HIG-20-001

HIG-20-001

[inst1]Christophorus Grab

# Measurement of simplified template cross sections of the Higgs boson
produced in association with or bosons in the $\PH\to\PQb\PAQb$ decay channel
in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

Differential cross sections are measured for the standard model Higgs boson
produced in association with vector bosons (, ) and decaying to a pair of
quarks. Measurements are performed within the framework of the simplified
template cross sections. The analysis relies on the leptonic decays of the and
bosons, resulting in final states with 0, 1, or 2 electrons or muons. The
Higgs boson candidates are either reconstructed from pairs of resolved -tagged
jets, or from single large distance parameter jets containing the particles
arising from two quarks. Proton-proton collision data at $\sqrt{s}=13\TeV$,
collected by the CMS experiment in 2016–2018 and corresponding to a total
integrated luminosity of 138, are analyzed. The inclusive signal strength,
defined as the product of the observed production cross section and branching
fraction relative to the standard model expectation, combining all analysis
categories, is found to be $\mu=1.15^{+0.22}_{-0.20}$. This corresponds to an
observed (expected) significance of 6.3 (5.6) standard deviations.

##  0.1 Introduction

The discovery of a Higgs boson () with a mass near 125by the ATLAS [1] and CMS
[2, 3] Collaborations at the CERN LHC was a major milestone in the
understanding of electroweak (EW) symmetry breaking in the standard model (SM)
of particle physics [4, 5, 6, 7, 8, 9]. Since its observation, most of the
Higgs boson production modes and many decay channels predicted by the SM have
been measured. In particular, decays of the Higgs boson into the $\PGg\PGg$,
$\PZ\PZ$, $\PW\PW$, $\PGt\PGt$, and $\PQb\PAQb$ channels have been observed
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. Higgs boson production in
association with a top quark-antiquark pair ($\ttbar$) [22, 23] has also been
observed. To date, all the measured properties [24, 25] are compatible with
the hypothesis that this particle is the Higgs boson predicted by the SM.
Evidence for the Higgs boson decaying to a pair of muons has been reported by
the CMS Collaboration, which further supports compatibility with the SM
hypothesis [26].

For an SM Higgs boson with a mass ($m_{\PH}$) of 125, the largest Yukawa
coupling that is directly accessible by studying a specific Higgs boson decay
channel is the coupling between the Higgs boson and quarks. The Yukawa
couplings to fermions are proportional to the fermion masses, and the quark is
the heaviest fermion into pairs of which the Higgs boson decay is
kinematically allowed. Therefore, out of all Higgs boson decay channels,
$\PH\to\PQb\PAQb$ decay has the largest branching fraction. This decay was
previously observed by the ATLAS and CMS Collaborations [21, 27]. Using the
data collected at $\sqrt{s}=7$, 8, and 13in 2011–2017, the CMS measurement of
the $\PH\to\PQb\PAQb$ decay in the vector boson (, $\PV=\PW,\PZ$) associated
production mode ($\PV\PH$) had a significance of 4.8 standard deviations over
the background-only hypothesis. The corresponding measured signal strength,
defined as the production cross section times branching fraction relative to
its SM expectation, was $\mu=1.01\pm 0.22$. In combination with other
production modes, the measured signal strength in the $\PH\to\PQb\PAQb$
channel was $1.04\pm 0.20$, corresponding to an observed significance of 5.6
standard deviations [27].

The large size of the data set delivered by the LHC between 2016 and 2018
permits both the ATLAS and CMS Collaborations to make detailed measurements of
the kinematic properties of VH production using the $\PH\to\PQb\PAQb$ decay
channel. The analysis targets the following decay channels of the or boson:
$\PZ\to\PGn\PAGn$, $\PW\to\Pell\PGn$, and $\PZ\to\Pell\Pell$. These decay
modes are referred to as 0-lepton, 1-lepton, and 2-lepton channels,
respectively. The leptons considered in the analysis are electrons and muons.

The cross section measurement is performed in exclusive regions of phase space
defined according to the type of vector boson, its transverse momentum
$\pt(\PV)$, and the presence of additional jets. A standardized scheme for
these measurement regions, called bins, is given by the definition of
simplified template cross sections (STXS) [28, 29], which were introduced to
reduce the theoretical dependence in the measurements and to allow for a
straightforward comparison of theoretical models with the measurements.

While inclusive cross section measurements are suitable for establishing a
given decay mode with limited sample size, using the STXS approach allows the
kinematic properties of the Higgs boson production to be probed in a model-
independent way. The sensitivity to the $\PV\PH$ production STXS bins is
expected to be dominated by measurements of the $\PH\to\PQb\PAQb$ decay
channel. An STXS measurement in this channel was performed by the ATLAS
Collaboration with data collected between 2015 and 2018. The corresponding
inclusive signal strength was measured to be $1.02^{+0.18}_{-0.17}$ [30].

This paper reports the study of $\PV\PH$ production with subsequent
$\PH\to\PQb\PAQb$ decay, using the full CMS proton-proton ($\Pp\Pp$) collision
data set collected in 2016–2018 at $\sqrt{s}=13\TeV$. The integrated
luminosities are 36.3, 41.5, and 59.8 for the 2016 [31], 2017 [32], and 2018
[33] data-taking periods, respectively, corresponding to a total of 138.
Unlike in the previous publications [34, 35, 27], dedicated categories are
introduced for topologies that arise when the Higgs boson is highly Lorentz-
boosted so that its decay products are reconstructed as a single merged jet. A
similar large-radius-jet topology was explored by the ATLAS Collaboration
[36].

The paper is organized as follows. A brief description of the CMS detector and
the simulated samples used in the analysis is given in Section 0.2. The event
reconstruction, with particular emphasis on the objects used in the
measurement, is described in Section 0.3. The trigger-level selection employed
in the analysis is given in Section 0.4. The event selection and
categorization, as well as the description of the STXS scheme, are documented
in Section 0.5. The sources of systematic uncertainties included in the
measurement are listed in Section 0.6. The analysis strategy and results are
discussed in Section 0.7 with tabulated versions provided in HEPData [37].
Section 0.8 provides the summary of the analysis results.

##  0.2 The CMS detector and simulated samples

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. Events of
interest are selected using a two-tiered trigger system [38]. The first level,
composed of custom hardware processors, uses information from the calorimeters
and muon detectors to select events at a rate of around 100kHz within a fixed
time interval of about 4. The second level, known as the high-level trigger,
consists of a farm of processors running a version of the full event
reconstruction software optimized for fast processing, and reduces the event
rate to around 1kHz before data storage. A more detailed description of the
CMS detector, together with a definition of the coordinate system used and the
relevant kinematic variables, can be found in Ref. [39].

Several Monte Carlo (MC) event generators are used to simulate signal and
background processes. The signal processes contain Higgs bosons, with
$m_{\PH}=125\GeV$, produced in association with or bosons. In this analysis,
only decays of the and bosons involving muons, electrons, and/or neutrinos are
considered. The quark-induced $\PZ\PH$ and $\PW\PH$ processes are generated at
next-to-leading order (NLO) using the v2 [40, 41, 42] event generator extended
with the MINLO procedure [43, 44], while the gluon-induced $\PZ\PH$ process
(denoted $\Pg\Pg\PZ\PH$) is generated at leading-order (LO) accuracy with v2.
The diboson processes $\PZ\PZ$, $\PW\PZ$, and $\PW\PW$ are simulated with [45]
v2.2.2 (v2.4.2) in 2016 (2017 and 2018) simulations at NLO using the FxFx
merging scheme [46] with up to two additional partons. For the analysis of
2017 and 2018 data, the same generator with NLO accuracy is used to simulate
$\PW$+jets and $\PZ$+jets processes. For the analysis of 2016 data, LO
accurate MadGraph simulations with the MLM matching scheme [47] are used to
simulate $\PW$+jets and $\PZ$+jets events, in inclusive and -quark-enriched
configurations. Corrections at NLO and next-to-NLO (NNLO) accuracy are applied
to the 2016 LO $\PW$+jets and $\PZ$+jets event samples to achieve NNLO
precision in shape and normalization. The NLO corrections are derived from
simulation, while the NNLO corrections are obtained from theoretical
calculations [48]. While the NLO samples provide a more precise modeling of
the kinematic properties of the $\PV$+jets processes, and are used for
analyzing the 2017 and 2018 data sets, the corrections for the LO samples used
in the analysis of the 2016 data are well validated. The production of
simulated samples at NLO requires more computing resources than the production
of samples at LO, which means the number of events in the NLO samples is
limited. Thus, the LO samples are used in the 2016 analysis to reduce the
uncertainty in the measurement arising from the limited size of the simulated
samples. All components of the NLO $\PV$+jets MC samples produced with
exclusive bins in jet multiplicity and $\pt(\PV)$ are merged to maximize the
statistical power of the analysis. Two or more samples contributing to
overlapping regions of the phase space are reweighted, such that the total
cross section of a given process is conserved. The simulated $\PW$+jets and
$\PZ$+jets samples at NLO are additionally corrected differentially in the
angular momentum separation between jets ($\Delta R(\text{jj})$), in the
region $\Delta R(\text{jj})<1$. This correction improves the agreement between
data and the NLO $\PV$+jets predictions. Shape-altering uncertainties
associated with this correction will be discussed in Section 0.6.

Samples of events, as well as those of single top quark events produced in the
$t$ channel, are generated with v2. Samples of single top quark events
produced in the $\PQt\PW$ and $s$ channels are generated with v1.

The production cross sections used to normalize the simulated samples of
signal and $\PV$+jets events are rescaled to NNLO quantum chromodynamic (QCD)
predictions, inclusively in $\pt(\PV)$, and NLO EW accuracy, combining the
results from Refs. [49, 50, 51], VH@NNLO [51, 52], and HAWK v2.0 [53]
generators, as described in Ref. [28]. The NLO EW correction is applied as a
function of $\pt(\PV)$. The production cross section for events is calculated
at NNLO with next-to-next-to-leading-logarithmic (NNLL) precision obtained
using v2.0 [54]. The parton distribution functions (PDFs) used to produce the
NLO samples in the 2017–2018 analyses are from the NNLO NNPDF3.1 set [55],
while the LO NNPDF3.0 set is used for the LO samples. For parton showering and
hadronization, all simulated samples are interfaced with 8.2 [56]. The
parameters for the underlying event description correspond to the CUETP8M1
tune for the samples compatible with the 2016 data set, and to the CP5 tune
for the simulation corresponding to the 2017 and 2018 data sets. These tunes
were derived in Ref. [57] based on the work described in Ref. [58]. For all
processes, the detector response is simulated with a detailed description of
the CMS detector, based on the package [59]. The event reconstruction is
performed with the same algorithms as for data. Additional interactions in the
same or nearby bunch crossings, referred to as pileup (PU), are generated with
and added to the simulated samples. The simulated events are weighted such
that the PU distribution in the simulation matches the one observed in data.

##  0.3 Event reconstruction

Events are reconstructed using a particle-flow (PF) algorithm [60], which aims
to reconstruct and identify each individual particle in an event (PF
candidate) with an optimized combination of information from the various
elements of the CMS detector. The energy of photons is obtained from the ECAL
measurement. The energy of electrons is determined from a combination of the
electron momentum at the primary interaction vertex as determined by the
tracker, the energy of the corresponding ECAL cluster, and the energy sum of
all bremsstrahlung photons spatially compatible with originating from the
electron track. The energy of muons is obtained from the curvature of the
corresponding track. The energy of charged hadrons is determined from a
combination of their momentum measured in the tracker and the matching ECAL
and HCAL energy deposits, corrected for the effect of hadronic showers on the
calorimeter response. Finally, the energy of neutral hadrons is obtained from
the corresponding corrected ECAL and HCAL energies. Events that are found to
be affected by reconstruction failures and detector malfunctions are
identified and rejected. Correction factors are applied to all reconstructed
objects and used to equalize the reconstruction and the identification
efficiencies in data and simulation.

The primary vertex (PV) is taken to be the vertex corresponding to the hardest
scattering in the event, evaluated using tracking information alone, as
described in Ref. [61]. The PV position is reconstructed using tracks
clustered with the deterministic annealing algorithm [62]. The reconstructed
PV is required to have a $z$ position within 24cm of the nominal detector
center, and a radial position within 2cm of the beam axis. Displaced tracks
originating from hadron decays are associated with secondary vertices.

and bosons are reconstructed using charged leptons and missing transverse
momentum ($\ptvecmiss$). With two opposite-charge, same-flavor leptons, a full
boson reconstruction is performed, which defines the 2-lepton channel. With
one charged lepton and interpreting the missing transverse momentum in the
event as the transverse momentum of a neutrino, the transverse momentum of the
boson candidate can be reconstructed, which defines the 1-lepton channel. In
events without charged leptons, the large missing transverse momentum is used
to estimate the $\PZ(\PGn\PGn)$ boson transverse momentum directly. This
defines the 0-lepton channel.

Electrons require the matching of a set of ECAL clusters, denoted as
superclusters, to a track in the silicon tracker. The electron reconstruction
is performed with the Gaussian sum filter algorithm [63]. Electrons are
preselected by requiring $\pt>7\GeV$, $\abs{\eta}<2.4$, $d_{xy}<0.05\unit{cm}$
and $d_{z}<0.2\unit{cm}$, where $d_{xy}$ and $d_{z}$ are the transverse and
longitudinal impact parameters associated with the electron tracks,
respectively.

A tighter identification is then performed using a multivariate approach (MVA
ID). In addition, a set of offline requirements on ECAL-based electron
quantities is applied. Two selections on the MVA ID discriminant are used,
defining two different working points based on the expected electron
identification efficiency of either $90\%$ (loose working point) or $80\%$
(tight working point). The loose working point is used when counting the
number of additional leptons beyond the selected muons and electrons in each
event, as well as for the event selection of the $\PZ(\Pe\Pe)\PH$ channel. The
tight working point is required to select events in the $\PW(\Pe\PGn)\PH$
channel. The electron threshold in the $\PW(\Pe\PGn)\PH$ channel is $30\GeV$.
For the $\PZ(\Pe\Pe)\PH$ channel, the thresholds are 25 and $17\GeV$ for the
two electrons. The working points and isolation requirements for the 2-lepton
channel are generally looser than in the 1-lepton channel because requiring
two leptons significantly reduces the background from QCD multijet events. In
the 1-lepton channel, tighter requirements are needed to reduce the multijet
background.

Muons are reconstructed from the combined fit of the tracker and muon detector
signals [64]. They are preselected by requiring the following conditions:
$\pt>5\GeV$, ${\abs{\eta}<2.4}$, ${d_{xy}<0.5\unit{cm}}$,
$d_{z}<1.0\unit{cm}$. Two working points corresponding to tight and loose muon
identification requirements are utilized to reduce the fraction of other
particles misidentified as muons. These working points depend on several of
the following identification criteria: the number of hits in the tracker and
muon system, the fit quality of the extrapolated muon track, and its
consistency with the reconstructed PV. The muon threshold in the
$\PW(\PGm\PGn)\PH$ channel is 25, and 25 and 15in the $\PZ(\PGm\PGm)\PH$
channel.

The isolation of a lepton is defined relative to its momentum by summing the
of PF candidates, excluding the lepton itself, in geometrical cones around the
lepton track direction at the event vertex. The cone size is expressed in
terms of $\Delta R=\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}$, where
$\Delta\phi$ ($\Delta\eta)$ is the difference in the azimuthal angle
(pseudorapidity) from the center of the cone to its edge. The lepton isolation
criteria reject most of the major background consisting of nonprompt leptons
produced in jets. The isolation cone for muons and electrons is $\Delta
R=0.4$, and the ratio of the sum of each particle’s within the cone to the
lepton must be smaller than 0.06.

Jets are reconstructed from PF candidates using the anti-clustering algorithm
with a distance parameter of 0.4 (AK4 jets) [65]. Jet momentum is determined
as the vectorial sum of all particle momenta in the jet, and is found from
simulation to be, on average, within 5–10% of the true momentum over the
entire spectrum and detector acceptance.

Pileup interactions can contribute additional tracks and calorimetric energy
depositions to the event, increasing the apparent jet momentum. To mitigate
this effect, tracks identified to be originating from PU vertices are
discarded and an offset correction [65] is applied to correct for remaining
contributions.

Jet energy corrections are derived from simulation studies so that the average
measured energy of jets is equal to that of particle-level jets. In situ
measurements of the momentum balance in dijet, photon+jet, $\PZ$+jet, and
multijet events are used to determine any residual differences between the jet
energy scale (JES) in data and simulation, and appropriate corrections are
made [65]. Additional selection criteria are applied to each jet to remove
jets potentially dominated by instrumental effects or reconstruction failures.
Jets that overlap geometrically ($\Delta R<0.4$) with preselected electrons or
muons are discarded. Only jets with $\abs{\eta}<2.5$ are considered. In the
$\PW\PH$ and $\PZ(\PGn\PGn)\PH$ channels, a minimum threshold of jet
$\pt>25\GeV$ is used, while a looser selection $(\pt>20\GeV)$ is applied in
the $\PZ(\Pell\Pell)\PH$ channel.

The jet energy resolution (JER) is about 15–20% at 20, 10% at 100, and 5% at
1, and the jet energies in simulation are smeared to ensure their resolutions
match those of jets in data [65].

Jets from final-state radiation (FSR), exceeding 20in momentum and fulfilling
jet quality criteria are recovered by an FSR recovery algorithm, which adds
the momenta of jets close to the Higgs boson candidate in the dijet mass
calculation.

A reweighting of the additional jet multiplicity spectrum in simulation is
applied to the NLO $\PW$+jets and $\PZ$+jets samples used in the analysis of
the 2017 and 2018 data sets. Here, additional jets are those jets that are
retained in the analysis selection, but that do not stem from the Higgs boson
candidate decay. This reweighting is continuous in $\pt(\PV)$ and achieves
improved modeling of the observed additional jet multiplicity distribution
from simulation compared to data. Several shape-altering systematic
uncertainties are associated with this reweighting, which will be discussed in
Section 0.6.

Jets that originate from the hadronization of quarks are identified by means
of an algorithm based on a deep neural network (DNN), named DeepCSV [66]. This
DNN has several probability outputs for jets resulting from quarks of
different flavors. The algorithm provides a continuous discriminant output
combining the information from track impact parameters and identified
secondary vertices within jets, and from low-$\pt$ leptons produced by heavy-
flavor quark decays present in the jet. A jet with a DeepCSV discriminant
value above a certain threshold is considered to be from the decay of a
hadron, called a -tagged jet. The efficiency for tagging jets and the rate at
which other jets are misidentified as jets depend on the chosen threshold of
the DeepCSV discriminant. The efficiency and the misidentification rate are
both parametrized as functions of the jet and $\eta$. The loose (tight)
threshold has the highest (lowest) efficiency for tagging jets, while allowing
the most (least) contamination from light, i.e., , , , and gluon () jets, as
well as jets. The working points are defined such that a specific target for
the misidentification (mistag) rate is achieved: 10, 1.0, and 0.1% for the
loose, medium, and tight working points, respectively. Each channel is
optimized separately, and results in the same selection: the higher-momentum
(leading) jet candidate must pass the medium identification working point of
the DeepCSV algorithm. For the lower-momentum (subleading) jet candidate, the
optimal selection requires the loose working point of the DeepCSV
discriminant. The tagging selections for all three channels will be discussed
in more detail in Section 0.5.

Some tagging inputs are used in the training of the multivariate discriminants
employed in the analysis signal region (SR) to separate signal from background
events. The tagging discriminant is corrected to equalize the efficiency in
data and simulation as a function of the tagging score and the kinematic
properties of each jet. An index with an integer value between 0 and 3
referring to the tagging working point requirement that the jet fulfills is
used as one of the inputs for deriving the signal-versus-background
discriminants.

The dijet invariant mass resolution is computed by applying a multivariate
regression analysis using a DNN trained on simulated jets stemming from events
[67]. The training includes input features that describe the jet energy and
direction, as well as properties of the secondary vertices of the jets.
Information about tracks associated with jets, jet constituents, low-$\pt$
electrons and muons in the jet associated with semileptonic hadron decays is
also used. The jet energy regression improves the precision of the jet four-
vectors, which leads to a 10–15% improvement in the dijet invariant mass
resolution, depending on the of the reconstructed Higgs boson candidate. The
momenta of the Higgs boson candidate jets are corrected by the application of
the jet energy regression described above, unless otherwise specified. After
the application of the regression, a dedicated smearing is applied to the jet
energy so that the dijet invariant mass resolution in simulation will match
the performance in data. The smearing parameters are extracted in events where
a jet recoils against a boson that decays into leptons. Because the boson is
balanced with the jet $\pt$, and given that the lepton momentum measurement is
precise, the ratio of the reconstructed jet momentum ($\pt^{j}$) to the boson
momentum ($\pt^{\Pell\Pell}$) enables a precise measurement of the jet
momentum and energy. The distribution of the energy difference between the
balance procedure and the jet energy regression is used to estimate the jet
energy regression scale and resolution corrections and uncertainties. The
selected events are divided into four regions of
$\alpha=\pt^{j}/\pt^{\Pell\Pell}$, and this procedure is applied in each
$\alpha$ bin.

In signal events where the Higgs boson has a exceeding 250, two jets
reconstructed with the AK4 clustering [68] algorithm will begin to overlap as
the opening angle between the jets shrinks. Therefore, these events are
reconstructed using a single large distance parameter of 0.8, producing what
are termed AK8 jets. The modified mass drop tagger algorithm (soft-drop
algorithm) [69] is applied to remove soft and wide-angle radiation from the
AK8 jets. This algorithm identifies two hard subjets within the AK8 jet. The
four-momenta of the two subjets are used to calculate the kinematic properties
of their corresponding AK8 jet.

The DeepAK8 algorithm [70] is used to tag boosted $\PH\to\PQb\PAQb$
topologies, exploiting AK8 jet (${\Delta R=0.8}$) reconstruction. The tagger
architecture is based on a set of convolutional kernels spanning multiple
candidates. It makes use of a multi-output feed-forward neural network with
low-level input features (PF candidates), in addition to the traditional
observables of the boosted-jet environment (tracks, jets, and secondary
vertices).

Ten features for each charged and neutral PF candidate are passed to one of
these convolutional kernels, ordered in candidate momentum, to learn the jet
substructure. The flavor content of the jet is learned by two other kernels.
One of these uses only charged constituents of the jet, sorted by the
displacement with respect to the PV, while the other one uses secondary
vertices. The DeepAK8 algorithm is decorrelated from the mass of the AK8 jet,
which is included in the training of the network. The level of decorrelation
is such that no accumulation of events in mass is observed even for very small
values of the background mistag rate. This means that the AK8 jet mass shape
associated with the background does not become similar to that of the signal
after selection with the DeepAK8 tagger. The DeepAK8 algorithm aims to
classify a variety of resonances in multiple decay modes. It provides
separation for the Lorentz-boosted $\PH\to\PQb\PAQb$ signal against several
background outputs: merged light jets, top quark jets, and QCD multijets.
Scale factors are used to correct the simulation to account for differences in
efficiency with respect to the data. These scale factor measurements are
applied to the $\PH\to\PQb\PAQb$ boosted signal output node by exploiting the
gluon splitting contribution to the boosted $\PQb\PAQb$ topology. The scale
factors are parametrized as a function of the AK8 jet and $\eta$, and are
available for two working points, as discussed in Section 0.5.

The value of $\ptvecmiss$ is crucial in the reconstruction of the
$\PW\to\Pell\PGn$ and $\PZ\to\PGn\PAGn$ decays. The $\ptvecmiss$ is computed
as the negative vector $\ptvec$ sum of all the PF candidates in an event, and
its magnitude is denoted as $\pt^{\text{miss}}$ [71]. The calculation of
$\ptvecmiss$ is modified to account for corrections to the energy scale of the
reconstructed jets in the event. Track-based missing transverse momentum,
denoted as $p_{\text{T,trk}}^{\text{miss}}$, is also used in the analysis.
Only tracks that have a above a minimum momentum threshold and an impact
parameter consistent with the PV are considered in the vectorial sum. The
tracks are also required to pass quality requirements, which were designed to
limit the contribution from misreconstructed tracks. The estimation of
$\ptvecmiss$ in simulated events is improved by correcting it for the
difference between raw and calibrated jets, including scale and resolution
corrections. In addition, a set of recommended filters to remove known issues
related to instrumental noise and problematic events is applied [71].

In background event topologies, such as $\ttbar$ events, there is additional
low-energy jet activity in the event (soft jets). A collection of additional
tracks associated with soft jets is built by requiring tracks to have
$\pt>300\MeV$, $d_{z}<2\unit{mm}$, a “high-purity” quality identification, and
not be associated with either the leptons from the vector boson decay nor with
the two selected -tagged jets in the event. After applying these requirements,
a collection of soft-track jets is clustered from these tracks using the anti-
clustering algorithm with a distance parameter of $\Delta R=0.4$. The soft-jet
activity is used as a discriminant variable for all the channels in both the
resolved and boosted topologies, as discussed in Section 0.5.

##  0.4 Trigger-level selection

Several triggers are used to collect events containing final-state particles
consistent with the signal processes considered. The trigger selection focuses
on the final state of the or boson produced in association with the Higgs
boson. The triggers used to select events in the 0-lepton channel make use of
and missing hadronic transverse momentum, . These quantities are derived from
the reconstructed objects as identified by the PF algorithm. Online, is
defined as the magnitude of the negative vector $\ptvec$ sum of all
reconstructed jets with $\pt>20\GeV$ and $\abs{\eta}<2.5$. The main triggers
used in each of the data-taking periods require the same threshold on
$\ptmiss$ and $\mht$. This threshold is 110in 2016, and 120in the 2017 and
2018 data-taking periods. In the 1-lepton channel, single-lepton triggers are
used. The threshold for electrons is 27in the 2016 data-taking period, rising
to 32in 2017–2018. For muons, the threshold is 24in the 2016 and 2018 data-
taking periods, and is increased to 27in 2017. Dilepton triggers are used to
select events in the 2-lepton channel. The thresholds for electrons are 23 and
12in all data-taking periods. For muons, the thresholds are 17 and 8in all
data-taking periods; the triggers used in 2017 and 2018 differ from those used
in 2016 by the additional requirement that the dimuon invariant mass must be
greater than 3.8. In addition to the thresholds, the triggers require the
leptons to pass stringent identification criteria. The trigger-level leptons
are also required to be isolated from other tracks and energy deposits in the
calorimeters.

##  0.5 Event selection and multivariate discriminants for signal extraction

Though there are numerous features that differ between the three lepton
channels and the $\PQb\PAQb$ reconstruction topologies, a unified analysis
strategy is used. Three control regions (CRs), each enriched in one of the
three primary backgrounds: $\ttbar$, $\PV$+light-quark jets ($\PV$+LF), and
$\PV+\PQb$ jets ($\PV$+HF), are defined for every channel and quark
reconstruction topology. Templates derived from simulation are fitted to the
data with the normalizations of the three primary backgrounds left
unconstrained and incorporating several systematic uncertainties that are
allowed to modify the shapes. The SRs are defined by requiring the dijet
invariant mass to be in the range 90–150and making a relatively tight
selection on the multivariate quark flavor discriminant that target two
quarks. The fitted observables in the SRs are all DNN outputs, binned such
that all bins contain roughly equal expected signal yields. The observables
used in the CRs vary between the channels, and are selected to constrain
particular features of the background model. All SRs and CRs are fitted
simultaneously to extract the background shapes, background normalizations,
and signal strengths.

The requirement of an identified boosted or bosons in the signal events
suppresses backgrounds from QCD multijet events, while also providing an
efficient trigger path when the or boson decays to charged leptons. Requiring
a large boost provides additional advantages. It further reduces the large
backgrounds from and production in association with jets, helps to suppress
the large background from top quark production in the signal channels
including neutrinos, and generally improves the invariant mass resolution of
the reconstructed Higgs boson candidates. Therefore, in addition to the
analysis targeting resolved events where the jets from the decay of the Higgs
boson candidate are reconstructed as separate AK4 jets, we also include
boosted events where the jets from the decay of the Higgs boson candidates are
both contained in a large distance parameter jet reconstructed with the AK8
algorithm.

The boosted analysis only considers events with $\pt(\PV)>250\GeV$. The SRs
and CRs for these analyses are optimized separately for the three analysis
channels (0-, 1-, and 2-lepton). Sections 0.5.2 and 0.5.3 describe the main
features of the resolved and boosted analyses.

Some of the events with $\pt(\PV)>250\GeV$ can be reconstructed in both the
resolved and boosted topologies. We refer to these as overlap events in what
follows. There are four categories that overlap events can enter, depending on
whether they are resolved or boosted, and whether they belong to the SRs or
the CRs. By studying all possible permutations in simulation, the following
priority ranking was selected: resolved SRs, boosted SRs, resolved CRs, and
boosted CRs. The reason for this choice is that the resolved SRs provide more
sensitivity, gauged as the uncertainty in the signal strength extracted using
an Asimov dataset, to the signal than the boosted SRs. Overlap events that
would be placed either in a resolved CR or a boosted SR are assigned to the
boosted SR to improve the sensitivity of the analysis. This ranking minimizes
the expected uncertainties in the STXS measurements and ensures that events
satisfying both resolved and boosted selection criteria are used only once.

###  0.5.1 Subcategorization in STXS bins

In all channels, additional subcategorizations are employed to maximize the
signal sensitivity to the different STXS bins. The STXS categorization
employed in the analysis is shown in Fig. 1. The STXS binning for the
V(leptonic)H process [29] uses a division into three production modes:
$\PW\PH$, $\PQq\PQq\to\PZ\PH$, and $\Pg\Pg\to\PZ\PH$. For each of these
production modes, the STXS classification defines bins in $\pt(\PV)$:
${[0,75)}$, ${[75,150)}$, ${[150,250)}$, ${[250,400)}$, ${>400\GeV}$.

The $[150,250)\GeV$ $\pt(\PV)$ STXS bin is split into two; one bin without any
additional jets and another bin with additional jets. This categorization
constitutes the STXS classification, that is, the generator-level division of
events into STXS bins. Not all of these bins are accessible in the analysis.
The gluon- and quark-induced $\PZ\PH$ production modes are merged because the
sensitivity to the separate processes is small with the currently available
data set. In addition, the two exclusive jet bins for the $\PW\PH$ process are
merged because of the low sensitivity to the bin with additional jets. Apart
from bins that are merged, there are also STXS bins that are not within the
analysis selection acceptance. This concerns the lowest $\pt(\PV)$ bin,
$[0,75)\GeV$, for all production processes, and the $[75,150)\GeV$ bin for the
$\PW\PH$ process. Negligible contributions from these bins can appear in the
analysis because generator-level quantities used to define the STXS bins do
not exactly match their reconstruction-level equivalents. Where these bins do
contribute to the analysis, their rates are fixed to the SM expectations. To
target the STXS bins, we define corresponding reconstruction-level categories,
constituting the subcategorization of the five channels considered in the
measurement.

  * •

For the 0-lepton channel, we define three categories in reconstructed
$\pt(\PV)$: ${[75,250]\GeV}$, ${[250,400)\GeV}$, and ${>400\GeV}$.

  * •

For the 2-lepton channel, we define four categories in reconstructed
$\pt(\PV)$: ${[75,150)\GeV}$, ${[150,250)\GeV}$, ${[250,400)\GeV}$, and
${>400\GeV}$.

  * •

In the 0- and 2-lepton channels, the ${150<\pt(\PV)<250\GeV}$ category is
further subdivided into a subcategory without any additional jets and another
with at least one additional jet.

  * •

For the 1-lepton channel, we define three categories in reconstructed
$\pt(\PV)$: ${[150,250)\GeV}$, ${[250,400)\GeV}$, and ${>400\GeV}$. There is
no further subdivision into additional jet categories.

Figure 1: Overview of the STXS bins for the three $\PV\PH$ production modes
[29]. The vertical axis reflects the $\pt(\PV)$ bin ranges and the horizontal
axis the number of additional jets. The general bin definitions are indicated
by the green boxes. No distinction is made between gluon- and quark-induced
production modes in the analysis. As mentioned in Section 0.5.1, some STXS
bins are not explicitly targeted by the analysis: contributions from these
bins are fixed to their SM expectations.

###  0.5.2 Analysis of the resolved jet topology

#### Analysis of the 0-lepton channel

The event topology of the 0-lepton channel is characterized by the presence of
large $\pt^{\text{miss}}$ due to the $\PZ\to\PGn\PAGn$ decay and a pair of
jets from the Higgs boson decay recoiling against the . Additional jet
activity ($N_{\text{aj}}$) is expected to be low, and no high-$\pt$ leptons
should be present. Events in the 0-lepton channel are selected using the high-
level trigger paths described in Section 0.4. The $\pt^{\text{miss}}$ is
required to be larger than 170. As a result of the trigger acceptance, we
require $\min(\pt^{\text{miss}},\mht)$ to exceed 100. The Higgs boson
candidate is reconstructed using the two jets with $\abs{\eta}<2.5$ that have
the highest tagging score. The leading and subleading jet must have $\pt>60$
and 35, respectively.

Since the system is supposed to be recoiling against the boson in 0-lepton
events, we require the difference in azimuthal angle between the directions of
the and boson to satisfy $\Delta\phi(\PZ,\PH)$ $>2.0$. We also demand the
invariant mass of the dijet system to be in the range 50–500and have
$\pt>120\GeV$. Events containing at least one isolated lepton with
${\pt>15\GeV}$ in the central region ($\abs{\eta}<2.5$) are rejected. To
reduce the QCD multijet background, a requirement of
$\Delta\phi(\text{\ptvecmiss, j})>0.5$ is applied for all jets with
${\pt>30\GeV}$ for both the SR and CRs. This means that events with any
energetic jets close to $\ptvecmiss$ in azimuth are not considered. Events are
split into four orthogonal categories, one SR and three CRs. The selections
used for the SR, as well as for the CRs enriched in the main background
processes ($\PZ$+$\PQb$ jets, $\PZ$+light-quark jets, and $\ttbar$), are
summarized in Table 0.5.2.

Definition of the SR and CRs for the resolved selection in the 0-lepton
channel. If the same selection is applied in all SRs and CRs, this is
indicated by the $\div$ symbol in the latter. The $M(\text{jj})$ and momenta
variables have units of . lcccc Variable & SR $\PZ$+$\PQb$ jets $\PZ$+light-
quark jets $\ttbar$
Common selection:
$\min(\pt^{\text{miss}},\mht)$ $>$100 $\div$ $\div$ $\div$
$\pt^{\text{miss}}$ $>$170 $\div$ $\div$ $\div$
$\pt(\mathrm{j_{1}})$ $>$60 $\div$ $\div$ $\div$
$\pt(\mathrm{j_{2}})$ $>$35 $\div$ $\div$ $\div$
${\pt}(\text{jj})$ $>$120 $\div$ $\div$ $\div$
$\Delta\phi(\PZ,\PH)$ $>$2.0 $\div$ $\div$ $\div$
$\Delta\phi(\text{\ptvecmiss, j})$ $>$0.5 $\div$ $\div$ $\div$
SR/CR difference:
$N_{\text{aj}}$ $\leq$1 $\leq$1 $\leq$1 $\geq$2
$M(\text{jj})$ $\in$[90–150] $\notin$[90–150]
$\text{btag}_{\text{max}}$ $>$medium $>$medium $<$medium $>$medium
$\text{btag}_{\text{min}}$ $>$loose $>$loose $<$loose $>$loose
$\Delta\phi(\ptvecmiss,\ptvecmiss{}_{\text{trk}})$ $<$0.5 $<$0.5 $<$0.5
$\min{\Delta\phi(\text{\ptvecmiss, j})}$ $<\pi/2$

#### Analysis of the 1-lepton channel

The topology of the 1-lepton events is characterized by the presence of a
single isolated lepton from the decay of the boson recoiling against two jets
from the decay of the Higgs boson. The presence of a single isolated lepton
provides a trigger path for this channel. The Higgs boson is reconstructed
using the two jets, with regressed $\pt>25\GeV$, that have the highest tag
scores. We require the Higgs and boson candidates to have $\pt>100$ and 150 ,
respectively. An additional requirement on the ratio of to its uncertainty,
$\pt^{\text{miss}}/\sigma(\pt^{\text{miss}})$, is applied to the CRs enriched
in $\PW$+$\PQb$ and $\PW$+light-quark jets. Events are not considered if they
contain additional leptons ($N_{\text{al}}$) with $\abs{\eta}<2.5$ and
$\pt>25\GeV$. The selected events are split into four orthogonal categories,
one SR and three CRs. The selections used for the SR, as well as for the CRs
enriched in the main background processes ($\PW$+$\PQb$ jets, $\PW$+light-
quark jets, and $\ttbar$), are summarized in Tab. 0.5.2.

Definition of the SR and CRs for the resolved selection of the 1-lepton
channel. If the same selection is applied in all SRs and CRs, this is
indicated by the $\div$ symbol in the latter. The $M(\text{jj})$ and momenta
variables have units of . lcccc Variable SR $\PW$+$\PQb$ jets $\PW$+light-
quark jets $\ttbar$
Common selection:
${\pt}(\text{jj})$ $>$100 $\div$ $\div$ $\div$
$\pt(\PV)$ $>$150 $\div$ $\div$ $\div$
$N_{\text{al}}$ $<$1 $\div$ $\div$ $\div$
$\pt(\mathrm{j_{1}})$ $>$25 $\div$ $\div$ $\div$
$\pt(\mathrm{j_{2}})$ $>$25 $\div$ $\div$ $\div$
$\Delta\phi(\text{lep},\ptvecmiss)$ $<$2 $\div$ $\div$ $\div$
SR/CR difference:
$\text{btag}_{\text{max}}$ $>$medium $>$medium [loose-medium] $>$tight
$\text{btag}_{\text{min}}$ $>$loose
$M(\text{jj})$ $\in$[90–150] $\in$[150–250] and $<$90 $<$250 $<$250
$N_{\text{aj}}$ $<$2 $<$2 $>$1
$\frac{\pt^{\text{miss}}}{\sigma(\pt^{\text{miss}})}$ $>$2 $>$2
$\Delta\phi(\PH,\PV)$ $>$2.5

#### Analysis of the 2-lepton channel

The topology of the signal events in the 2-lepton channel is characterized by
the presence of two isolated leptons from the decay of the boson, recoiling
against two jets from the decay of the Higgs boson. Figure 2 shows the
simulated invariant mass for signal events (without any additional recoiling
jet) generated with $m_{\PH}=125\GeV$ in the 2-lepton channel using the FSR
recovery algorithm mentioned in Section 0.3, along with the addition of the
jet energy regression (discussed in Section 0.3), and a kinematic fit. Since
there is no genuine $\ptvecmiss$ in the hard-scattering process for this event
topology, a kinematic fit is performed to improve the resolution of the dijet
invariant mass. In this fit, the mass of the dilepton system is constrained to
the boson mass (PDG), while the total of all the particles must sum to zero.
This kinematic fit imposes a balance between the boson $\pt$, which is
expected to be well measured because of the good momentum resolution for
high-$\pt$ leptons, and the vectorial sum of the jet momenta. The objects used
in the fit are the two -tagged jets that form the Higgs boson candidate after
application of the jet energy regression, and the FSR recovery algorithm, two
lepton candidates, and up to one jet produced by initial-state radiation
(ISR). As shown in Fig. 2, a large improvement in resolution in the region of
the mass peak is achieved when the kinematic fit, the jet energy regression,
and the FSR recovery algorithms are employed together.

Figure 2: Dijet invariant mass distributions in samples of simulated (2017
simulation) signal events passing the 2-lepton channel requirements without
any additional recoiling jet. Distributions are shown without the usage of the
FSR recovery algorithm (purple triangles), before (red triangles) and after
(blue squares) the energy corrections from the jet regression are applied, and
when a kinematic fit procedure (green circles) is used in addition to them.
The fitted mean and width of the core of the distribution, obtained by fitting
a Bukin function [72], are displayed in the figure. The statistical
uncertainties are smaller than the marker height.

The Higgs boson is reconstructed using the two highest tagging score jets that
have regressed $\pt>20\GeV$, and $\pt(\PV)$ is required to exceed 75. The
selections used to define the SR and CRs, which are enriched in the main
background processes ($\PZ$+$\PQb$ jets, $\PZ$+light-quark jets, $\ttbar$) are
summarized in Table 0.5.2. Similarly to the 0- and 1-lepton channels, the
$\ttbar$ CR is very pure in $\ttbar$ events. The additional requirement that
the dilepton invariant mass lie in the boson mass window ensures the
$\PZ$+jets purity in the corresponding regions is high.

Definition of the SR and CRs for the resolved selection in the 2-lepton
channel. If the same selection is applied in all SRs and CRs, this is
indicated by the $\div$ symbol in the latter. The $M(\text{jj})$, $M(\PV)$,
and momenta variables have units of . lcccc Variable SR $\PZ$+$\PQb$ jets
$\PZ$+light-quark jets $\ttbar$
$\pt(\PV)$ $>$75 $\div$ $\div$ $\div$
$\text{btag}_{\text{max}}$ $>$medium $>$medium $<$loose $>$tight
$\text{btag}_{\text{min}}$ $>$loose $>$loose $<$loose $>$loose
$M(\PV)$ $\in$[75–105] $\in$[85–97] $\in$[75–105] $\in$[10–75] and $>$120
$M(\text{jj})$ $\in$[90–150] $\notin$[90–150] $\in$[90–150]
$\ptvecmiss$ $<$60
$\Delta\phi(\PH,\PV)$ $>$2.5 $>$2.5

#### Multivariate discriminants in the SR and the heavy-flavor CRs

In order to improve the separation between signal and background, a DNN
classifier trained to discriminate the $\PV\PH$, $\PH\to\PQb\PAQb$ signal
against all background processes is used for all the channels. The same
network architecture is used for both the DNN signal/background classifier and
the DNN multiclass background classifier discussed later in this section. A
DNN classifier with six hidden fully connected layers is trained, with each
layer containing 512, 256, 128, 64, 64, and 64 nodes [73]. The final layer is
a softmax layer [73], giving the probability for an event to be in a
particular class. The classifier is trained to minimize the cross-entropy loss
function [73] using the Adam optimizer algorithm [74] trained on a minibatch
of size 1024.

The input features used in the DNN training encompass the kinematic properties
of the final state: the masses, momenta, and angles of the jets, dijet, vector
boson candidate, and leptons. The additional reconstructed jet multiplicity is
also used. While many potentially discriminating variables were considered,
variables that did not contribute to the analysis sensitivity were dropped.
The modeling of these variables in the simulation is also inspected. When the
modeling of such variables is observed to be unsatisfactory, the variables are
removed from the input feature list of the DNN training.

The full list of input variables is shown in Table 0.5.2 for the 0-, 1-, and
2-lepton channels. To evaluate the recoil jet multiplicity, used as an input
variable in the DNN training for the 2-lepton channel, recoil jets are
identified as ISR jets. They must have ${\pt>20\GeV}$, pass reconstruction
quality requirements, and cannot be part of the Higgs boson candidate or one
of the FSR jets.

Input variables used for the DNN training in the resolved SR of the 0-, 1-,
and 2-lepton channels. Reconstructed jets are classified as leading and
subleading based on their tag score.  llccc Variable Description 0-lepton
1-lepton 2-lepton
$M(\text{jj})$ Dijet invariant mass ✓ ✓ ✓
$\pt$(jj) Dijet transverse momentum ✓ ✓ ✓
$\ptvecmiss$ Missing transverse momentum ✓ ✓ ✓
$M_{\text{t}}$(V) Transverse mass of the vector boson ✓
$\pt(\PV)$ Transverse momentum of the vector boson ✓ ✓
$\pt(\mathrm{jj})$/ $\pt(\PV)$ Ratio of transverse momenta of the vector boson
and Higgs boson ✓ ✓
$\Delta\phi({\PV,\PH})$ Azimuthal angle between the vector boson and the dijet
directions ✓ ✓ ✓
$\text{btag}_{\text{max}}$ tagging score of leading jet ✓ ✓ ✓
$\text{btag}_{\text{min}}$ tagging score of subleading jet ✓ ✓ ✓
$\Delta\eta$(jj) Pseudorapidity difference between leading and subleading jet
✓ ✓ ✓
$\Delta\phi$(jj) Azimuthal angle between leading and subleading jet ✓ ✓
$\pt^{\text{max}}$($\mathrm{j_{1},j_{2}}$) Maximum transverse momentum of jet
✓ ✓
between leading and subleading jet
SA5 Number of soft-track jets with momentum greater than 5 ✓ ✓
$N_{\text{aj}}$ Number of additional jets ✓ ✓
$\text{btag}_{\text{max}}$ (add) Maximum tagging discriminant score among
additional jets ✓
$\pt^{\text{max}}$(add) Maximum transverse momentum among additional jets ✓
$\Delta\phi(\text{jet},\ptvecmiss)$ Azimuthal angle between additional jet and
$\ptvecmiss$ ✓
$\Delta\phi(\text{lep},\ptvecmiss)$ Azimuthal angle between lepton and
$\ptvecmiss$ ✓
$M_{\PQt}$ Reconstructed top quark mass ✓
$\pt(\mathrm{j_{1}})$ Transverse momentum of leading jet ✓
$\pt(\mathrm{j_{2}})$ Transverse momentum of subleading jet ✓
$M(\PV)$ Reconstructed vector boson mass ✓
$\Delta R(\PV,\PH)$ Angular separation between the vector boson and Higgs
boson ✓
$\Delta R(\PV,\PH)$ (kin) Angular separation between the vector boson and ✓
Higgs boson (reconstructed after kinematic fit)
$\sigma({M(\mathrm{jj})})$ Resolution of dijet invariant mass ✓
$N_{\text{rec}}$ Number of recoil jets ✓

Among the most discriminating variables for all channels are $M(\text{jj})$,
$\pt(\PV)$, the number of additional jets, and the angular separation between
the two jets forming the Higgs boson candidate. In the 2-lepton channel, both
regressed variables and variables evaluated after the kinematic fit are
employed. The tagging status of the jets is exploited by using the DeepCSV
working point information. The trainings are performed in categories defined
to target particular STXS bins for all channels, and the subsequent evaluation
is performed for the same STXS bins.

In the 0- and 1-lepton channels, a multiclass DNN is trained in the heavy-
flavor CR (HFDNN) to separate the different $\PV$+jets components (vector
boson production associated with light-quark, , and jets), single top quark,
and $\ttbar$ backgrounds. The same input features and DNN architecture as for
the signal/background classification are used (as shown in Table 0.5.2).
Instead of labels for the signal/background classification, the output of the
DNN is an $m$-dimensional vector of probabilities for the $m$ background
classes. Figure 3 shows the HFDNN discriminants in the 0- and 1-lepton heavy-
flavor CRs, after a maximum likelihood fit to the data. This is a simultaneous
fit of all SRs and CRs in the analysis.

Figure 3: Distribution of the HFDNN scores in the 0-lepton (left) and 1-lepton
(right) heavy-flavor CRs for the 2016 data set, after the fit to data. The
output nodes target enrichment in the $\PV$+light-quark (first bin),
$\PV$+$\PQc$ (second bin), $\PV$+$\PQb$ (third bin), $\PV$+$\PQb\PAQb$ (fourth
bin), single top quark (fifth bin), and $\ttbar$ (sixth bin) backgrounds. The
lower plots display the ratio of the data to the MC expectations. The vertical
bars on the points represent the statistical uncertainty in the ratio, and the
hatched area shows the MC uncertainty.

The DNN score is used as a discriminating variable in each resolved SR, while
different strategies are used in the resolved CRs, as discussed in Section
0.7.

###  0.5.3 Analysis of the merged-jet topology

The boosted topology is included in the measurement in addition to the
resolved analysis and targets the two STXS $\pt(\PV)$ bins above 250(i.e.,
250–400and $>400\GeV$). In the boosted event categories a single AK8 jet is
used to reconstruct the Higgs boson candidate, with the DeepAK8 tagging
algorithm applied to the Higgs boson candidate decay products. This accounts
for the kinematic properties of the event in the region where the two AK4 jets
start to overlap.

An important feature of the analysis is the usage of the tagging algorithm,
both as a selection variable to define SR and CRs, and as a discriminating
input feature of the MVA discriminant trained in the SR. The output of the
discriminant node against light-quark jets of the DeepAK8 algorithm
(DeepAK8bbVsLight) is used in this measurement, since it is observed to
provide the highest sensitivity for signal/background separation in the
boosted analysis region. The $\PH\to\PQb\PAQb$ boosted signal output node is
calibrated by performing data and simulation efficiency measurements, as
described in Section 0.3. The corresponding uncertainties are used as prior
constraints in the fit. No external efficiency measurements are available for
high-momentum light-quark, , and jets stemming from top quark decays. These
components are dominant in the $\PV$+LF, $\PV$+HF, and $\ttbar$ analysis CRs.
Therefore, the normalizations of these processes are extracted in situ in the
combined fit by including unconstrained parameters that scale the
normalizations of these processes in the CRs, as discussed in Section 0.7.

The resolved SRs and CRs for the 0-, 1-, and 2-lepton channels are defined
separately. The common preselection valid for all channels requires
$m_{\mathrm{SD}}>50\GeV$, $\pt(\PH)>250\GeV$, and $\pt(\PV)>250\GeV$. We
define three CRs, enriched in the main background processes ($\PV$+HF,
$\PV$+LF, and $\ttbar$), and one SR for the extraction of the boosted
$\PH\to\PQb\PAQb$ signal. To enrich the SR and CRs in merged jets, $\ttbar$,
and $\PV$+HF, the DeepAK8 discriminant must be larger than 0.8. For the light-
quark jets region, the complementary requirement ($<0.8$) is used. The value
of 0.8 was optimized to retain most of the signal while minimizing the
background contamination. In the 0- and 1-lepton channels, events in the
$\ttbar$ CRs are selected if they have at least one additional lepton and one
additional -tagged jet, identified using the medium DeepCSV working point. No
additional leptons or jets are allowed in the remaining CRs and SR. The dijet
soft-drop mass must be in the range 90–150 for the SR, outside this range for
the $\PV$+HF CR, and above 50for the $\PZ$+LF and $\ttbar$ CRs. Since all of
the boson decay products are visible in the 2-lepton final state, $M(\PV)$
must be in the range 75–105for the SR, $\PZ$+HF, and $\PZ$+LF CRs, and outside
this range for the $\ttbar$ CR. Table 0.5.3 summarizes the SR and CR
selections in the boosted analysis.

Selection criteria for the SR and CRs in the boosted topology for 0-, 1-, and
2-lepton channels. The DeepAK8bbVsLight designation represents the DeepAK8
discriminant for the light-quark flavor discrimination node. The
$M(\text{jj})$ and $M(\PV)$ variables have units of . lcccc Variable SR
$\PZ$+$\PQb$ jets $\PV$+light-quark jets $\ttbar$
0-lepton
DeepAK8bbVsLight $>$0.8 $>$0.8 $<$0.8 $>$0.8
$M(\text{jj})$ $\in$[90–150] $\in$[50–90] or $\in$[150–250] $>$50 $>$50
$N_{\text{al}}$ $=$0 $=$0 $=$0 $>$0
$N_{\text{aj}}$ $=$0 $=$0 $=$0 $>$0
1-lepton
DeepAK8bbVsLight $>$0.8 $>$0.8 $<$0.8 $>$0.8
$M(\text{jj})$ $\in$[90–150] $\in$[50–90] or $\in$[150–250] $>$50 $>$50
$N_{\text{al}}$ $=$0 $=$0 $=$0 $>$0
$N_{\text{aj}}$ $=$0 $=$0 $=$0 $>$0
2-lepton
DeepAK8bbVsLight $>$0.8 $>$0.8 $<$0.8 $>$0.8
$M(\text{jj})$ $\in$[90–150] $\in$[50–90] or $\in$[150–250] $\in$[90–150]
$>$50
$M(\PV)$ $\in$[75–105] $\in$[75–105] $\in$[75–105] $\notin$[75–105]

#### Multivariate discriminant in the SR for the boosted topology

Boosted decision trees (BDTs) were trained in the SRs for the 0-, 1-, and
2-lepton channels to separate the boosted Higgs boson decay signal from the
sum of all background processes. Input variable optimizations, as well as
overtraining checks, were performed. The input features of the BDT training
take into account the kinematic properties of the event and include
observables related to the AK8 jet candidate, such as its soft-drop mass and
$\pt$. The invariant mass of the dijet system, as well as additional variables
used for the DNN training in the resolved topology, are also added for events
in the overlap region between the boosted and resolved analyses. For purely
boosted events, these variables are set to default values in the BDT training
and are not considered further. This training strategy is found to improve the
BDT sensitivity by approximately 25% as a result of the very good
$M(\text{jj})$ resolution. Additionally, the properties of the reconstructed
vector boson recoiling against the Higgs boson candidate, and the DeepAK8
output node against light-quark-jet discrimination, are used as inputs in the
training. The inclusion of the DeepAK8 discriminant in the BDT training leads
to an improvement of around 30% in the expected sensitivity in this region.
Because the data-to-simulation corrections for the DeepAK8 tagger are
extracted for fixed operating points of the DeepAK8 discriminant and do not
correct the full shape of the output, binned working point values of the
discriminant are used in the training instead of its full shape. The
definition of the optimal working point used in the training is found from a
scan of the expected sensitivity when varying the choice of the DeepAK8
working point. For each configuration, a dedicated BDT is trained. The BDT
score is used as the fitted variable in the boosted SR, while the DeepAK8
output node is used in the boosted CRs. The $\PV$+HF, $\PV$+LF, and $\ttbar$
CRs make use of two bins in tagging score. The bin boundaries correspond to
the tagging working points: [0.8, 0.97, 1]. The $\PV$+LF CR uses two bins in
the complementary region, [0, 0.4, 0.8].

##  0.6 Systematic uncertainties

Several systematic uncertainties affect the normalizations of the simulated
signal and background processes, as well as the shapes of the multivariate
discriminants fitted in the SRs.

The theoretical uncertainty in the $\PH\to\PQb\PAQb$ branching fraction is
0.5% [28]. Theoretical uncertainties in the inclusive production cross
sections are derived from factorization ($\mu_{\mathrm{F}}$) and
renormalization ($\mu_{\mathrm{R}}$) scale variations and amount to 0.7, 0.6,
and 25% for the $\PW\PH$, quark-induced $\PZ\PH$, and gluon-induced $\PZ\PH$
processes, respecitvely [28]. Migration uncertainties between the STXS bins
are evaluated by studying the effect on the total cross section above each
$\pt(\PV)$ and $N_{\text{aj}}$ boundary by varying $\mu_{\mathrm{F}}$ and
$\mu_{\mathrm{R}}$ individually up and down by a factor of two around their
default values and neglecting the largest variations. The largest change is
used as the absolute uncertainty from this source. This change is then
employed to increase the process normalization in STXS bins above the given
$\pt(\PV)$ or $N_{\text{aj}}$ boundary, and decreasing it in the bins below.
These uncertainties are in the range of 3–11 and 30–40% for the quark- and
gluon-induced processes, respectively, depending on the STXS bin considered.
The gluon-induced process uncertainty is larger than for the quark-induced
process because of the larger bin migration in the former case. Acceptance
effects within each STXS bin are taken into account by varying
$\mu_{\mathrm{F}}$ and $\mu_{\mathrm{R}}$, including a normalization factor to
ensure these variations do not change the overall cross section of that
particular STXS bin.

Theoretical uncertainties due to the choice of PDFs and the value of the
strong coupling constant are derived for each signal and background process
following the recommendations given in Ref. [75] and are fully correlated
across data-taking years. They are 1.9% for the quark-induced $\PZ\PH$ and
$\PW\PH$ processes, and 2.4% for the gluon-induced $\PZ\PH$ process.
Uncertainties coming from the variations of $\mu_{\mathrm{F}}$ and
$\mu_{\mathrm{R}}$ are applied to all background processes and are fully
correlated across data-taking years. The NLO EW corrections to the $\PZ\PH$
and $\PW\PH$ processes have a 2% systematic uncertainty [28]. The
uncertainties in the diboson and single top quark production cross sections in
the high-$\pt(\PV)$ region are both set to 15%. These uncertainties are
derived from CMS measurements of these processes [76, 77]. However, they are
increased to account for the different in the phase space between the present
analysis and the previous measurement.

The shapes of various distributions of the background processes are derived
from simulated samples, and dedicated modeling uncertainties affecting both
the process normalization and the shape of the distribution are taken into
account. These shape uncertainties originate from the reweighting procedure
employed to improve the background modeling, and from the comparisons of the
$\PV$+jets simulation to the data, as discussed in Section 0.2. The
uncertainties are treated as uncorrelated between data-taking years and across
background processes to account for differences in the level of mismodeling
between processes and data-taking periods. Normalization uncertainties in the
NLO EW and NNLO QCD corrections applied to the high-$\pt(\PV)$ region in the
signal and $\PV$+jets samples are estimated as 2 and 5% for the NLO EW and
NNLO QCD corrections, respectively.

The uncertainty in the integrated luminosity measurement is 1.2, 2.3, and 2.5%
in the 2016, 2017, and 2018 data-taking periods, respectively [31, 32, 33].
These uncertainties are treated as partially correlated between the three
data-taking years. A 4.6% uncertainty in the total inelastic cross section
[78], used to evaluate the PU profile in data for reweighting to the simulated
PU profile, is applied.

The corrections applied to simulated samples to account for differences in the
electron and muon trigger, reconstruction, and identification efficiencies
with respect to data are affected by systematic uncertainties. These
uncertainties originate from choices made in the efficiency measurement
method, the selection applied to the leptons, and the limited size of the
simulated samples used in the measurement. They depend on the lepton and
$\eta$, and affect the process normalizations by 1–2%. An uncertainty in the
$\ptmiss$ trigger efficiency correction is included in the model and has a
$\approx$1% effect.

Uncertainties in the $\PQb$ tagging efficiency and misidentification rate
measurements used in the analysis of the resolved topology depend on the jet
flavor, and the and $\eta$ of the jet. These uncertainties are split into 9
independent sources and 15 and $\eta$ bins, and treated as uncorrelated
between the three data-taking periods. For the $\bbbar$ tagging efficiencies
used in the analysis of the boosted topology, uncertainties are provided for
the $\PH\to\PQb\PAQb$ signal output node of the DeepAK8 discriminant. They
account for the limited size of the simulated samples available for the
calibration; the relative uncertainty in the fraction of boosted jet
contributions present in the calibration; and for the normalizations of these
contributions. The tagging uncertainties are uncorrelated between the
efficiency working points and are parametrized in regions of jet (200–300,
300–400, 400–500, 500–600, $>$600).

Uncertainties in the JES and JER depend on the and $\eta$ of the jets, and
affect the kinematic properties of resolved and boosted jets, as well as the
in the event. The uncertainties in the JES are split into independent sources
[65] accounting for different experimental effects. Some of these uncertainty
sources are correlated between the different data-taking periods, , when that
uncertainty component depends on the size of the available data sample. For
-tagged jets, to which the previously described jet energy regression is
applied, dedicated uncertainties in the JES and JER corrections are applied,
as discussed in Section 0.3.

As described in Section 0.7, uncertainties in the shape of the $\pt(\PV)$
spectrum are introduced. This observable is important to improve the
discrimination between signal and background in the SR classifier. Because the
shape of the $\pt(\PV)$ spectrum is used in the fit model, the parameters that
scale the background process normalizations are parametrized continuously in
$\pt(\PV)$ rather than in individual STXS bins. To account for differences
between adjacent categories, extrapolation uncertainties in the shapes are
defined for each $\pt(\PV)$ boundary. These $\pt(\PV)$ migration uncertainties
are parametrized with a linear function that is applied in an uncorrelated way
to the various background processes. They are implemented with large prior
constraints in the fit model to mimic freely floating flat prior uncertainties
and are significantly constrained by the fit to data.

In addition to the $\pt(\PV)$-based uncertainties, freely floating parameters
that scale the normalizations of the main background components, , production
of a vector boson associated with heavy (, ) or light-quark jets and $\ttbar$,
are included in the fit as described in Section 0.7. These parameters are
constrained in the dedicated background-enriched regions discussed in Section
0.5, , $\PV$+HF, $\PV$+LF, and $\ttbar$. The parameters that scale the process
normalizations are treated as uncorrelated between lepton flavors (, ).

To account for the finite sizes of the simulated samples, each bin of the
simulated signal-plus-background template is allowed to vary within its
statistical uncertainty, independently from the other bins in the
distribution, following the Barlow–Beeston “light” approach [79]. The impact
of the systematic uncertainties in the measured cross sections in the
different STXS bins is discussed in Section 0.7.

##  0.7 Analysis results

The analysis targets the measurement of the Higgs boson signal strength using
$\PV\PH$ production with a subsequent $\PH\to\PQb\PAQb$ decay and interprets
the results in terms of the STXS categorization for the V(leptonic)H process,
as discussed in Section 0.5.1. Results are extracted from a simultaneous
maximum likelihood fit of the signal-plus-background model to the data
distributions in all SRs and CRs, based on the templates detailed in Table
0.7.

Discriminating variables fitted in each SR and CR. The DeepAK8bbVsLight
designation represents the DeepAK8 discriminant for the light-quark flavor
discrimination node.  ccccc SR $\ttbar$ CR $\PV$+LF CR $\PV$+HF CR
0-lepton, resolved DNN $\pt(\PV)$ $\pt(\PV)$ HFDNN
0-lepton, boosted BDT DeepAK8bbVsLight DeepAK8bbVsLight DeepAK8bbVsLight
1-lepton, resolved DNN $\pt(\PV)$ $\pt(\PV)$ HFDNN
1-lepton, boosted BDT DeepAK8bbVsLight DeepAK8bbVsLight DeepAK8bbVsLight
2-lepton, resolved DNN $\pt(\PV)$ $\pt(\PV)$ DeepCSV scores
2-lepton, boosted BDT DeepAK8bbVsLight DeepAK8bbVsLight DeepAK8bbVsLight

In the resolved and boosted SRs, the DNN and BDT classifiers presented in
Section 0.5 are employed. The variable used in the resolved CRs depends on the
channel. In the 0- and 1-lepton channels, the multiclassifier DNN described in
Section 0.5.2 is employed in the $\PV$+HF CR, while $\pt(\PV)$ is used in the
$\ttbar$ and $\PV$+LF CRs. In the 2-lepton channel, the fitted variable in the
$\PV$+HF CR is the score of the DeepCSV discriminant, binned to align with the
established working points, while the remaining CRs use $\pt(\PV)$ as in the
0- and 1-lepton channels. In the boosted CRs, the DeepAK8 discriminant for the
light-quark jet discrimination node (DeepAK8bbVsLight) is used in conjunction
with the selection requirement described in Section 0.5.3. For the $\PV$+HF
and $\ttbar$ CRs, the DeepAK8 discriminant is tagged as in the SR, while for
the $\PV$+LF CR, the DeepAK8 discriminant is required to be $<0.8$.

The analysis regions are partitioned into categories targeting specific STXS
bins in order to maximize the sensitivity to the different STXS bin signals,
as discussed in Section 0.5. The shapes and normalizations of all
distributions for the signal and background components are allowed to vary
within the systematic uncertainties described in Section 0.6. These
uncertainties are treated as independent nuisance parameters in the fit to the
data. For the nuisance parameters with shape-altering effects, alternative
templates that correspond to a variation of $\pm 1$ standard deviation of the
associated nuisance parameter are used.

To reduce the effects from statistical fluctuations on these alternative
templates in the SRs, a smoothing technique is applied to templates exhibiting
the largest fluctuations with respect to the nominal templates. The
normalization of the systematic variation is fixed, and the ratio of the
template with respect to the nominal is smoothed. The uncertainty sources that
show the largest fraction of bin-to-bin fluctuations are the JES, JER, and PU
uncertainties. For those uncertainties, the smoothing procedure is applied to
all processes in the analysis SRs. Freely floating parameters, termed process
scale factors, accounting for the difference in normalization between
simulation and data for the main background processes, namely $\ttbar$,
$\PV$+udsg, $\PV$+$\PQc$, $\PV$+$\PQb$, and $\PV$+$\PQb\PAQb$, are constrained
in the CRs and SRs. In the 0- and 2-lepton channels, the $\PV$+$\PQb$ and
$\PV$+$\PQb\PAQb$ components are split by employing freely floating
parameters. In the 1-lepton channel, a freely floating parameter for the
$\PV$+$\PQb\PAQb$ process is used in addition to a prior constraint that
governs the ratio of $\PV$+$\PQb$ to $\PV$+$\PQb\PAQb$. This implementation is
employed because the number of $\PV$+$\PQb$ events in the 1-lepton channel is
limited due to the tight tagging requirement applied in the selection.

To allow for shape variations in the categories targeting different STXS bins,
linear variations as a function of the reconstructed $\pt(\PV)$ are
constrained in the CRs. These linear variation uncertainties are
anticorrelated following the STXS categorization at the $\pt(\PV)$ boundaries
of 150(2-lepton channel only) and 250(all channels), for all processes.
Additional unconstrained parameters, used to measure flavor tagging scale
factors in situ in the boosted analysis regions, are employed to account for
the (mis)tagging efficiency difference between data and simulation for high-
momentum light-quark, $\PQc$, and $\PQb$ jets. The procedure is described in
more detail in Section 0.5.3. These parameters are treated as fully correlated
between channels, and are not correlated with the background process scale
factors. As discussed in Section 0.6, the process scale factors and in situ
flavor tagging scale factors are fully uncorrelated between lepton flavors.

Figure 4 shows the correlation matrix of the signal strengths split by STXS
bin for the analysis of all data-taking years combined. As expected (see
Section 0.5.1), the signal strengths for the medium $\pt(\PV)$ STXS bins with
0 and at least 1 jet exhibit the largest correlation ($-$21%). The fractional
contribution of each STXS bin to the total signal in each category is shown in
Fig. 5. The signal purity is higher in the 2-lepton channel than in the 0- and
1-lepton channels.

Figure 4: Correlation matrix of the parameters of interest in the STXS
measurement. The vector boson momenta have units of .

Figure 5: Contributions of the different STXS signal bins as a fraction of the
total signal yield in each SR. The vector boson momenta have units of .

The inclusive signal strength extracted from a simultaneous maximum likelihood
fit of the SRs and CRs, combining all three data-taking years, is
$\mu=1.15^{+0.22}_{-0.20}$, where the uncertainties include both the
statistical and systematic components. The individual signal strengths are
$\mu=1.43\pm 0.37$, $\mu=0.68\pm 0.36$, and $\mu=1.23\pm 0.30$ for the 2016,
2017, and 2018 data-taking years, respectively. Figure 6 shows the signal
strengths per analysis channel, as well as the signal strengths split by
production mode ($\PZ\PH$ or $\PW\PH$). The $p$-value compatibility of the
individual deviations of the three analysis channels from the SM expectation
($\mu=1$) is 64%, while the $p$-value compatibility of the three analysis
channels with the inclusive $\PV\PH$, $\PH\to\PQb\PAQb$ signal strength is
84%.

The measured signal strengths in the different STXS bins, fitting all data-
taking years (2016–2018) are shown in Fig. 7. These results are interpreted in
Fig. 8 as $\sigma\mathcal{B}$, the product of the production cross sections
and the branching fractions for $\to$ leptons and $\PH\to\PQb\PAQb$. To
convert the results to measurements of the production cross section alone,
theoretical uncertainties that modify the overall cross section of the
individual STXS bins, or the inclusive cross section, are removed from the
fit. These measured cross sections, along with the SM predictions, are given
in Table 0.7. The local inclusive observed (expected) significance of the
measured $\PZ\PH$ and $\PW\PH$ signals, over the background-only expectation,
is found to be 6.3 (5.6) standard deviations when taking into account all
three data-taking years. Examples of post-fit distributions of the DNN output
scores in the SRs of the 2018 data set are shown in Fig. 9 for the 0-, 1-, and
2-lepton channels in the category targeting the $250<\pt(\PV)<400\GeV$ STXS
bin. Figure 10 shows the distribution of events in all channels, sorted
according to the observed value of $\log_{10}$ (S/B), for the three data-
taking years combined; here, the signal (S) and background (B) yields are
determined from the discriminant scores used in the resolved and boosted
analyses.

Figure 6: Signal strengths (points) for the 0-, 1-, and 2-lepton channels
(left) and the $\PZ\PH$ and $\PW\PH$ production modes (right). The horizontal
red and blue bars on the points represent the systematic and total
uncertainties, respectively. The combined inclusive signal strength is shown
by the vertical line, with the green band giving the 68% confidence interval.
The results combine the 2016–2018 data-taking years. The first and the second
uncertainty values correspond to the statistical and systematic uncertainties,
respectively.

Figure 7: STXS signal strengths from the analysis of the 2016–2018 data. The
vertical dashed line corresponds to the SM value of the signal strength. The
first and the second uncertainty values correspond to the statistical and
systematic uncertainties, respectively. Figure 8: Measured values of
$\sigma\mathcal{B}$, defined as the product of the $\PV\PH$ production cross
sections multiplied by the branching fractions of $\to$ leptons and
$\PH\to\PQb\PAQb$, evaluated in the same STXS bins as for the signal
strengths, combining all years. In the lower panel, the ratio of the observed
results, with associated uncertainties, to the SM expectations is shown. If
the observed signal strength for a given STXS bin is negative, no value is
plotted for $\sigma\mathcal{B}$ in the upper panel.

Predicted and measured values of the product of the cross section and
branching fractions in the V(leptonic)H STXS process scheme. The SM
predictions for each bin are calculated using the inclusive values reported in
Ref. [28]. The uncertainties shown are the combined statistical and systematic
components. lccc STXS bin Expected $\sigma\mathcal{B}$ [fb] Observed
$\sigma\mathcal{B}$ [fb] $\mu$
ZH $75<\pt(\PZ)<150$ 50.0 $\pm$ 5.3 71 $\pm$ 38 1.4 $\pm$ 0.8
ZH $150<\pt(\PZ)<250$ 0 jets 9.0 $\pm$ 1.4 3.8 $\pm$ 4.1 0.4 $\pm$ 0.5
ZH $150<\pt(\PZ)<250$ $\geq$1 jets 10.1 $\pm$ 2.2 $<$0 $-$0.6 $\pm$ 1.0
ZH $250<\pt(\PZ)<400$ 4.5 $\pm$ 0.9 6.9 $\pm$ 2.2 1.5 $\pm$ 0.5
ZH $\pt(\PZ)>400$ 0.9 $\pm$ 0.1 1.6 $\pm$ 0.6 1.8 $\pm$ 0.8
WH $150<\pt(\PW)<250$ 24.9 $\pm$ 1.8 6 $\pm$ 16 0.2 $\pm$ 0.7
WH $250<\pt(\PW)<400$ 6.3 $\pm$ 0.5 11.9 $\pm$ 3.8 1.9 $\pm$ 0.6
WH $\pt(\PW)>400$ 1.4 $\pm$ 0.1 2.7 $\pm$ 1.1 1.9 $\pm$ 0.8

Figure 9: Post-fit distributions of the DNN discriminant in the
$250<\pt(\PV)<400\GeV$ category of the 0-lepton (top left), 1-lepton (top
right) and 2-lepton (bottom) channels for the electron final state using the
2018 data set. The background contributions after the maximum likelihood fit
are shown as filled histograms. The Higgs boson signal is also shown as a
filled histogram, and is normalized to the signal strength shown in Fig. 7.
The hatched band indicates the combined statistical and systematic uncertainty
in the sum of the signal and background templates. The ratio of the data to
the sum of the fitted signal and background is shown in the lower panel. The
distributions that enter the maximum likelihood fit use the same binning as
shown here.

Figure 10: Distributions of signal, background, and observed data event yields
sorted into bins of similar signal-to-background ratio, as given by the result
of the fit to the multivariate discriminants in the resolved and boosted
categories. All events in the signal regions of the 2016–2018 data set are
included. The red histogram indicates the Higgs boson signal assuming SM
yields ($\mu=1$) and the sum of all backgrounds is given by the gray
histogram. The lower panel shows the ratio of the observed data to the
background expectation, with the total uncertainty in the background
prediction indicated by the gray hatching. The red line indicates the sum of
signal assuming the SM prediction plus background contribution, divided by the
background.

Table 0.7 shows the contribution, in terms of absolute uncertainties, to the
uncertainty in the measured inclusive signal strength originating from the
various sources of systematic uncertainty. This contribution for a given group
of uncertainties is defined as the difference in quadrature between the total
uncertainty in the signal strength and the uncertainty in the signal strength
with the nuisance parameters of the corresponding group fixed to their best-
fit values. The total statistical uncertainty is defined as the uncertainty in
the signal strength when all the constrained nuisance parameters are fixed to
their best-fit values, while the total systematic uncertainty is defined as
the difference in quadrature between the total uncertainty in the signal
strength and the total statistical uncertainty. Table 0.7 breaks the total
uncertainty down into the following sources.

  * •

Theoretical uncertainties in the signal and background components.

  * •

Limited size of simulated samples.

  * •

Simulation modeling, including uncertainty sources associated with the
modeling of the $\PV$+jets background components, as discussed in Section 0.2.
Additionally, the $\pt(\PV)$ migration uncertainties are included in this
category.

  * •

Experimental uncertainties (tagging, integrated luminosity, JES and JER,
lepton identification, and trigger). The JES and JER components include the
dedicated uncertainty in mass scale and smearing that is applied for jets
subject to the jet energy regression.

The limited size of the NLO $\PV$+jets samples is the largest contribution to
the overall $\PV\PH$, ${\PH\to\PQb\PAQb}$ signal strength uncertainty.

The sources of systematic uncertainty in the inclusive signal strength
measurement and their positive and negative values. lc $\Delta\mu$
Background (theory) +0.043 $-$0.043
Signal (theory) +0.088 $-$0.059
MC sample size +0.078 $-$0.078
Simulation modeling +0.059 $-$0.059
tagging +0.050 $-$0.046
Jet energy resolution +0.036 $-$0.028
Int. luminosity +0.032 $-$0.027
Jet energy scale +0.025 $-$0.025
Lepton ident. +0.008 $-$0.007
Trigger ($\ptvecmiss$) +0.002 $-$0.001

###  0.7.1 Cross-check analysis: extraction of $\PV\PZ$ with $\PZ\to\bbbar$

The $\PV\PZ$ process, where the boson decays into a $\PQb\PAQb$ pair, has an
identical final state to the $\PV\PH$ process with $\PH\to\PQb\PAQb$.
Therefore, it is used to cross-check the methodology for the $\PV\PH$,
$\PH\to\PQb\PAQb$ analysis. The DNN and BDT discriminants in the resolved and
boosted SRs are trained using the simulated diboson sample as signal. All
other processes are considered as background. The $\PV\PZ$, $\PZ\to\PQb\PAQb$
analysis makes use of the same event categorization as the $\PV\PH$ analysis
discussed in Section 0.5, with the only modification being the requirement
that $M(\text{jj})$ lies in the range 60–120to define the SR for all channels.
The inclusive observed $\PV\PZ$, $\PZ\to\PQb\PAQb$ signal strength is
$\mu=1.25\pm 0.14$, corresponding to observed and expected significances well
above 5 standard deviations. The per-production process signal strenghts,
$\mu_{\PZ\PZ}$ and $\mu_{\PW\PZ}$, are $1.19\pm 0.09\stat\pm 0.11\syst$ and
$1.61\pm 0.18\stat\pm 0.24\syst$, respectively.

##  0.8 Summary

Measurements are presented of the cross section for the associated production
of the 125Higgs boson and a or boson, where the Higgs boson decays to and the
vector bosons decay to leptons. Proton-proton collision data collected by the
CMS experiment during 2016–2018 at ${\sqrt{s}=13\TeV}$ are used, corresponding
to an integrated luminosity of 138. Five decay channels are analyzed, and both
resolved as well as merged-jet topology are employed in each vector boson
decay mode. An additional subcategorization in the transverse momentum of the
vector boson and the number of additional jets in the event is applied to
maximize the sensitivity of different simplified template cross section bins.
The overall signal strength, combining all analysis categories, is found to be
$\mu=1.15^{+0.22}_{-0.20}$. The production of the Higgs boson in association
with a vector boson and decays to bottom quark pairs is established with an
observed (expected) significance of 6.3 (5.6) standard deviations.

###### 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 and other
centers 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, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: SC
(Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES,
FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS,
MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of
Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia);
BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST
(India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of
Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV,
CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New
Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD
(Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies
(Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
724704, 752730, 758316, 765710, 824093, and COST Action CA16108 (European
Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander
von Humboldt Foundation; the Science Committee, project no. 22rl-037
(Armenia); the Belgian Federal Science Policy Office; the Fonds pour la
Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-
Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-
Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science –
EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology
Commission, No. Z191100007219010 and Fundamental Research Funds for the
Central Universities (China); the Ministry of Education, Youth and Sports
(MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation,
grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under
Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and
under project number 400140256 - GRK2497; the Hellenic Foundation for Research
and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of
Sciences, the New National Excellence Program - ÚNKP, the NKFIH research
grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K
138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
(Hungary); the Council of Science and Industrial Research, India; ICSC –
National Research Center for High Performance Computing, Big Data and Quantum
Computing, funded by the EU NexGeneration program (Italy); the Latvian Council
of Science; the Ministry of Education and Science, project no. 2022/WK/14, and
the National Science Center, contracts Opus 2021/41/B/ST2/01369 and
2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia,
grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program
by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way
of making Europe”, and the Programa Estatal de Fomento de la Investigación
Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and
Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn
Academic into Its 2nd Century Project Advancement Project, and the National
Science, Research and Innovation Fund via the Program Management Unit for
Human Resources & Institutional Development, Research and Innovation, grant
B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the
SuperMicro Corporation; 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. Tumasyan10009-0000-0684-6742

Institut für Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341,
J.W. Andrejkovic, T. Bergauer0000-0002-5786-0293, S.
Chatterjee0000-0003-2660-0349, K. Damanakis0000-0001-5389-2872, M.
Dragicevic0000-0003-1967-6783, A. Escalante Del Valle0000-0002-9702-6359, P.S.
Hussain0000-0002-4825-5278, M. Jeitler20000-0002-5141-9560, N.
Krammer0000-0002-0548-0985, L. Lechner0000-0002-3065-1141, D.
Liko0000-0002-3380-473X, I. Mikulec0000-0003-0385-2746, P. Paulitsch, J.
Schieck20000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784, D.
Schwarz0000-0002-3821-7331, M. Sonawane0000-0003-0510-7010, S.
Templ0000-0003-3137-5692, W. Waltenberger0000-0002-6215-7228, C.-E.
Wulz20000-0001-9226-5812

Universiteit Antwerpen, Antwerpen, Belgium M.R. Darwish30000-0003-2894-2377,
T. Janssen0000-0002-3998-4081, T. Kello4, H. Rejeb Sfar, P. Van
Mechelen0000-0002-8731-9051

Vrije Universiteit Brussel, Brussel, Belgium E.S. Bols0000-0002-8564-8732, J.
D’Hondt0000-0002-9598-6241, A. De Moor0000-0001-5964-1935, M.
Delcourt0000-0001-8206-1787, H. El Faham0000-0001-8894-2390, S.
Lowette0000-0003-3984-9987, A. Morton0000-0002-9919-3492, D.
Müller0000-0002-1752-4527, A.R. Sahasransu0000-0003-1505-1743, S.
Tavernier0000-0002-6792-9522, W. Van Doninck, S. Van Putte0000-0003-1559-3606,
D. Vannerom0000-0002-2747-5095

Université Libre de Bruxelles, Bruxelles, Belgium B.
Clerbaux0000-0001-8547-8211, G. De Lentdecker0000-0001-5124-7693, L.
Favart0000-0003-1645-7454, D. Hohov0000-0002-4760-1597, J.
Jaramillo0000-0003-3885-6608, K. Lee0000-0003-0808-4184, M.
Mahdavikhorrami0000-0002-8265-3595, I. Makarenko0000-0002-8553-4508, A.
Malara0000-0001-8645-9282, S. Paredes0000-0001-8487-9603, L.
Pétré0009-0000-7979-5771, N. Postiau, L. Thomas0000-0002-2756-3853, M. Vanden
Bemden0009-0000-7725-7945, C. Vander Velde0000-0003-3392-7294, P.
Vanlaer0000-0002-7931-4496

Ghent University, Ghent, Belgium D. Dobur0000-0003-0012-4866, J.
Knolle0000-0002-4781-5704, L. Lambrecht0000-0001-9108-1560, G. Mestdach, C.
Rendón, A. Samalan, K. Skovpen0000-0002-1160-0621, M.
Tytgat0000-0002-3990-2074, N. Van Den Bossche0000-0003-2973-4991, B.
Vermassen, L. Wezenbeek0000-0001-6952-891X

Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke0000-0003-0252-3609, G. Bruno0000-0001-8857-8197, F.
Bury0000-0002-3077-2090, C. Caputo0000-0001-7522-4808, P.
David0000-0001-9260-9371, C. Delaere0000-0001-8707-6021, I.S.
Donertas0000-0001-7485-412X, A. Giammanco0000-0001-9640-8294, K.
Jaffel0000-0001-7419-4248, Sa. Jain0000-0001-5078-3689, V. Lemaitre, K.
Mondal0000-0001-5967-1245, A. Taliercio0000-0002-5119-6280, T.T.
Tran0000-0003-3060-350X, P. Vischia0000-0002-7088-8557, S.
Wertz0000-0002-8645-3670

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil G.A.
Alves0000-0002-8369-1446, E. Coelho0000-0001-6114-9907, C.
Hensel0000-0001-8874-7624, A. Moraes0000-0002-5157-5686, P. Rebello
Teles0000-0001-9029-8506

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, M. Alves Gallo Pereira0000-0003-4296-7028, M.
Barroso Ferreira Filho0000-0003-3904-0571, H. Brandao
Malbouisson0000-0002-1326-318X, W. Carvalho0000-0003-0738-6615, J.
Chinellato5, E.M. Da Costa0000-0002-5016-6434, G.G. Da
Silveira60000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680, V. Dos
Santos Sousa0000-0002-4681-9340, S. Fonseca De Souza0000-0001-7830-0837, J.
Martins70000-0002-2120-2782, C. Mora Herrera0000-0003-3915-3170, K. Mota
Amarilo0000-0003-1707-3348, L. Mundim0000-0001-9964-7805, H.
Nogima0000-0001-7705-1066, A. Santoro0000-0002-0568-665X, S.M. Silva Do
Amaral0000-0002-0209-9687, A. Sznajder0000-0001-6998-1108, M.
Thiel0000-0001-7139-7963, A. Vilela Pereira0000-0003-3177-4626

Universidade Estadual Paulista, Universidade Federal do ABC, São Paulo, Brazil
C.A. Bernardes60000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
P.G. Mercadante0000-0001-8333-4302, S.F. Novaes0000-0003-0471-8549, Sandra S.
Padula0000-0003-3071-0559

Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria A. Aleksandrov0000-0001-6934-2541, G.
Antchev0000-0003-3210-5037, R. Hadjiiska0000-0003-1824-1737, P.
Iaydjiev0000-0001-6330-0607, M. Misheva0000-0003-4854-5301, M. Rodozov, M.
Shopova0000-0001-6664-2493, G. Sultanov0000-0002-8030-3866

University of Sofia, Sofia, Bulgaria A. Dimitrov0000-0003-2899-701X, T.
Ivanov0000-0003-0489-9191, L. Litov0000-0002-8511-6883, B.
Pavlov0000-0003-3635-0646, P. Petkov0000-0002-0420-9480, A.
Petrov0009-0003-8899-1514, E. Shumka0000-0002-0104-2574

Instituto De Alta Investigación, Universidad de Tarapacá, Casilla 7 D, Arica,
Chile S. Thakur0000-0002-1647-0360

Beihang University, Beijing, China T. Cheng0000-0003-2954-9315, T.
Javaid80009-0007-2757-4054, M. Mittal0000-0002-6833-8521, L.
Yuan0000-0002-6719-5397

Department of Physics, Tsinghua University, Beijing, China M.
Ahmad0000-0001-9933-995X, G. Bauer9, Z. Hu0000-0001-8209-4343, S.
Lezki0000-0002-6909-774X, K. Yi9,100000-0002-2459-1824

Institute of High Energy Physics, Beijing, China G.M.
Chen80000-0002-2629-5420, H.S. Chen80000-0001-8672-8227, M.
Chen80000-0003-0489-9669, F. Iemmi0000-0001-5911-4051, C.H. Jiang, A.
Kapoor0000-0002-1844-1504, H. Liao0000-0002-0124-6999, Z.-A.
Liu110000-0002-2896-1386, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, R. Sharma0000-0003-1181-1426, J.
Tao0000-0003-2006-3490, J. Thomas-Wilsker0000-0003-1293-4153, J.
Wang0000-0002-3103-1083, H. Zhang0000-0001-8843-5209, J.
Zhao0000-0001-8365-7726

State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos0000-0002-8953-1232, Y. An0000-0003-1299-1879, Y.
Ban0000-0002-1912-0374, A. Levin0000-0001-9565-4186, C. Li0000-0002-6339-8154,
Q. Li0000-0002-8290-0517, X. Lyu, Y. Mao, S.J. Qian0000-0002-0630-481X, X.
Sun0000-0003-4409-4574, D. Wang0000-0002-9013-1199, J.
Xiao0000-0002-7860-3958, H. Yang

Sun Yat-Sen University, Guangzhou, China M. Lu0000-0002-6999-3931, Z.
You0000-0001-8324-3291

University of Science and Technology of China, Hefei, China N.
Lu0000-0002-2631-6770

Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam
Application (MOE) - Fudan University, Shanghai, China X.
Gao40000-0001-7205-2318, D. Leggat, H. Okawa0000-0002-2548-6567, Y.
Zhang0000-0002-4554-2554

Zhejiang University, Hangzhou, Zhejiang, China Z. Lin0000-0003-1812-3474, C.
Lu0000-0002-7421-0313, M. Xiao0000-0001-9628-9336

Universidad de Los Andes, Bogota, Colombia C. Avila0000-0002-5610-2693, D.A.
Barbosa Trujillo, A. Cabrera0000-0002-0486-6296, C. Florez0000-0002-3222-0249,
J. Fraga0000-0002-5137-8543

Universidad de Antioquia, Medellin, Colombia J. Mejia
Guisao0000-0002-1153-816X, F. Ramirez0000-0002-7178-0484, M.
Rodriguez0000-0002-9480-213X, J.D. Ruiz Alvarez0000-0002-3306-0363

University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia D. Giljanovic0009-0005-6792-6881, N.
Godinovic0000-0002-4674-9450, D. Lelas0000-0002-8269-5760, I.
Puljak0000-0001-7387-3812

University of Split, Faculty of Science, Split, Croatia Z. Antunovic, M.
Kovac0000-0002-2391-4599, T. Sculac0000-0002-9578-4105

Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic0000-0001-5847-0062,
B.K. Chitroda0000-0002-0220-8441, D. Ferencek0000-0001-9116-1202, S.
Mishra0000-0002-3510-4833, M. Roguljic0000-0001-5311-3007, A.
Starodumov120000-0001-9570-9255, T. Susa0000-0001-7430-2552

University of Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K.
Christoforou0000-0003-2205-1100, S. Konstantinou0000-0003-0408-7636, J.
Mousa0000-0002-2978-2718, C. Nicolaou, F. Ptochos0000-0002-3432-3452, P.A.
Razis0000-0002-4855-0162, H. Rykaczewski, H. Saka0000-0001-7616-2573, A.
Stepennov0000-0001-7747-6582

Charles University, Prague, Czech Republic M. Finger120000-0002-7828-9970, M.
Finger Jr.120000-0003-3155-2484, A. Kveton0000-0001-8197-1914

Escuela Politecnica Nacional, Quito, Ecuador E. Ayala0000-0002-0363-9198

Universidad San Francisco de Quito, Quito, Ecuador E. Carrera
Jarrin0000-0002-0857-8507

Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt Y. Assran13,14, S.
Elgammal14

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud0000-0001-8692-5458, Y. Mohammed0000-0001-8399-3017

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia S.
Bhowmik0000-0003-1260-973X, R.K. Dewanjee0000-0001-6645-6244, K.
Ehataht0000-0002-2387-4777, M. Kadastik, T. Lange0000-0001-6242-7331, S.
Nandan0000-0002-9380-8919, C. Nielsen0000-0002-3532-8132, J.
Pata0000-0002-5191-5759, M. Raidal0000-0001-7040-9491, L.
Tani0000-0002-6552-7255, C. Veelken0000-0002-3364-916X

Department of Physics, University of Helsinki, Helsinki, Finland P.
Eerola0000-0002-3244-0591, H. Kirschenmann0000-0001-7369-2536, K.
Osterberg0000-0003-4807-0414, M. Voutilainen0000-0002-5200-6477

Helsinki Institute of Physics, Helsinki, Finland S.
Bharthuar0000-0001-5871-9622, E. Brücken0000-0001-6066-8756, F.
Garcia0000-0002-4023-7964, J. Havukainen0000-0003-2898-6900, M.S.
Kim0000-0003-0392-8691, R. Kinnunen, T. Lampén0000-0002-8398-4249, K. Lassila-
Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén0009-0002-4847-8882, M. Lotti, L. Martikainen0000-0003-1609-3515, M.
Myllymäki0000-0003-0510-3810, M.m. Rantanen0000-0002-6764-0016, H.
Siikonen0000-0003-2039-5874, E. Tuominen0000-0002-7073-7767, J.
Tuominiemi0000-0003-0386-8633

Lappeenranta-Lahti University of Technology, Lappeenranta, Finland P.
Luukka0000-0003-2340-4641, H. Petrow0000-0002-1133-5485, T.
Tuuva${}^{\textrm{\textdagger}}$

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France C.
Amendola0000-0002-4359-836X, M. Besancon0000-0003-3278-3671, F.
Couderc0000-0003-2040-4099, M. Dejardin0009-0008-2784-615X, D. Denegri, J.L.
Faure, F. Ferri0000-0002-9860-101X, S. Ganjour0000-0003-3090-9744, P.
Gras0000-0002-3932-5967, G. Hamel de Monchenault0000-0002-3872-3592, V.
Lohezic0009-0008-7976-851X, J. Malcles0000-0002-5388-5565, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro150000-0002-9481-5168, P. Simkina0000-0002-9813-372X, M.
Titov0000-0002-1119-6614

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut
Polytechnique de Paris, Palaiseau, France C. Baldenegro
Barrera0000-0002-6033-8885, F. Beaudette0000-0002-1194-8556, A. Buchot
Perraguin0000-0002-8597-647X, P. Busson0000-0001-6027-4511, A.
Cappati0000-0003-4386-0564, C. Charlot0000-0002-4087-8155, F.
Damas0000-0001-6793-4359, O. Davignon0000-0001-8710-992X, B.
Diab0000-0002-6669-1698, G. Falmagne0000-0002-6762-3937, B.A. Fontana Santos
Alves0000-0001-9752-0624, S. Ghosh0009-0006-5692-5688, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi0009-0008-2093-8131, B.
Harikrishnan0000-0003-0174-4020, G. Liu0000-0001-7002-0937, J.
Motta0000-0003-0985-913X, M. Nguyen0000-0001-7305-7102, C.
Ochando0000-0002-3836-1173, L. Portales0000-0002-9860-9185, R.
Salerno0000-0003-3735-2707, U. Sarkar0000-0002-9892-4601, J.B.
Sauvan0000-0001-5187-3571, Y. Sirois0000-0001-5381-4807, A.
Tarabini0000-0001-7098-5317, E. Vernazza0000-0003-4957-2782, A.
Zabi0000-0002-7214-0673, A. Zghiche0000-0002-1178-1450

Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram160000-0001-7476-0158, J. Andrea0000-0002-8298-7560, D.
Apparu0009-0004-1837-0496, D. Bloch0000-0002-4535-5273, G.
Bourgatte0009-0005-7044-8104, J.-M. Brom0000-0003-0249-3622, E.C.
Chabert0000-0003-2797-7690, C. Collard0000-0002-5230-8387, D. Darej, U.
Goerlach0000-0001-8955-1666, C. Grimault, A.-C. Le Bihan0000-0002-8545-0187,
P. Van Hove0000-0002-2431-3381

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S.
Beauceron0000-0002-8036-9267, B. Blancon0000-0001-9022-1509, G.
Boudoul0009-0002-9897-8439, A. Carle, N. Chanon0000-0002-2939-5646, J.
Choi0000-0002-6024-0992, D. Contardo0000-0001-6768-7466, P.
Depasse0000-0001-7556-2743, C. Dozen170000-0002-4301-634X, H. El Mamouni, J.
Fay0000-0001-5790-1780, S. Gascon0000-0002-7204-1624, M.
Gouzevitch0000-0002-5524-880X, G. Grenier0000-0002-1976-5877, B.
Ille0000-0002-8679-3878, I.B. Laktineh, M. Lethuillier0000-0001-6185-2045, L.
Mirabito, S. Perries, L. Torterotot0000-0002-5349-9242, M. Vander
Donckt0000-0002-9253-8611, P. Verdier0000-0003-3090-2948, S. Viret

Georgian Technical University, Tbilisi, Georgia D. Lomidze0000-0003-3936-6942,
I. Lomidze0009-0002-3901-2765, Z. Tsamalaidze120000-0001-5377-3558

RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany V.
Botta0000-0003-1661-9513, L. Feld0000-0001-9813-8646, K.
Klein0000-0002-1546-7880, M. Lipinski0000-0002-6839-0063, D.
Meuser0000-0002-2722-7526, A. Pauls0000-0002-8117-5376, N.
Röwert0000-0002-4745-5470, M. Teroerde0000-0002-5892-1377

RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany S.
Diekmann0009-0004-8867-0881, A. Dodonova0000-0002-5115-8487, N.
Eich0000-0001-9494-4317, D. Eliseev0000-0001-5844-8156, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, D.
Fasanella0000-0002-2926-2691, B. Fischer0000-0002-3900-3482, T.
Hebbeker0000-0002-9736-266X, K. Hoepfner0000-0002-2008-8148, F.
Ivone0000-0002-2388-5548, M.y. Lee0000-0002-4430-1695, L. Mastrolorenzo, M.
Merschmeyer0000-0003-2081-7141, A. Meyer0000-0001-9598-6623, S.
Mondal0000-0003-0153-7590, S. Mukherjee0000-0001-6341-9982, D.
Noll0000-0002-0176-2360, A. Novak0000-0002-0389-5896, F. Nowotny, A.
Pozdnyakov0000-0003-3478-9081, Y. Rath, W. Redjeb0000-0001-9794-8292, F. Rehm,
H. Reithler0000-0003-4409-702X, A. Schmidt0000-0003-2711-8984, S.C. Schuler,
A. Sharma0000-0002-5295-1460, A. Stein0000-0003-0713-811X, F. Torres Da Silva
De Araujo180000-0002-4785-3057, L. Vigilante, S.
Wiedenbeck0000-0002-4692-9304, S. Zaleski

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany C.
Dziwok0000-0001-9806-0244, G. Flügge0000-0003-3681-9272, W. Haj
Ahmad190000-0003-1491-0446, O. Hlushchenko, T. Kress0000-0002-2702-8201, A.
Nowack0000-0002-3522-5926, O. Pooth0000-0001-6445-6160, A.
Stahl0000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A.
Zotz0000-0002-1320-1712

Deutsches Elektronen-Synchrotron, Hamburg, Germany H. Aarup
Petersen0009-0005-6482-7466, M. Aldaya Martin0000-0003-1533-0945, J.
Alimena0000-0001-6030-3191, P. Asmuss, S. Baxter0009-0008-4191-6716, M.
Bayatmakou0009-0002-9905-0667, H. Becerril Gonzalez0000-0001-5387-712X, O.
Behnke0000-0002-4238-0991, S. Bhattacharya0000-0002-3197-0048, F.
Blekman200000-0002-7366-7098, K. Borras210000-0003-1111-249X, D.
Brunner0000-0001-9518-0435, A. Campbell0000-0003-4439-5748, A.
Cardini0000-0003-1803-0999, C. Cheng, F. Colombina0009-0008-7130-100X, S.
Consuegra Rodríguez0000-0002-1383-1837, G. Correia Silva0000-0001-6232-3591,
M. De Silva0000-0002-5804-6226, G. Eckerlin, D. Eckstein0000-0002-7366-6562,
L.I. Estevez Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E.
Gallo200000-0001-7200-5175, A. Geiser0000-0003-0355-102X, A.
Giraldi0000-0003-4423-2631, G. Greau, A. Grohsjean0000-0003-0748-8494, V.
Guglielmi0000-0003-3240-7393, M. Guthoff0000-0002-3974-589X, A.
Jafari220000-0001-7327-1870, N.Z. Jomhari0000-0001-9127-7408, B.
Kaech0000-0002-1194-2306, M. Kasemann0000-0002-0429-2448, H.
Kaveh0000-0002-3273-5859, C. Kleinwort0000-0002-9017-9504, R.
Kogler0000-0002-5336-4399, M. Komm0000-0002-7669-4294, D.
Krücker0000-0003-1610-8844, W. Lange, D. Leyva Pernia0009-0009-8755-3698, K.
Lipka230000-0002-8427-3748, W. Lohmann240000-0002-8705-0857, R.
Mankel0000-0003-2375-1563, I.-A. Melzer-Pellmann0000-0001-7707-919X, M.
Mendizabal Morentin0000-0002-6506-5177, J. Metwally, A.B.
Meyer0000-0001-8532-2356, G. Milella0000-0002-2047-951X, M.
Mormile0000-0003-0456-7250, A. Mussgiller0000-0002-8331-8166, A.
Nürnberg0000-0002-7876-3134, Y. Otarid, D. Pérez Adán0000-0003-3416-0726, E.
Ranken0000-0001-7472-5029, A. Raspereza0000-0003-2167-498X, B. Ribeiro
Lopes0000-0003-0823-447X, J. Rübenach, A. Saggio0000-0002-7385-3317, M.
Savitskyi0000-0002-9952-9267, M. Scham25,210000-0001-9494-2151, V. Scheurer,
S. Schnake210000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger200000-0001-6699-6662, M. Shchedrolosiev0000-0003-3510-2093,
R.E. Sosa Ricardo0000-0002-2240-6699, D. Stafford, N.
Tonon${}^{\textrm{\textdagger}}$0000-0003-4301-2688, M. Van De
Klundert0000-0001-8596-2812, F. Vazzoler0000-0001-8111-9318, A. Velyka, A.
Ventura Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, D.
Walter0000-0001-8584-9705, Q. Wang0000-0003-1014-8677, Y.
Wen0000-0002-8724-9604, K. Wichmann, L. Wiens210000-0002-4423-4461, C.
Wissing0000-0002-5090-8004, S. Wuchterl0000-0001-9955-9258, Y.
Yang0009-0009-3430-0558, A. Zimermmane Castro Santos0000-0001-9302-3102

University of Hamburg, Hamburg, Germany A. Albrecht0000-0001-6004-6180, S.
Albrecht0000-0002-5960-6803, M. Antonello0000-0001-9094-482X, S.
Bein0000-0001-9387-7407, L. Benato0000-0001-5135-7489, M.
Bonanomi0000-0003-3629-6264, P. Connor0000-0003-2500-1061, K. De
Leo0000-0002-8908-409X, M. Eich, K. El Morabit0000-0001-5886-220X, F. Feindt,
A. Fröhlich, C. Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, M.
Hajheidari, J. Haller0000-0001-9347-7657, A. Hinzmann0000-0002-2633-4696, H.R.
Jabusch0000-0003-2444-1014, G. Kasieczka0000-0003-3457-2755, P. Keicher, R.
Klanner0000-0002-7004-9227, W. Korcari0000-0001-8017-5502, T.
Kramer0000-0002-7004-0214, V. Kutzner0000-0003-1985-3807, F.
Labe0000-0002-1870-9443, J. Lange0000-0001-7513-6330, A.
Lobanov0000-0002-5376-0877, C. Matthies0000-0001-7379-4540, A.
Mehta0000-0002-0433-4484, L. Moureaux0000-0002-2310-9266, M. Mrowietz, A.
Nigamova0000-0002-8522-8500, Y. Nissan, A. Paasch0000-0002-2208-5178, K.J.
Pena Rodriguez0000-0002-2877-9744, T. Quadfasel0000-0003-2360-351X, M.
Rieger0000-0003-0797-2606, O. Rieger, D. Savoiu0000-0001-6794-7475, J.
Schindler0009-0006-6551-0660, P. Schleper0000-0001-5628-6827, M.
Schröder0000-0001-8058-9828, J. Schwandt0000-0002-0052-597X, M.
Sommerhalder0000-0001-5746-7371, H. Stadie0000-0002-0513-8119, G.
Steinbrück0000-0002-8355-2761, A. Tews, M. Wolf0000-0003-3002-2430

Karlsruher Institut fuer Technologie, Karlsruhe, Germany S.
Brommer0000-0001-8988-2035, M. Burkart, E. Butz0000-0002-2403-5801, T.
Chwalek0000-0002-8009-3723, A. Dierlamm0000-0001-7804-9902, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, J.O. Gosewisch,
A. Gottmann0000-0001-6696-349X, F. Hartmann260000-0001-8989-8387, M.
Horzela0000-0002-3190-7962, U. Husemann0000-0002-6198-8388, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, M. Link, A.
Lintuluoto0000-0002-0726-1452, S. Maier0000-0001-9828-9778, S.
Mitra0000-0002-3060-2278, Th. Müller0000-0003-4337-0098, M. Neukum, M.
Oh0000-0003-2618-9203, G. Quast0000-0002-4021-4260, K.
Rabbertz0000-0001-7040-9846, J. Rauser, I. Shvetsov0000-0002-7069-9019, H.J.
Simonis0000-0002-7467-2980, N. Trevisani0000-0002-5223-9342, R.
Ulrich0000-0002-2535-402X, J. van der Linden0000-0002-7174-781X, R.F. Von
Cube0000-0002-6237-5209, M. Wassmer0000-0002-0408-2811, S.
Wieland0000-0003-3887-5358, R. Wolf0000-0001-9456-383X, S.
Wozniewski0000-0001-8563-0412, S. Wunsch, X. Zuo0000-0002-0029-493X

Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia
Paraskevi, Greece G. Anagnostou, P. Assiouras0000-0002-5152-9006, G.
Daskalakis0000-0001-6070-7698, A. Kyriakis, A. Stakia0000-0001-6277-7171

National and Kapodistrian University of Athens, Athens, Greece M.
Diamantopoulou, D. Karasavvas, P. Kontaxakis0000-0002-4860-5979, A.
Manousakis-Katsikakis0000-0002-0530-1182, A. Panagiotou, I.
Papavergou0000-0002-7992-2686, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K.
Vellidis0000-0001-5680-8357, I. Zisopoulos0000-0001-5212-4353

National Technical University of Athens, Athens, Greece G.
Bakas0000-0003-0287-1937, T. Chatzistavrou, G. Karapostoli0000-0002-4280-2541,
K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos0000-0002-8440-0487, G.
Tsipolitis, A. Zacharopoulou

University of Ioánnina, Ioánnina, Greece K. Adamidis, I. Bestintzanos, I.
Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios0009-0003-7233-0738, C.
Kamtsikis, P. Katsoulis, P. Kokkas0009-0009-3752-6253, P.G. Kosmoglou
Kioseoglou0000-0002-7440-4396, N. Manthos0000-0003-3247-8909, I.
Papadopoulos0000-0002-9937-3063, J. Strologas0000-0002-2225-7160

HUN-REN Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók270000-0002-4440-2701, G. Bencze, C. Hajdu0000-0002-7193-800X, D.
Horvath28,290000-0003-0091-477X, F. Sikler0000-0001-9608-3901, V.
Veszpremi0000-0001-9783-0315

MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd
University, Budapest, Hungary M. Csanád0000-0002-3154-6925, K.
Farkas0000-0003-1740-6974, M.M.A. Gadallah300000-0002-8305-6661, S.
Lökös310000-0002-4447-4836, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, A.J.
Rádl320000-0001-8810-0388, O. Surányi0000-0002-4684-495X, G.I.
Veres0000-0002-5440-4356

Institute of Nuclear Research ATOMKI, Debrecen, Hungary N.
Beni0000-0002-3185-7889, S. Czellar, J. Karancsi270000-0003-0802-7665, J.
Molnar, Z. Szillasi, D. Teyssier0000-0002-5259-7983

Institute of Physics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari330000-0003-0498-4265, G. Zilizi0000-0002-0480-0000

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo320000-0002-9110-9663, F. Nemes320000-0002-1451-6484, T.
Novak0000-0001-6253-4356

Panjab University, Chandigarh, India J. Babbar0000-0002-4080-4156, S.
Bansal0000-0003-1992-0336, S.B. Beri, V. Bhatnagar0000-0002-8392-9610, G.
Chaudhary0000-0003-0168-3336, S. Chauhan0000-0001-6974-4129, N.
Dhingra340000-0002-7200-6204, R. Gupta, A. Kaur0000-0002-1640-9180, A.
Kaur0000-0003-3609-4777, H. Kaur0000-0002-8659-7092, M.
Kaur0000-0002-3440-2767, S. Kumar0000-0001-9212-9108, P.
Kumari0000-0002-6623-8586, M. Meena0000-0003-4536-3967, K.
Sandeep0000-0002-3220-3668, T. Sheokand, J.B. Singh350000-0001-9029-2462, A.
Singla0000-0003-2550-139X

University of Delhi, Delhi, India A. Ahmed0000-0002-4500-8853, A.
Bhardwaj0000-0002-7544-3258, A. Chhetri0000-0001-7495-1923, B.C.
Choudhary0000-0001-5029-1887, A. Kumar0000-0003-3407-4094, M.
Naimuddin0000-0003-4542-386X, K. Ranjan0000-0002-5540-3750, S.
Saumya0000-0001-7842-9518

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S.
Baradia0000-0001-9860-7262, S. Barman360000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, D. Bhowmik, S. Dutta0000-0001-9650-8121, S.
Dutta, B. Gomber370000-0002-4446-0258, M. Maity36, P.
Palit0000-0002-1948-029X, G. Saha0000-0002-6125-1941, B.
Sahu0000-0002-8073-5140, S. Sarkar

Indian Institute of Technology Madras, Madras, India P.K.
Behera0000-0002-1527-2266, S.C. Behera0000-0002-0798-2727, S.
Chatterjee0000-0003-0185-9872, P. Kalbhor0000-0002-5892-3743, J.R.
Komaragiri380000-0002-9344-6655, D. Kumar380000-0002-6636-5331, A.
Muhammad0000-0002-7535-7149, L. Panwar380000-0003-2461-4907, R.
Pradhan0000-0001-7000-6510, P.R. Pujahari0000-0002-0994-7212, N.R.
Saha0000-0002-7954-7898, A. Sharma0000-0002-0688-923X, A.K.
Sikdar0000-0002-5437-5217, S. Verma0000-0003-1163-6955

Bhabha Atomic Research Centre, Mumbai, India K. Naskar390000-0003-0638-4378

Tata Institute of Fundamental Research-A, Mumbai, India T. Aziz, I.
Das0000-0002-5437-2067, S. Dugad, M. Kumar0000-0003-0312-057X, G.B.
Mohanty0000-0001-6850-7666, P. Suryadevara

Tata Institute of Fundamental Research-B, Mumbai, India S.
Banerjee0000-0002-7953-4683, M. Guchait0009-0004-0928-7922, S.
Karmakar0000-0001-9715-5663, S. Kumar0000-0002-2405-915X, G.
Majumder0000-0002-3815-5222, K. Mazumdar0000-0003-3136-1653, S.
Mukherjee0000-0003-3122-0594, A. Thachayath0000-0001-6545-0350

National Institute of Science Education and Research, An OCC of Homi Bhabha
National Institute, Bhubaneswar, Odisha, India S.
Bahinipati400000-0002-3744-5332, A.K. Das, C. Kar0000-0002-6407-6974, P.
Mal0000-0002-0870-8420, T. Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu410000-0003-4671-815X, A. Nayak410000-0002-7716-4981, P.
Saha0000-0002-7013-8094, S.K. Swain0000-0001-6871-3937, D.
Vats410009-0007-8224-4664

Indian Institute of Science Education and Research (IISER), Pune, India A.
Alpana0000-0003-3294-2345, S. Dube0000-0002-5145-3777, B.
Kansal0000-0002-6604-1011, A. Laha0000-0001-9440-7028, S.
Pandey0000-0003-0440-6019, A. Rastogi0000-0003-1245-6710, S.
Sharma0000-0001-6886-0726

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi42,430000-0001-5741-3357, E. Khazaie430000-0001-9810-7743, M.
Zeinali440000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S.
Chenarani450000-0002-1425-076X, S.M. Etesami0000-0001-6501-4137, M.
Khakzad0000-0002-2212-5715, M. Mohammadi Najafabadi0000-0001-6131-5987

University College Dublin, Dublin, Ireland M. Grunewald0000-0002-5754-0388

INFN Sezione di Baria, Università di Barib, Politecnico di Baric, Bari, Italy
M. Abbresciaa,b0000-0001-8727-7544, R. Alya,b,460000-0001-6808-1335, C.
Arutaa,b0000-0001-9524-3264, A. Colaleoa0000-0002-0711-6319, D.
Creanzaa,c0000-0001-6153-3044, L. Cristellaa,b0000-0002-4279-1221, N. De
Filippisa,c0000-0002-0625-6811, M. De Palmaa,b0000-0001-8240-1913, A. Di
Florioa,b0000-0003-3719-8041, W. Elmetenaweea,b0000-0001-7069-0252, F.
Erricoa,b0000-0001-8199-370X, L. Fiorea0000-0002-9470-1320, G.
Iasellia,c0000-0003-2546-5341, G. Maggia,c0000-0001-5391-7689, M.
Maggia0000-0002-8431-3922, I. Margjekaa,b0000-0002-3198-3025, V.
Mastrapasquaa,b0000-0002-9082-5924, S. Mya,b0000-0002-9938-2680, S.
Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b0000-0003-3279-6114, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, R.
Radognaa0000-0002-1094-5038, D. Ramosa0000-0002-7165-1017, A.
Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, Ü.
Sözbilira0000-0001-6833-3758, A. Stamerraa0000-0003-1434-1968, R.
Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631

INFN Sezione di Bolognaa, Università di Bolognab, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia0000-0001-8679-4443, L.
Brigliadoria, R. Campaninia,b0000-0002-2744-0597, P.
Capiluppia,b0000-0003-4485-1897, A. Castroa,b0000-0003-2527-0456, F.R.
Cavalloa0000-0002-0326-7515, M. Cuffiania,b0000-0003-2510-5039, G.M.
Dallavallea0000-0002-8614-0420, T. Diotalevia,b0000-0003-0780-8785, F.
Fabbria0000-0002-8446-9660, A. Fanfania,b0000-0003-2256-4117, P.
Giacomellia0000-0002-6368-7220, L. Giommia,b0000-0003-3539-4313, C.
Grandia0000-0001-5998-3070, L. Guiduccia,b0000-0002-6013-8293, S. Lo