EXO-21-011

EXO-21-011

# Search for long-lived heavy neutral leptons in proton-proton collision
events with a lepton-jet pair associated with a secondary vertex at
$\sqrt{s}=13\TeV$

The CMS Collaboration

###### Abstract

A search for long-lived heavy neutral leptons (HNLs) using proton-proton
collision data corresponding to an integrated luminosity of 138collected at
$\sqrt{s}=13\TeV$ with the CMS detector at the CERN LHC is presented. Events
are selected with a charged lepton originating from the primary vertex
associated with the proton-proton interaction, as well as a second charged
lepton and a hadronic jet associated with a secondary vertex that corresponds
to the semileptonic decay of a long-lived HNL. No excess of events above the
standard model expectation is observed. Exclusion limits at 95% confidence
level are evaluated for HNLs that mix with electron and/or muon neutrinos.
Limits are presented in the mass range of 1–16.5, with excluded square mixing
parameter values reaching as low as $2\times 10^{-7}$. For masses above 11,
the presented limits exceed all previous results in the semileptonic decay
channel, and for some of the considered scenarios are the strongest to date.

##  0.1 Introduction

The discovery of neutrino oscillations [1, 2, 3] has provided experimental
evidence that neutrinos have nonzero masses [4]. Cosmological constraints [5,
6, 7], as well as direct measurements [8, 9], indicate that the neutrino
masses are orders of magnitude smaller than the masses of other fermions in
the standard model (SM) of particle physics. A possible way to generate gauge-
invariant mass terms for the SM neutrinos and explain their small mass scale
is the see-saw mechanism, which requires the introduction of new heavy states
with right-handed chirality [10, 11, 12, 13, 14, 15, 16, 17]. These new
states, referred to as heavy neutral leptons (HNLs), can also provide
explanations for other open questions in high-energy physics, such as the
nature of dark matter [18, 19] or the matter-antimatter asymmetry in the
universe [20, 21, 22].

The HNLs are singlets of the SM gauge groups, and thus do not interact with
the SM particles through the electroweak or strong interaction. They do,
however, form mixed mass eigenstates with the SM neutrinos and hence can be
produced and decay via the electroweak interaction through mixing. For an
explanation of both neutrino observations and cosmological problems, an SM
extension with HNLs generally needs to provide three HNL generations with
couplings to all SM neutrino generations [23]. In the following, we will refer
to the HNL mass eigenstates and indicate them with the symbol N. A wide range
of HNL masses $m_{\HepParticle{N}{}{}}$ and mixing parameters
$V_{\Pell{\HepParticle{N}{}{}}}$ can yield mass values for the SM neutrinos
consistent with observed neutrino oscillations and offer an explanation for
the matter-antimatter asymmetry in the universe. In the case of, ,
$m_{\HepParticle{N}{}{}}=10\GeV$, these requirements are found to be fulfilled
for a range $10^{-11}<\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}<10^{-5}$ [22];
a significant part of which can be probed in proton-proton ($\Pp\Pp$)
collisions at the CERN LHC.

The experimental signature of HNL models has been studied extensively [24, 25,
26, 27, 17, 28, 29, 30, 31], and searches have been conducted at the LHC [32,
33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], at other
colliders [50, 51, 52, 53, 54], and in fixed-target experiments [55, 56, 57,
58, 59, 60, 61, 62, 63, 64, 65], covering a mass range from a few keV to
several [31]. The proper lifetime $t$ of HNLs follows an exponential
distribution
$P(t)=\PGt_{\HepParticle{N}{}{}}^{-1}\exp(-t/\PGt_{\HepParticle{N}{}{}})$ with
mean proper lifetime $\PGt_{\HepParticle{N}{}{}}$ depending on
$m_{\HepParticle{N}{}{}}$ and $V_{\Pell{\HepParticle{N}{}{}}}$ as [25]

$\PGt_{\HepParticle{N}{}{}}\propto
m_{\HepParticle{N}{}{}}^{-5}\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{-2}.$ (1)

For $m_{\HepParticle{N}{}{}}<20\GeV$, the value of
$\PGt_{\HepParticle{N}{}{}}$ can become so large that many HNLs will decay
after a time $t$ long enough for the HNL decay products to emerge from a
secondary vertex (SV) that is spatially displaced from the primary vertex (PV)
and separately reconstructable. Collider searches for HNL production can thus
be optimized either for short-lived HNL scenarios with prompt signatures, or
for long-lived HNL scenarios with displaced signatures.

This paper reports a search for boson decays to an HNL and a charged lepton
$\Pell_{1}$, with a subsequent HNL decay to a second charged lepton
$\Pell_{2}$ and two quarks. The search is optimized for displaced HNL decays
with $1<m_{\HepParticle{N}{}{}}<20\GeV$, where the large boost of the HNL in
the boson decay causes the two quarks to be merged into a single jet that can
also encompass $\Pell_{2}$. The experimental signature thus consists of a
prompt $\Pell_{1}$ originating from the PV, and a nonprompt $\Pell_{2}$
encompassed in a jet originating from an SV. We consider only electrons and
muons for $\Pell_{1}$ and $\Pell_{2}$, and refer to them as “leptons” in the
following. Two possible decay modes are considered for the HNLs: lepton-
number-conserving (LNC) decays with $\Pell_{1}$ and $\Pell_{2}$ of opposite
sign (OS), and lepton-number-violating (LNV) decays with same-sign (SS)
leptons. The case where both LNC and LNV decays are possible is referred to as
the “Majorana” nature of the HNL, whereas the “Dirac” nature refers to HNLs
with only LNC decays. Examples of leading-order (LO) Feynman diagrams of the
studied process with either an LNC or LNV decay are shown in Fig. 1.

Figure 1: Examples of LO Feynman diagrams for production and decay of an HNL
(indicated with the symbol N) resulting in a final state with two charged
leptons and two quarks. In the left diagram, the HNL is a Dirac particle and
thus the two charged leptons must have opposite charge. In the right diagram,
the HNL is a Majorana particle and the two charged leptons can have the same
charge.

The analyzed $\Pp\Pp$ collision data were recorded by the CMS experiment at
$\sqrt{s}=13\TeV$ in 2016–2018, and correspond to an integrated luminosity of
138. We select events with one prompt lepton and apply a dedicated
reconstruction of the SV with an associated nonprompt lepton and a jet. Events
are grouped into the four lepton flavor categories $\Pe\Pe$, $\PGm\PGm$,
$\Pe\PGm$, and $\PGm\Pe$, where the first (second) symbol refers to the flavor
of $\Pell_{1}$ ($\Pell_{2}$). A machine-learning algorithm that uses
information related to the nonprompt lepton, the jet, and the SV is trained to
distinguish between HNL signal and SM background events. Background
contributions from SM processes can arise when a displaced lepton forms an SV
by overlapping with a random track, when a photon converts into a lepton pair
in the detector material, or when a heavy-flavor hadron decay results in a
displaced lepton associated with a genuine SV. These contributions are
estimated from control samples in data. For the interpretation of our results,
we consider simplified models with a single HNL that couples exclusively to
electron and/or muon neutrinos (Refs. [66, 67, 68, 69] discuss limitations of
this approach). The $\Pe\Pe$ ($\PGm\PGm$) channel is used to constrain
$\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$
($\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$) in HNL scenarios with an exclusive
coupling to electron (muon) neutrinos, whereas the $\Pe\PGm$ and $\PGm\Pe$
channels are used together to constrain
$\abs{V_{\Pe{\HepParticle{N}{}{}}}V_{\PGm{\HepParticle{N}{}{}}}}^{2}/(\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}+\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2})$
in HNL scenarios with couplings to both electron and muon neutrinos. Exclusion
limits at 95% confidence level () are derived on the respective
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ values as functions of
$m_{\HepParticle{N}{}{}}$. Tabulated results are provided in the HEPData
record for this analysis [70].

Recent analyses by the CMS experiment have used the same data set to search
for HNLs in various mass ranges and final-state signatures. The search
presented in Ref. [44] targets the same decay channel and
$m_{\HepParticle{N}{}{}}$ range as the one presented in this paper, but uses a
deep-neural-network-based jet tagger to identify the displaced decay products
without explicitly reconstructing an SV. The explicit SV reconstruction
employed in the analysis presented here results in more stringent limits in
the region with $m_{\HepParticle{N}{}{}}>10\GeV$ and high
$\PGt_{\HepParticle{N}{}{}}$ values. Searches for HNL production and decay
resulting in events with three leptons have been performed in the mass range
$10\GeV<m_{\HepParticle{N}{}{}}<1.5\TeV$, where all leptons are required to
originate from the PV [46], and in the mass range
$1<m_{\HepParticle{N}{}{}}<20\GeV$, where two of the leptons are required to
originate from an SV [40]. Other results target the range
$1<m_{\HepParticle{N}{}{}}<3\GeV$ by reconstructing HNLs with
$c\PGt_{\HepParticle{N}{}{}}>0.1\unit{m}$ decaying in the muon system [45] or
by searching for HNLs in meson decays [47]. A summary of all HNL searches
performed by the CMS Collaboration is presented in Ref. [49].

##  0.2 The CMS experiment and event reconstruction

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are measured in gas-ionization detectors
embedded in the steel flux-return yoke outside the solenoid. More detailed
descriptions of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, can be found in Refs. [71,
72].

Events of interest are selected using a two-tiered trigger system. The first
level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed latency of about 4 [73]. 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 [74].

Charged-particle tracks are reconstructed across the entire $\eta$ range of
the tracker, enabling the detection of charged particles with transverse
momentum () as low as 0.1and originating from distances up to 60cm from the
beam line. These charged-particle tracks are then clustered to form vertices.
To reconstruct vertices corresponding to $\Pp\Pp$ interactions, the clustering
is performed based on the $z$ coordinate of the closest approach of the tracks
to the beam line. The vertex position is estimated with an adaptive vertex
fitter [75] using the collection of tracks compatible with originating from
the same interaction. From the vertices reconstructed in this way, the PV is
taken to be the one corresponding to the hardest scattering in the event,
evaluated using tracking information alone, as described in Section 9.4.1 of
Ref. [76].

The impact parameter of a track is defined as the distance of the track from
the PV at its point of closest approach. The three-dimensional (3D) impact
parameter is referred to as $d_{xyz}$, the two-dimensional (2D) value in the
plane transverse to the beam line as $d_{xy}$, and the longitudinal value
along the beam line as $d_{z}$. The silicon tracker used in 2016 measured
charged particles within the range $\abs{\eta}<2.5$. For nonisolated particles
of $1<\pt<10\GeV$ and $\abs{\eta}<1.4$, the track resolutions were typically
1.5% in and 25–90 (45–150)in $d_{xy}$ ($d_{z}$) [77]. At the start of 2017, a
new pixel detector was installed [78]; the upgraded tracker measured particles
up to $\abs{\eta}<3.0$ with typical resolutions of 1.5% in and 20–75in
$d_{xy}$ for nonisolated particles of $1<\pt<10\GeV$ [79].

To reconstruct vertices corresponding to the decay of long-lived particles
displaced from the PV, we employ a modified version of the inclusive vertex
finder (IVF) algorithm [80, 81] originally developed for the identification of
displaced hadron decays. The IVF clusters tracks that have at least six hits
in the tracker and $\pt>0.8\GeV$, and is seeded with tracks that additionally
have a 3D impact parameter significance, defined as $d_{xyz}$ divided by its
uncertainty, above 1.2. The 3D (2D) distance of the SV to the PV is referred
to as $\Delta_{\mathrm{3D}}$ ($\Delta_{\mathrm{2D}}$). All SVs are retained
that have a significance of $\Delta_{\mathrm{3D}}$ ($\Delta_{\mathrm{2D}}$)
above 0.5 (2.5), while no explicit requirements are applied on the values of
$\Delta_{\mathrm{3D}}$ or $\Delta_{\mathrm{2D}}$ directly. From the high
number of SVs reconstructed with the IVF, we select the candidate vertex
corresponding to the HNL decay by requiring that the SV contains the track
associated with $\Pell_{2}$, selected as described in Section 0.4. If more
than one SV contains the $\Pell_{2}$ track, we select the SV with the highest
associated .

The particle-flow (PF) algorithm [82] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the PV, the energy of the
corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons
spatially compatible with originating from the electron track. The energy of
muons is obtained from the curvature of the corresponding track. The energy of
charged hadrons is determined from a combination of their momentum measured in
the tracker and the matching ECAL and HCAL energy deposits, corrected for the
response function of the calorimeters to hadronic showers. Finally, the energy
of neutral hadrons is obtained from the corresponding corrected ECAL and HCAL
energies.

Reconstructed PF particles are clustered into jets with the anti-algorithm
[83, 84] using 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 $\Pp\Pp$ interactions within the
same or nearby bunch crossings (pileup) can contribute with additional tracks
and calorimetric energy depositions, increasing the apparent jet momentum. To
mitigate this effect, tracks identified to be originating from pileup vertices
are discarded and an offset correction is applied to account for remaining
contributions [85]. 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 dijet,
photon+jet, +jet, and multijet events are used to determine any residual
differences between the jet energy scale in data and simulation, and
appropriate corrections are made [86]. Additional selection criteria are
applied to each jet to remove jets potentially dominated by instrumental
effects or reconstruction failures [85]. Finally, jets are required to have
$\pt>20\GeV$ and $\abs{\eta}<2.4$.

Electrons are reconstructed within the geometrical acceptance of the CMS
tracking system, $\abs{\eta}<2.5$. The electron momentum is estimated by
combining the energy measurement in the ECAL with the momentum measurement in
the tracker. The momentum resolution for electrons with $\pt\approx 45\GeV$
from $\PZ\to\Pe\Pe$ decays ranges from 1.6 to 5%. It is generally better in
the barrel region than in the endcaps, and also depends on the bremsstrahlung
energy emitted by the electron as it traverses the material in front of the
ECAL [87, 88].

Muons are measured in the range $\abs{\eta}<2.4$, with detection planes made
using three technologies: drift tubes, cathode strip chambers, and resistive
plate chambers. The efficiency to reconstruct and identify muons is greater
than 96%. Matching muons to tracks measured in the silicon tracker results in
a relative resolution, for muons with up to 100, of 1% in the barrel and 3% in
the endcaps [89].

##  0.3 Data and simulated samples

Data events have been recorded with single-electron and single-muon triggers.
For the single-electron trigger, the presence of at least one isolated
electron at the HLT is required with a threshold of 27 (32)in 2016 (2017–2018)
[90]. For the single-muon trigger, the presence of at least one isolated muon
at the HLT is required with a threshold of 24throughout all data-taking years
[91].

Monte Carlo (MC) event simulation is used to generate HNL signal events for
the evaluation of the signal selection efficiency, and SM background events
for the validation of the background estimation. For each signal and
background process, separate simulated event samples are generated
corresponding to the conditions of the three data-taking years. For the 2016
(2017–2018) samples, the NNPDF 3.0 [92] (3.1 [93]) parton distribution
functions (PDFs) are used in the simulation of the hard process. All
generators are interfaced with the 8.226 (8.230) simulation [94] for parton
showering and hadronization, using the CUETP8M1 [95] (CP5 [96]) tune for the
underlying event modeling in case of the event modeling for the 2016
(2017–2018) samples. Simulated minimum bias events are superimposed on the
generated events to reproduce the pileup distribution obtained from the
measured instantaneous luminosity. For all simulated events, the CMS detector
response is modeled with the toolkit [97].

Background samples for boson production in association with jets
($\PW\mkern-1.0mu\text{+jets}$) are generated at next-to-LO (NLO) with the
2.4.2 event generator [98], including up to two additional partons in the
matrix-element calculation. For Drell–Yan (DY) dilepton production, event
samples are generated at LO (NLO) with up to four (two) additional partons in
the matrix-element calculation with . Top quark (pair and single production)
and diboson ($\PW\PW$, $\PW\PZ$, $\PZ\PZ$, $\PW\PGg$, $\PZ\PGg$) processes are
simulated with the v2 [99, 100, 101, 102, 103, 104, 105, 106] and event
generators at NLO. Samples generated with at LO (NLO) use the MLM [107] (FxFx
[108]) merging algorithm. For the background from events comprised uniquely of
jets produced through the strong interaction (QCD multijet events), event
samples are generated at LO with .

Signal events for HNL production are simulated at LO with , using a model that
extends the SM particle content by introducing up to three right-handed
neutrinos [25, 109, 110]. Event samples are generated for scenarios with pure
electron neutrino couplings, pure muon neutrino couplings, and equal electron
and muon neutrino couplings, covering the parameter ranges
$1<m_{\HepParticle{N}{}{}}<20\GeV$ and
$10^{-9}<\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}<10^{-1}$. The cross section
of the HNL event samples is calculated at LO with , and scaled to next-to-NLO
(NNLO) precision using a $K$-factor of 1.089 derived from the comparison of SM
boson production samples generated at LO accuracy with , using the same
settings as the signal simulation, and the cross section calculated at NNLO
accuracy with the fewz v3.1 program [111, 112, 113, 114].

For a fixed value of $m_{\HepParticle{N}{}{}}$, we emulate an event sample
with a different $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ value by applying
a global weight to match the expected cross section and a per-event weight to
correct the proper lifetime distribution. The cross section is proportional to
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ [110] and the global weight is
accordingly calculated as the ratio of the new to the simulated
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ value. To reweight from
$\PGt_{\HepParticle{N}{}{}}$ values $\tau_{0}$ to $\tau_{1}$, the per-event
weights for events with proper lifetime $t$ are calculated as

$W(t)=\frac{P(t;\tau_{1})}{P(t;\tau_{0})}=\frac{\tau_{0}}{\tau_{1}}\exp\bigg{[}-t\bigg{(}\frac{1}{\tau_{1}}-\frac{1}{\tau_{0}}\bigg{)}\bigg{]}.$
(2)

The simulation is performed assuming Majorana HNLs, , both LNC and LNV decays
are included. To obtain an event sample for Dirac HNLs, we select only events
with LNC decays. The production cross section is the same for Majorana and
Dirac HNLs, and thus the global weight is only adjusted for the smaller number
of generated events in the sample. The per-event weights are calculated with
Eq. (2) but using that $\tau_{1}$ is twice as large for a Dirac HNL with
respect to a Majorana HNL of same $m_{\HepParticle{N}{}{}}$ and
$\PGt_{\HepParticle{N}{}{}}$.

##  0.4 Event selection

To select prompt and nonprompt leptons and to reduce the contribution from
particles misidentified as leptons, additional identification (ID) criteria
are applied to the reconstructed electrons and muons. We employ two standard
sets of electron ID criteria defined in Ref. [87] that both have an efficiency
of 90%, where the first is based on sequential selection of requirements on
seven observables (“sequential ID”), and the second is based on a multivariate
ID discriminant trained with an extended set of observables (“multivariate
ID”). For muons, we use the set of “medium” ID criteria defined in Ref. [89]
that has an efficiency of 99.5%. In addition, the relative isolation
$I_{\text{rel}}$, defined as the scalar sum of surrounding particles divided
by the lepton , is used to quantify how isolated a reconstructed lepton is
from other particles in the event. The isolation sum requires particles to be
within a cone of
$\DR=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}<0.3$, where
$\Delta\eta$ and $\Delta\phi$ are the $\eta$ and azimuthal angle differences
between the lepton and the particle, respectively. Charged hadrons are
included only if they originate from the PV, while all neutral hadrons and
photons within the cone are included and additional corrections for the
contribution of neutral particles from pileup interactions are applied [87,
89]. Finally, we employ selection criteria for the impact parameters of the
tracks associated with the electrons and muons, as defined in Section 0.2.

Selection criteria for electrons and muons. Numbers in parentheses indicate
values applied in the 2017–2018 data sets, when different from those for 2016.
Criterion Prompt Nonprompt Electrons Muons Electrons Muons [] $>$30 (34) $>$30
$>$7 $>$7 $\abs{\eta}$ $<$2.5 $<$2.4 $<$2.5 $<$2.4 $I_{\text{rel}}$ $<$0.1
$<$0.1 $\abs{d_{xy}}$ [cm] $<$0.02 $<$0.01 $>$0.02 $>$0.02 $\abs{d_{z}}$ [cm]
$<$0.04 $<$0.1 $<$10 $<$10 ID full full reduced reduced

The selection criteria for prompt and nonprompt electrons and muons are listed
in Table 0.4. Prompt electrons and muons are required to have a above a
threshold chosen larger than the HLT single-lepton trigger thresholds, to be
isolated, and to originate from the PV. Nonprompt electrons and muons are
selected with a much lower threshold and no isolation requirement. Instead of
consistency with the PV, a minimal requirement on $d_{xy}$ and a loose maximal
requirement on $d_{z}$ are imposed. In addition, we require that electrons and
muons pass a set of ID criteria as introduced before, where prompt leptons
have to pass the “full” set and nonprompt leptons only a “reduced” set without
criteria that are inefficient for displaced leptons. For prompt electrons, the
multivariate ID is used. For nonprompt electrons, the sequential ID is used
except for the requirements on the maximum number of missing hits in the pixel
detector and the veto on photon conversions, as defined in Ref. [87]. We use
the medium ID for both prompt and nonprompt muons, but remove the requirements
on the fraction of valid tracker hits associated with the muon track and on
the fit quality of the global muon track fit [89] for nonprompt muons.

Figure 2: Selection efficiencies of nonprompt electrons (upper) and muons
(lower), evaluated in simulated HNL signal events as functions of the
generated lepton (left) and transverse displacement of the generated SV
(right). Three HNL signal scenarios are shown, with
$m_{\HepParticle{N}{}{}}=3\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=9.9\times 10^{-5}$ (HNL3,
corresponding to $c\PGt_{\HepParticle{N}{}{}}=23\unit{mm}$),
$m_{\HepParticle{N}{}{}}=5\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=1.6\times 10^{-6}$ (HNL5,
corresponding to $c\PGt_{\HepParticle{N}{}{}}=92\unit{mm}$), and
$m_{\HepParticle{N}{}{}}=10\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=5.7\times 10^{-7}$ (HNL10,
corresponding to $c\PGt_{\HepParticle{N}{}{}}=7\unit{mm}$). The error bars in
the plot represent statistical uncertainties.

The selection efficiencies for nonprompt electrons and muons are evaluated in
HNL signal samples with different
$(m_{\HepParticle{N}{}{}},\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2})$ scenarios
and shown in Fig. 2 as functions of the generated and the transverse
displacement $\Delta_{\mathrm{2D}}$ of the generated SV. The simulated HNL
signal events used for this evaluation are selected by requiring a generated
nonprompt electron (muon) with $\pt>7\GeV$ and $\abs{\eta}<2.5$ (2.4), and a
generated HNL decay vertex with $\Delta_{\mathrm{2D}}<50\cm$. Higher selection
efficiencies are found for nonprompt muons when compared to electrons,
consistent with the better resolution and higher efficiency in the basic
reconstruction of muons compared to electrons [87, 89], resulting in a better
sensitivity of the analysis to $\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$ than
$\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$.

Events are selected with exactly one prompt electron or muon ($\Pell_{1}$)
that is matched geometrically to the trigger selection, exactly one nonprompt
electron or muon ($\Pell_{2}$), and one jet matched to $\Pell_{2}$ by the
requirement $\DR<0.7$. To reconstruct the HNL decay vertex, the $\Pell_{2}$
track is required to be part of an SV reconstructed by the IVF, as described
in Section 0.2. The SV reconstruction is validated in simulation by comparing
properties of the reconstructed SV with the generated HNL decay vertex. In
Fig. 3, the SV reconstruction efficiency is shown as a function of
$\Delta_{\mathrm{2D}}$, using HNL signal events with an SV that contains the
nonprompt-lepton track and is matched to the generated HNL decay vertex. The
vertex matching is defined by the criterion
$\abs{\Delta_{\mathrm{3D}}-\Delta_{\mathrm{3D}}^{\text{gen}}}/\Delta_{\mathrm{3D}}^{\text{gen}}<0.1$,
where $\Delta_{\mathrm{3D}}^{\text{gen}}$ is the 3D displacement of the
generated HNL decay vertex.

Figure 3: The SV reconstruction efficiency in simulated HNL signal events as a
function of the SV displacement for vertices with a nonprompt electron (left)
or muon (right). Three HNL signal scenarios are shown, with
$m_{\HepParticle{N}{}{}}=3\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=9.9\times 10^{-5}$ (HNL3,
corresponding to $c\PGt_{\HepParticle{N}{}{}}=23\unit{mm}$),
$m_{\HepParticle{N}{}{}}=5\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=1.6\times 10^{-6}$ (HNL5,
corresponding to $c\PGt_{\HepParticle{N}{}{}}=92\unit{mm}$), and
$m_{\HepParticle{N}{}{}}=10\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=5.7\times 10^{-7}$ (HNL10,
corresponding to $c\PGt_{\HepParticle{N}{}{}}=7\unit{mm}$). The error bars in
the plot represent statistical uncertainties.

Further requirements to remove background contributions are imposed on the
invariant mass of the two selected leptons, $m(\Pell_{1}\Pell_{2})>10\GeV$,
and on their angular separation, $\abs{\Delta\phi(\Pell_{1},\Pell_{2})}>0.4$.
Events with at least one additional jet separated from $\Pell_{1}$ by
$\DR>0.4$, as expected for top quark production, are removed and used as an
orthogonal event selection for the validation of the background prediction
described in Section 0.6. Events where $\Pell_{1}$ and $\Pell_{2}$ are OS
muons and have $85<m(\Pell_{1}\Pell_{2})<95\GeV$ are removed to suppress DY
dimuon background contributions. This DY veto is introduced because of a
correlation between the observables used in the background estimation observed
in the OS dimuon channels, as described in Section 0.6, but is not required
for the other channels. The event selection criteria are summarized in Table
0.4.

Summary of the event selection criteria.  Event selection criteria
$N(\text{prompt $\Pell_{1}$})$ $=$ 1 $N(\text{nonprompt $\Pell_{2}$})$ = 1
$N(\text{jets})$ = 1 $\DR(\Pell_{2},\text{jet})$ $<$ 0.7 $\Pell_{2}$ $\in$ SV
$m(\Pell_{1}\Pell_{2})$ $>$ 10 $\abs{\Delta\phi(\Pell_{1},\Pell_{2})}$ $>$ 0.4
$m(\PGmpm\PGmmp)$ $\notin$ [85,95]

Separate event categories are defined based on the flavors of $\Pell_{1}$ and
$\Pell_{2}$ ($\Pe\Pe$, $\Pe\PGm$, $\PGm\Pe$, and $\PGm\PGm$), and for the
cases of SS and OS charges. The invariant mass of $\Pell_{1}$ and the tracks
belonging to the selected SV, $m(\Pell_{1},\text{SV})$, is an important
observable for the discrimination between signal and background events. For
HNL signal events, $m(\Pell_{1},\text{SV})$ is kinematically limited by the
boson mass since it includes all charged particles originating from the boson
decay in the hard interaction but does not account for potential neutral
hadrons and photons formed in the hadronic decay. The distribution of
$m(\Pell_{1},\text{SV})$ predicted from simulated event samples is shown in
Fig. 4, separately for the different event categories.

Figure 4: The $m(\Pell_{1},\text{SV})$ distribution of predicted events yields
after applying the selection summarized in Table 0.4, for the $\Pe\Pe$ (upper
left), $\PGm\PGm$ (upper right), $\Pe\PGm$ (lower left), and $\PGm\Pe$ (lower
right) categories. The filled histograms display the predicted background
yields. The lines show the predicted yields for three HNL signal scenarios,
with $m_{\HepParticle{N}{}{}}=3\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=9.9\times 10^{-5}$ (HNL3),
$m_{\HepParticle{N}{}{}}=5\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=1.6\times 10^{-5}$ (HNL5), and
$m_{\HepParticle{N}{}{}}=10\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=5.7\times 10^{-7}$ (HNL10). The HNL
signal yield is normalized to the total background yield. The last bins
include the overflow.

##  0.5 Identification of the SV from an HNL decay

A multivariate discriminant based on machine learning is employed to
distinguish between events with a reconstructed SV originating from an HNL
decay and background events where, , the jet accidentally overlaps with the
nonprompt lepton. We employ a particle-flow network (PFN) [115], an
application of the deep sets theorem [116], that uses per-particle information
of the particles associated with the jet as well as per-event information
associated with the nonprompt lepton, the jet, and the SV. The PFN
architecture consists of two sections, with three fully connected neural
network layers of 128 nodes each per section. First, each individual particle
is passed through the per-particle section, which is provided with the per-
particle information and produces a 256-dimensional output representing the
latent space. The per-particle outputs are added up in a summation layer, and
the sums are provided together with event-level variables as inputs to the
second section that processes the full event information. The two sections are
combined into a single network, which allows for a simultaneous optimization
of both the per-particle and per-event sections. The PFN output is a single
score value between zero and one, where high (low) values are assigned to
signal-like (background-like) events.

Up to 50 PF particles associated with the selected jet are used for the per-
particle section. For each particle, the , $\eta$, $\phi$, charge, particle
type as identified by the PF algorithm, impact parameter properties and
uncertainties, number of hits in the pixel and strip detectors of the tracker
system [77], and the association with tracks and SVs are provided as input
variables to the PFN.

The per-event input variables are associated with the selected jet, nonprompt
lepton, and SV. For the jet, the kinematic properties and the number of
constituent particles are provided. For the nonprompt lepton, the kinematic
properties, $I_{\text{rel}}$, impact parameter properties and uncertainties,
and the number of hits in the pixel detector are used. Additionally, the
between lepton and jet, the transverse component of the lepton momentum with
respect to the jet axis, and the ratio between the lepton and the jet are
provided. For the SV, the kinematic properties including the invariant mass
$m_{\text{SV}}$ of the tracks associated with the SV, the number of tracks,
the normalized $\chi^{2}$ of the reconstruction as determined with the IVF,
and the distance from the PV are used.

The PFN is trained with MC samples for the HNL signal, and the SM background
samples described in Section 0.3. Only the QCD multijet sample is excluded
from the training, because of its large statistical uncertainties. Only events
passing the event selection summarized in Table 0.4 are used for the training.
To account for the upgrade of the pixel detector in 2017 [78], PFNs are
trained separately for the 2016 data set and the combined 2017–2018 data sets.
Separate PFNs are trained for nonprompt electrons and muons, and for
$m_{\HepParticle{N}{}{}}$ up to and above 5. The reason for the split into a
low- and high-mass PFN is the strong dependence of some of the input variables
for HNL signal events on the $m_{\HepParticle{N}{}{}}$ scenario, and also
heavy-flavor background events are more similar to low-mass than to high-mass
HNL events. For the low-mass (high-mass) PFN, the training is performed using
an admixture of MC signal samples with HNL masses in the range of 1–5
(6–20)and with different $\PGt_{\HepParticle{N}{}{}}$ values.

The PFN training is performed with the keras v2.1.5 deep learning library
[117] interfaced to TensorFlow v1.6 [118]. Parametric rectified linear
functions are used for the activation, and the dropout parameter is set to
50%. The full training sample is divided into training (60%), validation
(30%), and testing (10%) sets. The actual training is performed on the
training set, while the validation set is only evaluated after each training
epoch. The testing set is used for the final validation after the full
training is done. The PFN is trained for a maximum of 100 epochs, and stopped
earlier if the accuracy on the training data does not increase for four full
training epochs. The model from the epoch with the best accuracy on the
validation data is used as the final model. The PFN score is shown for
simulated HNL and background events in Fig. 5.

Figure 5: The PFN score distribution of predicted events yields after applying
the selection summarized in Table 0.4, for the combined $\Pe\Pe$ and $\PGm\Pe$
(left) or $\PGm\PGm$ and $\Pe\PGm$ (right) categories, using the low-mass
(upper) or high-mass (lower) PFNs. The filled histograms display the predicted
background yields, where the QCD multijet background displays huge statistical
fluctuations for PFN scores above 0.2. The lines show the predicted yields for
four HNL signal scenarios, with $m_{\HepParticle{N}{}{}}=3\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=9.9\times 10^{-5}$ (HNL3),
$m_{\HepParticle{N}{}{}}=5\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=1.6\times 10^{-6}$ (HNL5),
$m_{\HepParticle{N}{}{}}=6\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=2.0\times 10^{-6}$ (HNL6), and
$m_{\HepParticle{N}{}{}}=10\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=5.7\times 10^{-7}$ (HNL10). The HNL
signal yield is normalized to the total background yield.

A validation of differences in the shape of the PFN score between data and
simulated events is performed to ensure that the signal efficiency is the same
in data and simulation. The meson decays $\PKzS\to\PGpp\PGpm$ are a
representative long-lived decay process that can be used to emulate HNL
decays. Following a procedure similar to that described in Ref. [119], a pure
sample of $\PZ\to\MM$ events is selected, requiring two OS muons with
$m(\PGmpm\PGmmp)$ within 10of the boson mass [120] to facilitate the data-to-
simulation comparison and normalization. These events are then required to
contain an SV formed by two tracks with an invariant mass within 15of the
meson mass [120]. The meson is typically found within a jet with additional PF
candidates. The identification of the SV and jet is performed with the same
configuration employed for HNL events. All PF particles in the jet, including
those not associated with the meson decay, are used as inputs to the PFN.
Since no displaced lepton is present in the meson decay, the with the higher
is chosen to emulate the displaced lepton for the PFN. In Fig. 6, the result
of this comparison is shown for the low-mass PFNs. The total candidate yield
derived from MC samples is normalized to the yield in data, to facilitate the
shape comparison of the PFN score. Agreement between the data and the MC
prediction within 20% is found over the whole range of the PFN score, both in
the electron and muon channels, and an agreement of better than 10% for the
bins with the highest PFN scores. A similar level of agreement is found for
the high-mass PFNs.

Figure 6: Predicted and observed event yields in the PFN score distribution
for $\PKzS\to\PGpp\PGpm$ decays in $\PZ\to\MM$ events, using the low-mass PFNs
for the electron (left) and muon (right) channels, where the with higher is
treated as the lepton. The prediction is scaled to match the overall data
yield. The lower panels show the data-to-prediction ratio.

##  0.6 Background estimation

After applying all event selection criteria, the main sources of SM background
contributions are top quark, $\PW\mkern-1.0mu\text{+jets}$, DY, and QCD
multijet production. Their relative contributions vary across the different
channels. To further suppress background events and enrich the event sample
used for the background estimation with events that have properties similar to
expected HNL signal events, all events are required to have a PFN score of at
least 0.2. The selection after this additional requirement will be referred to
as the signal region (SR), and is generally different when using low- or high-
mass PFNs. Contributions from DY production are found in the $\Pe\Pe$ and
$\PGm\PGm$ categories, where one lepton is poorly reconstructed and identified
as nonprompt, and combined with a random track to form a compatible SV.
Background contributions from top quark ($\PW\mkern-1.0mu\text{+jets}$)
production typically have one genuine prompt lepton and a second lepton inside
a jet that is reconstructed in genuine (accidental) association with an SV.
The QCD multijet events contribute especially to the $\PGm\PGm$ and $\Pe\PGm$
categories, and neither the prompt nor nonprompt lepton is genuine. A final
background source arises from displaced photon conversions in the detector
material and contributes mainly to the $\Pe\Pe$ and $\PGm\Pe$ categories,
which have a photon radiated from a prompt lepton and subsequently converted
into an $\EE$ pair with one electron receiving most of the momentum.

Background estimates for displaced signatures based on MC simulation are known
to be poorly modeled. Thus, we employ a background estimation method using
data control samples and rely on the discriminating powers of the
$m(\Pell_{1},\text{SV})$ distribution described in Section 0.4 and the PFN
score described in Section 0.5.

A target region A is defined by requiring $50<m(\Pell_{1},\text{SV})<85\GeV$
and that the PFN score is above a threshold value $x$. Values of $0.97\leq
x\leq 0.998$ are chosen separately for each event category, optimized to
achieve minimal but nonzero background yields while preserving a reasonable
level of signal efficiency. To further increase the sensitivity to the HNL
signal, the SR is binned in $m_{\text{SV}}$ and the transverse displacement
$\Delta_{\mathrm{2D}}$ between PV and SV. An ABCD method [121] is then
employed to predict the background contribution in region A from the observed
yields in three sideband regions B, C, and D, based on inverting one or both
of the $m(\Pell_{1},\text{SV})$ and PFN score requirements. The region
definitions are listed in Table 0.6 and shown schematically in Fig. 7. The
expected number of background events in region A, $N_{\text{A}}$, is then
estimated from the observed number of events in the sideband regions as
$N_{\text{A}}=N_{\text{B}}N_{\text{C}}/N_{\text{D}}$. This evaluation is done
separately for each bin in $m_{\text{SV}}$ and $\Delta_{\mathrm{2D}}$, and
separately using the low- and high-mass PFNs.

Definition of target and sideband regions used in the ABCD background
estimation method for the signal (SR), validation (VR), and control (CR)
regions. The threshold value $x$ is chosen between 0.97 and 0.998 separately
for each event category, as described in the text.  Region $N(\text{jets})$
PFN score $m(\Pell_{1},\text{SV})$ [] SR-A $=$1 $>$$x$ $>$50 and $<$85 SR-B
$=$1 $>$0.2 and $<$$x$ $>$50 and $<$85 SR-C $=$1 $>$$x$ $<$50 or $>$85 SR-D
$=$1 $>$0.2 and $<$$x$ $<$50 or $>$85 VR-AB $>$1 $>$0.2 $>$50 and $<$85 VR-CD
$>$1 $>$0.2 $<$50 or $>$85 CR-A $\geq$1 $>$$x$ $>$85 and $<$110 CR-B $\geq$1
$>$0.2 and $<$$x$ $>$85 and $<$110 CR-C $\geq$1 $>$$x$ $<$50 or $>$110 CR-D
$\geq$1 $>$0.2 and $<$$x$ $<$50 or $>$110

Figure 7: Illustration of the target and sideband region definitions for the
ABCD method applied to the SR, in terms of $N(\text{jets})$,
$m(\Pell_{1},\text{SV})$, and the PFN score.

The ABCD method relies on the absence of correlation between the PFN score and
$m(\Pell_{1},\text{SV})$. An orthogonal event selection with
$N(\text{jets})>1$ is used to define a validation region (VR) divided into a
target region with $50<m(\Pell_{1},\text{SV})<85\GeV$ and a sideband region
outside of this interval, as listed in Table 0.6. In Fig. 8, the PFN scores in
data are compared for VR events in the target and sideband regions and,
notably, no difference is observed between the shapes.

Figure 8: The PFN scores in data in the VR, shown for the electron (left) and
muon (right) trainings with the low-mass (upper) and high-mass (lower)
samples. The distributions are normalized to unity to facilitate a shape
comparison. Statistical uncertainties are indicated with error bars and shaded
areas. The lower panels show the ratio of the VR-AB to the VR-CD yields.

The Pearson correlation coefficients, which measure the strength and direction
of the linear relation, are calculated between $m(\Pell_{1},\text{SV})$ and
the PFN score, and $p$-values are evaluated to determine the statistical
significance of the correlation [122]. We find correlation coefficients very
close to zero in most cases, and also the $p$-values indicate a weak or
negligible linear dependence of the PFN score on $m(\Pell_{1},\text{SV})$.
Only in the OS $\PGm\PGm$ category with the low-mass PFN is a relevant
statistical dependence found, attributed to DY dimuon events.

To correct for the bias in the background estimation from the correlation
between $m(\Pell_{1},\text{SV})$ and the PFN score in DY dimuon events, we
apply correction factors to the ABCD estimate. The correction factors are
computed by comparing the direct DY MC prediction in region A with the ABCD
estimate derived from the DY MC prediction in the other regions, and scaled
with the fraction of the DY MC prediction to the total MC background
prediction in region A. Each bin of the OS $\PGm\PGm$ categories when using
the low-mass PFNs is corrected separately.

For an additional check of the ABCD background estimation method, we define a
control region (CR) that includes all events from the SR and VR outside the
interval $50<m(\Pell_{1},\text{SV})<85\GeV$, , excluding the target regions of
the SR and VR. The target region of the CR is defined by the requirement
$85<m(\Pell_{1},\text{SV})<110\GeV$, and the corresponding sideband
definitions are listed in Table 0.6. By comparing the ABCD estimate for event
yields in the target region to the observed data, we assess the accuracy of
the background prediction method. The results of this comparison are shown in
Figs. 9–10. A good closure of the background prediction estimate is found when
taking the large statistical uncertainty of the data into account.

Figure 9: Predicted and observed event yields in the CR for the low-mass PFNs
in the SS (upper) and OS (lower) categories, binned by flavor channel,
$m_{\text{SV}}$ (as specified below the flavor channel), and
$\Delta_{\mathrm{2D}}$. The hashed bands represent the total uncertainty in
the background prediction. The lower panels show the data-to-prediction ratio
and the background prediction uncertainty is split into statistical and
systematic contributions.

Figure 10: Predicted and observed event yields in the CR for the high-mass
PFNs in the SS (upper) and OS (lower) categories, binned by flavor channel,
$m_{\text{SV}}$ (as specified below the flavor channel), and
$\Delta_{\mathrm{2D}}$. The hashed bands represent the total uncertainty in
the background prediction. The lower panels show the data-to-prediction ratio
and the background prediction uncertainty is split into statistical and
systematic contributions.

##  0.7 Systematic uncertainties

The predicted number of background events, as well as the expected number of
signal events in the different SR bins for the different event categories, are
affected by several sources of systematic uncertainty. Several theoretical and
experimental uncertainties affect the HNL signal yields, with some changing
only the overall normalization and others also affecting the shape of the
signal prediction in the SR bins. For the background yields, two dedicated
uncertainties are evaluated that both affect the shape of the prediction. A
summary of the different systematic uncertainty sources is given in Table 0.7.

Summary of systematic uncertainty sources in the signal and background
predictions.  Source Type Uncertainty [%] Signal prediction NNLO $K$-factor
Normalization 4 Integrated luminosity Normalization 1.6 Pileup modeling Shape
4.6 () trigger efficiency Shape 1 ($<$1) Prompt () selection efficiency Shape
2–4 (1–3) Nonprompt () selection efficiency Shape 1–20 ($<$1) Tracking
efficiency Shape 7.3 Jet energy scale & resolution Shape 1–2 PFN score
Normalization 10 Background prediction CR closure Shape 20–30 DY scale factor
(OS $\PGm\PGm$, low mass) Shape 20–50

The $K$-factor applied to the normalization of HNL signal events has an
uncertainty of 4%, evaluated by performing variations of the factorization and
renormalization scales and of the PDFs in the NNLO boson production
calculation. Additionally, the spectrum of the simulated boson in SM
$\PW\mkern-1.0mu\text{+jets}$ production at either NLO or LO accuracy was
compared, and all differences were found to be covered by the $K$-factor
uncertainty.

The integrated luminosity of the three data-taking years is measured with
uncertainties between 1.2 and 2.5% [123, 124, 125], with a total uncertainty
of 1.6% for the combined data set. The modeling uncertainty of pileup
interactions is assessed by varying the total $\Pp\Pp$ inelastic cross section
in simulation by $\pm$4.6%.

The efficiencies of the trigger and prompt-lepton selections are measured in
data and simulation with the “tag-and-probe” method applied to
$\PZ\to\Pell^{+}\Pell^{-}$ events [126]. Differences between efficiencies in
data and simulation are corrected for with scale factors, and the
uncertainties in these scale factors are considered for the HNL signal yield.
The uncertainties in the trigger scale factors are found to be about 1% (less
than 1%) for the single-electron (single-muon) trigger, and in the selection
scale factors to be 2–4 (1–3)% for electrons (muons).

The selection efficiency for nonprompt electrons is studied in asymmetric
photon conversions events, following the procedure described in Ref. [40].
Events with $\PZ\to\Pell^{+}\Pell^{-}\PGg\to\Pell^{+}\Pell^{-}\Pepm(\Pemp)$,
where $(\Pemp)$ is a low-electron that is not reconstructed in the detector,
are selected and data-to-simulation differences are evaluated as a function of
a displacement variable. Systematic uncertainties in the range 1–20% are
found, depending on the SR bin.

The nonprompt-muon selection efficiency is studied with a tag-and-probe method
in $\PBpm\to\JPsi\PKpm\to\MM\PKpm$ events, where the muon pair from the meson
decay provides a good proxy to nonprompt muons in HNL decays [40]. The
differences between the efficiencies measured in data and simulated events are
found to be small, with a systematic uncertainty of less than 1%.

The track and SV reconstruction efficiency is studied using meson decays to
two charged particles, which provide an event signature with two displaced
tracks originating from a common vertex [119]. We find that reconstruction
inefficiencies originate almost entirely from the track reconstruction
inefficiency, while the SV reconstruction is almost 100% efficient. Scale
factors are applied as a function of and displacement to correct the
simulation for differences with data, and we assign half the difference from
unity as a systematic uncertainty.

Uncertainties in the momentum scale and resolution of prompt leptons [87, 89]
are found to be negligible. Possible discrepancies between data and simulation
in the momentum scale and resolution of nonprompt tracks are assessed by
comparing the mean values and standard deviations of Gaussian fits to the
reconstructed candidate mass, in bins of SV displacement. All discrepancies
are found to be below 1% and are neglected.

The systematic uncertainties associated with the jet energy scale and
resolution [86] are evaluated by independently scaling the energy of jets up
and down by their uncertainty. These variations result in a variation of the
HNL signal yield by 1–2%.

The PFN score validation in meson decays, as described in Section 0.5, is used
to evaluate a systematic uncertainty that covers differences between data and
simulation in the PFN score for HNL events. For high values of the PFN score,
discrepancies between data and simulation of up to 10% are observed,
consistent for different PFN trainings and data-taking years. Thus, we assign
an overall 10% uncertainty in the HNL yield.

The predicted background yields in the SR are subject to statistical
uncertainties in the observed event yields in the sideband regions, resulting
in uncertainties of up to 50% in the SR yields. An additional systematic
uncertainty in the background prediction is evaluated from the agreement
between predicted and observed yields in the CR, resulting in an uncertainty
of 20–30% depending on the channel. As discussed in Section 0.6, an additional
scale factor is applied to the background prediction in the OS $\PGm\PGm$ low-
mass channel. The limited size of the DY MC sample used for the derivation of
this scale factor results in an uncertainty of 20–50%, depending on the SR
bin.

##  0.8 Results

The yields in the four flavor channels are analyzed with both the low- and
high-mass PFNs, and binned according to $m_{\text{SV}}$ and
$\Delta_{\mathrm{2D}}$. For the low-mass analysis, two $m_{\text{SV}}$ bins
$<$2 and $>$2are formed, and each is divided into three $\Delta_{\mathrm{2D}}$
bins $<$4, 4–10, and $>$10cm. For the high-mass analysis, the two
$m_{\text{SV}}$ bins are $<$6 and $>$6, each divided by $\Delta_{\mathrm{2D}}$
as $<$1, 1–5, and $>$5cm. The observed data and the predicted background
yields from the ABCD method in these bins are shown in Fig. 11 (12) for the
low-mass (high-mass) PFNs. The number of observed events in data is in
agreement with the SM background expectations within the statistical and
systematic uncertainties. No significant data excess is found in any final
state or SR bin.

Figure 11: Predicted and observed SR event yields for the SS (upper) and OS
(lower) categories of the low-mass PFNs, binned by flavor channel,
$m_{\text{SV}}$ (as specified below the flavor channel), and
$\Delta_{\mathrm{2D}}$. The hashed band represents the total systematic and
statistical uncertainty in the background prediction. Signal predictions are
shown for two HNL production hypotheses, with $m_{\HepParticle{N}{}{}}=3\GeV$
and $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=9.9\times 10^{-5}$ (HNL3), and
$m_{\HepParticle{N}{}{}}=5\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=1.6\times 10^{-6}$ (HNL5). The lower
panels show the data-to-prediction ratio and the background prediction
uncertainty is split into statistical, main systematic, and DY scale factor
contributions.

Figure 12: Predicted and observed SR event yields for the SS (upper) and OS
(lower) categories of the high-mass PFNs, binned by flavor channel,
$m_{\text{SV}}$ (as specified below the flavor channel), and
$\Delta_{\mathrm{2D}}$. The hashed band represents the total systematic and
statistical uncertainty in the background prediction. Signal predictions are
shown for two HNL production hypotheses, with $m_{\HepParticle{N}{}{}}=6\GeV$
and $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=2.0\times 10^{-7}$ (HNL6), and
$m_{\HepParticle{N}{}{}}=6\GeV$ and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}=5.7\times 10^{-7}$ (HNL10). The
lower panels show the data-to-prediction ratio and the background prediction
uncertainty is split into statistical and systematic contributions.

For each $(m_{\HepParticle{N}{}{}},\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2})$
HNL signal scenario, we evaluate upper limits on the production cross section
using the modified frequentist criterion [127, 128]. A profile likelihood
ratio is used as test statistic [129], employing the asymptotic approximation
[130] in the limit setting procedure. The binned likelihood is constructed
from the observed data yields and the expected signal and background yields in
the bins defined for the SR. The expected signal yields are estimated from
simulation, and the expected background yields are predicted through the ABCD
method described in Section 0.6. Systematic uncertainties, which include
effects on the shapes of the distributions, are incorporated in the likelihood
as constrained nuisance parameters. The binned profile likelihood fits are
performed with the CMS statistical analysis tool combine [131], which is based
on the RooFit [132] and RooStats [133] frameworks.

We derive exclusion limits on $\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$,
$\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$, and
$\abs{V_{\Pe{\HepParticle{N}{}{}}}V_{\PGm{\HepParticle{N}{}{}}}}^{2}/(\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}+\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2})$
as functions of $m_{\HepParticle{N}{}{}}$, separately for the cases of
Majorana and Dirac HNLs, using a grid of points in the
$(m_{\HepParticle{N}{}{}},\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2})$ parameter
space. A signal scenario is excluded if the predicted cross section is
incompatible with the observed data at 95% . The obtained limits are connected
with straight lines between neighboring mass points. We consider values of
$1<m_{\HepParticle{N}{}{}}<20\GeV$ with a step size of 0.5 or 1. For each mass
point, per-event reweighting is employed to interpolate across the entire
range of interest in $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$, using all
available MC samples, as described in Section 0.3. The obtained exclusion
limits are presented in Fig. 13.

Figure 13: Exclusion limits at 95% on $\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$
(upper row), $\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$ (middle row), and
$\abs{V_{\Pe{\HepParticle{N}{}{}}}V_{\PGm{\HepParticle{N}{}{}}}}^{2}/(\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}+\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2})$
(lower row) as functions of $m_{\HepParticle{N}{}{}}$ for a Majorana (left)
and Dirac (right) HNL. The solid (dashed) black curve indicates the observed
(expected) exclusion, where the parameter combinations inside the curve are
excluded.

The exclusion limits exhibit both an upper and a lower curve, corresponding to
HNL scenarios with short and long lifetimes. The results of this study extend
existing exclusion limits into the parameter space characterized by longer
lifetimes and, correspondingly, smaller
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ values. In Table 0.8, the observed
limits are summarized for those mass points where the lowest values of
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ are excluded per scenario.

Comparison of lowest and highest $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$
values excluded at 95% for Majorana and Dirac HNLs with different coupling
scenarios. For each scenario, the $m_{\HepParticle{N}{}{}}$ value where the
lowest $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ value is excluded is shown.
Type Coupling $m_{\HepParticle{N}{}{}}$ 95% exclusion [] Lowest value Highest
value Majorana $\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$ 10 $9.1\times 10^{-7}$
$5.6\times 10^{-5}$ Majorana $\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$ 14
$2.9\times 10^{-7}$ $4.8\times 10^{-6}$ Majorana
$\abs{V_{\Pe{\HepParticle{N}{}{}}}V_{\PGm{\HepParticle{N}{}{}}}}^{2}/(\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}+\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2})$
12 $1.3\times 10^{-7}$ $6.7\times 10^{-6}$ Dirac
$\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$ 11.5 $6.9\times 10^{-7}$ $6.8\times
10^{-5}$ Dirac $\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$ 13.5 $2.7\times
10^{-7}$ $2.1\times 10^{-5}$ Dirac
$\abs{V_{\Pe{\HepParticle{N}{}{}}}V_{\PGm{\HepParticle{N}{}{}}}}^{2}/(\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}+\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2})$
12 $1.2\times 10^{-7}$ $1.8\times 10^{-5}$

Other experiments and searches have established more stringent limits for some
of the mass range probed in this analysis. The CHARM experiment [56] has
excluded electron (muon) couplings for $1.0<m_{\HepParticle{N}{}{}}<2.1$
(1.9), with $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ values as low as
$10^{-7}$. The previous CMS search for HNLs decaying in the muon system [45]
found more stringent limits in the mass range 1–2.8, with electron (muon)
couplings excluded with $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ values as
low as 0.9 (0.5)${}\times 10^{-6}$. The displaced trilepton search [40] and
the displaced-jet-tagger analysis in the same final state [44] probe similar
$m_{\HepParticle{N}{}{}}$ and $\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$
regions as this work. In the mass range 2–10, the two searches exhibit
sensitivities that are comparable to our results. For masses of 10–17and
$\abs{V_{\Pell{\HepParticle{N}{}{}}}}^{2}$ values below $10^{-5}$, the
requirement of an SV in this analysis results in the removal of a significant
amount of background from the SRs, resulting in more stringent limits when
compared to Ref. [44]. For masses above 10and higher coupling values (,
shorter lifetime), however, the displaced-jet-tagger approach shows a better
sensitivity. Compared to the recent prompt trilepton search [46], the limits
on $\abs{V_{\Pe{\HepParticle{N}{}{}}}}^{2}$ and
$\abs{V_{\PGm{\HepParticle{N}{}{}}}}^{2}$ presented here are more stringent in
the probed mass range by more than one order of magnitude.

##  0.9 Summary

A search for long-lived heavy neutral leptons (HNLs) has been presented using
proton-proton collision events with one prompt lepton and a system of a
nonprompt lepton and a jet associated with a secondary vertex. The data set
corresponds to 138and was collected by the CMS experiment at the LHC in
2016–2018. A dedicated machine-learning method is developed and utilized to
identify the secondary vertex associated with the HNL decay. No excess of
events above the standard model background prediction obtained from control
samples in data is found. Exclusion limits at 95% confidence level are
evaluated for different HNL coupling scenarios as functions of the HNL mass
and the mixing parameter with standard model neutrinos. The obtained exclusion
limits cover HNL masses from 1 to 16.5and squared mixing parameters as low as
$2\times 10^{-7}$, depending on the scenario. These results exceed previous
experimental constraints derived in the single-lepton decay channel in the
mass range 11–16.5. For some of the considered coupling scenarios and mass
ranges, the presented limits are the strongest to date.

###### 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 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), among
others, 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 and FAIR – Future Artificial Intelligence Research, funded by the
NextGenerationEU 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 B39G670016
(Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro
Corporation; the Welch Foundation, contract C-1845; and the Weston Havens
Foundation (USA).

## References

  * [1] Super-Kamiokande Collaboration, “Evidence for oscillation of atmospheric neutrinos”,  Phys. Rev. Lett. 81 (1998) 1562, 10.1103/PhysRevLett.81.1562, arXiv:hep-ex/9807003.
  * [2] SNO Collaboration, “Direct evidence for neutrino flavor transformation from neutral-current interactions in the Sudbury Neutrino Observatory”,  Phys. Rev. Lett. 89 (2002) 011301, 10.1103/PhysRevLett.89.011301, arXiv:nucl-ex/0204008.
  * [3] KamLAND Collaboration, “First results from KamLAND: Evidence for reactor antineutrino disappearance”,  Phys. Rev. Lett. 90 (2003) 021802, 10.1103/PhysRevLett.90.021802, arXiv:hep-ex/0212021.
  * [4] S. Bilenky, “Neutrino oscillations: From a historical perspective to the present status”,  Nucl. Phys. B 908 (2016) 2, 10.1016/j.nuclphysb.2016.01.025, arXiv:1602.00170.
  * [5] Planck Collaboration, “Planck 2018 results. VI. cosmological parameters”,  Astron. Astrophys. 641 (2020) A6, 10.1051/0004-6361/201833910, arXiv:1807.06209. [Erratum: 10.1051/0004-6361/201833910e].
  * [6] eBOSS Collaboration, “Completed SDSS-IV extended baryon oscillation spectroscopic survey: Cosmological implications from two decades of spectroscopic surveys at the Apache Point Observatory”,  Phys. Rev. D 103 (2021) 083533, 10.1103/PhysRevD.103.083533, arXiv:2007.08991.
  * [7] Z. Sakr, “A short review on the latest neutrinos mass and number constraints from cosmological observables”,  Universe 8 (2022) 284, 10.3390/universe8050284.
  * [8] J. Formaggio, A. de Gouvêa, and R. Robertson, “Direct measurements of neutrino mass”,  Phys. Rept. 914 (2021) 1, 10.1016/j.physrep.2021.02.002, arXiv:2102.00594.
  * [9] KATRIN Collaboration, “Direct neutrino-mass measurement with sub-electronvolt sensitivity”,  Nature Phys. 18 (2022) 160, 10.1038/s41567-021-01463-1, arXiv:2105.08533.
  * [10] P. Minkowski, “${\PGm\to\Pe\PGg}$ at a rate of one out of $10^{9}$ muon decays?”,  Phys. Lett. B 67 (1977) 421, 10.1016/0370-2693(77)90435-X.
  * [11] T. Yanagida, “Horizontal gauge symmetry and masses of neutrinos”, in  Proc. Workshop on the Unified Theories and the Baryon Number in the Universe: Tsukuba, Japan, February 13–14, 1979.  1979\.  [Conf. Proc. C 7902131 (1979) 95].
  * [12] M. Gell-Mann, P. Ramond, and R. Slansky, “Complex spinors and unified theories”, in  Proc. Supergravity Workshop: Stony Brook NY, USA, September 27–28, 1979.  1979\.  arXiv:1306.4669.  [Conf. Proc. C 790927 (1979) 315].
  * [13] S. Glashow, “The future of elementary particle physics”,  NATO Sci. Ser. B 61 (1980) 687, 10.1007/978-1-4684-7197-7_15.
  * [14] R. Mohapatra and G. Senjanović, “Neutrino mass and spontaneous parity nonconservation”,  Phys. Rev. Lett. 44 (1980) 912, 10.1103/PhysRevLett.44.912.
  * [15] J. Schechter and J. Valle, “Neutrino masses in $\mathrm{SU}(2)\otimes\mathrm{U}(1)$ theories”,  Phys. Rev. D 22 (1980) 2227, 10.1103/PhysRevD.22.2227.
  * [16] R. Shrock, “General theory of weak leptonic and semileptonic decays. I. leptonic pseudoscalar meson decays, with associated tests for, and bounds on, neutrino masses and lepton mixing”,  Phys. Rev. D 24 (1981) 1232, 10.1103/PhysRevD.24.1232.
  * [17] Y. Cai, T. Han, T. Li, and R. Ruiz, “Lepton number violation: Seesaw models and their collider tests”,  Front. Phys. 6 (2018) 40, 10.3389/fphy.2018.00040, arXiv:1711.02180.
  * [18] S. Dodelson and L. Widrow, “Sterile neutrinos as dark matter”,  Phys. Rev. Lett. 72 (1994) 17, 10.1103/PhysRevLett.72.17, arXiv:hep-ph/9303287.
  * [19] A. Boyarsky et al., “Sterile neutrino dark matter”,  Prog. Part. Nucl. Phys. 104 (2019) 1, 10.1016/j.ppnp.2018.07.004, arXiv:1807.07938.
  * [20] M. Fukugita and T. Yanagida, “Baryogenesis without grand unification”,  Phys. Lett. B 174 (1986) 45, 10.1016/0370-2693(86)91126-3.
  * [21] E. Chun et al., “Probing leptogenesis”,  Int. J. Mod. Phys. A 33 (2018) 1842005, 10.1142/S0217751X18420058, arXiv:1711.02865.
  * [22] M. Drewes, Y. Georis, and J. Klarić, “Mapping the viable parameter space for testable leptogenesis”,  Phys. Rev. Lett. 128 (2022) 051801, 10.1103/PhysRevLett.128.051801, arXiv:2106.16226.
  * [23] T. Asaka, S. Blanchet, and M. Shaposhnikov, “The MSM, dark matter and neutrino masses”,  Phys. Lett. B 631 (2005) 151, 10.1016/j.physletb.2005.09.070, arXiv:hep-ph/0503065.
  * [24] F. del Aguila and J. Aguilar-Saavedra, “Distinguishing seesaw models at LHC with multi-lepton signals”,  Nucl. Phys. B 813 (2009) 22, 10.1016/j.nuclphysb.2008.12.029, arXiv:0808.2468.
  * [25] A. Atre, T. Han, S. Pascoli, and B. Zhang, “The search for heavy Majorana neutrinos”,  JHEP 05 (2009) 030, 10.1088/1126-6708/2009/05/030, arXiv:0901.3589.
  * [26] V. Tello et al., “Left-right symmetry: from LHC to neutrinoless double beta decay”,  Phys. Rev. Lett. 106 (2011) 151801, 10.1103/PhysRevLett.106.151801, arXiv:1011.3522.
  * [27] F. Deppisch, P. Bhupal Dev, and A. Pilaftsis, “Neutrinos and collider physics”,  New J. Phys. 17 (2015) 075019, 10.1088/1367-2630/17/7/075019, arXiv:1502.06541.
  * [28] S. Pascoli, R. Ruiz, and C. Weiland, “Heavy neutrinos with dynamic jet vetoes: multilepton searches at $\sqrt{s}=14$, 27, and 100”,  JHEP 06 (2019) 049, 10.1007/JHEP06(2019)049, arXiv:1812.08750.
  * [29] J. Alimena et al., “Searching for long-lived particles beyond the standard model at the Large Hadron Collider”,  J. Phys. G 47 (2020) 090501, 10.1088/1361-6471/ab4574, arXiv:1903.04497.
  * [30] A. Abdullahi et al., “The present and future status of heavy neutral leptons”,  J. Phys. G 50 (2023) 020501, 10.1088/1361-6471/ac98f9, arXiv:2203.08039.
  * [31] C. Antel et al., “Feebly interacting particles: FIPs 2022 workshop report”,  Eur. Phys. J. C 83 (2023) 1122, 10.1140/epjc/s10052-023-12168-5, arXiv:2305.01715.
  * [32] CMS Collaboration, “Search for heavy Majorana neutrinos in ${\PGmpm\PGmpm}+{}$jets and ${\Pepm\Pepm}+{}$jets events in ${\Pp\Pp}$ collisions at $\sqrt{s}={7\TeV}$”,  Phys. Lett. B 717 (2012) 109, 10.1016/j.physletb.2012.09.012, arXiv:1207.6079.
  * [33] CMS Collaboration, “Search for heavy Majorana neutrinos in ${\PGmpm\PGmpm}+{}$jets events in proton-proton collisions at $\sqrt{s}={8\TeV}$”,  Phys. Lett. B 748 (2015) 144, 10.1016/j.physletb.2015.06.070, arXiv:1501.05566.
  * [34] ATLAS Collaboration, “Search for heavy Majorana neutrinos with the ATLAS detector in ${\Pp\Pp}$ collisions at $\sqrt{s}={8\TeV}$”,  JHEP 07 (2015) 162, 10.1007/JHEP07(2015)162, arXiv:1506.06020.
  * [35] CMS Collaboration, “Search for heavy Majorana neutrinos in ${\Pepm\Pepm}+{}$jets and ${\Pepm\PGmpm}+{}$jets events in proton-proton collisions at $\sqrt{s}={8\TeV}$”,  JHEP 04 (2016) 169, 10.1007/JHEP04(2016)169, arXiv:1603.02248.
  * [36] CMS Collaboration, “Search for heavy neutral leptons in events with three charged leptons in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  Phys. Rev. Lett. 120 (2018) 221801, 10.1103/PhysRevLett.120.221801, arXiv:1802.02965.
  * [37] CMS Collaboration, “Search for heavy Majorana neutrinos in same-sign dilepton channels in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JHEP 01 (2019) 122, 10.1007/JHEP01(2019)122, arXiv:1806.10905.
  * [38] ATLAS Collaboration, “Search for heavy neutral leptons in decays of bosons produced in 13 ${\Pp\Pp}$ collisions using prompt and displaced signatures with the ATLAS detector”,  JHEP 10 (2019) 265, 10.1007/JHEP10(2019)265, arXiv:1905.09787.
  * [39] LHCb Collaboration, “Search for heavy neutral leptons in ${\PWp\to\PGmp\PGmpm\,\text{jet}}$ decays”,  Eur. Phys. J. C 81 (2021) 248, 10.1140/epjc/s10052-021-08973-5, arXiv:2011.05263.
  * [40] CMS Collaboration, “Search for long-lived heavy neutral leptons with displaced vertices in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JHEP 07 (2022) 081, 10.1007/JHEP07(2022)081, arXiv:2201.05578.
  * [41] ATLAS Collaboration, “Search for heavy neutral leptons in decays of bosons using a dilepton displaced vertex in $\sqrt{s}={13\TeV}$ ${\Pp\Pp}$ collisions with the ATLAS detector”,  Phys. Rev. Lett. 131 (2023) 061803, 10.1103/PhysRevLett.131.061803, arXiv:2204.11988.
  * [42] CMS Collaboration, “Probing heavy Majorana neutrinos and the Weinberg operator through vector boson fusion processes in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  Phys. Rev. Lett. 131 (2023) 011803, 10.1103/PhysRevLett.131.011803, arXiv:2206.08956.
  * [43] ATLAS Collaboration, “Search for Majorana neutrinos in same-sign ${\PW\PW}$ scattering events from ${\Pp\Pp}$ collisions at $\sqrt{s}={13\TeV}$”,  Eur. Phys. J. C 83 (2023) 824, 10.1140/epjc/s10052-023-11915-y, arXiv:2305.14931.
  * [44] CMS Collaboration, “Search for long-lived heavy neutral leptons with lepton flavour conserving or violating decays to a jet and a charged lepton”,  JHEP 03 (2024) 105, 10.1007/JHEP03(2024)105, arXiv:2312.07484.
  * [45] CMS Collaboration, “Search for long-lived heavy neutral leptons decaying in the CMS muon detectors in proton-proton collisions at $\sqrt{s}={13\TeV}$”, 2024. arXiv:2402.18658. Accepted by Phys. Rev. D.
  * [46] CMS Collaboration, “Search for heavy neutral leptons in final states with electrons, muons, and hadronically decaying tau leptons in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JHEP 06 (2024) 123, 10.1007/JHEP06(2024)123, arXiv:2403.00100.
  * [47] CMS Collaboration, “Search for long-lived heavy neutrinos in the decays of mesons produced in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JHEP 06 (2024) 183, 10.1007/JHEP06(2024)183, arXiv:2403.04584.
  * [48] ATLAS Collaboration, “Search for heavy Majorana neutrinos in ${\Pepm\Pepm}$ and ${\Pepm\PGmpm}$ final states via ${\PW\PW}$ scattering in ${\Pp\Pp}$ collisions at $\sqrt{s}={13\TeV}$ with the ATLAS detector”, 2024\. arXiv:2403.15016. Submitted to Phys. Lett. B.
  * [49] CMS Collaboration, “Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton-proton collisions at $\sqrt{s}={13\TeV}$ at the CMS experiment”, 2024. arXiv:2405.17605. Submitted to Phys. Rept.
  * [50] DELPHI Collaboration, “Search for neutral heavy leptons produced in decays”,  Z. Phys. C 74 (1997) 57, 10.1007/s002880050370. [Erratum: 10.1007/BF03546181].
  * [51] Belle Collaboration, “Search for heavy neutrinos at Belle”,  Phys. Rev. D 87 (2013) 071102, 10.1103/PhysRevD.87.071102, arXiv:1301.1105. [Erratum: 10.1103/PhysRevD.95.099903].
  * [52] BaBar Collaboration, “Search for heavy neutral leptons using tau lepton decays at BaBaR”,  Phys. Rev. D 107 (2023) 052009, 10.1103/PhysRevD.107.052009, arXiv:2207.09575.
  * [53] Belle Collaboration, “Search for a heavy neutrino in decays at Belle”,  Phys. Rev. Lett. 131 (2023) 211802, 10.1103/PhysRevLett.131.211802, arXiv:2212.10095.
  * [54] Belle Collaboration, “Search for a heavy neutral lepton that mixes predominantly with the tau neutrino”,  Phys. Rev. D 109 (2024) L111102, 10.1103/PhysRevD.109.L111102, arXiv:2402.02580.
  * [55] WA66 Collaboration, “Search for heavy neutrino decays in the BEBC beam dump experiment”,  Phys. Lett. B 160 (1985) 207, 10.1016/0370-2693(85)91493-5.
  * [56] CHARM Collaboration, “A search for decays of heavy neutrinos in the mass range 0.5–2.8”,  Phys. Lett. B 166 (1986) 473, 10.1016/0370-2693(86)91601-1.
  * [57] NA3 Collaboration, “Mass and lifetime limits on new longlived particles in 300 interactions”,  Z. Phys. C 31 (1986) 21, 10.1007/BF01559588.
  * [58] CHARM II Collaboration, “Search for heavy isosinglet neutrinos”,  Phys. Lett. B 343 (1995) 453, 10.1016/0370-2693(94)01422-9.
  * [59] A. Vaitaitis et al., “Search for neutral heavy leptons in a high-energy neutrino beam”,  Phys. Rev. Lett. 83 (1999) 4943, 10.1103/PhysRevLett.83.4943, arXiv:hep-ex/9908011.
  * [60] T2K Collaboration, “Search for heavy neutrinos with the T2K near detector ND280”,  Phys. Rev. D 100 (2019) 052006, 10.1103/PhysRevD.100.052006, arXiv:1902.07598.
  * [61] NA62 Collaboration, “Search for heavy neutral lepton production in decays to positrons”,  Phys. Lett. B 807 (2020) 135599, 10.1016/j.physletb.2020.135599, arXiv:2005.09575.
  * [62] NA62 Collaboration, “Search for decays to a muon and invisible particles”,  Phys. Lett. B 816 (2021) 136259, 10.1016/j.physletb.2021.136259, arXiv:2101.12304.
  * [63] ArgoNeuT Collaboration, “New constraints on tau-coupled heavy neutral leptons with masses $m_{\PN}=280$–970”,  Phys. Rev. Lett. 127 (2021) 121801, 10.1103/PhysRevLett.127.121801, arXiv:2106.13684.
  * [64] MicroBooNE Collaboration, “Search for long-lived heavy neutral leptons and Higgs portal scalars decaying in the MicroBooNE detector”,  Phys. Rev. D 106 (2022) 092006, 10.1103/PhysRevD.106.092006, arXiv:2207.03840.
  * [65] MicroBooNE Collaboration, “Search for heavy neutral leptons in electron-positron and neutral-pion final states with the MicroBooNE detector”,  Phys. Rev. Lett. 132 (2024) 041801, 10.1103/PhysRevLett.132.041801, arXiv:2310.07660.
  * [66] A. Abada, N. Bernal, M. Losada, and X. Marcano, “Inclusive displaced vertex searches for heavy neutral leptons at the LHC”,  JHEP 01 (2019) 093, 10.1007/JHEP01(2019)093, arXiv:1807.10024.
  * [67] J.-L. Tastet, O. Ruchayskiy, and I. Timiryasov, “Reinterpreting the ATLAS bounds on heavy neutral leptons in a realistic neutrino oscillation model”,  JHEP 12 (2021) 182, 10.1007/JHEP12(2021)182, arXiv:2107.12980.
  * [68] I. Boiarska, A. Boyarsky, O. Mikulenko, and M. Ovchynnikov, “Constraints from the CHARM experiment on heavy neutral leptons with tau mixing”,  Phys. Rev. D 104 (2021) 095019, 10.1103/PhysRevD.104.095019, arXiv:2107.14685.
  * [69] A. Abada, P. Escribano, X. Marcano, and G. Piazza, “Collider searches for heavy neutral leptons: beyond simplified scenarios”,  Eur. Phys. J. C 82 (2022) 1030, 10.1140/epjc/s10052-022-11011-7, arXiv:2208.13882.
  * [70] HEPData record for this analysis, 2024.  10.17182/hepdata.150534.
  * [71] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [72] CMS Collaboration, “Development of the CMS detector for the CERN LHC Run 3”,  JINST 19 (2024) P05064, 10.1088/1748-0221/19/05/P05064, arXiv:2309.05466.
  * [73] CMS Collaboration, “Performance of the CMS trigger in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [74] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [75] W. Waltenberger, R. Frühwirth, and P. Vanlaer, “Adaptive vertex fitting”,  J. Phys. G 34 (2007) N343, 10.1088/0954-3899/34/12/N01.
  * [76] CMS Collaboration, “Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid”, CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015.  10.17181/CERN.VU8I.D59J.
  * [77] CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,  JINST 9 (2014) P10009, 10.1088/1748-0221/9/10/P10009, arXiv:1405.6569.
  * [78] CMS Tracker Group Collaboration, “The CMS Phase-1 pixel detector upgrade”,  JINST 16 (2021) P02027, 10.1088/1748-0221/16/02/P02027, arXiv:2012.14304.
  * [79] CMS Collaboration, “Track impact parameter resolution for the full pseudo rapidity coverage in the 2017 dataset with the CMS Phase-1 pixel detector”, CMS Detector Performance Note CMS-DP-2020-049, 2020.
  * [80] CMS Collaboration, “Measurement of ${\PB\PAB}$ angular correlations based on secondary vertex reconstruction at $\sqrt{s}={7\TeV}$”,  JHEP 03 (2011) 136, 10.1007/JHEP03(2011)136, arXiv:1102.3194.
  * [81] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in ${\Pp\Pp}$ collisions at 13”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [82] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [83] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [84] M. Cacciari, G. P. Salam, and G. Soyez, “ user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [85] CMS Collaboration, “Pileup mitigation at CMS in 13 data”,  JINST 15 (2020) P09018, 10.1088/1748-0221/15/09/P09018, arXiv:2003.00503.
  * [86] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in ${\Pp\Pp}$ collisions at 8”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [87] CMS Collaboration, “Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC”,  JINST 16 (2021) P05014, 10.1088/1748-0221/16/05/P05014, arXiv:2012.06888.
  * [88] CMS Collaboration, “ECAL 2016 refined calibration and Run 2 summary plots”, CMS Detector Performance Note CMS-DP-2020-021, 2020.
  * [89] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [90] CMS Collaboration, “Single- and double-electron trigger efficiencies using the full Run 2 data set”, CMS Detector Performance Note CMS-DP-2020-016, 2020.
  * [91] CMS Collaboration, “Performance of the CMS muon trigger system in proton-proton collisions at $\sqrt{s}={13\TeV}$”,  JINST 16 (2021) P07001, 10.1088/1748-0221/16/07/P07001, arXiv:2102.04790.
  * [92] NNPDF Collaboration, “Parton distributions for the LHC run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [93] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [94] T. Sjöstrand et al., “An introduction to 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [95] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [96] CMS Collaboration, “Extraction and validation of a new set of CMS 8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [97] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [98] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [99] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [100] S. Frixione, G. Ridolfi, and P. Nason, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,  JHEP 09 (2007) 126, 10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  * [101] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [102] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in : $s$\- and $t$-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: 10.1007/JHEP02(2010)011].
  * [103] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the box”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [104] E. Re, “Single-top ${\PW\PQt}$-channel production matched with parton showers using the method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [105] T. Melia, P. Nason, R. Röntsch, and G. Zanderighi, “${\PWp\PWm}$, ${\PW\PZ}$ and ${\PZ\PZ}$ production in the box”,  JHEP 11 (2011) 078, 10.1007/JHEP11(2011)078, arXiv:1107.5051.
  * [106] P. Nason and G. Zanderighi, “${\PWp\PWm}$, ${\PW\PZ}$ and ${\PZ\PZ}$ production in the -box-v2”,  Eur. Phys. J. C 74 (2014) 2702, 10.1140/epjc/s10052-013-2702-5, arXiv:1311.1365.
  * [107] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [108] R. Frederix and S. Frixione, “Merging meets matching in ”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [109] D. Alva, T. Han, and R. Ruiz, “Heavy Majorana neutrinos from ${\PW\PGg}$ fusion at hadron colliders”,  JHEP 02 (2015) 072, 10.1007/JHEP02(2015)072, arXiv:1411.7305.
  * [110] C. Degrande, O. Mattelaer, R. Ruiz, and J. Turner, “Fully-automated precision predictions for heavy neutrino production mechanisms at hadron colliders”,  Phys. Rev. D 94 (2016) 053002, 10.1103/PhysRevD.94.053002, arXiv:1602.06957.
  * [111] K. Melnikov and F. Petriello, “Electroweak gauge boson production at hadron colliders through $\mathcal{O}({\alpS^{2}})$”,  Phys. Rev. D 74 (2006) 114017, 10.1103/PhysRevD.74.114017, arXiv:hep-ph/0609070.
  * [112] R. Gavin, Y. Li, F. Petriello, and S. Quackenbush, “2.0: A code for hadronic production at next-to-next-to-leading order”,  Comput. Phys. Commun. 182 (2011) 2388, 10.1016/j.cpc.2011.06.008, arXiv:1011.3540.
  * [113] R. Gavin, Y. Li, F. Petriello, and S. Quackenbush, “ physics at the LHC with 2.1”,  Comput. Phys. Commun. 184 (2013) 208, 10.1016/j.cpc.2012.09.005, arXiv:1201.5896.
  * [114] Y. Li and F. Petriello, “Combining QCD and electroweak corrections to dilepton production in ”,  Phys. Rev. D 86 (2012) 094034, 10.1103/PhysRevD.86.094034, arXiv:1208.5967.
  * [115] P. Komiske, E. Metodiev, and J. Thaler, “Energy flow networks: deep sets for particle jets”,  JHEP 01 (2019) 121, 10.1007/JHEP01(2019)121, arXiv:1810.05165.
  * [116] M. Zaheer et al., “Deep sets”, in  Proc. 31st Conference on Neural Information Processing Systems (NIPS 2017): Long Beach CA, USA, December 04–09, 2017, p. 3391.  2017\.  arXiv:1703.06114.
  * [117] F. Chollet et al., “keras”, 2015.  Software available from https://keras.io.
  * [118] M. Abadi et al., “TensorFlow: Large-scale machine learning on heterogeneous systems”, 2015.  Software available from http://tensorflow.org. 10.5281/zenodo.4724125.
  * [119] CMS Collaboration, “Displaced tracking and vertexing calibration using neutral mesons”, CMS Detector Performance Note CMS-DP-2024-010, 2024.
  * [120] Particle Data Group, R. L. Workman et al., “Review of particle physics”,  Prog. Theor. Exp. Phys. 2022 (2022) 083C01, 10.1093/ptep/ptac097.
  * [121] S. Choi and H. Oh, “Improved extrapolation methods of data-driven background estimations in high energy physics”,  Eur. Phys. J. C 81 (2021) 643, 10.1140/epjc/s10052-021-09404-1, arXiv:1906.10831.
  * [122] B. Rémillard, “Tests of independence”, in  International encyclopedia of statistical science, M. Lovric, ed., p. 1598.  Springer-Verlag, Berlin, 2011.  10.1007/978-3-642-04898-2_592.
  * [123] CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at $\sqrt{s}={13\TeV}$ in 2015 and 2016 at CMS”,  Eur. Phys. J. C 81 (2021) 800, 10.1140/epjc/s10052-021-09538-2, arXiv:2104.01927.
  * [124] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}={13\TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018\.
  * [125] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}={13\TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019\.
  * [126] CMS Collaboration, “Measurements of inclusive and cross sections in ${\Pp\Pp}$ collisions at $\sqrt{s}={7\TeV}$”,  JHEP 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.
  * [127] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.
  * [128] A. L. Read, “Presentation of search results: The technique”,  J. Phys. G 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [129] ATLAS and CMS Collaborations, and LHC Higgs Combination Group, “Procedure for the LHC Higgs boson search combination in Summer 2011”, Technical Report CMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011.
  * [130] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [131] CMS Collaboration, “The CMS statistical analysis and combination tool: combine”, 2024. arXiv:2404.06614. Accepted by Comput. Softw. Big Sci.
  * [132] W. Verkerke and D. Kirkby, “The RooFit toolkit for data modeling”, in  Proc. 13th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2003): La Jolla CA, United States, March 24–28, 2003.  2003\.  arXiv:physics/0306116.  [eConf C0303241 (2003) MOLT007].
  * [133] L. Moneta et al., “The RooStats project”, in  Proc. 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2010): Jaipur, India, February 22–27, 2010.  2010\.  arXiv:1009.1003.  [PoS (ACAT2010) 057]. 10.22323/1.093.0057.

##  .10 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 T. Janssen0000-0002-3998-4081, T.
Van Laer, P. Van Mechelen0000-0002-8731-9051

Vrije Universiteit Brussel, Brussel, Belgium N. Breugelmans, J.
D’Hondt0000-0002-9598-6241, S. Dansana0000-0002-7752-7471, A. De
Moor0000-0001-5964-1935, M. Delcourt0000-0001-8206-1787, F. Heyen, S.
Lowette0000-0003-3984-9987, I. Makarenko0000-0002-8553-4508, D.
Müller0000-0002-1752-4527, S. Tavernier0000-0002-6792-9522, M.
Tytgat30000-0002-3990-2074, 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. Bilin0000-0003-1439-7128,
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, 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, M.A. Shahzad, 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, G. Gokbulut0000-0002-0175-6454, Y.
Hong0000-0003-4752-2458, J. Knolle0000-0002-4781-5704, L.
Lambrecht0000-0001-9108-1560, D. Marckx0000-0001-6752-2290, K. Mota
Amarilo0000-0003-1707-3348, 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, J. De Favereau De
Jeneret0000-0003-1775-8574, C. Delaere0000-0001-8707-6021, I.S.
Donertas0000-0001-7485-412X, A. Giammanco0000-0001-9640-8294, A.O.
Guzel0000-0002-9404-5933, 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, M. Alves Gallo Pereira0000-0003-4296-7028, E.
Coelho0000-0001-6114-9907, G. Correia Silva0000-0001-6232-3591, C.
Hensel0000-0001-8874-7624, T. Menezes De Oliveira0009-0009-4729-8354, C. Mora
Herrera40000-0003-3915-3170, A. Moraes0000-0002-5157-5686, P. Rebello
Teles0000-0001-9029-8506, M. Soeiro, A. Vilela Pereira40000-0003-3177-4626

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, 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, M.
Macedo0000-0002-6173-9859, J. Martins70000-0002-2120-2782, 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

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, B.
Lopes Da Costa, I. Maietto Silverio0000-0003-3852-0266, 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, D. Laroze0000-0002-6487-8096, 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, Z. Liang, 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, J. Zhao0000-0001-8365-7726

State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos0000-0002-8953-1232, Y. Ban0000-0002-1912-0374, S.
Deng0000-0002-2999-1843, B. Guo, C. Jiang0009-0008-6986-388X, A.
Levin0000-0001-9565-4186, C. Li0000-0002-6339-8154, Q. Li0000-0002-8290-0517,
Y. Mao, S. Qian, S.J. Qian0000-0002-0630-481X, X. Qin, X.
Sun0000-0003-4409-4574, D. Wang0000-0002-9013-1199, H. Yang, L.
Zhang0000-0001-7947-9007, Y. Zhao, C. Zhou0000-0001-5904-7258

Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong
Joint Laboratory of Quantum Matter, South China Normal University, Guangzhou,
China S. Yang0000-0002-2075-8631

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, B. Li, J.
Zhang0000-0003-3314-2534

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 F. Ramirez0000-0002-7178-0484, C.
Rendón, M. Rodriguez0000-0002-9480-213X, A.A. Ruales
Barbosa0000-0003-0826-0803, 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, A. Petkovic, 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, C.
Leonidou0009-0008-6993-2005, 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

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

Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt Y. Assran17,18, B. El-
mahdy, S. Elgammal18

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

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia 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, N. Bin Norjoharuddeen0000-0002-8818-7476, E.
Brücken0000-0001-6066-8756, F. Garcia0000-0002-4023-7964, P.
Inkaew0000-0003-4491-8983, K.T.S. Kallonen0000-0001-9769-7163, 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, 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, M.
Kumar0000-0003-0312-057X, V. Lohezic0009-0008-7976-851X, J.
Malcles0000-0002-5388-5565, F. Orlandi0009-0001-0547-7516, L.
Portales0000-0002-9860-9185, A. Rosowsky0000-0001-7803-6650, M.Ö.
Sahin0000-0001-6402-4050, A. Savoy-Navarro190000-0002-9481-5168, 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, G.
Boldrini0000-0001-5490-605X, 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, 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, L. Urda
Gómez0000-0002-7865-5010, 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.
Agram200000-0001-7476-0158, 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, R. Haeberle0009-0007-5007-6723, A.-C. Le
Bihan0000-0002-8545-0187, M. Meena0000-0003-4536-3967, O.
Poncet0000-0002-5346-2968, G. Saha0000-0002-6125-1941, M.A.
Sessini0000-0003-2097-7065, P. Van Hove0000-0002-2431-3381, P.
Vaucelle0000-0001-6392-7928

Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique
des Particules, CNRS/IN2P3, Villeurbanne, France A. Di
Florio0000-0003-3719-8041

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France D.
Amram, 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.
Dozen210000-0002-4301-634X, 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, E. Jourd‘huy, 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 D.
Chokheli0000-0001-7535-4186, I. Lomidze0009-0002-3901-2765, Z.
Tsamalaidze160000-0001-5377-3558

RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany V.
Botta0000-0003-1661-9513, S. Consuegra Rodríguez0000-0002-1383-1837, 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, D. Pérez Adán0000-0003-3416-0726, 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,
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 Araujo220000-0002-4785-3057,
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, 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, J.
Bach0000-0001-9572-6645, 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, F.
Blekman230000-0002-7366-7098, K. Borras240000-0003-1111-249X, A.
Campbell0000-0003-4439-5748, A. Cardini0000-0003-1803-0999, C. Cheng, F.
Colombina0009-0008-7130-100X, 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. Gallo230000-0001-7200-5175, A.
Geiser0000-0003-0355-102X, V. Guglielmi0000-0003-3240-7393, M.
Guthoff0000-0002-3974-589X, A. Hinzmann0000-0002-2633-4696, 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.
Lipka250000-0002-8427-3748, W. Lohmann260000-0002-8705-0857, 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, K. Moral
Figueroa0000-0003-1987-1554, A. Mussgiller0000-0002-8331-8166, L.P.
Nair0000-0002-2351-9265, J. Niedziela0000-0002-9514-0799, A.
Nürnberg0000-0002-7876-3134, Y. Otarid, J. Park0000-0002-4683-6669, E.
Ranken0000-0001-7472-5029, A. Raspereza0000-0003-2167-498X, D.
Rastorguev0000-0001-6409-7794, J. Rübenach, L. Rygaard, A.
Saggio0000-0002-7385-3317, M. Scham27,240000-0001-9494-2151, S.
Schnake240000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger230000-0001-6699-6662, D. Selivanova0000-0002-7031-9434, K.
Sharko0000-0002-7614-5236, M. Shchedrolosiev0000-0003-3510-2093, D. Stafford,
F. Vazzoler0000-0001-8111-9318, A. Ventura Barroso0000-0003-3233-6636, R.
Walsh0000-0002-3872-4114, D. Wang0000-0002-0050-612X, Q.
Wang0000-0003-1014-8677, Y. Wen0000-0002-8724-9604, K. Wichmann, L.
Wiens240000-0002-4423-4461, 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, C.c. Kuo, 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, U.
Elicabuk, N. Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, A.
Gottmann0000-0001-6696-349X, F. Hartmann280000-0001-8989-8387, 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, L. Sowa, L.
Stockmeier, K. Tauqeer, 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. Papadopoulos28, 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, 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. Tsipolitis0000-0002-0805-0809, 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 C.
Hajdu0000-0002-7193-800X, D. Horvath29,300000-0003-0091-477X, K. Márton, A.J.
Rádl310000-0001-8810-0388, 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, A. Fehérkuti320000-0002-5043-2958, M.M.A.
Gadallah330000-0002-8305-6661, Á. Kadlecsik0000-0001-5559-0106, P.
Major0000-0002-5476-0414, G. Pásztor0000-0003-0707-9762, G.I.
Veres0000-0002-5440-4356

Faculty of Informatics, University of Debrecen, Debrecen, Hungary 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.
Novak0000-0001-6253-4356

Panjab University, Chandigarh, India J. Babbar0000-0002-4080-4156, S.
Bansal0000-0003-1992-0336, S.B. Beri, V. Bhatnagar0000-0002-8392-9610, G.
Chaudhary0000-0003-0168-3336, S. Chauhan0000-0001-6974-4129, N.
Dhingra340000-0002-7200-6204, 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, M.K. Saini, S. Saumya0000-0001-7842-9518

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S.
Baradia0000-0001-9860-7262, S. Barman350000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, S. Das Gupta, 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, G.
Dash0000-0002-7451-4763, P. Jana0000-0001-5310-5170, P.
Kalbhor0000-0002-5892-3743, S. Kamble0000-0001-7515-3907, J.R.
Komaragiri360000-0002-9344-6655, D. Kumar360000-0002-6636-5331, P.R.
Pujahari0000-0002-0994-7212, N.R. Saha0000-0002-7954-7898, A.
Sharma0000-0002-0688-923X, A.K. Sikdar0000-0002-5437-5217, R.K. Singh, P.
Verma, S. Verma0000-0003-1163-6955, A. Vijay

Tata Institute of Fundamental Research-A, Mumbai, India S. Dugad, G.B.
Mohanty0000-0001-6850-7666, B. Parida0000-0001-9367-8061, M. Shelake, P.
Suryadevara

Tata Institute of Fundamental Research-B, Mumbai, India A.
Bala0000-0003-2565-1718, S. Banerjee0000-0002-7953-4683, R.M. Chatterjee, 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.
Bahinipati370000-0002-3744-5332, C. Kar0000-0002-6407-6974, D.
Maity380000-0002-1989-6703, P. Mal0000-0002-0870-8420, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu380000-0003-4671-815X, K. Naskar380000-0003-0638-4378, A.
Nayak380000-0002-7716-4981, S. Nayak, K. Pal, P. Sadangi, S.K.
Swain0000-0001-6871-3937, S. Varghese380009-0000-1318-8266, D.
Vats380009-0007-8224-4664

Indian Institute of Science Education and Research (IISER), Pune, India S.
Acharya390009-0001-2997-7523, A. Alpana0000-0003-3294-2345, S.
Dube0000-0002-5145-3777, B. Gomber390000-0002-4446-0258, P.
Hazarika0009-0006-1708-8119, B. Kansal0000-0002-6604-1011, A.
Laha0000-0001-9440-7028, B. Sahu390000-0002-8073-5140, S.
Sharma0000-0001-6886-0726, K.Y. Vaish0009-0002-6214-5160

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi400000-0001-5741-3357, A. Jafari410000-0001-7327-1870, M.
Zeinali420000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S. Bashiri,
S. Chenarani430000-0002-1425-076X, S.M. Etesami0000-0001-6501-4137, Y.
Hosseini0000-0001-8179-8963, M. Khakzad0000-0002-2212-5715, E.
Khazaie440000-0001-9810-7743, M. Mohammadi Najafabadi0000-0001-6131-5987, S.
Tizchang450000-0002-9034-598X

University College Dublin, Dublin, Ireland M. Felcini0000-0002-2051-9331, M.
Grunewald0000-0002-5754-0388

INFN Sezione di Baria, Università di Barib, Politecnico di Baric, Bari, Italy
M. Abbresciaa,b0000-0001-8727-7544, 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, W.
Elmetenaweea,b,460000-0001-7069-0252, L. Fiorea0000-0002-9470-1320, G.
Iasellia,c0000-0003-2546-5341, L. Longoa0000-0002-2357-7043, M. Loukaa,b, G.
Maggia,c0000-0001-5391-7689, M. Maggia0000-0002-8431-3922, I.
Margjekaa0000-0002-3198-3025, 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. Radognaa,b0000-0002-1094-5038, D.
Ramosa0000-0002-7165-1017, A. Ranieria0000-0001-7912-4062, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,c0000-0002-1924-983X, Ü.
Sözbilira0000-0001-6833-3758, A. Stamerraa,b0000-0003-1434-1968, D.
Troianoa,b0000-0001-7236-2025, R. Vendittia,b0000-0001-6925-8649, 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, P. Capiluppia,b0000-0003-4485-1897, A.
Castro${}^{\textrm{\textdagger}}$a,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.
Fasanellaa0000-0002-2926-2691, 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,470000-0003-3249-9208, M.
Lorussoa,b0000-0003-4033-4956, L. Lunertia0000-0002-8932-0283, S.
Marcellinia0000-0002-1233-8100, G. Masettia0000-0002-6377-800X, F.L.
Navarriaa,b0000-0001-7961-4889, G. Paggia,b0009-0005-7331-1488, A.
Perrottaa0000-0002-7996-7139, F. Primaveraa,b0000-0001-6253-8656, A.M.
Rossia,b0000-0002-5973-1305, S. Rossi Tisbenia,b0000-0001-6776-285X, 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,480000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, A.
Lapertosaa0000-0001-6246-6787, R. Potenzaa,b, A.
Tricomia,b,480000-0002-5071-5501, 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.
Meola490000-0002-8233-7277, 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, F. Brivioa0000-0001-9523-6451, F.
Cetorellia,b0000-0002-3061-1553, 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, A.
Peregoa,b0009-0002-5210-6213, 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,500000-0001-6471-5492, P.
Paoluccia,280000-0002-8773-4781

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,510000-0002-2205-5737, M.
Biasottoa,520000-0003-2834-8335, 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, T. Dorigoa0000-0002-1659-8727, S.
Fantinela0000-0002-0079-8708, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, E. Lusiania0000-0001-8791-7978, M.
Margonia,b0000-0003-1797-4330, 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, 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

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy C.
Aimèa0000-0003-0449-4717, 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, M.E. Asciotia,b, G.M. Bileia0000-0002-4159-9123,
C. Carrivalea,b, 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,530000-0002-7676-3106, 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 C.A.
Alexea,c0000-0003-4981-2790, 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, A.C. Marinia0000-0003-2351-0487, 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 Marcoa,b0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, F. Erricoa,b0000-0001-8199-370X, E.
Longoa,b0000-0001-6238-6787, 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, L. Menzioa,b, P. Meridiania0000-0002-8480-2259,
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, R.
Whitea,b0000-0001-5793-526X

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,
J. Kim, D. Lee, 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, B. Tae, 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, Y. Ryou

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.