.8 are expected. The corresponding signal
strength is $\mu=1.06^{+0.31}_{-0.29}$. All systematic uncertainties are
assumed to be uncorrelated in the combination, except for cross section
uncertainties derived from theory, which are assumed to be fully correlated.
Treating all uncertainties as uncorrelated has a negligible effect on the
significance. Table 0.7.2 lists these results.

The expected and observed significances and the observed signal strengths for
${\cmsSymbolFace{V}}\PH$ production with $\PH\to\bbbar$ for Run 1 data [18],
Run 2 (2016) data, and for the combination of the two. Significance values are
given in numbers of standard deviations.  Data used Significance Significance
Signal strength expected observed observed Run 1 2.5 2.1
$0.89_{-0.42}^{+0.44}$ Run 2 2.8 3.3 $1.19_{-0.38}^{+0.40}$ Combined 3.8 3.8
$1.06_{-0.29}^{+0.31}$

##  0.8 Summary

A search for the standard model (SM) Higgs boson (H) when produced in
association with an electroweak vector boson and decaying to a pair is
reported for the $\cPZ(\nu\nu)\PH$, $\PW(\mu\nu)\PH$, $\PW(\Pe\nu)\PH$,
$\cPZ(\mu\mu)\PH$, and $\cPZ(\Pe\Pe)\PH$ processes. The search is performed in
data samples corresponding to an integrated luminosity of 35.9at
$\sqrt{s}=13\TeV$, recorded by the CMS experiment at the LHC. The observed
signal significance, for $m_{\PH}=125.09$, is 3.3 standard deviations, where
the expectation from the SM Higgs boson production is 2.8. The corresponding
signal strength is $\mu=1.2\pm 0.4$.

The combination of this result with the one from the same measurement
performed by the CMS Collaboration in Run 1 of the LHC using proton-proton
collisions at $\sqrt{s}=7$ and 8with data samples corresponding to integrated
luminosities of up to 5.1 and 18.9, respectively, yields an observed signal
significance of 3.8 standard deviations, where 3.8 are expected from the SM
signal. The corresponding signal strength is $\mu=1.06^{+0.31}_{-0.29}$.

The result presented in this article provides evidence for the decay of the
Higgs boson into a pair of b quarks with a rate consistent with the SM
expectation.

###### Acknowledgements.

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

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##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Maerschalk, A. Marinov, T. Seva, E. Starling, C. Vander
Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University,
Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
S. Salva, M. Tytgat, W. Verbeke, N. Zaganidis Université Catholique de
Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior,
C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio
de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J.
Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira
Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,
Brazil
S. Ahujaa, C.A. Bernardesa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy of Bulgaria Academy
of Sciences
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,
G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, S. Zhang, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F.
Zhang5 Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, J.D. Ruiz Alvarez, M.A. Segura Delgado University of Split, Faculty
of Electrical Engineering, Mechanical Engineering and Naval Architecture,
Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran8,9, S. Elgammal9, A. Mahrous10 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, H.
Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of Technology,
Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, P. Gras, G. Hamel de Monchenault, P. Jarry, I. Kucher, C.
Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P. Busson,
L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, S. Lisniak, A.
Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P.
Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T. Strebler, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram11, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte11, X. Coubez, J.-C. Fontaine11, D. Gelé, U.
Goerlach, M. Jansová, A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul
de l’Institut National de Physique Nucleaire et de Physique des Particules,
CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, R. Chierici, D. Contardo, P. Depasse, H.
El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille,
F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov12, V. Sordini, M. Vander Donckt, S. Viret Georgian Technical
University, Tbilisi, Georgia
T. Toriashvili13 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze7 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, V. Zhukov12 RWTH Aachen University, III.
Physikalisches Institut A, Aachen, Germany
A. Albert, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg,
T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K.
Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T.
Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, D. Teyssier, S. Thüer
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl14 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras15, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo16, J. Garay Garcia, A.
Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb,
J. Hauk, M. Hempel17, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, I.
Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, J. Leonard, K. Lipka, W.
Lohmann17, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, G. Mittag, J. Mnich,
A. Mussgiller, E. Ntomari, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R.
Shevchenko, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, T. Lapsien, D. Marconi, M. Meyer, M. Niedziela,
D. Nowatschin, F. Pantaleo14, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper,
A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Institut für Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann14, S.M. Heindl, U. Husemann, F.
Kassel14, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou National Technical
University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres18 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, Á. Hunyadi, F. Sikler, V. Veszpremi
Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók18, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati21, S. Bhowmik, P. Mal, K. Mandal, A. Nayak22, D.K. Sahoo21, N.
Sahoo, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur,
S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of
Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep, S. Dey, S. Dutt,
S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S.
Nandan, A. Purohit, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, S. Thakur
Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty14, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity23, G. Majumder, K. Mazumdar, T. Sarkar23, N. Wickramage24
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani25, E. Eskandari Tadavani, S.M. Etesami25, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi26, F. Rezaei
Hosseinabadi, B. Safarzadeh27, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, F. Erricoa,b, L. Fiorea, G. Iasellia,c, S.
Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A.
Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A.
Sharmaa, L. Silvestrisa,14, R. Vendittia, P. Verwilligena INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria,
A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, S.
Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, A.M.
Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione di Catania a,
Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, P. Lenzia,b, M. Meschinia, S. Paolettia, L.
Russoa,28, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera14 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,14, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,29, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,14, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,14, P. Paoluccia,14, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, P. De Castro
Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, M. Margonia,b, A.T. Meneguzzoa,b, N. Pozzobona,b,
P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, E. Torassaa, M. Zanettia,b, P.
Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia,
Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V.
Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN
Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore di Pisa c,
Pisa, Italy
K. Androsova, P. Azzurria,14, G. Bagliesia, T. Boccalia, L. Borrello, R.
Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L. Gianninia,c, A.
Giassia, M.T. Grippoa,28, F. Ligabuea,c, T. Lomtadzea, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-Navarroa,30, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b,14, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,b,
S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, R.
Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonbuk National University, Jeonju, Korea
A. Lee Chonnam National University, Institute for Universe and Elementary
Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali31, F. Mohamad Idris32, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz33, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana,
Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. 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
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev35,36, V. Palichik, V.
Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin, A.
Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim37, E. Kuznetsova38, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin36 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov39, M. Danilov39, P. Parygin, D. Philippov, S. Polikarpov, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin36, I. Dremin36, M. Kirakosyan36, 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, V.
Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, M. Perfilov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov41, Y.Skovpen41, D. Shtol41 State Research Center of Russian
Federation, Institute for High Energy Physics, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic42, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain
J. Alcaraz Maestre, I. Bachiller, M. Barrio Luna, M. Cerrada, N. Colino, B. De
La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, D.
Moran, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, A. Quintario Olmeda, I.
Redondo, L. Romero, M.S. Soares, A. Álvarez Fernández Universidad Autónoma de
Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, M. Missiroli Universidad de Oviedo, Oviedo,
Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R.
González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P. Vischia, J.M.
Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-Universidad de
Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, J. Duarte Campderros,
M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, C.
Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, T.
Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar
Cortabitarte CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, P. Bloch, A. Bocci, C. Botta, T. Camporesi, R. Castello,
M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V.
Daponte, A. David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du
Pree, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita,
G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, P. Harris, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O.
Karacheban17, J. Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M.
Krammer1, C. Lange, P. Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M.
Mannelli, A. Martelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P.
Milenovic43, F. Moortgat, M. Mulders, H. Neugebauer, J. Ngadiuba, S.
Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G.
Petrucciani, A. Pfeiffer, M. Pierini, D. Rabady, A. Racz, T. Reis, G.
Rolandi44, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M.
Selvaggi, A. Sharma, P. Silva, P. Sphicas45, A. Stakia, J. Steggemann, M.
Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns46, M. Verweij,
W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada47, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr Institute for Particle Physics and Astrophysics (IPA), Zurich,
Switzerland
M. Backhaus, L. Bäni, P. Berger, L. Bianchini, B. Casal, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, G.
Kasieczka, T. Klijnsma, W. Lustermann, B. Mangano, M. Marionneau, M.T.
Meinhard, D. Meister, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi,
J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat, M. Reichmann, D.A.
Sanz Becerra, M. Schönenberger, L. Shchutska, V.R. Tavolaro, K. Theofilatos,
M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
T.K. Aarrestad, C. Amsler48, M.F. Canelli, A. De Cosa, R. Del Burgo, S.
Donato, C. Galloni, T. Hreus, B. Kilminster, D. Pinna, G. Rauco, P. Robmann,
D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, A. Zucchetta National
Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci49, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos50, E.E. Kangal51, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut52, K. Ozdemir53, D. Sunar
Cerci49, B. Tali49, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar54, K. Ocalan55, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
E. Gülmez, M. Kaya56, O. Kaya57, S. Tekten, E.A. Yetkin58 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, I. Köseoglu Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold59,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev60, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A.
Cockerill, J.A. Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D.
Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams
Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, S.
Casasso, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies, A. De Wit, M.
Della Negra, R. Di Maria, A. Elwood, Y. Haddad, G. Hall, G. Iles, T. James, R.
Lane, C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T.
Matsushita, J. Nash, A. Nikitenko6, V. Palladino, M. Pesaresi, D.M. Raymond,
A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, S. Summers, A.
Tapper, K. Uchida, M. Vazquez Acosta61, T. Virdee14, N. Wardle, D.
Winterbottom, J. Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, A. Garabedian, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J.
Pazzini, S. Piperov, S. Sagir, R. Syarif, D. Yu University of California,
Davis, Davis, USA
R. Band, C. Brainerd, D. Burns, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, W. Ko, R.
Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout, M. Shi, J.
Smith, D. Stolp, K. Tos, M. Tripathi, Z. Wang University of California, Los
Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, J. Heilman, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M.
Olmedo Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, M. Tadel, A. Vartak, S.
Wasserbaech62, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta University
of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta,
M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A. Ovcharova, H. Qu,
J. Richman, D. Stuart, I. Suarez, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C. Pena, M.
Spiropulu, J.R. Vlimant, S. Xie, Z. Zhang, R.Y. Zhu Carnegie Mellon
University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, T.
Mulholland, K. Stenson, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, J. Chu, S. Dittmer, K. Mcdermott, N. Mirman, J.R.
Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M. Tan,
Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National Accelerator
Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln,
R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K. Maeshima, N. Magini,
J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V.
O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, B. Schneider, E.
Sexton-Kennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait, N.
Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C.
Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber, A. Whitbeck University
of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, H. Trauger, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki63, W. Clarida, K. Dilsiz64, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya65, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul66, Y. Onel, F. Ozok67, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P.