HIG-22-011

HIG-22-011

# Search for $\PZ\PZ$ and $\PZ\PH$ production in the $\PQb\PAQb\PQb\PAQb$
final state using proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for $\PZ\PZ$ and $\PZ\PH$ production in the $\PQb\PAQb\PQb\PAQb$
final state is presented, where is the standard model (SM) Higgs boson. The
search uses an event sample of proton-proton collisions corresponding to an
integrated luminosity of 133collected at a center-of-mass energy of 13with the
CMS detector at the CERN LHC. The analysis introduces several novel techniques
for deriving and validating a multi-dimensional background model based on
control samples in data. A multiclass multivariate classifier customized for
the $\PQb\PAQb\PQb\PAQb$ final state is developed to derive the background
model and extract the signal. The data are found to be consistent, within
uncertainties, with the SM predictions. The observed (expected) upper limits
at 95% confidence level are found to be 3.8 (3.8) and 5.0 (2.9) times the SM
prediction for the $\PZ\PZ$ and $\PZ\PH$ production cross sections,
respectively.

##  0.1 Introduction

The observation of Higgs boson pair ($\PH\PH$) production is an important goal
of the High-Luminosity LHC (HL-LHC) program [1]. This process is sensitive to
the self-coupling ($\lambda$) of the Higgs boson, a crucial parameter of the
standard model (SM) that has not yet been measured [2]. Estimates of the
sensitivity to the $\PH\PH$ processes indicate that the SM cross section is at
the edge of what is observable with an integrated luminosity of 3000at the HL-
LHC [3]. An observation of $\PH\PH$ production and a measurement of $\lambda$
will require the combination of the $\PQb\PAQb\PGm\PGm$, $\PQb\PAQb\PGt\PGt$,
and $\PQb\PAQb\PQb\PAQb$ (4) decay modes [4, 5, 6, 7, 8, 9, 10]. The 4channel
has the largest $\PH\PH$ decay branching fraction but suffers from a large
background composed of jets produced through the strong interaction, referred
to as quantum chromodynamic (QCD) multijet events. This background is
challenging to model with simulation; the QCD multijet predictions lack
sufficient accuracy and it is not possible to generate sufficiently large
samples. Extracting all available information from the 4decay mode will thus
require the development and validation of a multi-dimensional background model
based on control samples in data.

In previous $\PH\PH$ $\to$ 4searches [9, 4, 11, 12, 13, 14, 15], the QCD
multijet background is determined in a signal-free control region using a
variant of the “ABCD” or matrix method [16, 17, 18]. The background prediction
in the signal region (SR) requires an extrapolation to a different region of
phase space. This extrapolation is a significant source of systematic
uncertainty that limits the ultimate sensitivity of the analysis. A common
approach for assessing this extrapolation uncertainty is to validate the
background prediction in a third, statistically independent validation region
(VR) [9, 4, 11, 12, 13, 14, 15] that is dominated by background. This strategy
can address how accurately the background model extrapolates to a different
region of phase space, but does not directly test the extrapolation into the
SR. In addition, it inevitably suffers from a lack of statistical power in the
phase space with the highest signal-to-background ratio; the selection that
makes the VR background-dominated depletes the most sensitive phase space of
the SR.

This paper introduces a new method to overcome these limitations by validating
the background model with data samples obtained using hemisphere mixing [19,
20], referred to as synthetic data samples. These synthetic data samples allow
for the validation of the extrapolation of the background model to the
relevant SR and avoid the problem of low statistical power in the most signal-
like phase space. This technique also provides a way to determine the expected
variance of the background prediction, resulting from the finite size of the
data sample used to fit the model.

The $\PZ\PZ\to 4\PQb$ and $\PZ\PH\to 4\PQb$ processes share the final state
and all the experimental challenges of the $\PH\PH\to 4\PQb$ analysis, but
have production cross sections that are expected to be 31 ($\PZ\PZ$) [21] and
8 ($\PZ\PH$) [22] times larger than $\PH\PH$. In addition, the $\PZ\PZ$ and
$\PZ\PH$ processes are well established experimentally, both having been
observed and measured using channels in which the Z decays to leptons [23, 24,
25, 26, 27].

This paper presents a search for $\PZ\PZ\to$ 4and $\PZ\PH\to$ 4production
using 133of proton-proton ($\Pp\Pp$) collisions at $\sqrt{s}=13\TeV$,
collected by the CMS experiment at the LHC. The analysis introduces several
new techniques for deriving and validating the background model. A multiclass
multivariate classifier, which uses convolutional layers to solve the
combinatoric jet-pairing problem, has been designed with an architecture
customized to the 4final state. The classifier is used both for signal-versus-
background discrimination as well as for the derivation and validation of the
background model. While these techniques are developed and demonstrated in the
$\PZ\PZ$ and $\PZ\PH$ $\to$ 4searches, they are directly applicable to the
$\PH\PH\to 4\PQb$ analysis and the measurement of $\lambda$.

The remainder of the paper is organized as follows. The CMS detector and the
reconstruction and identification of physics objects used in this analysis are
described in Section 0.2. Section 0.3 discusses the data and the simulated
events used. Details of the event selection are presented in Section 0.4.
Section 0.5 describes the architecture of the multivariate classifier used
throughout the analysis. The background modeling method is described in
Section 0.6 and its validation in Section 0.7. The construction of the
synthetic data samples are described in Section 0.7.1, and the evaluation of
the background uncertainties in Section 0.7.2. Other sources of systematic
uncertainty are detailed in Section 0.8. Finally, the results are reported in
Section 0.9 and a summary is provided in Section 0.10. The tabulated results
are provided in a HEPData record [28].

##  0.2 The CMS detector

The CMS apparatus [29, 30] is a multipurpose, nearly hermetic detector,
designed to trigger on [31, 32] and identify electrons, muons, photons, and
(charged and neutral) hadrons [33, 34, 35]. The central feature of the CMS
detector is a superconducting solenoid, providing a magnetic field of 3.8T.
Within the solenoid volume are a silicon pixel and strip tracker, a lead
tungstate crystal electromagnetic calorimeter, and a brass and scintillator
hadron calorimeter, 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. 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. [29].

Events of interest are selected using a two-tiered trigger system. The first
level (L1), composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed latency of 4 [31]. The second level, known as the high-level
trigger (HLT), consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage [32].

The event reconstruction is based on the particle-flow (PF) algorithm [36],
which aims to reconstruct and identify each individual particle (PF candidate)
in an event with an optimized combination of information from the various
elements of the CMS detector. The PF candidates are classified as electrons,
muons, photons, and charged or neutral hadrons. The primary vertex is taken to
be the vertex corresponding to the hardest scattering in the event, evaluated
using tracking information alone, as described in Section 9.4.1 of Ref. [37].

Jets are reconstructed from PF candidates, clustered using the anti-algorithm
[38, 39] with a distance parameter of 0.4. The 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 whole
spectrum and detector acceptance. Additional collisions within the same or
nearby bunch crossings (pileup) can give rise to jets not coming from the
hard-scattering process or contribute additional tracks and calorimetric
energy depositions, increasing the apparent jet momentum. To mitigate this
effect, tracks identified as originating from pileup vertices are discarded
and an offset correction is applied to account for remaining contributions
[40]. Jet energy corrections are derived from simulation studies so that the
average measured energy of jets becomes identical to that of particle-level
jets. In situ measurements of the momentum balance in dijets,
$\gamma+\mathrm{jets}$, $\PZ+\mathrm{jets}$, and multijet events are used to
determine any residual differences between the jet energy scale in data and in
simulation, and appropriate corrections are applied [41, 42]. Jets originating
from quarks are identified using the DeepJet algorithm [43], a deep neural
network combining secondary vertex properties, track-based variables, and PF
jet constituents (neutral and charged particle candidates). The efficiency of
(light flavor and gluon) jets to pass the -tagging requirement used in this
analysis is data-set dependent and varies within the 50–60% (0.05–0.5%) range.

##  0.3 Data and simulation

This analysis is performed on data collected during three years of data taking
at $\sqrt{s}$ = 13. The combined data set corresponds to an integrated
luminosity of 133collected during 2016–2018 [44, 45, 46].

Events are selected at L1 using triggers requiring the presence of at least
four jets in the tracker acceptance ($\abs{\eta}<2.5$) and large , defined as
the scalar sum of the of the reconstructed jets in the event. During the 2016
data taking, events are required to have either $\HT>280\GeV$ or at least four
jets with $\pt>50\GeV$. In the 2017 data set, events are required to have
$\HT>280\GeV$ and the four leading jets are required to pass staggered
thresholds of 70, 55, 40, and 35. In the 2018 data set, the requirement was
raised to 320and the lowest jet threshold was raised to 40.

Events are selected in the HLT using a combination of triggers requiring the
presence of jets coming from the hadronization of quarks (jets). Events are
required to have at least four jets, at least three of which are identified as
arising from a bottom quark ($\PQb$ tagged). In the 2016 data set, events are
required to have either at least four jets with transverse momentum
$\pt>45\GeV$, or two or more jets with $\pt>90\GeV$ and two or more jets with
$\pt>30\GeV$. In the 2017 data set, an requirement of 300was added to match
the threshold at L1, and the four highest-jets were required to pass staggered
thresholds of 75, 60, 45, and 40. The threshold was raised to 330in 2018. The
tagging was performed in HLT using the CSV algorithm [47] in 2016–2017, and
with the DeepCSV algorithm [43] in 2018. Following the selection described in
Section 0.4, this combination of triggers has an approximate efficiency of 20%
for simulated signals with di-boson invariant mass near 200, rising to 90%
efficiency for masses greater than 600.

Simulated events are used to model $\PZ\PZ$, $\PZ\PH$, and $\PH\PH$ events and
the background from top quark pair () production. The process is generated at
next-to-leading-order (NLO) accuracy in QCD [48] with v2.0 [49, 50]. The
dominant background from QCD multijet events is modeled using control samples
in data.

Events from $\PZ\PZ$ production are generated with v2.4.2 [51] at NLO QCD with
the FxFx merging scheme [52] and include up to two additional partons. The SM
prediction for the total $\PZ\PZ$ production cross section in $\Pp\Pp$
collisions at $\sqrt{s}=13\TeV$ is $15.0^{+0.7}_{-0.6}$pb, taken from Ref.
[21].

The quark-induced $\PZ\PH$ signal process is generated at NLO accuracy [53]
using the v2 event generator extended with the MiNLO procedure [54, 55], while
the gluon-induced $\PZ\PH$ process is simulated at LO accuracy with v2. The SM
prediction for the total $\PZ\PH$ production cross section, computed at next-
to-next-to-leading-order accuracy in QCD, is $0.88\pm 0.03$pb for
$m_{\mathrm{H}}=125\GeV$ [22]. The SM branching fractions of 58.2 and 15.1%
are taken for H $\to$ bb and Z $\to$ bb decays, respectively [22, 56, 57],
again assuming $m_{\mathrm{H}}=125\GeV$.

Events from $\PH\PH$ production used to train the classifier are simulated at
NLO accuracy [58] with v2. The dominant SM $\PH\PH$ production mode in
$\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$ is through the gluon-fusion
mechanism; the predicted cross section, computed at next-to-next-to-leading-
order accuracy, is $31.1^{+2.1}_{-7.2}$fb [59, 60, 61, 62, 63, 64, 65, 66].

For both signal and background events, multiple-parton interactions, parton
shower, and hadronization effects are simulated with v8.226 for 2016 and
v8.230 for 2017–2018 [67]. For 2016, the CP5 tune [68] is used for the samples
and the CUETP8M1 tune [69] is used for all the others; for 2017–2018, the CP5
tune is used throughout all samples. The NNPDF 3.0 [70] (NNPDF 3.1 [71])
parton distribution functions (PDFs) are used to simulate the samples
corresponding to the 2016 (2017–2018) data sets. For all simulated samples,
the CMS detector response is modeled with [72]. Pileup collisions are
simulated and added to the hard-scattering process for all samples. The
simulated events are weighted to match the distribution of reconstructed
primary vertices observed in data.

##  0.4 Event selection

Selected events must have at least four jets with $\pt>40$and $\abs{\eta}<2.4$
that are -tagged by the DeepJet algorithm. Events passing this selection are
referred to as the four-tag sample. The four jets with the highest tagging
score are paired to form or candidates (“boson-candidate jets” in what
follows). A dedicated correction to the jet energy scale, based on a
regression technique that takes properties of the jets into account, is
applied to the boson-candidate jets. This improves the determination of the
jet momentum by up to 15% [73].

Initially, all three possible jet pairings are considered. To reduce the
combinatorial background, pairs of jets with an invariant mass roughly
consistent with the and masses are retained. Jet pairings must satisfy

$\displaystyle 52$
$\displaystyle<m_{\mathrm{jj}}^{\text{lead}}<180\GeV\quad\text{and}$ (1)
$\displaystyle 50$ $\displaystyle<m_{\mathrm{jj}}^{\text{subl}}<173\GeV,$

where $m_{\mathrm{jj}}^{\text{lead}}$ and $m_{\mathrm{jj}}^{\text{subl}}$ are
the invariant masses of the leading and subleading boson candidates,
respectively. The leading boson candidate is defined as the one with the
highest scalar sum of jet . From simulation it is found that this sorting
tends to bias the leading dijet mass distribution upwards by 2% and the
subleading — downwards by 2%. This is taken into account when defining the
dijet mass regions used in the analysis.

The angle between the decay products of the bosons in the laboratory frame
provides another handle to reduce the background. This angle depends on the
Lorentz boost of the bosons and, thus, on the four-jet invariant mass
$m_{\mathrm{4j}}$. The pairings of jets associated with the boson candidates
satisfy the following requirements

$\displaystyle\frac{360\GeV}{m_{\mathrm{4j}}}-0.5<$
$\displaystyle\Delta\text{R}_{\text{jj}}^{\text{lead}}<\max\bigg{[}1.5,\frac{650\GeV}{m_{\mathrm{4j}}}+0.5\bigg{]}\quad\text{and}$
(2) $\displaystyle\frac{235\GeV}{m_{\mathrm{4j}}}<$
$\displaystyle\Delta\text{R}_{\text{jj}}^{\text{subl}}<\max\bigg{[}1.5,\frac{650\GeV}{m_{\mathrm{4j}}}+0.7\bigg{]},$

where $\Delta\text{R}_{\text{jj}}^{\text{lead}}$ and
$\Delta\text{R}_{\text{jj}}^{\text{subl}}$ are the angular separations between
the jets in the leading and subleading boson candidates, respectively. The
angular separation is defined as $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$, where $\Delta\phi$
($\Delta\eta$) is the difference in azimuthal angle (pseudorapidity) between
the two boson candidates. These requirements reject jet pairings that are
inconsistent with a or decay. This selection is based on that of the previous
ATLAS measurement in Ref. [11], loosened to increase the signal acceptance.

Events in which all pairings fail the criteria in Eq. (2) are retained to
increase the size of the data set used to train various classifiers in the
analysis. If a pairing in these events satisfies one of the aforementioned
requirements, it is chosen. When multiple pairings pass the same number of
requirements, one is chosen randomly to give each event a location in the
$m_{\mathrm{jj}}^{\text{lead}}$-$m_{\mathrm{jj}}^{\text{subl}}$ plane without
biasing the background distribution.

The SR is defined using four overlapping regions in the dijet mass plane
targeting $\PZ\PZ$, $\PZ\PH$, and $\PH\PH$ decays. These regions are defined
using variables $X_{\mathrm{\PZ\PZ}}$, $X_{\mathrm{\PZ\PH}}$,
$X_{\mathrm{\PH\PZ}}$, and $X_{\mathrm{\PH\PH}}$, defined as

$X_{\mathrm{B}_{1}\mathrm{B}_{2}}=\sqrt{\Bigg{(}\frac{m_{\mathrm{jj}}^{\text{lead}}-m_{\mathrm{B_{1}}}}{\sigma_{m_{\mathrm{jj}}^{\text{lead}}}}\Bigg{)}^{2}+\Bigg{(}\frac{m_{\mathrm{jj}}^{\text{subl}}-m_{\mathrm{B_{2}}}}{\sigma_{m_{\mathrm{jj}}^{\text{subl}}}}\Bigg{)}^{2}},$
(3)

where $m_{\mathrm{B_{1}}}$ and $m_{\mathrm{B_{2}}}$ correspond to the nominal
or masses, corrected for the bias mentioned above, depending on the signal
targeted. The denominators in each term is the approximate mass resolution,
estimated from simulation to be 10% of the reconstructed boson mass. The SR is
defined as the union of the requirements: $X_{\mathrm{\PZ\PZ}}<2.6$,
$X_{\mathrm{\PZ\PH}}<1.9$, $X_{\mathrm{\PH\PZ}}<1.9$, and
$X_{\mathrm{\PH\PH}}<1.9$. Figure 1 shows signal yield from simulation
normalized to the expected yield (left) and the four-tag events from data
(right), as a function of $m_{\mathrm{jj}}^{\text{lead}}$ and
$m_{\mathrm{jj}}^{\text{subl}}$. The four regions used to define the SRs are
shown by the red dashed contours.

Figure 1: Signal yield from simulation (left) and from four-tag events in data
(right), as a function of $m_{\mathrm{jj}}^{\text{lead}}$and
$m_{\mathrm{jj}}^{\text{subl}}$. The color scale to the right of each plot
gives the range of values. The signal region is defined by the union of the
regions enclosed by the dashed red contours.

The fractions of $\PZ\PZ$ and $\PZ\PH$ signal events within the detector
acceptance from simulation, multiplied by the efficiency of each selection
step are shown in the left and right plots of Fig. 2, respectively, as a
function of the true four-body invariant mass, $m_{4\PQb}^{\text{gen}}$. The
cumulative effect of each selection requirement is shown for the $\PZ\PZ$ and
$\PZ\PH$ channels. The product of the acceptance and efficiency is limited at
low mass by the and jet requirements in the trigger. At high mass, the
acceptance for resolving four jets drops and tagging efficiency decreases for
high jets. The trigger requirement significantly limits the efficiency at
lower masses. The larger cross section of the $\PZ\PZ$ process compensates for
the lower acceptance, leading to similar expected event yields.

Figure 2: Event selection acceptance times efficiency as a function of the
generated four-body mass $m_{4\PQb}^{\text{gen}}$ for the $\PZ\PZ$ (left) and
$\PZ\PH$ (right) signals. The plots show the cumulative efficiency with
respect to the inclusive sample. The expected $m_{4\PQb}^{\text{gen}}$
distributions of the inclusive $\PZ\PZ$ and $\PZ\PH$ events are shown by the
gray-shaded areas with arbitrary normalization.

The output from a multivariate classifier, described in Section 0.5, is used
as the final discriminant between the signal and background. The classifier,
referred to as the signal-versus-background (SvB) classifier, is trained to
output the probability that an event is from one of five classes: multijet, ,
$\PZ\PZ$, $\PZ\PH$, and $\PH\PH$. The probability of a signal event, defined
as the sum of the probabilities of the event being from the $\PZ\PZ$,
$\PZ\PH$, or $\PH\PH$ sample, is used in the final signal extraction.

Orthogonal $\PZ\PZ$ and $\PZ\PH$ SRs are defined according to which of the
signal probabilities is the largest. A combined fit is performed in the
$\PZ\PZ$ and $\PZ\PH$ regions to events with a signal probability larger than
1%, to extract the $\PZ\PZ$ and $\PZ\PH$ fitted signal strengths.

##  0.5 Hierarchical combinatorial residual network

A multivariate classifier is used both for signal-versus-background
discrimination as well as for the derivation and validation of the background
model. An architecture, referred to as the hierarchical combinatorial residual
(HCR) network, is specifically developed for the four-jet diboson topology.
The HCR consists of a series of convolutional neural networks, each employing
phase-symmetric convolutional filters [74] and residual learning [75], to
process kinematic information from the boson-candidate jets; information from
additional jets in the event is included using an attention block [76]. This
network architecture provides a clean solution to the combinatorial jet-
pairing problem, allowing the same set of weights to be optimized for all
pairings. The convolutional layers are arranged hierarchically, first
processing a jet image to form a dijet image, then processing the dijet image
to form a quadjet image.

Figure 3 shows a high-level sketch of the HCR architecture. The initial jet
image is formed from pixels representing each of the boson-candidate jets,
using the jet $\pt,\,\eta,\,\phi,$ and mass values as features. Three copies
of the jet pixels are arranged to form a one-dimensional image such that pairs
of adjacent pixels represent the three possible jet pairings. Adjacent pairs
of jet pixels are convolved to form a dijet image using a single set of
weights. The second layer processes the six dijet pixels to form a three-pixel
quadjet image. These three pixels are then combined to produce a single event-
level pixel. Each convolution projects the input image into an eight-
dimensional space; the dimension of this embedded space is a hyperparameter
that controls the size of the network. A final output layer projects these
features into a $N_{c}$-dimensional space, where $N_{c}$ is the number of
input classes used in training. These outputs are converted into the
probabilities that a given event belongs to the corresponding class used in
training by applying softmax function.

Figure 3: A high-level sketch of the HCR classifier architecture. Boson-
candidate jets are shown on the left with the three possible jet pairings. The
HCR architecture is shown on the right. The boxes represent pixels, with the
labels indicating which jet, dijet, or quadjet the pixel refers to. The
different jet pairings on the left are each represented within the network, as
indicated by the color coding. The output P(class) corresponds to the the
probability that an event belongs to the corresponding class used in training.

Classifiers with the HCR architecture are used throughout the analysis. The
SvB classifier employs the HCR architecture to construct the variable used in
the final signal extraction. The HCR classifiers are also used to define the
background model (Section 0.6) and in the construction of the synthetic data
sets (Section 0.7.1).

##  0.6 Background model

After the event selection is applied, 95% of the expected background consists
of multijet events, which are modeled using data. The remaining 5% are events,
which are modeled with simulation. Backgrounds from other sources, including
processes involving single-production, are found to have a negligible
contribution.

The QCD multijet background is modeled with an independent data set selected
using the same trigger and selection requirements as used in the SR, except
for the tagging requirement: at least four jets with $\pt>40\GeV$ are
required, exactly three of which are required to be tagged. To increase the
size of this three-tag control sample, the tagging requirement is loosened
such that the efficiency to correctly identify a jet is 70–80% depending on
the data set. The size of three-tag sample is about 17 times larger than that
of the four-tag sample. The three-tag sample consists of 90% multijet events
and 10% events. Simulations indicate that the three-tag sample has a
negligible contribution of signal events.

A product of two weights is applied to the three-tag events to model the
multijet background in the four-tag sample. The first weight, referred to as
the jet combinatorial model (JCM) weight, accounts for additional jet activity
in the four-tag sample. The second weight, referred to as the four-vs-three
(FvT) weight, corrects for kinematic differences between the three- and four-
tag samples.

The weights are derived using a signal-depleted sideband (SB) region of the
$m_{\mathrm{jj}}^{\text{lead}}-m_{\mathrm{jj}}^{\text{subl}}$ plane, shown in
Fig. 1. The SB is defined as the region inside the mass window defined by Eq.
(1) but outside the SR. The boundaries of the SB region are chosen to provide
sufficient statistical precision, while ensuring that the kinematic properties
of events in the SB are representative of those in the SR.

###  0.6.1 Jet combinatorial model

The four-tag sample has a larger jet multiplicity than the three-tag sample,
since the requirement of exactly three -tagged jets in the three-tag sample
biases the jet multiplicity. This effect is modeled using an extension of the
combinatorial technique introduced in Ref. [11]. For each three-tag event, all
possible combinations of jets not satisfying the looser tagging requirement,
referred to as anti-b-tagged jets, are considered. At least one anti--tagged
jet is treated as a jet. The anti--tagged jets that are treated as jets are
referred to as pseudo-tagged jets. A constant per-jet transfer factor $f$ is
assigned to each pseudo-tagged jet and a factor $(1-f)$ to the remaining anti
--tagged jets. Correlations in the transfer factor among jets arise because
jets are produced in pairs. These correlations are modeled with a pair-
enhancement term $e$, that is included in combinations with an even number of
tagged plus pseudo-tagged jets. This enhancement term is expected to be
suppressed at higher jet multiplicities since the probability of observing an
odd number of -tagged jets increases because of mistagging and jets falling
outside of the detector acceptance. The pair-enhancement factor is thus
divided by $n^{d}$, where $n$ is the number of anti-b-tagged jets and $d$ is a
free parameter that controls the suppression of $e$ with jet multiplicity.
Finally, an overall normalization $t$ is included to account for the looser
tagging requirement in the three-tag sample. The per-event JCM weight is thus
computed as:

$w_{\text{JCM}}=\begin{cases}t\sum_{i=1}^{n}\binom{n}{i}f^{i}(1-f)^{n-i}(1+\re/n^{d})&(3+i)\quad\text{even}\\\
t\sum_{i=1}^{n}\binom{n}{i}f^{i}(1-f)^{n-i}&(3+i)\quad\text{odd},\end{cases}$
(4)

where $i$ is the number of pseudo-tagged jets and $\binom{n}{i}$ is the
binomial coefficient.

The JCM parameters are determined by a combined fit to the jet and -tagged jet
multiplicity distributions in the SB region. Selected jets are required to
satisfy the same kinematic requirements, $\pt>40$and $\abs{\eta}<2.4$, as the
boson-candidate jets. The jet and -tagged jet multiplicity distributions are
shown in Fig. 4 (left) and (right), respectively, along with the results of
the fit. All events have at least four jets that are tagged (or pseudo tagged)
in the four-tag (three-tag) sample. The black data points show the observed
four-tag data; the yellow distribution displays the multijet background
estimate prior to the JCM correction, and the blue distribution is from the
simulation. The multijet background prior to the JCM correction is given by
the -subtracted three-tag data, normalized to the -subtracted four-tag data.
By construction, the multijet background cannot model the -tagged jet
multiplicity prior to the JCM correction. The red histogram shows the result
of the JCM fit, which provides a good description of both the jet and -tagged
jet multiplicities.

Figure 4: Jet (left) and -tagged jet (right) multiplicity distributions in the
SB region. The black data points show the observed four-tag data, the blue
distribution the simulation, and the yellow histogram the three-tag multijet
prior to the JCM corrections. The red histogram shows the result of the fit to
the JCM model. The quality of the fit is given by the $\chi^{2}$ per degrees
of freedom (dof) and corresponding $p$-value in the legend. The lower panels
display the ratio of the data to the fit prediction.

###  0.6.2 Kinematic reweighting

After correction with the JCM weights, differences remain between the three-
and four-tag samples arising from the kinematic dependence of the tagging
efficiency and from a different mixture of underlying scattering processes in
the two selections. These differences are highlighted in Fig. 5, which shows
the opening angles $\Delta R(j,j)$ for dijet pairs, chosen such that the
boson-candidate jets with the smallest opening angle form one pair (close
dijet) and the remaining boson-candidate jets form the other (complement
dijet). The opening angles are shown for events in the SB region. The multijet
model, shown in yellow, includes the JCM weights. The four-tag sample is
dominated by gluon splitting to from an underlying two-to-two scattering
process. This produces a topology with two -tagged dijets, each with a small
$\Delta\text{R}(j,j)$. The three-tag sample contains this process in addition
to a mixture of processes where the anti-b-tagged boson-candidate jet can be
produced without gluon splitting, leading to a broader distribution of opening
angles. Significant differences between the three- and four-tag samples are
also observed in other jet, dijet, and event-level distributions. Figure 6
shows the modeling of the final discriminating variables — the SvB signal
probabilities for $\PZ\PZ$ ($\mathrm{P}_{\PZ\PZ}$) and $\PZ\PH$
($\mathrm{P}_{\PZ\PH}$) — in the SB region.

Figure 5:  Distributions of $\Delta R(j,j)_{\text{close}}$ (left) and $\Delta
R(j,j)_{\text{complement}}$ (right). The four-tag SB events are shown by the
points. The QCD multijet distribution (yellow region) is from the three-tag SB
sample after the JCM correction but before the FvT kinematic reweighting, and
the distribution (blue region) is from simulation. The lower panels display
the ratio of the four-tag data to the total background, which is the sum of
the QCD multijet and distributions. The hatched area gives the statistical
uncertainty in the background.

Figure 6:  Distributions of the signal probabilities for $\PZ\PZ$ (left) and
$\PZ\PH$ (right) in the SB region, respectively. The four-tag SB events are
shown by the points. The QCD multijet distribution (yellow region) is from the
three-tag SB sample after the JCM correction but before the FvT kinematic
reweighting, and the distribution (blue region) is from simulation. The lower
panels display the ratio of the four-tag data to the total background, which
is the sum of the QCD multijet and distributions. The hatched area gives the
statistical uncertainty in the background.

The residual kinematic differences between the three- and four-tag samples are
corrected using weights derived from a multivariate classifier with the HCR
architecture. This classifier, referred to as the FvT classifier, is trained
with four classification targets: four-tag data, four-tag simulation, three-
tag data, and three-tag simulation. The FvT classifier is trained using data
and the simulation in the SB region. The JCM weights are applied to the three-
tag data and three-tag simulation prior to training. The output probabilities
are used to reweight the three-tag data to the four-tag multijet background.
The FvT weights are given by

$w_{\text{FvT}}=\frac{P(\text{M}_{\mathrm{4b}})}{P(\text{D}_{\mathrm{3b}})}\equiv\frac{P(\text{D}_{\mathrm{4b}})-P(\text{\ttbar}_{\mathrm{4b}})}{P(\text{D}_{\mathrm{3b}})},$
(5)

where $P(\text{D}_{\mathrm{4b}})$, $P(\text{\ttbar}_{\mathrm{4b}})$, and
$P(\text{D}_{\mathrm{3b}})$ are the class-assignment probabilities coming from
the FvT classifier output, and $\text{M}_{\text{4b}}$ refers to the QCD
multijet four-tag event. The final background model is given by the four-tag
simulation plus the three-tag data weighted by the product of the JCM and FvT
weights.

Figure 7 shows the improvement in modeling of the close and complement
candidate opening angles when the FvT weights are applied. The FvT weights
also correct the modeling of the other observed discrepancies in jet, dijet,
and event-level distributions. The impact of the FvT reweighting on the
modeling of the SvB signal probabilities in the SB region is shown in Fig. 8.

Figure 7: The $\Delta\text{R}(j,j)$ distributions shown in Figure 5 after
including the FvT corrections to the QCD multijet prediction.

Figure 8: Distribution of signal probabilities for $\PZ\PZ$ (left) and
$\PZ\PH$ (right) events in the SB region after including the FvT corrections
to the QCD multijet prediction.

##  0.7 Background validation

The accurate modeling of the SvB signal probabilities in Fig. 8 comes with two
major caveats. The first is that the FvT classifier is trained with the four-
tag SB region data and thus has access to all of the relevant SvB information
during training. The final analysis in the SR requires an extrapolation of the
background model to a different region of phase space. This extrapolation is a
significant source of systematic uncertainty that cannot be assessed in the
region used to train the classifier. The second major caveat is that the
sensitivity in the SR is dominated by SvB signal probabilities above
${\approx}$ 0.9, for which there is little statistical power in the SB region.

These problems are addressed by validating the background model with synthetic
data sets that allow the extrapolation of the background model to be tested
precisely in regions of high signal probability. Section 0.7.1 describes the
construction of the synthetic data sets, and Section 0.7.2 describes how it is
used to assess the systematic uncertainties in the background model.

###  0.7.1 Synthetic data sets from hemisphere mixing

A synthetic data set is generated by splitting individual events into
hemispheres and then combining similar hemispheres from different events. This
mixing procedure suppresses correlations among the jet four-vectors due to the
presence of a signal, while preserving the kinematic distributions of the
4background. The relevant event-level correlations in the background — which
primarily arise from gluon splitting in an underlying two-to-two scattering
process — are captured in the correlation between hemispheres and are
independent of the dijet substructure within the hemispheres.

The mixing algorithm is based on the technique developed in a previous CMS
$\PH\PH\to 4\PQb$ analysis [20]. The first step involves creating a collection
of hemispheres (hemisphere library) from events in the four-tag data set. Each
event is split into two hemispheres using the plane orthogonal to the
transverse thrust axis [20], which is chosen based on the assumption that it
is a good proxy for the initial gluon directions in the underlying scattering
process. Jets on one side of the plane are assigned to one hemisphere, those
on the other side are assigned to the other hemisphere. Four variables are
computed using the sum of the four-vectors of all the jets in that hemisphere:
the invariant mass, the longitudinal momentum, and the transverse momentum
perpendicular and parallel to the transverse thrust axis. The jet and jet
multiplicities are also computed for each hemisphere. The library is created
with events that pass the jet kinematic requirements but before the dijet
invariant mass requirement given in Eq. (1) is applied.

The mixing is performed in a second pass over the data. Events are split into
hemispheres as before, and the corresponding hemisphere summary variables are
calculated. Each hemisphere in the event is replaced with its nearest neighbor
in the hemisphere library. Nearest neighbors are defined as hemispheres with
the same jet and jet multiplicities that minimize the distance between
hemispheres, defined as

$d(h_{i},h_{j})=\sqrt{\sum_{k}\left(\frac{v_{k}(h_{i})-v_{k}(h_{j})}{\sigma_{v_{k}}}\right)^{2}},$
(6)

where $h_{i}$ and $h_{j}$ are two hemispheres and the sum runs over the four
hemisphere summary variables $v_{k}$ and $\sigma_{v_{k}}$ is the root mean
square of the corresponding variable calculated from all hemispheres in the
library. During the nearest neighbor replacement a check is made that the
matching hemispheres do not come from the same event. Finally, the nearest
neighbor hemispheres are rotated in the azimuthal angle $\phi$ to match the
direction of the transverse thrust axis of the input event.

This analysis introduces two important improvements to the mixing strategy.
The first is the use of the three-tag data set in mixing. Four-tag events are
used to create the hemisphere library, however, the three-tag data set is used
in the second pass when creating the mixed data sample. In the three-tag
sample, the pseudo-tagged jets are treated as tagged when matching
hemispheres; this ensures that events in the resulting mixed data sample all
have four -tagged jets. An illustration of the mixing procedure is given in
Fig. 9. Mixing the three-tag data eliminates a potential bias from signal
contamination and allows for the construction of a synthetic data sample that
is fifteen times larger than the four-tag data sample.

Figure 9:  An illustration of the hemisphere mixing procedure, adapted from
Ref. [20]. Three-tag events are divided into two halves by cutting along the
axis perpendicular to the transverse thrust axis. In a preliminary step, each
event in the four-tag data set is split into two hemispheres that are
collected in a library of hemispheres. Once the library is created, each
three-tag event is used as a basis for creating a synthetic event. These are
constructed by picking the two hemispheres from the library that are most
similar to the hemispheres making up the original event.

The other important improvement to the mixing algorithm is the introduction of
corrections accounting for the background contamination. The presence of the
events complicates the mixing strategy, since these events have significant
hemisphere-level correlations coming from the decays of the top quarks. Mixing
a hemisphere with a QCD multijet hemisphere would significantly distort the
background. The mixed events are thus not expected to provide a good model of
the unmixed background.

The subtraction of the background needs to be done at the level of the
hemispheres in the library, with three-tag data being mixed — not from
projections into histograms, as is more typical in high energy physics. The
component in the hemisphere library and the three-tag data sample are
subtracted statistically using event weights derived from a multivariate
classifier with the HCR architecture. This classifier is trained with two
classes, data and simulation, separately on the three- and four-tag data. The
outputs from the classifier are used to determine the probability
$P(\text{M})$ that an event in the three- or four-tag data sample is a
multijet event. When constructing the mixed data sample, an input three-tag
event is accepted for mixing with probability $P(\text{M})$. Similarly,
nearest-neighbor hemispheres are accepted as replacements with probability
$P(\text{M})$, as calculated in their corresponding four-tag event. When a
hemisphere is rejected, the next-nearest neighbor is considered in turn.

The size of the four-tag data sample in the SB region, used to train the FvT
classifier, is a fundamental limitation of the background determination
procedure. When validating the background, it is critical that the synthetic
data have the same statistical power as the nominal four-tag sample. To ensure
this, the mixed data are subsampled to match the size of the QCD multijet
background in the four-tag sample. The relative size of the mixed data sample
allows fifteen independent subsamples to be created. Events from the four-tag
simulation are added to each subsample according to the expected yield. These
fifteen synthetic data sets are referred to as mixed models.

The advantages of using the mixed models to validate the background can be
seen in Fig. 10. The $\PZ\PZ$ ($\PZ\PH$) SvB signal probabilities are shown in
the upper (lower) figures in the SB region, left, and in the SR, right. The
four-tag data are shown by the black points. The three-tag data distribution
before applying the kinematic FvT corrections, from which the background
prediction is extrapolated, is shown in yellow. The average of the mixed
models, shown in red, provides a high-event-count proxy for the four-tag
background that can be used to validate the background prediction in regions
of high signal probability.

Figure 10:  Distribution of signal probabilities for $\PZ\PZ$ (upper row) and
$\PZ\PH$ (lower row) events in the sideband (left) and signal regions (right).
The four-tag events are shown by the points. The QCD multijet distribution
before the FvT corrections is given by the yellow region, and the simulated
distribution by the blue area. The average of the mixed models (red) provides
a high-event-count proxy of the 4background (black) that allows the
extrapolation of the background model to be tested precisely. The lower panels
display the ratio of the four-tag data to the average of the mixed models
(red) and to the QCD multijet distribution (black).

###  0.7.2 Background model uncertainties

The background procedure, as described in Section 0.6, is repeated by treating
the mixed models as four-tag data. The procedure is performed separately using
each of the fifteen mixed models. Differences among the fifteen QCD multijet
predictions and the comparison of the predicted background to the observed SR
yield are used to assign systematic uncertainties in the nominal background
model. These systematic uncertainties are assessed in three steps. First, the
differences among the fifteen QCD multijet predictions are quantified. These
differences arise from the finite size of the data sample in the SB region
used to train the FvT classifier. The second step measures the systematic
uncertainty from extrapolating to the SR, by comparing the background
predictions to the observed yields in the mixed models. The final step checks
if biases in the background model can mimic a signal. This process is carried
out independently for the final $\PZ\PZ$ and $\PZ\PH$ selections.

The differences in the background models are quantified by comparing the QCD
multijet predictions of each mixed model in the SR to their average. These
differences are parameterized by a set of orthogonal Fourier basis functions
added to the background predictions. Each of the multijet predictions, with
unconstrained coefficients for the basis function corrections, are fit
separately to the average. Basis functions with increasing frequency
components are added until the pulls of adjacent SvB signal probability bins
from all the mixed-model predictions are consistent with being uncorrelated. A
pull is defined as the difference between the observed and expected values,
divided by the uncertainty in the difference. A systematic uncertainty in each
coefficient is assigned based on the root mean square of the fitted values.
This uncertainty accounts for the expected variance of a single background
prediction due to the finite size of the data sample in the SB region.

This procedure is carried out separately for the $\PZ\PZ$ and $\PZ\PH$ signal
probabilities. Five (four) basis functions, with uncertainties in the basis-
function coefficients of up to 3%, are needed to characterize the differences
among the $\PZ\PZ$ ($\PZ\PH$) background predictions.

The systematic uncertainty from extrapolating the background prediction is
evaluated by comparing the SR predictions to the observed yields in the mixed
models. A combined background model is fit to the average of the observed SR
yields. Averaging the fifteen mixed models improves the precision with which
the extrapolation uncertainty can be determined. The combined background model
consists of the estimated , the average of the QCD multijet predictions, and
the basis-function corrections determined above. The coefficients of the basis
functions are treated as nuisance parameters constrained using the systematic
uncertainties assigned in the previous step.

The extrapolation uncertainty is quantified using the basis-function
coefficients determined from fitting the mixed models. The fit is repeated,
sequentially removing constraints on the nuisance parameters, until the fit
has a $p$-value greater than 5% and an F-test [77] does not prefer more
unconstrained basis function coefficients. Nonzero fitted coefficients
represent a systematic difference between the predicted and observed
background. Systematic uncertainties in the background extrapolation are
assigned by adding the magnitude of the fitted coefficients in quadrature with
their uncertainty. These extrapolation uncertainties are treated as
uncorrelated from the variance uncertainties assigned in the previous step.

Figure 11 illustrates the process in determining the extrapolation uncertainty
in the $\PZ\PZ$ (left) and $\PZ\PH$ (right) SRs. The upper panels compare the
mixed model SvB signal probability in the SR, given by the black points, to
the pre-fit background prediction, shown as stacked yellow and blue
histograms, and the post-fit background prediction, shown in red. The lower
panels show the pulls. For the $\PZ\PZ$ SR, none of the fit parameters need to
be unconstrained to satisfy the goodness-of-fit or the F-test criteria. For
the $\PZ\PH$ SR, the goodness-of-fit criteria is not satisfied until two
parameters are unconstrained, at which point the F-test criterion is also
satisfied. The extrapolation uncertainty is ${\lesssim}\ 1\%$ for most
parameters and at most ${\approx}\ 3\%({\approx}\ 5\%)$ in the $\PZ\PZ$
($\PZ\PH$) region.

Figure 11:  The distributions of the $\PZ\PZ$ (left) and $\PZ\PH$ (right)
signal probabilities. The black data points show the average of the mixed
models. The yellow and blue distributions show the average of the QCD multijet
models and the simulation, respectively. The red histogram displays the post-
fit results of the data fit to the background model. The $\PZ\PZ$ channel data
distribution is fit with all five basic coefficients constrained, while the
$\PZ\PH$ channel distribution has two of the four coefficients unconstrained.
The lower panels give the pre- (blue) and post-fit (red) pulls.

The mixed models can also be used to test if biases in the background model
can mimic the signature of a signal, a possibility to which many analyses with
backgrounds estimated from control samples in data are blind. To assess the
risk of fitting a spurious signal, the fit to the averaged mixed models in the
second step is repeated with and without an unconstrained signal template. For
this test, the coefficients of the basis functions in the background model are
constrained with the systematic uncertainties assigned in the previous two
steps. An F-test is performed that compares the background-only model to the
model including an unconstrained signal template. In both SRs, it is found
that allowing for a spurious signal does not lead to a significant improvement
in the model fit, therefore no additional systematic uncertainty is assigned.

##  0.8 Systematic uncertainties

A maximum likelihood fit to the four-tag data is performed on the distribution
of the SvB signal probability simultaneously in the $\PZ\PZ$ and $\PZ\PH$ SRs.
Systematic uncertainties are treated as nuisance parameters with either
Gaussian (shape uncertainties) or log-normal (normalization uncertainties)
function priors included in the likelihood function. All systematic
uncertainties are considered as shape uncertainties with the exception of the
luminosity, predicted signal cross section, and branching fraction
uncertainties, which are treated as normalization uncertainties.

Table 0.8 summarizes the impact of different sources of uncertainty in the
$\PZ\PZ$ and $\PZ\PH$ signal sensitivity. The table shows the relative
contributions of the various sources of uncertainty in the measured signal
strength, quoted in terms of a percentage of the total uncertainty. The
contributions from the leading sources of uncertainty – background modeling,
tagging, and jet energy scale and resolution – are listed separately. The
remaining uncertainties, described below, are included in the row labeled
“Others”. The statistical uncertainty accounts for over half of the total
uncertainty, while the remaining uncertainty is primarily due to experimental
uncertainties in the background model and the tagging efficiency.

Summary of the relative uncertainties form the various sources in the measured
signal strength, expressed as a percentage of the total uncertainty for the
$\PZ\PZ$ and $\PZ\PH$ channels. The two uncertainties coming from the
background modeling are given separately in parentheses, as well as their sum.
The total systematic uncertainties shown include the effects of correlations.
Source $\PZ\PZ$ $\PZ\PH$ Statistical uncertainty 75 77 Total systematic
uncertainty 67 64 Background model 61 56 (Variance) (46) (46) (Extrapolation)
(40) (33) b tagging 9 17 Jet energy scale and resolution 9 5 Others 24 24

The uncertainties in the background model are described in Section 0.7.
Similar results are obtained when characterizing the shape differences using a
Fourier or a shifted Legendre polynomial basis. Combined, they account for
${\approx}\ 60\%$ of the total uncertainty in the measured signal strengths
despite being constrained to the percent level with the mixed data samples.

The efficiency of the tagging requirement in the simulated samples is
corrected to match the efficiency measured in data [47]. These corrections,
along with their corresponding uncertainties, are determined in bins of jet ,
$\eta$, and the DeepJet tagging score. The largest uncertainties in the
tagging efficiency arise from contamination of light-flavor jets in heavy-
flavor control regions. To evaluate the impact of the tagging uncertainties,
the per-jet uncertainty in the tagging corrections is propagated to the final
SvB distribution. The tagging uncertainties result in a roughly flat
${\pm}20\%$ variation in signal yield, with no significant variations in the
SvB shape, and contribute 10–20% of the total uncertainty.

Uncertainties in the modeling of the jet energy scale and resolution, and the
jet energy scale correction in the simulation are estimated by propagating
variations of the calibrations [41] to the final SvB discriminant
distributions. These variations change the reconstructed energy and direction
of simulated jets and can thus result in event migration across regions and
signal probability bins. The combined jet energy and resolution uncertainties
contribute 5–10% of the total uncertainty.

The efficiency of the trigger requirement in the simulated samples is adjusted
to match the efficiency measured in data. The efficiencies for jets to pass
the various trigger thresholds, based on their and tagging scores, are
measured in a sample where both top quarks decay leptonically. Systematic
uncertainties on the measured trigger efficiency are evaluated and applied to
the expected signal yield. The largest trigger uncertainty comes from the
calculation of per-event trigger efficiencies using the measured per-jet
efficiencies. The total uncertainty in the trigger efficiency is estimated to
be ${\approx}\ 5\%$ for both the $\PZ\PZ$ and $\PZ\PH$ signals.

The total uncertainty in the $\PZ\PZ$ ($\PZ\PH$) cross section prediction is
6.6% (4.1%). These uncertainties include the effects of varying the
renormalization and factorization scales and the parton distribution function
(PDF) of the proton. The uncertainty from the choice of the factorization and
renormalization scales in the calculation of the matrix element for the hard-
scattering process is estimated by varying each scale by factors of 0.5 and 2,
excluding anticorrelated combinations, to calculate the envelope around the
central value. In order to estimate the impact on the results due to the
uncertainty on the proton PDF, event weights corresponding to the different
set of NNPDF [70] replicas are applied to the simulation. The uncertainty in
the $\to$ $\PQb\PAQb$ branching fraction is $\pm$1.3% [22].

The uncertainty in the total integrated luminosity for each data set has been
measured in Refs. [44, 45, 46]. A correlation scheme is used for the three
sets of uncertainties based on correlated features in calibration methods,
measurements, and data sets, resulting in an uncertainty of 1.6% for the full
data set.

The systematic uncertainty in the signal yields and distributions due to
pileup is found to be negligibly small.

##  0.9 Results

The measured and expected $\PZ\PZ$ and $\PZ\PH$ signal strengths are reported
in terms of the signal strength modifier $\mu$, defined as the ratio of the
value of the cross section to the expected SM theoretical cross section,
$\sigma/\sigma_{\text{SM}}$. The method [78, 79] is used to determine the
upper limits on the signal strengths at 95% confidence level (CL).

The final fit procedure is validated using synthetic data samples without
statistical fluctuations, and also by treating one of the mixed models as the
observed four-tag data. In both tests, the behavior of the systematic
uncertainties is as expected and the resulting best fit signal strengths were
consistent with zero.

Figure 12 shows the results of the combined fit of the SvB signal probability
distribution to the signal plus background model. The resulting signal
strengths and corresponding 95% CL upper limits are shown in Table 0.9. The
values if only the statistical uncertainties are included are shown in
parentheses. The upper limits are determined under the assumption that no
signal exists. Despite the significant difference in the $\PZ\PZ$ and $\PZ\PH$
cross sections, the upper limits of the signal strengths are similar. This is
due to the increased signal selection efficiency and lower background levels
in the SR for the $\PZ\PH$ channel in comparison to those for the $\PZ\PZ$
channel. The current results are limited by the size of the data set and the
systematic uncertainties associated with the background model.

Figure 12:  Distributions of signal probabilities for $\PZ\PZ$ (left) and
$\PZ\PH$ (right) channels (points), along with the post-fit QCD multijet
(yellow region) plus (blue region) distributions. The $\PZ\PH$ and $\PZ\PZ$
signal distributions scaled to the fitted signal strengths are shown, stacked
on top of the background prediction. The expected $\PZ\PH$ (red histograms)
and $\PZ\PZ$ (green histograms) signal channel distributions are also shown
separately, multiplied by 100 for visibility. The lower panels display the
ratio of the data to the result of the signal plus background fit, with the
hatched area showing the uncertainty in the combined fit.

Expected and observed $\PZ\PZ$ and $\PZ\PH$ signal strengths and their
corresponding 95% CL upper limits. The expected signal strengths and the
corresponding expected upper limits shown in parentheses include only the
statistical uncertainties. The upper limits are obtained from a fit to the SvB
signal probabilities under the hypothesis of no $\PZ\PZ\to 4\PQb$ or
$\PZ\PH\to 4\PQb$ signal.  $\PZ\PZ$ $\PZ\PH$ Signal strength expected (stat.
only) $1.0\ _{-1.7}^{+1.9}$ ($1.0\ _{-1.3}^{+1.4}$) $1.0\ _{-1.4}^{+1.5}$
($1.0\ _{-1.1}^{+1.1}$) Signal strength observed $0.0\ _{-1.7}^{+2.0}$ $2.2\
_{-0.8}^{+0.9}$ Expected upper limit at $95\%$ CL (stat. only) 3.8 (2.8) 2.9
(2.3) Observed upper limit at $95\%$ CL 3.8 5.0

##  0.10 Summary

A search for $\PZ\PZ$ and $\PZ\PH$ production in the 4final state is
presented. The search uses the full 2016–2018 data set of proton-proton
collisions at a center-of-mass energy of 13recorded with the CMS detector at
the LHC, corresponding to an integrated luminosity of 133. The analysis
benefits from a multiclass multivariate classifier, which uses convolutions to
solve the combinatoric jet pairing problem, and has been designed with an
architecture customized to the 4final state. The classifier is used both for
signal-versus-background discrimination and for the derivation and validation
of the background model. A novel technique for assessing the background
modeling uncertainties, using a synthetic data sample, produced using a
hemisphere mixing procedure, allows both the uncertainty in the background
model and its variance to be measured with a precision better than the
statistical uncertainties in the selected signal-region events. While these
techniques are developed and demonstrated in the $\PZ\PZ$ and $\PZ\PH$ $\to$
4searches, they are directly applicable to the $\PH\PH\to 4\PQb$ analysis. The
observed (expected) 95% CL upper limits on the $\PZ\PZ\to 4\PQb$ and
$\PZ\PH\to 4\PQb$ production cross sections correspond to 3.8 (3.8) and 5.0
(2.9) times the standard model prediction, respectively.

###### 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); ERC PRG, RVTT3 and MoER TK202 (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); LMTLT (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, 101115353,101002207, 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 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K
147048, 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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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Hayrapetyan, 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, P.S. Hussain0000-0002-4825-5278, M.
Jeitler20000-0002-5141-9560, N. Krammer0000-0002-0548-0985, A.
Li0000-0002-4547-116X, D. Liko0000-0002-3380-473X, I.
Mikulec0000-0003-0385-2746, 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, P. Van Mechelen0000-0002-8731-9051

Vrije Universiteit Brussel, Brussel, Belgium E.S. Bols0000-0002-8564-8732, J.
D’Hondt0000-0002-9598-6241, S. Dansana0000-0002-7752-7471, A. De
Moor0000-0001-5964-1935, M. Delcourt0000-0001-8206-1787, S.
Lowette0000-0003-3984-9987, I. Makarenko0000-0002-8553-4508, D.
Müller0000-0002-1752-4527, S. Tavernier0000-0002-6792-9522, M.
Tytgat40000-0002-3990-2074, G.P. Van Onsem0000-0002-1664-2337, S. Van
Putte0000-0003-1559-3606, D. Vannerom0000-0002-2747-5095

Université Libre de Bruxelles, Bruxelles, Belgium B.
Clerbaux0000-0001-8547-8211, A.K. Das, G. De Lentdecker0000-0001-5124-7693, H.
Evard0009-0005-5039-1462, L. Favart0000-0003-1645-7454, P.
Gianneios0009-0003-7233-0738, D. Hohov0000-0002-4760-1597, J.
Jaramillo0000-0003-3885-6608, A. Khalilzadeh, F.A. Khan0009-0002-2039-277X, K.
Lee0000-0003-0808-4184, M. Mahdavikhorrami0000-0002-8265-3595, A.
Malara0000-0001-8645-9282, S. Paredes0000-0001-8487-9603, 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 M. De Coen0000-0002-5854-7442, D.
Dobur0000-0003-0012-4866, Y. Hong0000-0003-4752-2458, J.
Knolle0000-0002-4781-5704, L. Lambrecht0000-0001-9108-1560, G. Mestdach, K.
Mota Amarilo0000-0003-1707-3348, C. Rendón, A. Samalan, K.
Skovpen0000-0002-1160-0621, N. Van Den Bossche0000-0003-2973-4991, J. van der
Linden0000-0002-7174-781X, L. Wezenbeek0000-0001-6952-891X

Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke0000-0003-0252-3609, A. Bethani0000-0002-8150-7043, G.
Bruno0000-0001-8857-8197, C. Caputo0000-0001-7522-4808, C.
Delaere0000-0001-8707-6021, I.S. Donertas0000-0001-7485-412X, A.
Giammanco0000-0001-9640-8294, Sa. Jain0000-0001-5078-3689, V. Lemaitre, J.
Lidrych0000-0003-1439-0196, P. Mastrapasqua0000-0002-2043-2367, T.T.
Tran0000-0003-3060-350X, 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, T. Menezes De Oliveira0009-0009-4729-8354, A.
Moraes0000-0002-5157-5686, P. Rebello Teles0000-0001-9029-8506, M. Soeiro

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, S.
Fonseca De Souza0000-0001-7830-0837, R. Gomes De Souza, J.
Martins70000-0002-2120-2782, C. Mora Herrera0000-0003-3915-3170, L.
Mundim0000-0001-9964-7805, H. Nogima0000-0001-7705-1066, J.P.
Pinheiro0000-0002-3233-8247, A. Santoro0000-0002-0568-665X, 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, B.
Orzari0000-0003-4232-4743, 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.
Shopova0000-0001-6664-2493, G. Sultanov0000-0002-8030-3866

University of Sofia, Sofia, Bulgaria A. Dimitrov0000-0003-2899-701X, 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. Keshri0000-0003-3280-2350, S. Thakur0000-0002-1647-0360

Beihang University, Beijing, China T. Cheng0000-0003-2954-9315, T.
Javaid0009-0007-2757-4054, L. Yuan0000-0002-6719-5397

Department of Physics, Tsinghua University, Beijing, China Z.
Hu0000-0001-8209-4343, J. Liu, K. Yi8,90000-0002-2459-1824

Institute of High Energy Physics, Beijing, China G.M.
Chen100000-0002-2629-5420, H.S. Chen100000-0001-8672-8227, M.
Chen100000-0003-0489-9669, F. Iemmi0000-0001-5911-4051, C.H. Jiang, A.
Kapoor110000-0002-1844-1504, H. Liao0000-0002-0124-6999, Z.-A.
Liu120000-0002-2896-1386, R. Sharma130000-0003-1181-1426, J.N. Song12, J.
Tao0000-0003-2006-3490, C. Wang10, J. Wang0000-0002-3103-1083, Z. Wang10, H.
Zhang0000-0001-8843-5209

State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos0000-0002-8953-1232, Y. Ban0000-0002-1912-0374, A.
Levin0000-0001-9565-4186, C. Li0000-0002-6339-8154, Q. Li0000-0002-8290-0517,
Y. Mao, S.J. Qian0000-0002-0630-481X, X. Sun0000-0003-4409-4574, D.
Wang0000-0002-9013-1199, H. Yang, L. Zhang0000-0001-7947-9007, C.
Zhou0000-0001-5904-7258

Sun Yat-Sen University, Guangzhou, China Z. You0000-0001-8324-3291

University of Science and Technology of China, Hefei, China K.
Jaffel0000-0001-7419-4248, N. Lu0000-0002-2631-6770

Nanjing Normal University, Nanjing, China G. Bauer14

Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam
Application (MOE) - Fudan University, Shanghai, China X.
Gao150000-0001-7205-2318

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, J.A. Reyes Vega

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, A.
Sculac0000-0001-7938-7559

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

Institute Rudjer Boskovic, Zagreb, Croatia P. Bargassa0000-0001-8612-3332, V.
Brigljevic0000-0001-5847-0062, B.K. Chitroda0000-0002-0220-8441, D.
Ferencek0000-0001-9116-1202, K. Jakovcic, S. Mishra0000-0002-3510-4833, A.
Starodumov160000-0001-9570-9255, T. Susa0000-0001-7430-2552

University of Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K.
Christoforou0000-0003-2205-1100, A. Hadjiagapiou, S.
Konstantinou0000-0003-0408-7636, J. Mousa0000-0002-2978-2718, C. Nicolaou, L.
Paizanos, 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. Finger0000-0002-7828-9970, M.
Finger Jr.0000-0003-3155-2484, 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 H.
Abdalla170000-0002-4177-7209, Y. Assran18,19

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M. Abdullah Al-Mashad0000-0002-7322-3374, M.A. Mahmoud0000-0001-8692-5458

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia 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 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, K.T.S. Kallonen0000-0001-9769-7163, R. Kinnunen, T.
Lampén0000-0002-8398-4249, K. Lassila-Perini0000-0002-5502-1795, S.
Lehti0000-0003-1370-5598, T. Lindén0009-0002-4847-8882, 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

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 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, F.
Orlandi0009-0001-0547-7516, L. Portales0000-0002-9860-9185, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro200000-0002-9481-5168, P. Simkina0000-0002-9813-372X, M.
Titov0000-0002-1119-6614, M. Tornago0000-0001-6768-1056

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut
Polytechnique de Paris, Palaiseau, France 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, M.
Chiusi0000-0002-1097-7304, F. Damas0000-0001-6793-4359, O.
Davignon0000-0001-8710-992X, A. De Wit0000-0002-5291-1661, I.T.
Ehle0000-0003-3350-5606, B.A. Fontana Santos Alves0000-0001-9752-0624, S.
Ghosh0009-0006-5692-5688, A. Gilbert0000-0001-7560-5790, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi0009-0008-2093-8131, B.
Harikrishnan0000-0003-0174-4020, L. Kalipoliti0000-0002-5705-5059, G.
Liu0000-0001-7002-0937, J. Motta0000-0003-0985-913X, M.
Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, R.
Salerno0000-0003-3735-2707, 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.
Agram210000-0001-7476-0158, J. Andrea0000-0002-8298-7560, D.
Apparu0009-0004-1837-0496, D. Bloch0000-0002-4535-5273, J.-M.
Brom0000-0003-0249-3622, E.C. Chabert0000-0003-2797-7690, C.
Collard0000-0002-5230-8387, S. Falke0000-0002-0264-1632, U.
Goerlach0000-0001-8955-1666, C. Grimault, R. Haeberle0009-0007-5007-6723,
A.-C. Le Bihan0000-0002-8545-0187, M. Meena0000-0003-4536-3967, G.
Saha0000-0002-6125-1941, M.A. Sessini0000-0003-2097-7065, 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, N. Chanon0000-0002-2939-5646, D.
Contardo0000-0001-6768-7466, P. Depasse0000-0001-7556-2743, C.
Dozen220000-0002-4301-634X, H. El Mamouni, J. Fay0000-0001-5790-1780, S.
Gascon0000-0002-7204-1624, M. Gouzevitch0000-0002-5524-880X, C. Greenberg, G.
Grenier0000-0002-1976-5877, B. Ille0000-0002-8679-3878, I.B. Laktineh, M.
Lethuillier0000-0001-6185-2045, L. Mirabito, S. Perries, A.
Purohit0000-0003-0881-612X, M. Vander Donckt0000-0002-9253-8611, P.
Verdier0000-0003-3090-2948, J. Xiao0000-0002-7860-3958

Georgian Technical University, Tbilisi, Georgia G. Adamov, I.
Lomidze0009-0002-3901-2765, Z. Tsamalaidze160000-0001-5377-3558

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, F.
Engelke0000-0002-9288-8144, J. Erdmann0000-0002-8073-2740, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B.
Fischer0000-0002-3900-3482, T. Hebbeker0000-0002-9736-266X, K.
Hoepfner0000-0002-2008-8148, F. Ivone0000-0002-2388-5548, A.
Jung0000-0002-2511-1490, M.y. Lee0000-0002-4430-1695, F.
Mausolf0000-0003-2479-8419, M. Merschmeyer0000-0003-2081-7141, A.
Meyer0000-0001-9598-6623, S. Mukherjee0000-0001-6341-9982, D.
Noll0000-0002-0176-2360, F. Nowotny, A. Pozdnyakov0000-0003-3478-9081, Y.
Rath, W. Redjeb0000-0001-9794-8292, F. Rehm, H. Reithler0000-0003-4409-702X,
U. Sarkar0000-0002-9892-4601, V. Sarkisovi0000-0001-9430-5419, A.
Schmidt0000-0003-2711-8984, A. Sharma0000-0002-5295-1460, J.L.
Spah0000-0002-5215-3258, A. Stein0000-0003-0713-811X, F. Torres Da Silva De
Araujo230000-0002-4785-3057, 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
Ahmad240000-0003-1491-0446, 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, S. Amoroso, Y. An0000-0003-1299-1879, S.
Baxter0009-0008-4191-6716, M. Bayatmakou0009-0002-9905-0667, H. Becerril
Gonzalez0000-0001-5387-712X, O. Behnke0000-0002-4238-0991, A.
Belvedere0000-0002-2802-8203, S. Bhattacharya0000-0002-3197-0048, F.
Blekman250000-0002-7366-7098, K. Borras260000-0003-1111-249X, 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. Gallo250000-0001-7200-5175, A.
Geiser0000-0003-0355-102X, A. Giraldi0000-0003-4423-2631, V.
Guglielmi0000-0003-3240-7393, M. Guthoff0000-0002-3974-589X, A.
Hinzmann0000-0002-2633-4696, A. Jafari270000-0001-7327-1870, L.
Jeppe0000-0002-1029-0318, B. Kaech0000-0002-1194-2306, M.
Kasemann0000-0002-0429-2448, 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.
Lipka280000-0002-8427-3748, W. Lohmann290000-0002-8705-0857, F.
Lorkowski0000-0003-2677-3805, R. Mankel0000-0003-2375-1563, I.-A. Melzer-
Pellmann0000-0001-7707-919X, M. Mendizabal Morentin0000-0002-6506-5177, A.B.
Meyer0000-0001-8532-2356, G. Milella0000-0002-2047-951X, A.
Mussgiller0000-0002-8331-8166, L.P. Nair0000-0002-2351-9265, A.
Nürnberg0000-0002-7876-3134, Y. Otarid, J. Park0000-0002-4683-6669, D. Pérez
Adán0000-0003-3416-0726, E. Ranken0000-0001-7472-5029, A.
Raspereza0000-0003-2167-498X, D. Rastorguev0000-0001-6409-7794, B. Ribeiro
Lopes0000-0003-0823-447X, J. Rübenach, A. Saggio0000-0002-7385-3317, M.
Scham30,260000-0001-9494-2151, S. Schnake260000-0003-3409-6584, P.
Schütze0000-0003-4802-6990, C. Schwanenberger250000-0001-6699-6662, D.
Selivanova0000-0002-7031-9434, K. Sharko0000-0002-7614-5236, M.
Shchedrolosiev0000-0003-3510-2093, R.E. Sosa Ricardo0000-0002-2240-6699, D.
Stafford, F. Vazzoler0000-0001-8111-9318, A. Ventura
Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, Q.
Wang0000-0003-1014-8677, Y. Wen0000-0002-8724-9604, K. Wichmann, L.
Wiens260000-0002-4423-4461, C. Wissing0000-0002-5090-8004, 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, S. Bollweg, M.
Bonanomi0000-0003-3629-6264, P. Connor0000-0003-2500-1061, K. El
Morabit0000-0001-5886-220X, Y. Fischer0000-0002-3184-1457, E.
Garutti0000-0003-0634-5539, A. Grohsjean0000-0003-0748-8494, J.
Haller0000-0001-9347-7657, 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, 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, B.
Raciti0009-0005-5995-6685, M. Rieger0000-0003-0797-2606, 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, A.
Gottmann0000-0001-6696-349X, F. Hartmann310000-0001-8989-8387, R.
Hofsaess0009-0008-4575-5729, M. Horzela0000-0002-3190-7962, U.
Husemann0000-0002-6198-8388, J. Kieseler0000-0003-1644-7678, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, J.M.
Lawhorn0000-0002-8597-9259, M. Link, A. Lintuluoto0000-0002-0726-1452, B.
Maier0000-0001-5270-7540, S. Maier0000-0001-9828-9778, S.
Mitra0000-0002-3060-2278, M. Mormile0000-0003-0456-7250, Th.
Müller0000-0003-4337-0098, M. Neukum, M. Oh0000-0003-2618-9203, E.
Pfeffer0009-0009-1748-974X, M. Presilla0000-0003-2808-7315, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, B.
Regnery0000-0003-1539-923X, N. Shadskiy0000-0001-9894-2095, I.
Shvetsov0000-0002-7069-9019, H.J. Simonis0000-0002-7467-2980, M.
Toms0000-0002-7703-3973, N. Trevisani0000-0002-5223-9342, R.F. Von
Cube0000-0002-6237-5209, M. Wassmer0000-0002-0408-2811, S.
Wieland0000-0003-3887-5358, F. Wittig, R. Wolf0000-0001-9456-383X, X.
Zuo0000-0002-0029-493X

Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia
Paraskevi, Greece G. Anagnostou, G. Daskalakis0000-0001-6070-7698, A.
Kyriakis, A. Papadopoulos31, A. Stakia0000-0001-6277-7171

National and Kapodistrian University of Athens, Athens, Greece P.
Kontaxakis0000-0002-4860-5979, G. Melachroinos, Z.
Painesis0000-0001-5061-7031, A. Panagiotou, I. Papavergou0000-0002-7992-2686,
I. Paraskevas0000-0002-2375-5401, 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, E.
Siamarkou, G. Tsipolitis, A. Zacharopoulou

University of Ioánnina, Ioánnina, Greece K. Adamidis, I. Bestintzanos, I.
Evangelou0000-0002-5903-5481, C. Foudas, 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.
Bartók320000-0002-4440-2701, C. Hajdu0000-0002-7193-800X, D.
Horvath33,340000-0003-0091-477X, K. Márton, A.J. Rádl350000-0001-8810-0388, 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. Gadallah360000-0002-8305-6661, Á.
Kadlecsik0000-0001-5559-0106, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, G.I.
Veres0000-0002-5440-4356

Faculty of Informatics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari0000-0003-0498-4265, G. Zilizi0000-0002-0480-0000

Institute of Nuclear Research ATOMKI, Debrecen, Hungary G. Bencze, S. Czellar,
J. Molnar, Z. Szillasi

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo370000-0002-9110-9663, F. Nemes370000-0002-1451-6484, 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.
Dhingra380000-0002-7200-6204, 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, K.
Sandeep0000-0002-3220-3668, T. Sheokand, J.B. Singh0000-0001-9029-2462, 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, A.
Kumar0000-0002-5180-6595, 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. Barman390000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, S. Dutta0000-0001-9650-8121, S. Dutta, S.
Sarkar

Indian Institute of Technology Madras, Madras, India M.M.
Ameen0000-0002-1909-9843, P.K. Behera0000-0002-1527-2266, S.C.
Behera0000-0002-0798-2727, S. Chatterjee0000-0003-0185-9872, P.
Jana0000-0001-5310-5170, P. Kalbhor0000-0002-5892-3743, J.R.
Komaragiri400000-0002-9344-6655, D. Kumar400000-0002-6636-5331, 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

Tata Institute of Fundamental Research-A, Mumbai, India S. Dugad, M.
Kumar0000-0003-0312-057X, G.B. Mohanty0000-0001-6850-7666, P. Suryadevara

Tata Institute of Fundamental Research-B, Mumbai, India A.
Bala0000-0003-2565-1718, S. Banerjee0000-0002-7953-4683, R.M. Chatterjee, R.K.
Dewanjee410000-0001-6645-6244, M. Guchait0009-0004-0928-7922, Sh.
Jain0000-0003-1770-5309, A. Jaiswal, S. Kumar0000-0002-2405-915X, G.
Majumder0000-0002-3815-5222, K. Mazumdar0000-0003-3136-1653, S.
Parolia0000-0002-9566-2490, A. Thachayath0000-0001-6545-0350

National Institute of Science Education and Research, An OCC of Homi Bhabha
National Institute, Bhubaneswar, Odisha, India S.
Bahinipati420000-0002-3744-5332, C. Kar0000-0002-6407-6974, D.
Maity430000-0002-1989-6703, P. Mal0000-0002-0870-8420, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu430000-0003-4671-815X, K. Naskar430000-0003-0638-4378, A.
Nayak430000-0002-7716-4981, P. Sadangi, S.K. Swain0000-0001-6871-3937, S.
Varghese430009-0000-1318-8266, D. Vats430009-0007-8224-4664

Indian Institute of Science Education and Research (IISER), Pune, India S.
Acharya440009-0001-2997-7523, A. Alpana0000-0003-3294-2345, S.
Dube0000-0002-5145-3777, B. Gomber440000-0002-4446-0258, P.
Hazarika0009-0006-1708-8119, B. Kansal0000-0002-6604-1011, A.
Laha0000-0001-9440-7028, B. Sahu440000-0002-8073-5140, S.
Sharma0000-0001-6886-0726, K.Y. Vaish0009-0002-6214-5160

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi450000-0001-5741-3357, E. Khazaie460000-0001-9810-7743, M.
Zeinali470000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S. Bashiri,
S. Chenarani480000-0002-1425-076X, S.M. Etesami0000-0001-6501-4137, M.
Khakzad0000-0002-2212-5715, M. Mohammadi Najafabadi0000-0001-6131-5987, S.
Tizchang0000-0002-9034-598X

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,c,490000-0001-6808-1335, A.
Colaleoa,b0000-0002-0711-6319, D. Creanzaa,c0000-0001-6153-3044, B.
D’Anzia,b0000-0002-9361-3142, N. De Filippisa,c0000-0002-0625-6811, M. De
Palmaa,b0000-0001-8240-1913, A. Di Florioa,c0000-0003-3719-8041, W.
Elmetenaweea,b,490000-0001-7069-0252, L. Fiorea0000-0002-9470-1320, G.
Iasellia,c0000-0003-2546-5341, M. Loukaa,b, 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, G. Ramirez-Sancheza,c0000-0001-7804-5514, D.
Ramosa0000-0002-7165-1017, A. Ranieria0000-0001-7912-4062, 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, A.
Zazaa,b0000-0002-0969-7284

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, 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, D.
Fasanellaa,b0000-0002-2926-2691, P. Giacomellia0000-0002-6368-7220, L.
Giommia,b0000-0003-3539-4313, C. Grandia0000-0001-5998-3070, L.
Guiduccia,b0000-0002-6013-8293, S. Lo Meoa,500000-0003-3249-9208, L.
Lunertia,b0000-0002-8932-0283, S. Marcellinia0000-0002-1233-8100, G.
Masettia0000-0002-6377-800X, F.L. Navarriaa,b0000-0001-7961-4889, A.
Perrottaa0000-0002-7996-7139, F. Primaveraa,b0000-0001-6253-8656, A.M.
Rossia,b0000-0002-5973-1305, T. Rovellia,b0000-0002-9746-4842, G.P.
Sirolia,b0000-0002-3528-4125

INFN Sezione di Cataniaa, Università di Cataniab, Catania, Italy S.
Costaa,b,510000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,510000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153

INFN Sezione di Firenzea, Università di Firenzeb, Firenze, Italy P.
Assiourasa0000-0002-5152-9006, G. Barbaglia0000-0002-1738-8676, G.
Bardellia,b0000-0002-4662-3305, B. Camaiania,b0000-0002-6396-622X, A.
Cassesea0000-0003-3010-4516, R. Ceccarellia0000-0003-3232-9380, V.
Ciullia,b0000-0003-1947-3396, C. Civininia0000-0002-4952-3799, R.
D’Alessandroa,b0000-0001-7997-0306, E. Focardia,b0000-0002-3763-5267, T.
Kelloa, G. Latinoa,b0000-0002-4098-3502, P. Lenzia,b0000-0002-6927-8807, M.
Lizzoa0000-0001-7297-2624, M. Meschinia0000-0002-9161-3990, S.
Paolettia0000-0003-3592-9509, A. Papanastassioua,b, G.
Sguazzonia0000-0002-0791-3350, L. Viliania0000-0002-1909-6343

INFN Laboratori Nazionali di Frascati, Frascati, Italy L.
Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124, S.
Meola520000-0002-8233-7277, D. Piccolo0000-0001-5404-543X

INFN Sezione di Genovaa, Università di Genovab, Genova, Italy P.
Chatagnona0000-0002-4705-9582, F. Ferroa0000-0002-7663-0805, E.
Robuttia0000-0001-9038-4500, S. Tosia,b0000-0002-7275-9193

INFN Sezione di Milano-Bicoccaa, Università di Milano-Bicoccab, Milano, Italy
A. Benagliaa0000-0003-1124-8450, G. Boldrinia,b0000-0001-5490-605X, F.
Brivioa0000-0001-9523-6451, F. Cetorellia0000-0002-3061-1553, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, R.
Gerosaa,b0000-0001-8359-3734, A. Ghezzia,b0000-0002-8184-7953, P.
Govonia,b0000-0002-0227-1301, L. Guzzia0000-0002-3086-8260, M.T.
Lucchinia,b0000-0002-7497-7450, M. Malbertia0000-0001-6794-8419, S.
Malvezzia0000-0002-0218-4910, A. Massironia0000-0002-0782-0883, D.
Menascea0000-0002-9918-1686, L. Moronia0000-0002-8387-762X, M.
Paganonia,b0000-0003-2461-275X, S. Palluottoa,b0009-0009-1025-6337, D.
Pedrinia0000-0003-2414-4175, B.S. Pinolinia, G. Pizzatia,b, S.
Ragazzia,b0000-0001-8219-2074, T. Tabarelli de Fatisa,b0000-0001-6262-4685

INFN Sezione di Napolia, Università di Napoli ’Federico II’b, Napoli, Italy;
Università della Basilicatac, Potenza, Italy; Scuola Superiore Meridionale
(SSM)d, Napoli, Italy S. Buontempoa0000-0001-9526-556X, A.
Cagnottaa,b0000-0002-8801-9894, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, F. Fabozzia,c0000-0001-9821-4151, A.O.M.
Iorioa,b0000-0002-3798-1135, L. Listaa,b,530000-0001-6471-5492, P.
Paoluccia,310000-0002-8773-4781, B. Rossia0000-0002-0807-8772, C.
Sciaccaa,b0000-0002-8412-4072

INFN Sezione di Padovaa, Università di Padovab, Padova, Italy; Università di
Trentoc, Trento, Italy R. Ardinoa0000-0001-8348-2962, P.
Azzia0000-0002-3129-828X, N. Bacchettaa,540000-0002-2205-5737, M.
Benettonia0000-0002-4426-8434, D. Biselloa,b0000-0002-2359-8477, P.
Bortignona0000-0002-5360-1454, G. Bortolatoa,b, A.
Bragagnoloa,b0000-0003-3474-2099, A.C.M. Bullaa0000-0001-5924-4286, R.
Carlina,b0000-0001-7915-1650, P. Checchiaa0000-0002-8312-1531, T.
Dorigoa0000-0002-1659-8727, U. Gasparinia,b0000-0002-7253-2669, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, F.
Marinia0000-0002-2374-6433, A.T. Meneguzzoa,b0000-0002-5861-8140, M.
Migliorinia,b0000-0002-5441-7755, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b0000-0003-3466-7500, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, A. Triossia,b0000-0001-5140-9154, S.
Venturaa0000-0002-8938-2193, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,550000-0002-0820-0483, C. Aimèa,b0000-0003-0449-4717, A.
Braghieria0000-0002-9606-5604, S. Calzaferria0000-0002-1162-2505, D.
Fiorinaa0000-0002-7104-257X, P. Montagnaa,b0000-0001-9647-9420, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia,b0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778

INFN Sezione di Perugiaa, Università di Perugiab, Perugia, Italy S.
Ajmala,b0000-0002-2726-2858, G.M. Bileia0000-0002-4159-9123, D.
Ciangottinia,b0000-0002-0843-4108, L. Fanòa,b0000-0002-9007-629X, M.
Magherinia,b0000-0003-4108-3925, V. Mariania,b0000-0001-7108-8116, M.
Menichellia0000-0002-9004-735X, F. Moscatellia,560000-0002-7676-3106, A.
Rossia,b0000-0002-2031-2955, A. Santocchiaa,b0000-0002-9770-2249, D.
Spigaa0000-0002-2991-6384, T. Tedeschia,b0000-0002-7125-2905

INFN Sezione di Pisaa, Università di Pisab, Scuola Normale Superiore di Pisac,
Pisa, Italy; Università di Sienad, Siena, Italy P.
Asenova,b0000-0003-2379-9903, P. Azzurria0000-0002-1717-5654, G.
Bagliesia0000-0003-4298-1620, R. Bhattacharyaa0000-0002-7575-8639, L.
Bianchinia,b0000-0002-6598-6865, T. Boccalia0000-0002-9930-9299, E.
Bossinia0000-0002-2303-2588, D. Bruschinia,c0000-0001-7248-2967, R.
Castaldia0000-0003-0146-845X, M.A. Cioccia,b0000-0003-0002-5462, M.
Cipriania,b0000-0002-0151-4439, V. D’Amantea,d0000-0002-7342-2592, R.
Dell’Orsoa0000-0003-1414-9343, S. Donatoa0000-0001-7646-4977, A.
Giassia0000-0001-9428-2296, F. Ligabuea,c0000-0002-1549-7107, D. Matos
Figueiredoa0000-0003-2514-6930, A. Messineoa,b0000-0001-7551-5613, M.
Musicha,b0000-0001-7938-5684, F. Pallaa0000-0002-6361-438X, A.
Rizzia,b0000-0002-4543-2718, G. Rolandia,c0000-0002-0635-274X, S. Roy
Chowdhurya0000-0001-5742-5593, T. Sarkara0000-0003-0582-4167, A.
Scribanoa0000-0002-4338-6332, P. Spagnoloa0000-0001-7962-5203, R.
Tenchinia0000-0003-2574-4383, G. Tonellia,b0000-0003-2606-9156, N.
Turinia,d0000-0002-9395-5230, F. Vasellia,c0009-0008-8227-0755, A.
Venturia0000-0002-0249-4142, P.G. Verdinia0000-0002-0042-9507

INFN Sezione di Romaa, Sapienza Università di Romab, Roma, Italy C. Baldenegro
Barreraa,b0000-0002-6033-8885, P. Barriaa0000-0002-3924-7380, C.
Basilea,b0000-0003-4486-6482, M. Campanaa,b0000-0001-5425-723X, F.
Cavallaria0000-0002-1061-3877, L. Cunqueiro Mendeza,b0000-0001-6764-5370, D.
Del Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, F. Erricoa,b0000-0001-8199-370X, E.
Longoa,b0000-0001-6238-6787, P. Meridiania0000-0002-8480-2259, J.
Mijuskovica,b0009-0009-1589-9980, G. Organtinia,b0000-0002-3229-0781, F.
Pandolfia0000-0001-8713-3874, R. Paramattia,b0000-0002-0080-9550, C.
Quarantaa,b0000-0002-0042-6891, S. Rahatloua,b0000-0001-9794-3360, C.
Rovellia0000-0003-2173-7530, F. Santanastasioa,b0000-0003-2505-8359, L.
Soffia0000-0003-2532-9876

INFN Sezione di Torinoa, Università di Torinob, Torino, Italy; Università del
Piemonte Orientalec, Novara, Italy N. Amapanea,b0000-0001-9449-2509, R.
Arcidiaconoa,c0000-0001-5904-142X, S. Argiroa,b0000-0003-2150-3750, M.
Arneodoa,c0000-0002-7790-7132, N. Bartosika0000-0002-7196-2237, R.
Bellana,b0000-0002-2539-2376, A. Belloraa,b0000-0002-2753-5473, C.
Biinoa0000-0002-1397-7246, C. Borcaa,b0009-0009-2769-5950, N.
Cartigliaa0000-0002-0548-9189, M. Costaa,b0000-0003-0156-0790, R.
Covarellia,b0000-0003-1216-5235, N. Demariaa0000-0003-0743-9465, L.
Fincoa0000-0002-2630-5465, M. Grippoa,b0000-0003-0770-269X, B.
Kiania,b0000-0002-1202-7652, F. Leggera0000-0003-1400-0709, F.
Luongoa,b0000-0003-2743-4119, C. Mariottia0000-0002-6864-3294, L.
Markovica,b0000-0001-7746-9868, S. Masellia0000-0001-9871-7859, A.
Meccaa,b0000-0003-2209-2527, E. Migliorea,b0000-0002-2271-5192, M.
Montenoa0000-0002-3521-6333, R. Mulargiaa0000-0003-2437-013X, M.M.
Obertinoa,b0000-0002-8781-8192, G. Ortonaa0000-0001-8411-2971, L.
Pachera,b0000-0003-1288-4838, N. Pastronea0000-0001-7291-1979, M.
Pelliccionia0000-0003-4728-6678, M. Ruspaa,c0000-0002-7655-3475, F.
Sivieroa,b0000-0002-4427-4076, V. Solaa,b0000-0001-6288-951X, A.
Solanoa,b0000-0002-2971-8214, A. Staianoa0000-0003-1803-624X, C.
Tarriconea,b0000-0001-6233-0513, D. Trocinoa0000-0002-2830-5872, G.
Umoreta,b0000-0002-6674-7874, E. Vlasova,b0000-0002-8628-2090, R.
Whitea0000-0001-5793-526X

INFN Sezione di Triestea, Università di Triesteb, Trieste, Italy S.
Belfortea0000-0001-8443-4460, V. Candelisea,b0000-0002-3641-5983, M.
Casarsaa0000-0002-1353-8964, F. Cossuttia0000-0001-5672-214X, K. De
Leoa0000-0002-8908-409X, G. Della Riccaa,b0000-0003-2831-6982

Kyungpook National University, Daegu, Korea S. Dogra0000-0002-0812-0758, J.
Hong0000-0002-9463-4922, C. Huh0000-0002-8513-2824, B. Kim0000-0002-9539-6815,
D.H. Kim0000-0002-9023-6847, J. Kim, H. Lee, S.W. Lee0000-0002-1028-3468, C.S.
Moon0000-0001-8229-7829, Y.D. Oh0000-0002-7219-9931, M.S.
Ryu0000-0002-1855-180X, S. Sekmen0000-0003-1726-5681, Y.C.
Yang0000-0003-1009-4621

Department of Mathematics and Physics - GWNU, Gangneung, Korea M.S.
Kim0000-0003-0392-8691

Chonnam National University, Institute for Universe and Elementary Particles,
Kwangju, Korea G. Bak0000-0002-0095-8185, P. Gwak0009-0009-7347-1480, H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187

Hanyang University, Seoul, Korea E. Asilar0000-0001-5680-599X, J.
Choi0000-0002-6024-0992, D. Kim0000-0002-8336-9182, T.J.
Kim0000-0001-8336-2434, J.A. Merlin

Korea University, Seoul, Korea S. Choi0000-0001-6225-9876, S. Han, B.
Hong0000-0002-2259-9929, K. Lee, K.S. Lee0000-0002-3680-7039, S.
Lee0000-0001-9257-9643, J. Park, S.K. Park, J. Yoo0000-0003-0463-3043

Kyung Hee University, Department of Physics, Seoul, Korea J.
Goh0000-0002-1129-2083, S. Yang0000-0001-6905-6553

Sejong University, Seoul, Korea H. S. Kim0000-0002-6543-9191, Y. Kim, S. Lee

Seoul National University, Seoul, Korea J. Almond, J.H. Bhyun, J.
Choi0000-0002-2483-5104, W. Jun0009-0001-5122-4552, J. Kim0000-0001-9876-6642,
S. Ko0000-0003-4377-9969, H. Kwon0009-0002-5165-5018, H.
Lee0000-0002-1138-3700, J. Lee0000-0001-6753-3731, J. Lee0000-0002-5351-7201,
B.H. Oh0000-0002-9539-7789, S.B. Oh0000-0003-0710-4956, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026

University of Seoul, Seoul, Korea W. Jang0000-0002-1571-9072, D.Y. Kang, Y.
Kang0000-0001-6079-3434, S. Kim0000-0002-8015-7379, B. Ko, J.S.H.
Lee0000-0002-2153-1519, Y. Lee0000-0001-5572-5947, I.C.
Park0000-0003-4510-6776, Y. Roh, I.J. Watson0000-0003-2141-3413

Yonsei University, Department of Physics, Seoul, Korea S.
Ha0000-0003-2538-1551, H.D. Yoo0000-0002-3892-3500

Sungkyunkwan University, Suwon, Korea M. Choi0000-0002-4811-626X, M.R.
Kim0000-0002-2289-2527, H. Lee, Y. Lee0000-0001-6954-9964, I.
Yu0000-0003-1567-5548

College of Engineering and Technology, American University of the Middle East
(AUM), Dasman, Kuwait T. Beyrouthy

Riga Technical University, Riga, Latvia K. Dreimanis0000-0003-0972-5641, A.
Gaile0000-0003-1350-3523, G. Pikurs, A. Potrebko0000-0002-3776-8270, M.
Seidel0000-0003-3550-6151

University of Latvia (LU), Riga, Latvia N.R. Strautnieks0000-0003-4540-9048

Vilnius University, Vilnius, Lithuania M. Ambrozas0000-0003-2449-0158, A.
Juodagalvis0000-0002-1501-3328, A. Rinkevicius0000-0002-7510-255X, G.
Tamulaitis0000-0002-2913-9634

National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia N. Bin Norjoharuddeen0000-0002-8818-7476, I.
Yusuff570000-0003-2786-0732, Z. Zolkapli

Universidad de Sonora (UNISON), Hermosillo, Mexico J.F.
Benitez0000-0002-2633-6712, A. Castaneda Hernandez0000-0003-4766-1546, H.A.
Encinas Acosta, L.G. Gallegos Maríñez, M. León Coello0000-0002-3761-911X, J.A.
Murillo Quijada0000-0003-4933-2092, A. Sehrawat0000-0002-6816-7814, L.
Valencia Palomo0000-0002-8736-440X

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala0000-0002-8294-8692, H. Castilla-Valdez0009-0005-9590-9958, H. Crotte
Ledesma, E. De La Cruz-Burelo0000-0002-7469-6974, I. Heredia-De La
Cruz580000-0002-8133-6467, R. Lopez-Fernandez0000-0002-2389-4831, C.A.
Mondragon Herrera, A. Sánchez Hernández0000-0001-9548-0358

Universidad Iberoamericana, Mexico City, Mexico C. Oropeza
Barrera0000-0001-9724-0016, M. Ramírez García0000-0002-4564-3822

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I.
Bautista0000-0001-5873-3088, I. Pedraza0000-0002-2669-4659, H.A. Salazar
Ibarguen0000-0003-4556-7302, C. Uribe Estrada0000-0002-2425-7340

University of Montenegro, Podgorica, Montenegro I. Bubanja0009-0005-4364-277X,
N. Raicevic0000-0002-2386-2290

University of Canterbury, Christchurch, New Zealand P.H.
Butler0000-0001-9878-2140

National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A.
Ahmad0000-0002-4770-1897, M.I. Asghar, A. Awais0000-0003-3563-257X, M.I.M.
Awan, H.R. Hoorani0000-0002-0088-5043, W.A. Khan0000-0003-0488-0941

AGH University of Krakow, Faculty of Computer Science, Electronics and
Telecommunications, Krakow, Poland V. Avati, L. Grzanka0000-0002-3599-854X, M.
Malawski0000-0001-6005-0243

National Centre for Nuclear Research, Swierk, Poland H.
Bialkowska0000-0002-5956-6258, M. Bluj0000-0003-1229-1442, B.
Boimska0000-0002-4200-1541, M. Górski0000-0003-2146-187X, M.
Kazana0000-0002-7821-3036, M. Szleper0000-0002-1697-004X, P.
Zalewski0000-0003-4429-2888

Institute of Experimental Physics, Faculty of Physics, University of Warsaw,
Warsaw, Poland K. Bunkowski0000-0001-6371-9336, K. Doroba0000-0002-7818-2364,
A. Kalinowski0000-0002-1280-5493, M. Konecki0000-0001-9482-4841, J.
Krolikowski0000-0002-3055-0236, A. Muhammad0000-0002-7535-7149

Warsaw University of Technology, Warsaw, Poland K. Pozniak0000-0001-5426-1423,
W. Zabolotny0000-0002-6833-4846

Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal M. Araujo0000-0002-8152-3756, D. Bastos0000-0002-7032-2481, C. Beirão
Da Cruz E Silva0000-0002-1231-3819, A. Boletti0000-0003-3288-7737, M.
Bozzo0000-0002-1715-0457, T. Camporesi0000-0001-5066-1876, G. Da
Molin0000-0003-2163-5569, P. Faccioli0000-0003-1849-6692, M.
Gallinaro0000-0003-1261-2277, J. Hollar0000-0002-8664-0134, N.
Leonardo0000-0002-9746-4594, T. Niknejad0000-0003-3276-9482, A.
Petrilli0000-0003-0887-1882, M. Pisano0000-0002-0264-7217, J.
Seixas0000-0002-7531-0842, J. Varela0000-0003-2613-3146, J.W. Wulff

Faculty of Physics, University of Belgrade, Belgrade, Serbia P.
Adzic0000-0002-5862-7397, P. Milenovic0000-0001-7132-3550

VINCA Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia
M. Dordevic0000-0002-8407-3236, J. Milosevic0000-0001-8486-4604, V. Rekovic

Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain M. Aguilar-Benitez, J. Alcaraz
Maestre0000-0003-0914-7474, Cristina F. Bedoya0000-0001-8057-9152, Oliver M.
Carretero0000-0002-6342-6215, M. Cepeda0000-0002-6076-4083, M.
Cerrada0000-0003-0112-1691, N. Colino0000-0002-3656-0259, B. De La
Cruz0000-0001-9057-5614, A. Delgado Peris0000-0002-8511-7958, A. Escalante Del
Valle0000-0002-9702-6359, D. Fernández Del Val0000-0003-2346-1590, J.P.
Fernández Ramos0000-0002-0122-313X, J. Flix0000-0003-2688-8047, M.C.
Fouz0000-0003-2950-976X, O. Gonzalez Lopez0000-0002-4532-6464, S. Goy
Lopez0000-0001-6508-5090, J.M. Hernandez0000-0001-6436-7547, M.I.
Josa0000-0002-4985-6964, D. Moran0000-0002-1941-9333, C. M. Morcillo
Perez0000-0001-9634-848X, Á. Navarro Tobar0000-0003-3606-1780, C. Perez
Dengra0000-0003-2821-4249, A. Pérez-Calero Yzquierdo0000-0003-3036-7965, J.
Puerta Pelayo0000-0001-7390-1457, I. Redondo0000-0003-3737-4121, D.D. Redondo
Ferrero0000-0002-3463-0559, L. Romero, S. Sánchez Navas0000-0001-6129-9059, L.
Urda Gómez0000-0002-7865-5010, J. Vazquez Escobar0000-0002-7533-2283, C.
Willmott

Universidad Autónoma de Madrid, Madrid, Spain J.F. de
Trocóniz0000-0002-0798-9806

Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologías
Espaciales de Asturias (ICTEA), Oviedo, Spain B. Alvarez
Gonzalez0000-0001-7767-4810, J. Cuevas0000-0001-5080-0821, J. Fernandez
Menendez0000-0002-5213-3708, S. Folgueras0000-0001-7191-1125, I. Gonzalez
Caballero0000-0002-8087-3199, J.R. González Fernández0000-0002-4825-8188, P.
Leguina0000-0002-0315-4107, E. Palencia Cortezon0000-0001-8264-0287, C. Ramón
Álvarez0000-0003-1175-0002, V. Rodríguez Bouza0000-0002-7225-7310, A. Soto
Rodríguez0000-0002-2993-8663, A. Trapote0000-0002-4030-2551, C. Vico
Villalba0000-0002-1905-1874, P. Vischia0000-0002-7088-8557

Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria,
Santander, Spain S. Bhowmik0000-0003-1260-973X, S. Blanco
Fernández0000-0001-7301-0670, J.A. Brochero Cifuentes0000-0003-2093-7856, I.J.
Cabrillo0000-0002-0367-4022, A. Calderon0000-0002-7205-2040, J. Duarte
Campderros0000-0003-0687-5214, M. Fernandez0000-0002-4824-1087, G.
Gomez0000-0002-1077-6553, C. Lasaosa García0000-0003-2726-7111, R. Lopez
Ruiz0009-0000-8013-2289, C. Martinez Rivero0000-0002-3224-956X, P. Martinez
Ruiz del Arbol0000-0002-7737-5121, F. Matorras0000-0003-4295-5668, P. Matorras
Cuevas0000-0001-7481-7273, E. Navarrete Ramos0000-0002-5180-4020, J. Piedra
Gomez0000-0002-9157-1700, L. Scodellaro0000-0002-4974-8330, I.
Vila0000-0002-6797-7209, J.M. Vizan Garcia0000-0002-6823-8854

University of Colombo, Colombo, Sri Lanka M.K. Jayananda0000-0002-7577-310X,
B. Kailasapathy590000-0003-2424-1303, D.U.J. Sonnadara0000-0001-7862-2537,
D.D.C. Wickramarathna0000-0002-6941-8478

University of Ruhuna, Department of Physics, Matara, Sri Lanka W.G.D.
Dharmaratna600000-0002-6366-837X, K. Liyanage0000-0002-3792-7665, N.
Perera0000-0002-4747-9106, N. Wickramage0000-0001-7760-3537

CERN, European Organization for Nuclear Research, Geneva, Switzerland D.
Abbaneo0000-0001-9416-1742, C. Amendola0000-0002-4359-836X, E.
Auffray0000-0001-8540-1097, G. Auzinger0000-0001-7077-8262, J. Baechler, D.
Barney0000-0002-4927-4921, A. Bermúdez Martínez0000-0001-8822-4727, M.
Bianco0000-0002-8336-3282, B. Bilin0000-0003-1439-7128, A.A. Bin
Anuar0000-0002-2988-9830, A. Bocci0000-0002-6515-5666, C.
Botta0000-0002-8072-795X, E. Brondolin0000-0001-5420-586X, C.
Caillol0000-0002-5642-3040, G. Cerminara0000-0002-2897-5753, N.
Chernyavskaya0000-0002-2264-2229, D. d’Enterria0000-0002-5754-4303, A.
Dabrowski0000-0003-2570-9676, A. David0000-0001-5854-7699, A. De
Roeck0000-0002-9228-5271, M.M. Defranchis0000-0001-9573-3714, M.
Deile0000-0001-5085-7270, M. Dobson0009-0007-5021-3230, L.
Forthomme0000-0002-3302-336X, G. Franzoni0000-0001-9179-4253, W.
Funk0000-0003-0422-6739, S. Giani, D. Gigi, K. Gill0009-0001-9331-5145, F.
Glege0000-0002-4526-2149, L. Gouskos0000-0002-9547-7471, M.
Haranko0000-0002-9376-9235, J. Hegeman0000-0002-2938-2263, B. Huber, V.
Innocente0000-0003-3209-2088, T. James0000-0002-3727-0202, P.
Janot0000-0001-7339-4272, O. Kaluzinska0009-0001-9010-8028, S.
Laurila0000-0001-7507-8636, P. Lecoq0000-0002-3198-0115, E.
Leutgeb0000-0003-4838-3306, C. Lourenço0000-0003-0885-6711, L.
Malgeri0000-0002-0113-7389, M. Mannelli0000-0003-3748-8946, A.C.
Marini0000-0003-2351-0487, M. Matthewman, A. Mehta0000-0002-0433-4484, F.
Meijers0000-0002-6530-3657, S. Mersi0000-0003-2155-6692, E.
Meschi0000-0003-4502-6151, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, F. Moortgat0000-0001-7199-0046, M.
Mulders0000-0001-7432-6634, I. Neutelings0009-0002-6473-1403, S. Orfanelli, F.
Pantaleo0000-0003-3266-4357, G. Petrucciani0000-0003-0889-4726, A.
Pfeiffer0000-0001-5328-448X, M. Pierini0000-0003-1939-4268, D.
Piparo0009-0006-6958-3111, H. Qu0000-0002-0250-8655, D.
Rabady0000-0001-9239-0605, M. Rovere0000-0001-8048-1622, H.
Sakulin0000-0003-2181-7258, S. Scarfi0009-0006-8689-3576, C. Schwick, M.
Selvaggi0000-0002-5144-9655, A. Sharma0000-0002-9860-1650, K.
Shchelina0000-0003-3742-0693, P. Silva0000-0002-5725-041X, P.
Sphicas610000-0002-5456-5977, A.G. Stahl Leiton0000-0002-5397-252X, A.
Steen0009-0006-4366-3463, S. Summers0000-0003-4244-2061, D.
Treille0009-0005-5952-9843, P. Tropea0000-0003-1899-2266, A. Tsirou, D.
Walter0000-0001-8584-9705, J. Wanczyk620000-0002-8562-1863, J. Wang, S.
Wuchterl0000-0001-9955-9258, P. Zehetner0009-0002-0555-4697, P.
Zejdl0000-0001-9554-7815, W.D. Zeuner

Paul Scherrer Institut, Villigen, Switzerland T.
Bevilacqua630000-0001-9791-2353, L. Caminada630000-0001-5677-6033, A.
Ebrahimi0000-0003-4472-867X, W. Erdmann0000-0001-9964-249X, R.
Horisberger0000-0002-5594-1321, Q. Ingram0000-0002-9576-055X, H.C.
Kaestli0000-0003-1979-7331, D. Kotlinski0000-0001-5333-4918, C.
Lange0000-0002-3632-3157, M. Missiroli630000-0002-1780-1344, L.
Noehte630000-0001-6125-7203, T. Rohe0009-0005-6188-7754

ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich,
Switzerland T.K. Aarrestad0000-0002-7671-243X, K.
Androsov620000-0003-2694-6542, M. Backhaus0000-0002-5888-2304, G. Bonomelli,
A. Calandri0000-0001-7774-0099, C. Cazzaniga0000-0003-0001-7657, K.
Datta0000-0002-6674-0015, A. De Cosa0000-0003-2533-2856, G.
Dissertori0000-0002-4549-2569, M. Dittmar, M. Donegà0000-0001-9830-0412, F.
Eble0009-0002-0638-3447, M. Galli0000-0002-9408-4756, K.
Gedia0009-0006-0914-7684, F. Glessgen0000-0001-5309-1960, C.
Grab0000-0002-6182-3380, N. Härringer0000-0002-7217-4750, T.G. Harte, D.
Hits0000-0002-3135-6427, W. Lustermann0000-0003-4970-2217, A.-M.
Lyon0009-0004-1393-6577, R.A. Manzoni0000-0002-7584-5038, M.
Marchegiani0000-0002-0389-8640, L. Marchese0000-0001-6627-8716, C. Martin
Perez0000-0003-1581-6152, A. Mascellani620000-0001-6362-5356, F. Nessi-
Tedaldi0000-0002-4721-7966, F. Pauss0000-0002-3752-4639, V.
Perovic0009-0002-8559-0531, S. Pigazzini0000-0002-8046-4344, C.
Reissel0000-0001-7080-1119, T. Reitenspiess0000-0002-2249-0835, B.
Ristic0000-0002-8610-1130, F. Riti0000-0002-1466-9077, R.
Seidita0000-0002-3533-6191, J. Steggemann620000-0003-4420-5510, D.
Valsecchi0000-0001-8587-8266, R. Wallny0000-0001-8038-1613

Universität Zürich, Zurich, Switzerland C. Amsler640000-0002-7695-501X, P.
Bärtschi0000-0002-8842-6027, M.F. Canelli0000-0001-6361-2117, K.
Cormier0000-0001-7873-3579, J.K. Heikkilä0000-0002-0538-1469, M.
Huwiler0000-0002-9806-5907, W. Jin0009-0009-8976-7702, A.
Jofrehei0000-0002-8992-5426, B. Kilminster0000-0002-6657-0407, S.
Leontsinis0000-0002-7561-6091, S.P. Liechti0000-0002-1192-1628, A.
Macchiolo0000-0003-0199-6957, P. Meiring0009-0001-9480-4039, U.
Molinatti0000-0002-9235-3406, A. Reimers0000-0002-9438-2059, P. Robmann, S.
Sanchez Cruz0000-0002-9991-195X, M. Senger0000-0002-1992-5711, E. Shokr, F.
Stäger0009-0003-0724-7727, Y. Takahashi0000-0001-5184-2265, R.
Tramontano0000-0001-5979-5299

National Central University, Chung-Li, Taiwan C. Adloff65, D. Bhowmik, C.M.
Kuo, W. Lin, P.K. Rout0000-0001-8149-6180, P.C. Tiwari400000-0002-3667-3843,
S.S. Yu0000-0002-6011-8516

National Taiwan University (NTU), Taipei, Taiwan L. Ceard, Y.
Chao0000-0002-5976-318X, K.F. Chen0000-0003-1304-3782, P.s. Chen, Z.g. Chen,