SUS-21-004

SUS-21-004

# Search for top squark pair production in a final state with at least one
hadronically decaying tau lepton in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

A search for pair production of the supersymmetric partner of the top quark,
the top squark, in proton-proton collisions at $\sqrt{s}=13\TeV$ is presented
in final states containing at least one hadronically decaying tau lepton and
large missing transverse momentum. This final state is highly sensitive to
scenarios of supersymmetry in which the decay of the top squark to tau leptons
is enhanced. The search uses a data sample corresponding to an integrated
luminosity of 138, which was recorded with the CMS detector during 2016–2018.
No significant excess is observed with respect to the standard model
predictions. Exclusion limits at 95% confidence level on the masses of the top
squark and the lightest neutralino are presented under the assumptions of
simplified models. The results probe top squark masses up to 1150for a nearly
massless neutralino. This search covers a relatively less explored parameter
space in the context of supersymmetry, and the exclusion limit is the most
stringent to date for the model considered here.

##  0.1 Introduction

The standard model (SM) of particle physics is the most successful theoretical
description of the fundamental particles of nature and their interactions.
However, it has various shortcomings. Several theories have been proposed to
address these deficiencies, among which Supersymmetry (SUSY) [1, 2, 3, 4, 5,
6, 7, 8, 9] is one of the most widely studied. It assumes a new symmetry
between bosons and fermions, thereby introducing a bosonic (fermionic)
superpartner for every SM fermion (boson). The fermionic superpartners of the
SU(2)$\times$U(1) gauge fields of the SM, known as gauginos and higgsinos, are
combined resulting in mass eigenstates that are referred to as charginos and
neutralinos, or collectively as electroweakinos. In $R$-parity conserving SUSY
models [10], the weakly interacting lightest neutralino can be interpreted as
a dark matter candidate. The superpartners of the left- and right-handed top
quarks are the top squarks, and , respectively. The combination of these
bosonic fields results in mass eigenstates and , where is defined to be the
lighter of the two. The top squarks play an important role in stabilizing the
Higgs boson () mass calculation by canceling the dominant top quark loop
corrections [11, 12, 13]. Depending on the mixing scenario [14, 15], the mass
of can be within the reach of the CERN LHC for the top squark to effectively
cancel the divergent contributions of the top quark to the Higgs boson mass.
Hence it is important to search for top squark production at the LHC.

The minimal supersymmetric standard model (MSSM) is the simplest SUSY
extension of the SM, and it incorporates a wide variety of SUSY
phenomenologies. Both the gauge and Yukawa components [9] of the chargino and
neutralino are involved in their interaction with fermion-sfermion pairs. As a
result, higgsino-like chargino and neutralino preferentially couple to third-
generation fermion-sfermion pairs through the large Yukawa coupling. Moreover,
the Yukawa coupling to tau lepton-slepton pairs can be large for a high value
of even if the higgsino component is relatively small. Here the quantity is
defined as the ratio of the vacuum expectation values of the two Higgs
doublets in the MSSM. For a large value of , the lighter state of the
superpartner of the tau lepton can be significantly less massive than the
superpartners of the first and second generation leptons. Consequently, the
chargino decays predominantly as
$\PSGcpmDo\to{\HepSusyParticle{\PGt}{1}{\pm}}\Xspace\PGnGt$ or
$\PGtpm\PSGnGt$, where is the superpartner of the tau neutrino. The decay
probabilities of the electron and muon channels are thus greatly reduced [16,
17]. Throughout this paper, charge conjugation symmetry is assumed and equal
branching fractions are considered for decays to
${\HepSusyParticle{\PGt}{1}{+}}\Xspace\PGnGt$ and $\PGtp\PSGnGt$. Since the
tau lepton decays to hadrons more often than to electrons and muons [18], and
the hadronic decay mode has lower background contribution relative to the
signal, searches for SUSY signals in electron and muon channels are less
sensitive to higgsino-like and high scenarios. In this search, we focus on the
signal of top squark pair production in a final state with two tau leptons, at
least one of them decaying hadronically, probing part of the MSSM parameter
space where the lightest charginos and neutralino preferentially couple to
third-generation fermions.

To address both the gauge hierarchy problem and the possibility of
preferential couplings between electroweakinos and third-generation particles,
we focus on the top squark decay chains
$\PSQtDo\to\PQb\PSGcpDo\to\PQb{\HepSusyParticle{\PGt}{1}{+}}\Xspace\PGnGt\to\PQb\PGtp\PSGczDo\PGnGt$
and $\PSQtDo\to\PQb\PSGcpDo\to\PQb\PGtp\PSGnGt\to\PQb\PGtp\PSGczDo\PGnGt$ and
their charge conjugate reactions. We assume $R$-parity conservation and
consider to be the lightest SUSY particle (LSP). Being neutral and weakly
interacting, leaves no recorded signal in the detector. The decay chains are
depicted by the four diagrams in Fig. 1 within the framework of simplified
model spectra (SMS) [19, 20], where a branching ratio of 50% is assumed for
both $\PSGcpDo\to{\HepSusyParticle{\PGt}{1}{+}}\Xspace\PGnGt$ and
$\PSGcpDo\to\PGtp\PSGnGt$.

Figure 1: Diagrams of top squark pair production in $\Pp\Pp$ collisions at the
LHC, and the decays that lead to final states with pairs of quarks and tau
leptons accompanied by neutrinos and LSPs, within the framework of SMS.

This search is performed using proton-proton ($\Pp\Pp$) collision events at a
center-of-mass energy of 13, recorded by the CMS experiment at the LHC. The
data sample corresponds to integrated luminosities of 36.3, 41.5, and
59.8collected during the 2016, 2017, and 2018 operating periods of the LHC,
respectively. Signal-like events are characterized by the presence of at least
one hadronically decaying tau lepton , jets identified as likely to have
originated from the fragmentation of quarks, , -tagged jets, and a large
missing transverse momentum. The other tau lepton decays either hadronically
or leptonically, $\PGt_{\Pell}$, to an electron or a muon. Events with both
tau leptons decaying leptonically are not considered as they constitute about
only 13% of the final states. The semileptonic final states are referred to as
$\Pe\tauh$ and $\PGm\tauh$ (or collectively as $\Pell\tauh$) categories, and
the fully hadronic final state as the $\tauh\tauh$ category. Contributions
from SM processes are estimated using a combination of Monte Carlo (MC)
simulated samples and control samples in data.

Both the CMS [21, 22, 23, 24, 25, 26, 27, 28] and ATLAS [29, 30, 31, 32, 33]
Collaborations have performed searches for top squark pair production in
leptonic and hadronic final states, establishing limits on top squark masses
in the framework of SMS models. The final state used in this search has also
been studied by the ATLAS Collaboration [34, 35]. However the results are not
directly comparable as the ATLAS searches were optimized for a gauge-mediated
SUSY-breaking scenario, where the top squark decays as
$\PSQtDo\to\PQb{\HepSusyParticle{\PGt}{1}{+}}\Xspace\PGnGt$, resulting in
different kinematics of the final state particles. A search performed by the
CMS Collaboration [36] using 2016 and 2017 data has studied the same signal
model as this study, albeit in the $\tauh\tauh$ final state alone. This study
expands upon the search in Ref. [36] by including the 2018 data and the
$\Pell\tauh$ final states, and employs improved identification and tagging
algorithms. Since we interpret the results in the framework of simplified
models, previous constraints from direct chargino and tau slepton production
searches by LEP, and LHC experiments have not been imposed in this analysis.
The HEPData record for the analysis can be found in Ref. [37].

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter that provides a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, is reported in Ref. [38].

Events of interest are selected using a two-tiered trigger system [39]. 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 less than 4 [40]. The second level, known as the
high-level trigger, consists of a farm of processors running a version of the
full event reconstruction software optimized for fast processing, and it
reduces the event rate to around 1kHz before data storage.

##  0.3 Event simulation

Simulated samples are used to estimate several SM backgrounds as well as to
predict signal rates. The background and signal samples are generated with
representative distributions of additional $\Pp\Pp$ interactions per bunch
crossing, referred to as pileup. These samples are produced for each year of
data taking separately to account for different pileup and detector conditions
in the three years. Additionally, the simulated samples for each year are
reweighted such that their pileup profiles match that measured in the data of
the corresponding year.

For background processes, the v2 [41, 42, 43, 44, 45] MC event generator is
used for the pair production of top quarks ($\ttbar$) and the single top quark
$t$-channel process, whereas v1 [46] is used for the process. The (v2.2.2 for
2016, v2.4.2 for 2017 and 2018) [47] event generator is used at leading order
(LO) for modeling the Drell–Yan+jets (DY+jets) and +jets backgrounds; these
two LO MC samples are normalized to cross sections calculated with the fewz
v3.1 program [48] at NNLO order in pQCD. The event generator is also used to
simulate the diboson (VV and VH) and $\ttbar$V (V = or ) processes at NLO in
pQCD. For the 2016 analysis, the parton showering and hadronization are
simulated with v8.212 [49]. All samples use the CUETP8M1 [50] underlying event
tune, except for simulation, which uses the CUETP8M2T4 [51] tune. For the 2017
and 2018 analyses, v8.230 with the CP5 [52] tune is used. The CMS detector
response is modeled using [53], and simulated events are then reconstructed in
the same way as collision data.

The signal is simulated based on simplified SUSY models. The signal process of
top squark production, shown in Fig. 1, is simulated at LO using followed by
v8.212 with the tune CUETP8M1 for 2016 and tune CP2 [52] for the 2017 and 2018
analyses. The signal cross sections are evaluated using NNLO plus next-to-
leading logarithmic (NLL) calculations in QCD [54, 55, 56, 57, 58]. The
detector response for the signal sample is simulated using the fast CMS
detector simulation (FastSim) [59]. For all simulated signal and background
events, small discrepancies observed between simulation and data are corrected
by adding several scale factors, as discussed in Section 0.7. Additional
corrections are applied to the signal to account for differences between
FastSim and simulations.

We assume a branching fraction of 50% for each of the two decay modes of the
chargino, $\PSGcpDo\to{\HepSusyParticle{\PGt}{1}{+}}\Xspace\PGnGt$ and
$\PSGcpDo\to\PGtp\PSGnGt$. Each of the four diagrams in Fig. 1 therefore
contributes to 25% of the generated signal events. The masses of SUSY
particles appearing in the decay chain are parameterized as

$\displaystyle m_{\PSGcmDo}-m_{\PSGczDo}$ $\displaystyle=0.5\
(m_{\PSQtDo}-m_{\PSGczDo}),$ (1) $\displaystyle m_{\PSGtDo}-m_{\PSGczDo}$
$\displaystyle=x\ (m_{\PSGcmDo}-m_{\PSGczDo}),$ $\displaystyle\text{where }x$
$\displaystyle\in[0.25,\ 0.5,\ 0.75],$ $\displaystyle\text{and }m_{\PSGnGt}$
$\displaystyle=m_{\PSGtDo}.$

In this parameterization, the chargino mass is fixed to have the mean of the
top squark and $\PSGczDo$ masses. The masses of the leptonic superpartners are
set by the value of $x$ for a given pair of top squark and $\PSGczDo$ masses.
A graphical representation of the mass parameterization is presented in Fig.
2.

Figure 2: A graphical representation of the mass parameterization described in
Eq. 1.

Therefore, the kinematic properties of the final state particles in each of
the decay chains depicted in Fig. 1 depend on the choice of $x$:

  * •

$x=0.25$: The mass of the lepton superpartner is closer to that of the than to
that of the . Hence, the upper left diagram in Fig. 1 produces tau leptons
with lower energy with respect to the ones produced in the upper right. The
lower two diagrams both typically produce two tau leptons with a large
difference in energy.

  * •

$x=0.75$: The masses of the 1±and the are relatively close, so the upper left
diagram in Fig. 1 produces more energetic tau leptons than the upper right.
The lower two diagrams produce the same energy asymmetry as in the case of
$x=0.25$.

  * •

$x=0.5$: The tau leptons in all four diagrams have similar energies.

In fact, when all four diagrams are taken into account, the distributions of
the kinematic properties are found to be very similar for the three different
values of $x$, for a given set of chargino and LSP masses. It is important to
note, however, that parameterizing the chargino mass to a point other than
halfway between the $\PSQtDo$ and $\PSGczDo$ masses does affect the overall
sensitivity of this search. These cases are not explored in this paper.

##  0.4 Event reconstruction

The particle-flow (PF) algorithm [60] reconstructs each individual particle in
an event, with an optimized combination of information from various components
of the CMS detector. The energies of electrons and photons are measured in the
ECAL. The momentum of electrons is determined by a combined measurement of the
track momentum in the tracker, the energy of the matching ECAL supercluster,
and the energy of all bremsstrahlung photons consistent with originating from
the track. The momentum of muons is obtained from the bending of the
corresponding tracks in the tracker and the muon spectrometer, which comprises
three technologies: drift tubes, cathode strip chambers, and resistive-plate
chambers. The energy of charged hadrons, which is corrected for zero-
suppression effects and for the response function of the calorimeters to
hadronic showers, is determined from a combination of the momentum measured in
the tracker and the energy of the matching ECAL and HCAL clusters. Finally,
the energy of neutral hadrons is obtained from the corrected energies in the
corresponding ECAL and HCAL clusters.

Vertices reconstructed in an event are required to be within 24cm of the
center of the detector in the $z$ direction (along the beam), and to have a
transverse displacement from the beam line of less than 2cm. The primary
vertex (PV) is taken to be the vertex corresponding to the hardest scattering
in the event, evaluated using tracking information alone, as described in
Section 9.4.1 of Ref. [61].

Reconstruction of jets is performed by clustering PF objects using the anti-
algorithm [62, 63] with a distance parameter of $R=0.4$. Jet momentum is
determined as the vector sum of all particle momenta in the jet, and is
typically within 5–10% of the momentum of the particle level jet over the
entire spectrum within the detector acceptance. Pileup interactions contribute
to spurious tracks and calorimetric energy deposits, increasing the apparent
jet momentum. To mitigate this effect, tracks identified as originating from
pileup vertices are discarded, and an offset is applied to correct for the
remaining contributions [64]. Jets are calibrated using information from both
simulation and data [64]. Additional selection criteria are applied to remove
jets that are potentially dominated by instrumental effects or reconstruction
failures [65]. Only jets with $\pt>20\GeV$ and $\abs{\eta}<2.4$ are considered
in this analysis.

Jets originating from the fragmentation of quarks are identified as -tagged
jets [66] using the DeepJet algorithm [67, 68]. The algorithm employs
properties of reconstructed secondary vertices and charged and neutral
particle constituents of the jet as inputs to a convolutional deep neural
network. The “medium” (“loose”) selection or working point (WP) of this
algorithm corresponds to a signal efficiency of about 80 (90)%, with a
mistagging probability of about 1 (10)% for light jets (from gluons and up,
down and strange quarks) and about 11 (50)% for jets originating from charm
quarks. The medium DeepJet WP is used to identify -tagged jets in the search
regions, whereas the loose WP is used to veto events in control regions (CRs),
as described in Section 0.6.2.

Electrons are identified using the “tight” WP of a boosted decision tree
algorithm [69] that uses inputs based on the spatial distribution of the
shower, track–cluster matching criteria, and consistency between the cluster
energy and the track momentum. This WP corresponds to a signal efficiency of
80%, with a mistagging probability of about 1.0 (1.8)% for hadrons in the
barrel (endcaps). The relative energy resolution ranges 0.8–5.2% for electrons
with between 10 and 300; 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 [69, 70]. Only
electrons with $\pt>30(36)\GeV$ and $\abs{\eta}<2.4$ are considered in this
analysis of the 2016 (2017 and 2018) samples. The stricter requirement on the
electron in 2017 and 2018 samples is because of increased pileup in those
years, which necessitated a higher single-electron trigger threshold.

Muon reconstruction uses a global fit combining information from the tracker
and muon spectrometers. Muon candidates are required to pass the “medium” WP
of the algorithm that uses criteria on the geometrical matching between the
tracks in the tracker and the muon spectrometers, and on the quality of the
global fit. This WP corresponds to a signal efficiency of more than 98%, with
a misidentification probability of about 0.15 (0.40)% for pions (kaons) [71].
Muons with between 2 and 100, matched to the tracks measured in the silicon
tracker, results in a resolution of 1% in the barrel and 3% in the endcaps.
The muons are measured with a resolution better than 7% in the barrel with a
of up to 1 [71].

The present search considers only muons with $\pt>28\GeV$ and
$\abs{\eta}<2.4$. The requirement on the muon is determined by the single-muon
trigger threshold.

Isolation criteria are imposed on the lepton (electron and muon) candidates to
reject leptons originating from hadronic decays. The isolation variable used
for this purpose is defined as the scalar sum of reconstructed charged and
neutral particles, excluding the lepton candidate, within a cone of radius
$\Delta R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ = 0.3 (0.4)
around the electron (muon) candidate track, divided by the of the lepton
candidate, where $\phi$ is the azimuthal angle in radians. Charged particles
not originating from the primary vertex are excluded from this sum, and a
correction is applied to account for the neutral components originating from
pileup, as described in Ref. [70]. This relative isolation is required to be
less than 15 (20)% for electrons (muons).

Hadronic tau lepton candidates are reconstructed from one charged hadron and
up to two neutral pions, or three charged hadrons and up to one neutral pion,
consistent with originating from the decay of a tau lepton, using the hadrons-
plus-strips algorithm [72]. To distinguish between jets originating from
quarks or gluons and genuine hadronic tau lepton decays, the discriminant of a
deep neural network algorithm called DeepTau [73] is used. The candidates are
selected with the “tight” WP of the above discriminant, which has an
efficiency of ${\approx}$60% and a misidentification probability of
${\approx}$0.5%. The “loose” (“very loose”) WP, which has an efficiency of
${\approx}$80 (${\approx}$90)% and a misidentification probability of
${\approx}$1.5 (${\approx}$3.5)% is used for estimating the background from
misidentified candidates in the $\tauh\tauh$ ($\Pell\tauh$) category. In this
analysis, only with $\pt>30$ (40)and $\abs{\eta}<2.3$ (2.1) are used for the
$\Pell\tauh$ ($\tauh\tauh$) category. The stricter requirement on the and
$\abs\eta$ of the candidate in the $\tauh\tauh$ category is because of a
higher double-trigger threshold.

The missing transverse momentum vector, , is computed as the negative vector
sum of all the PF objects in an event, and its magnitude is denoted as [74].
The is modified to account for the energy calibration of all the PF candidates
in an event, clustered into jets or not.

##  0.5 Event selection

The sources of in the signal events are the neutrinos and the weakly
interacting neutralinos, whose kinematic properties are correlated with those
of the visible objects (in particular the and $\PGt_{\Pell}$ candidates). In
contrast, in the SM background processes is primarily due to neutrinos. This
difference can be exploited by first constructing the transverse mass ,
defined as follows:

$\displaystyle\mT^{2}(\vec{\pt}^{\text{vis}},\vec{\pt}^{\text{inv}})$
$\displaystyle=m^{2}_{\text{vis}}+m^{2}_{\text{inv}}+2(\et^{\text{vis}}\et^{\text{inv}}-\vec{\pt}^{\text{vis}}\cdot\vec{\pt}^{\text{inv}}),$
(2)

where $\et^{2}=m^{2}+\pt^{2}$ for either visible or invisible particles. Here
the masses of the visible (vis) and invisible (inv) particles are denoted by
$m_{\text{vis}}$ and $m_{\text{inv}}$, respectively. The value of has a
maximum at the mass of the parent of the visible and the invisible particles
when there is only one source of missing momentum in the system. To account
for pair-produced particles where both have visible and invisible decay
products, the “stransverse mass ()” [75, 76] is defined as:

$\displaystyle\mTii^{2}(\text{vis1},\text{vis2},\ptmiss)$
$\displaystyle=\min_{\vec{\pt}^{\text{\text{inv1}}}+\vec{\pt}^{\text{\text{inv2}}}=\ptvecmiss}[\max\\{\mT^{2}(\vec{\pt}^{\text{\text{vis1}}},\vec{\pt}^{\text{\text{inv1}}}),\mT^{2}(\vec{\pt}^{\text{\text{vis2}}},\vec{\pt}^{\text{\text{inv2}}})\\}].$
(3)

Since the momenta of the individual invisible particles in Eq. (3) are
unknown, is divided into two components ($\vec{\pt}^{\text{\text{inv1}}}$ and
$\vec{\pt}^{\text{\text{inv2}}}$) in such a way that the value of is
minimized. If is computed using the two $\tauh$ candidates (or the
$\PGt_{\Pell}$ and $\tauh$ candidates for the $\Pell\tauh$ category) as the
visible objects, “vis1” and “vis2”, then its upper limit in the signal will be
at the chargino mass. This is different from the SM background processes. For
example in events, the upper limit is at the boson mass. In searches where the
masses of the invisible particles are unknown, the calculation of requires an
assumption on their masses. For this analysis, it was chosen to consider them
as massless [77].

The signal and background processes can be further separated by utilizing the
total visible momentum of the system. This is characterized using the quantity
for the $\tauh\tauh$ category, defined as the scalar sum of the of all jets
and the candidates in the event. Jets lying within a cone of $\Delta R=0.3$
around either of the two selected $\tauh$ candidates are excluded from this
sum to avoid double counting. Since is a measure of the visible transverse
momentum of the system, it is sensitive to the mass of the top squark. For the
$\Pell\tauh$ category, we construct an analogous quantity $S_{\mathrm{T}}$,
which includes the additional contribution from the lepton .

Events in the $\tauh\tauh$ category are selected using $\tauh\tauh$ triggers
where both candidates are required to have $\abs{\eta}<2.1$, and $\pt>35$ or
40depending on the trigger logic. The trigger has an efficiency of
${\approx}$95% for candidates that pass the offline selection. For the
$\Pe\tauh$ ($\PGm\tauh$) category, single-electron (single-muon) trigger is
used, with the trigger efficiency of ${\sim}90$ (${\sim}95$)% for electron
(muon) candidates that pass the offline selection. The single-electron and
single-muon triggers have thresholds of 27 (34)and 24 (27)respectively, in
2016 (2017 and 2018). The triggers described above are emulated using
simulation, and the efficiencies measured therein are corrected to match those
measured in the data.

For the offline selection, events are required to have $\ptmiss>50$,
$\HT>100$(for the $\tauh\tauh$ category only), $S_{\mathrm{T}}>100$(for the
$\Pell\tauh$ category only), and at least one -tagged jet with
$\abs{\eta}<2.4$ and $\pt>25$ (20)for the $\Pell\tauh$ ($\tauh\tauh$)
category. The $\Pe\tauh$ ($\PGm\tauh$) categories require exactly one electron
(muon) and exactly one of opposite-sign charge, while the $\tauh\tauh$
category requires two of opposite-sign charges. Additionally, events in the
$\tauh\tauh$ category having $40<m(\tauh\tauh)<90\GeV$ are vetoed in order to
suppress the contribution from DY+jets events. Here $m(\tauh\tauh)$ is the
invariant mass of the two candidate system. Events in the $\Pell\tauh$
category are vetoed if they have any extra , , or to avoid any overlap between
the $\Pe\tauh$, $\PGm\tauh$, and $\tauh\tauh$ categories. This veto also helps
to reduce the contribution from rare SM background processes like VV and
$\ttbar$V. The requirements on and the number of -tagged jets ($n_{\PQb}$)
help to reduce the contributions from DY+jets and SM events comprised uniquely
of jets produced through the strong interaction, referred to as multijet
events.

Distributions of the variables , , and (or $S_{\mathrm{T}}$) after this
selection are shown in Figs. 3–5 for data and the predicted background, along
with representative signal distributions. The SM backgrounds are estimated
using the methods described in Section 0.6.

Figure 3:  Distributions of the search variables , , and after event selection
described in Sec. 0.5 for data and predicted backgrounds, corresponding to the
$\tauh\tauh$ category. The histograms for the background processes are
stacked, and the signal distributions expected for a few representative sets
of model parameter values are overlaid: $x=0.5$ and [$m_{\PSQtDo}$,
$m_{\PSGczDo}$] = [300, 100], [500, 350], [800, 300], and [1000, 1]. The lower
panel indicates the ratio of the observed number of events to the total
predicted number of background events. The shaded bands indicate the
statistical and systematic uncertainties in the predicted backgrounds, added
in quadrature. The last bin includes the overflow.

Figure 4:  Distributions of the search variables , , and $S_{\mathrm{T}}$
after event selection described in Sec. 0.5 for data and predicted
backgrounds, corresponding to the $\Pe\tauh$ category. The histograms for the
background processes are stacked, and the signal distributions expected for a
few representative sets of model parameter values are overlaid: $x=0.5$ and
[$m_{\PSQtDo}$, $m_{\PSGczDo}$] = [300, 100], [500, 350], [800, 300], and
[1000, 1]. The lower panel indicates the ratio of the observed number of
events to the total predicted number of background events. The shaded bands
indicate the statistical and systematic uncertainties in the predicted
backgrounds, added in quadrature. The last bin includes the overflow.

Figure 5:  Distributions of the search variables , , and $S_{\mathrm{T}}$
after event selection described in Sec. 0.5 for data and predicted
backgrounds, corresponding to the $\PGm\tauh$ category. The histograms for the
background processes are stacked, and the signal distributions expected for a
few representative sets of model parameter values are overlaid: $x=0.5$ and
[$m_{\PSQtDo}$, $m_{\PSGczDo}$] = [300, 100], [500, 350], [800, 300], and
[1000, 1]. The lower panel indicates the ratio of the observed number of
events to the total predicted number of background events. The shaded bands
indicate the statistical and systematic uncertainties in the predicted
backgrounds, added in quadrature. The last bin includes the overflow.

Signal events with different top squark and LSP masses have decay products
with different kinematics and populate different regions of the phase space.
For example, regions with low , , and (or $S_{\mathrm{T}}$) are sensitive to
signals with low top squark masses. On the other hand, events with high , ,
and (or $S_{\mathrm{T}}$) are sensitive to models with high top squark and low
LSP masses. To obtain the highest sensitivity over the entire phase space, the
selected events are categorized in 15 bins as a function of the measured , ,
and (or $S_{\mathrm{T}}$), as illustrated in Fig. 6.

Figure 6: The 15 signal regions defined in bins of , , and . The bin
boundaries for $S_{\mathrm{T}}$ are the same as those for .

##  0.6 Background estimation

The most significant backgrounds contributing to the SR are single top quark
and processes in which the top quark decays to a lepton and a neutrino. With
the from the neutrino, these events have a final state that is very similar to
that of the signal model. Events from and single top quark production having
two genuine decays (one lepton and one genuine decay) account for about 47
(75–78)% of the total SM background in the $\tauh\tauh$ ($\Pell\tauh$)
category. The contribution from these processes is estimated from simulation.
The predicted yield in each SR bin is multiplied by a correction factor
derived from CRs in data and simulation.

Events with one or two jets that are misidentified as candidates arise mostly
from single top quark and processes. The contribution from multijet events is
significantly diminished because of the requirements $\ptmiss>50\GeV$ and
$n_{\PQb}\geq 1$, the latter of which also reduces the contribution from +jets
events with a misidentified candidate. The contribution from processes with
one or more jets misidentified as a candidate, estimated using data CRs, is
about 42 (18)% of the total background in the $\tauh\tauh$ ($\Pell\tauh$)
category.

The background contribution from DY+jets events via $\PZ/\PGg^{*}\to\PGt\PGt$
decays is typically small in the most sensitive bins, amounting to about 9% in
the $\tauh\tauh$ category and a few percent in the $\Pell\tauh$ category. This
background is estimated using simulation for both the categories. To account
for residual discrepancies between data and the LO DY+jets sample, correction
factors for simulated events are derived as functions of the dimuon invariant
mass and from a DY-enriched dimuon CR in data and simulation [78]. Other less
significant backgrounds, such as +jets, $\PV\PV$, $\PV\PH$, and $\ttbar\PV$
can also contribute to the SR via vector bosons decaying to leptons and the
Higgs boson decaying to a pair of tau leptons. The total contribution from
these processes, which is estimated from simulation, is below 1%.

###  0.6.1 Tau leptons from top quark production

The estimation of the background from and single top quark processes
(collectively called top quark events) with two genuine decays (or one genuine
decay and a lepton) is extrapolated from an $\Pe\PGm$ CR, based on the method
described in Refs. [36, 79]. The single top quark events contributing to this
final state are mostly from the ${\PQt}{\PW}$ process. The contribution from
these processes in the SR is obtained by multiplying the predicted yield in
each SR bin from simulation by correction factor derived from a CR enriched in
top quark events.

The CR enriched in top quark events is identified by selecting events with an
$\Pe\PGm$ pair with opposite-sign charge. These events are selected with
$\Pe\PGm$ triggers, and are required to satisfy the same requirements on ,
$S_{\mathrm{T}}$, and $n_{\PQb}$ as those of the SR. The $\Pe\PGm$ triggers
are ${\approx}$95% efficient for lepton candidates. To reduce possible DY
contamination in this CR coming from the tail of the $\Pe\PGm$ invariant mass
distribution in the process $\PZ/\Pgg^{*}\to\PGt\PGt\to\Pe\PGm$, events are
vetoed if the invariant mass of the $\Pe\PGm$ system is
$60<m_{\Pe\PGm}<120\GeV$. This selection on the dilepton invariant mass is
more useful for reducing the DY contribution in the $\PGm\PGm$ CR (discussed
later), but is also applied here to be consistent. The purity of top quark
events in the CR, , the fraction of top quark events in most of the bins is
${\gtrsim}$85% in simulation, as shown in Fig. 7, in the upper panels of each
subfigure. The small contamination from other processes is found to have no
significant effect on the results.

Residual differences between data and simulation are quantified by scale
factors (SFs). For a given SR bin ($i$) we define

$\displaystyle\text{SF}_{i}$ $\displaystyle=\frac{N^{\Pe\PGm\ \text{CR}}_{i,\
\text{data}}}{N^{\Pe\PGm\ \text{CR}}_{i,\ \text{MC}}},$ (4)

where the numerator and the denominator represent the yields in the CR in data
and simulation, respectively. The contamination from the signal process in the
CR is found to be negligible. The single top quark and backgrounds are treated
together when deriving and applying the SFs since it is difficult to find a CR
that is highly pure in single top quark events alone, and also has sufficient
event count to obtain the SFs bin by bin. The ratio of single top quark to
yields in the CR bins is very similar to that in the corresponding SR bins,
that is, the relative kinematics of the two processes are similar in the CR
and SR. The corrected and single top yield in simulation in each bin of the SR
is then obtained as:

$\displaystyle N^{\text{SR}}_{i,\ \text{corr. top}}$
$\displaystyle=N^{\text{SR}}_{i,\ \text{top}\ \text{MC}}\,\text{SF}_{i}$
$\displaystyle=\frac{N^{\Pe\PGm\ \text{CR}}_{i,\ \text{data}}\,\
N^{\text{SR}}_{i,\ \text{top}\ \text{MC}}}{N^{\Pe\PGm\ \text{CR}}_{i,\
\text{MC}}},$ (5)

where $N^{\text{SR}}_{i,\ \text{top MC}}$ is the prediction from simulated and
single top events in the SR. Only the contribution from events with two
genuine candidates (or one genuine candidate and a lepton) is corrected using
the procedure described above. The SFs in different bins, shown in Fig. 7
(middle row) for 2016, 2017, and 2018 data, are mostly found to be within
${\approx}$10% of unity. We note that bins 14 and 15 in the CR are merged and
a single SF is used for both bins in subsequent calculations to reduce the
statistical uncertainty.

To cross check the validity of this method, the same technique is applied to
an independent top-quark-enriched CR with an oppositely charged $\PGm\PGm$
pair in the final state. These events are selected with single-muon triggers
that reach ${\approx}$95% efficiency. The event selection for the $\PGm\PGm$
CR is the same as that for the $\Pe\PGm$ CR. This cross check evaluates the
effect of possible contamination from DY events, since the branching fraction
of $\PZ/\PGg^{*}\to\PGm\PGm$ is much higher than that of
$\PZ/\PGg^{*}\to\PGt\PGt\to\Pe\PGm$. It is also useful for checking any
dependence of SFs on lepton reconstruction. The differences between the SFs
calculated in the main and cross check CRs, shown in Fig. 7 (lower row) are
small (within ${\approx}$10% in most cases), and are taken as an uncertainty
in SFs. These are added in quadrature to the statistical uncertainty in SFs,
and propagated to the uncertainty in the final top quark background
prediction. The different sources of systematic uncertainties in the terms
estimated from simulation in the numerator and denominator of Eq. 5, are
included in the final prediction. These uncertainties are described in Sec.
0.7.

Figure 7:  The purities in top quarks, the scale factors SF from simulation to
data, and the $\text{SF}^{\Pe\PGm}-\text{SF}^{\PGm\PGm}$ differences in the
various bins (as defined in Fig. 6) of the top enriched CR, where the purity
is estimated from simulation. The upper left, upper right, and lower
subfigures correspond to 2016, 2017, and 2018 data, respectively. To mitigate
the effect of statistical fluctuations, bins 14 and 15 are merged to provide
the same SF in both bins for subsequent calculations.

###  0.6.2 Misidentified hadronically decaying tau lepton candidates

A major component of the total background originates from processes with a
quark or gluon jet that is misidentified as a candidate. The largest sources
of such events in the SR are semileptonic and single top quark decays. Events
with one genuine electron (muon) and a jet misidentified as a candidate
contribute to the $\Pe\tauh$ ($\PGm\tauh$) category whereas those with one or
two misidentified candidates contribute to the $\tauh\tauh$ category. These
background contributions are estimated from CRs in data and simulation, which
are obtained by requiring candidates to pass a looser WP but fail the tight
requirements. The yields observed in the CRs are extrapolated to the SR in a
way described next for the $\tauh\tauh$ and $\Pell\tauh$ categories. The
$\tauh\tauh$ category receives contributions from events with one or two
misidentified candidates, whereas the $\Pell\tauh$ category contains events
with only one misidentified candidate. Hence different extrapolation methods
are used for the two categories.

#### Estimation for the $\tauh\tauh$ category

The misidentified background in the $\tauh\tauh$ category is estimated
following the strategy described in Refs. [36, 80]. For a genuine
(misidentified) passing the loose identification requirements, we define $g$
($f$) as the probability that it also passes the tight identification
requirements. The number of $\tauh\tauh$ events where the candidate with the
highest is genuine and that with the second-highest is misidentified, is
denoted as $N_{\text{gf}}$. Other terms, $N_{\text{fg}}$, $N_{\text{gg}}$, and
$N_{\text{ff}}$ are defined similarly. The number of $\tauh\tauh$ events where
the candidate with the highest passes the tight identification criteria and
that with the second-highest fails, but passes the loose criteria, is denoted
as $N_{\text{TL}}$. Other terms, $N_{\text{LT}}$, $N_{\text{LL}}$, and
$N_{\text{TT}}$ are defined similarly. The events are required to satisfy the
selections described in Sec. 0.5. If $N$ is the total number of events, the
following set of equations can be established:

$\displaystyle
N=N_{\text{gg}}+N_{\text{fg}}+N_{\text{gf}}+N_{\text{ff}}=N_{\text{TT}}+N_{\text{LT}}+N_{\text{TL}}+N_{\text{LL}},$
(6) $\displaystyle
N_{\text{LL}}=(1-g_{1})(1-g_{2})N_{\text{gg}}+(1-f_{1})(1-g_{2})N_{\text{fg}}+(1-g_{1})(1-f_{2})N_{\text{gf}}+(1-f_{1})(1-f_{2})N_{\text{ff}},$
$\displaystyle
N_{\text{LT}}=(1-g_{1})g_{2}N_{\text{gg}}+(1-f_{1})g_{2}N_{\text{fg}}+(1-g_{1})f_{2}N_{\text{gf}}+(1-f_{1})f_{2}N_{\text{ff}},$
$\displaystyle
N_{\text{TL}}=g_{1}(1-g_{2})N_{\text{gg}}+f_{1}(1-g_{2})N_{\text{fg}}+g_{1}(1-f_{2})N_{\text{gf}}+f_{1}(1-f_{2})N_{\text{ff}},$
$\displaystyle
N_{\text{TT}}=g_{1}g_{2}N_{\text{gg}}+f_{1}g_{2}N_{\text{fg}}+g_{1}f_{2}N_{\text{gf}}+f_{1}f_{2}N_{\text{ff}},$

where the subscripts 1 and 2 on $g$ and $f$ refer to the candidates with the
highest and second-highest , respectively. The above equations can be inverted
to give the numbers of genuine and misidentified candidate events in the SR:

$N_{\text{TT}}=N^{\text{gen}}_{\text{TT}}+N^{\text{misid}}_{\text{TT}},$ (7)

where

$\displaystyle N^{\text{gen}}_{\text{TT}}$
$\displaystyle=g_{1}g_{2}N_{\text{gg}},$ $\displaystyle
N^{\text{misid}}_{\text{TT}}$
$\displaystyle=f_{1}g_{2}N_{\text{fg}}+g_{1}f_{2}N_{\text{gf}}+f_{1}f_{2}N_{\text{ff}}.$

Here $N^{\text{gen}}_{\text{TT}}$ represents the number of events in the SR
with two genuine candidates in the final state and
$N^{\text{misid}}_{\text{TT}}$ stands for the number of events in the SR with
one or two misidentified candidates.

The probability $g$ is evaluated using simulation for different decay modes of
the reconstructed candidate, as a function of its . It is observed to be about
80% with a mild dependence on the of the for the decay modes containing either
one charged hadron and up to two neutral pions, or three charged hadrons and
no neutral pions. For the decay mode with three charged hadrons and one
neutral pion, $g$ is found to vary between 50 and 70% depending on the of the
candidate.

The misidentification rate $f$ is estimated from data using a multijet-
enriched CR. This CR is defined by requiring a same-sign pair (satisfying the
selection criteria used in the SR), and by requiring $\ptmiss<50\GeV$. There
can be small correlations between the probabilities of the two candidates to
pass the tight criteria. This causes the value of $f$ to differ by a few
percent depending on which of the two candidates is required to pass the tight
criteria. This difference is included as an uncertainty in $f$. The
misidentification rate is measured as a function of its for different decay
modes of the reconstructed candidate; it varies between 25 and 45%. In
simulation studies [80] we find that the misidentification rate also depends
on the flavor of the parton corresponding to the jet that is misidentified as
a candidate. Since the jet flavor cannot be reliably determined in data, an
additional 30% uncertainty in $f$ is included [81]. This uncertainty is
evaluated as the relative difference between the average and the maximum of
the misidentification rates corresponding to the different jet flavors being
up, down, strange, charm, and bottom quarks, and gluons, estimated using
simulated +jets events.

#### Estimation for the $\Pell\tauh$ category

The misidentified background in the $\Pell\tauh$ category is estimated by
selecting a sideband region (SbR) where all the SR selections are applied,
except that the candidate is required to pass the very loose (VL) WP but fail
the tight (T) identification criteria. This identification requirement is
indicated as “VL & !T” in the following discussion. The yields from this SbR
are extrapolated to obtain the contribution from misidentified candidates to
the $\Pell\tauh$ SR. The extrapolation factor is determined in a CR [78]
enriched in +jets events containing a misidentified candidate. This CR is
obtained from data by requiring exactly one , exactly one , $\ptmiss>50\GeV$
and $60<\mT<130\GeV$ where is the transverse mass computed using and the
transverse momentum of the . Events with any -tagged jet passing the loose WP
are vetoed to remove any overlap between this CR and both the SR and SbR. The
fraction of +jets events in this CR is calculated to be ${\approx}83\%$ using
simulation. The remaining contribution from non-+jets events is estimated from
simulation and subtracted from the data. The ratio, $R$, of the number of
misidentified events in SR to that in SbR is defined in the +jets CR as:

$R=\frac{N_{\text{data}}^{\text{CR}}(\tauh^{\text{T}})-N_{\text{non-{\PW}+jets
MC}}^{\text{CR}}(\tauh^{\text{T}})}{N_{\text{data}}^{\text{CR}}(\tauh^{\text{VL
\& !T}})-N_{\text{non-{\PW}+jets MC}}^{\text{CR}}(\tauh^{\text{VL \& !T}})}.$
(8)

Here $N_{\text{data}}^{\text{CR}}$ is the number of events in the +jets CR
obtained from data and $N_{\text{MC, non-{\PW}+jets}}^{\text{CR}}$ is the
number of simulated events except +jets in the same CR. The value of $R$ is
calculated as a function of the candidate’s and $\eta$, and it varies between
15 and 30%. A variation of 50% in the non-+jets contribution is found to
change the value of $R$ by up to 10%, which is included as an uncertainty in
$R$. Similar to $f$ in Sec. 0.6.2, $R$ is also found to vary by
${\approx}30\%$ in simulation depending on the flavor of the parton
corresponding to the jet that is misidentified as a . Hence an uncertainty of
30% in $R$ is included. The contribution from misidentified candidates to each
of the $\Pell\tauh$ SR bins is then evaluated as

$N^{\text{misid, SR}}=R\,N^{\text{misid,
SbR}}=R\,[N_{\text{data}}^{\text{SbR}}-N_{\text{MC, genuine
\tauh}}^{\text{SbR}}],$ (9)

where $N_{\text{data}}^{\text{SbR}}$ is the number of events obtained in the
sideband region from data, and $N_{\text{MC, genuine \tauh}}^{\text{SbR}}$
represents the contribution to the sideband region from simulated events where
the $\tauh$ candidate is genuine. The contribution to $N^{\text{misid, SbR}}$
from events with candidates in a particular and $\eta$ range is multiplied by
$R$ measured in the same range.

##  0.7 Systematic uncertainties

There are several sources of systematic uncertainties that are propagated to
the prediction of the final signal and background yields. For the $\tauh\tauh$
category, the most significant is the uncertainty in the modeling of the
trigger (8–12%). The uncertainty due to identification and isolation (ID-iso)
requirements [72] is 6–8%. In the $\Pell\tauh$ category, the major uncertainty
arises from ID-iso requirements (3–4%), followed by the uncertainty in the SF
for top quark events (${\approx}4\%$). The other sources of uncertainty
affecting all processes include the jet energy scale (JES) and jet energy
resolution (JER), the energy scale, the effect of unclustered components in
calculating , pileup reweighting, and the tagging efficiency. The simulation
is reweighted to make its pileup vertex distribution identical to that of the
data. The uncertainty in estimating the number of pileup interactions is
estimated by varying the total inelastic cross section by $\pm$4.6% [82]. This
is propagated as an uncertainty in the pileup reweighting factor applied to
the simulation.

Since the and single top quark contribution in the SR is obtained by
multiplying the simulated yield by SFs (Eq. 5), several MC uncertainties
cancel in the ratio. However, some small residual effects arising from JER,
JES, and unclustered energies may remain after the first order cancellation.
This is because the spectrum in $\Pe\PGm$ and $\PGm\PGm$ CRs is different from
that in $\tauh\tauh$ SR where extra neutrinos exist. These small uncertainties
are also included in the estimation of the and single top quark contributions.
As mentioned earlier, the difference between the SFs obtained in the $\Pe\PGm$
and $\PGm\PGm$ CRs, added in quadrature with the statistical uncertainty, is
assumed to be the uncertainty in this method. The flavor dependence of the
misidentification rates $f$ and $R$ is accounted for by including an
uncertainty of 30% in the rates. The misidentification rate in the
$\tauh\tauh$ category has an additional uncertainty of about 4%, which arises
from small correlations between the probabilities of the two candidates to
pass the tight identification criteria.

The factorization ($\mu_{\text{F}}$) and renormalization ($\mu_{\text{R}}$)
scales used in the simulation are varied up and down by a factor of two to
account for missing higher order corrections, while avoiding the cases in
which one is doubled and the other is halved. The SysCalc package [83] is used
for this purpose. The resulting uncertainty is estimated to be less than 6%
for both signal and background processes estimated from simulation. The
uncertainty in the measured integrated luminosity amounts to 1.2, 2.3, and
2.5% in 2016, 2017, and 2018 [84, 85, 86], respectively. The uncertainty in
the boson correction applied to DY+jets events is assumed to be equal to the
deviation of the correction factor from unity. This correction is derived as a
function of the boson . A normalization uncertainty of 15% is assigned to the
production cross sections of the background processes that are evaluated
directly from simulation [87, 88, 89, 90, 91, 92].

Since the simulation of the detector for signal events is performed using
FastSim, the signal yields are corrected to account for the differences in the
, , and identification efficiencies with respect to the simulation used for
the backgrounds. The statistical uncertainty associated with this correction
is propagated to the final results as a part of the systematic uncertainties.
The FastSim package has a worse resolution than the full simulation that can
potentially result in an artificial enhancement of the signal yields.
Therefore the signal yields are corrected, and the uncertainty in the
resulting correction to the yield is estimated to be less than ${\approx}8\%$.

The region with $\ptmiss>400\GeV$ ($\mTii>110\GeV$) in the background MC
simulation was not adequately modeled in 2017 (2018). To account for this, an
additional uncertainty of 40 (38)% is applied to background MC events with
$\ptmiss>400\GeV$ ($\mTii>110\GeV$) in 2017 (2018) samples. These uncertainty
values correspond to the sizes of the discrepancies observed in those regions,
in the $\Pe\PGm$ and $\PGm\PGm$ CRs.

The uncertainties in the signal and background from all sources are presented
in Tables 0.7, 0.7 and 0.7 for the $\tauh\tauh$, $\Pe\tauh$ and $\PGm\tauh$
categories respectively. Upper and lower numbers correspond to the relative
uncertainties due to the upward and downward variations of the signal or
background yields due to the variations of the respective source within
uncertainties. These values are the weighted averages of the relative
uncertainties in the various search bins with the weights being the predicted
yields in the respective bins. The uncertainty from a given source is
considered to be correlated across the 15 search bins, whereas the different
sources are treated as uncorrelated with each other. In addition, the
statistical uncertainties are also included and are considered to be
uncorrelated across the bins.

Relative systematic uncertainties for the $\tauh\tauh$ category from various
sources in signal and background yields. These values are averages of the
relative uncertainties in the different search regions, weighted by the yields
in the respective bins. For the asymmetric uncertainties, the upper (lower)
entry is the uncertainty due to the upward (downward) variation, which can be
in the same direction as a result of taking the weighted average. In the
header row, the top squark and LSP masses in are indicated in parentheses. The
uncertainty values shown here are prior to the maximum likelihood fit
described in Sec. 0.8.  Uncertainty source $x=0.5$ $\PSQtDo(300)$
$\PSGczDo(100)$ $x=0.5$ $\PSQtDo(500)$ $\PSGczDo(350)$ $x=0.5$ $\PSQtDo(800)$
$\PSGczDo(300)$ $x=0.5$ $\PSQtDo(1000)$ $\PSGczDo(1)$ $\ttbar$ \+ single t
DY+jets Other SM Misid.  Signal cross section $\pm$6.7% $\pm$7.5% $\pm$9.5%
$\pm$11% FastSim $\ptmiss$ resolution $\pm$7.8% $\pm$6% $\pm$4.5% $\pm$2.3%
$\tauh$ FastSim/ $+$4.0% $-$3.9% $+$3.1% $-$3.0% $+$6.3% $-$6.1% $+$15.9%
$-$14.5% JER $<$0.1% $<$0.1% $-$1.1% $+$0.8% $<$0.1% $+$0.11% $+$0.23% $<$0.1%
$+$1.3% $-$1.3% $+$8.4% $-$3.6% $+$1.7% $-$3.0% 2018 uncertainty $<$0.1% $<$
0.1% $+$0.29% $-$0.29% $+$0.38% $-$0.38% Pileup $-$0.3% $+$0.3% $+$0.57%
$-$0.62% $+$0.2% $-$0.2% $<$ 0.1% $<$0.1% $+$1.7% $-$1.8% $-$1.8% $+$1.9% JES
$+$1.4% $-$0.5% $+$0.54% $-$0.12% $<$0.1% $<$0.1% $<$0.1% $<$0.1% $+$2.6%
$-$2.6% $+$8.5% $-$6.0% $+$2.4% $-$1.9% $\tauh$ ID-iso $+$6.5% $-$8.1% $+$6.4%
$-$8.1% $+$6.6% $-$8.1% $+$6.6% $-$8.2% $+$6.6% $-$8.1% $+$6.5% $-$8.1%
$+$6.8% $-$8.1% unclustered energy $+$0.47% $+$0.33% $-$0.46% $-$0.26%
$+$0.13% $<$0.1% $<$0.1% $<$0.1% $+$1.2% $-$1.2% $+$4.9% $-$4.6% $+$1.7%
$-$0.2% Background normalization $\pm 15\%$ $\pm 15\%$ $\tauh$ energy scale
$+$2.5% $-$2.7% $+$2.4% $-$3.5% $+$1.1% $-$1.3% $+$1.1% $-$1.1% $+$1.7%
$-$1.8% $+$3.6% $-$3.4% $+$1.7% $-$4.6% $\mu_{\text{R}}$ and $\mu_{\text{F}}$
scales $+$0.8% $-$0.8% $+$1.7% $-$1.8% $+$0.57% $-$0.64% $+$0.41% $-$0.46%
$+$2.1% $-$2.9% $+$4.1% $-$3.4% Luminosity $\pm$2.1% $\pm$2.1% $\pm$2.1%
$\pm$2.1% $\pm$2.1% $\pm$2.1% tagging $<$0.1% $<$0.1% $<$0.1% $<$0.1% $<$0.1%
$<$0.1% $+$0.14% $-$0.15% $+$7.7% $-$7.8% $+$7.9% $-$8.0% 2017 uncertainty
$<$0.1% $<$0.1% $<$0.1% $<$0.1% $+$1.2% $-$1.2% Trigger $+$7.9% $-$7.5%
$+$7.8% $-$7.5% $+$8.0% $-$7.7% $+$8.1% $-$7.8% $+$11.8% $-$11.2% $+$11.6%
$-$10.9% $+$11.6% $-$10.9% $\ttbar$ \+ single t SF $\pm$3.4% $\pt$ reweighting
$+$2.0% $-$2.0% $\tauh$ misid. rate (parton flavor) $+$36.5% $-$31.8% $\tauh$
misid. rate (correlations) $+$3.7% $-$3.8%

Relative systematic uncertainties for the $\Pe\tauh$ category from various
sources in signal and background yields. These values are averages of the
relative uncertainties in the different search regions, weighted by the yields
in the respective bins. For the asymmetric uncertainties, the upper (lower)
entry is the uncertainty due to the upward (downward) variation, which can be
in the same direction as a result of taking the weighted average. In the
header row, the top squark and LSP masses in are indicated in parentheses. The
uncertainty values shown here are prior to the maximum likelihood fit
described in Sec. 0.8.  Uncertainty source $x=0.5$ $\PSQtDo(300)$
$\PSGczDo(100)$ $x=0.5$ $\PSQtDo(500)$ $\PSGczDo(350)$ $x=0.5$ $\PSQtDo(800)$
$\PSGczDo(300)$ $x=0.5$ $\PSQtDo(1000)$ $\PSGczDo(1)$ $\ttbar$ Single
(DY+jets) \+ Other SM Misid.  Signal cross-section $\pm$6.9% $\pm$7.5%
$\pm$9.5% $\pm$11% FastSim resolution $\pm$0.6% $\pm$0.5% $<$0.1% $<$0.1%
FastSim/ $\pm$0.9% $\pm$0.8% $\pm$1.1% $\pm$1.6% $\Pe$ FastSim/ $\pm$1.7%
$\pm$1.4% $\pm$3.1% $\pm$3.1% JER $+$0.1% $+$0.2% $<$0.1% $+$0.1% $+$2.5%
$+$0.1% $-$0.4% $-$1.5% $-$0.1% $+$0.1% $+$0.3% $-$0.4% 2018 $\mTii$
uncertainty $<$0.1% $<$0.1% $<$0.1% $<$0.1% JES $+$0.2% $-$0.2% $+$0.1%
$+$0.1% $+$3.2% $+$0.4% $-$0.2% $-$0.3% $-$0.1 $-$0.1% $-$2.0% $-$0.4%
$\mu_{\text{R}}$ and $\mu_{\text{F}}$ scale $+$0.5% $+$1.02% $+$0.5% $+$0.3%
$+$3.2% $+$5.5% $-$0.4% $-$1.1% $-$0.5% $-$0.4% $-$4.6% $-$5.5% Id-iso $+$3.2%
$+$3.2% $+$3.2% $+$3.2% $+$3.1% $+$3.1% $+$3.1% $+$1.7% $-$3.9% $-$4.3%
$-$4.1% $-$4.1% $-$3.7% $-$3.9% $-$3.7% $-$1.4% Pileup $+$0.3% $+$1.3% $+$0.7%
$+$0.7% $+$0.2% $+$0.5% $-$0.3% $-$1.3% $-$0.7% $-$0.7% $-$0.2% $-$0.5%
unclustered energy $+$0.6% $+$0.8% $+$0.2% $<$0.1% $+$3.6% $+$0.2% $-$0.4%
$-$0.7% $-$0.2% $-$0.1% $-$1.9% $-$0.4% Background normalization $\pm$15%
Luminosity $\pm$2.1% $\pm$2.1% $\pm$2.1% $\pm$2.1% $\pm$2.1% b tagging
$\pm$0.1% $<$0.1% $\pm$0.2% $\pm$0.5% $\pm$4.9% $\pm$0.8% 2017 $\ptmiss$
uncertainty $<$0.1% $<$0.1% $<$0.1% $<$0.1% Trigger $<$0.1% $<$0.1% $<$0.1%
$<$0.1% $<$0.1% $<$0.1% $<$0.1% $<$0.1% energy scale $-$0.6% $-$0.1% $-$0.1%
$<$0.1% $<$0.1% $+$0.1% $+$1.5% $<$0.1% $-$0.7% $-$0.4% $-$0.1% $<$0.1%
$<$0.1% $-$0.1% $-$3.4% $\ttbar$ \+ single SF $\pm$3.8% $\pm$4.0% misid. rate
(parton flavor) $\pm$ 30% Non-+jets background modeling in $R$ $\pm$10%

Relative systematic uncertainties for the $\PGm\tauh$ category from various
sources in signal and background yields. These values are averages of the
relative uncertainties in the different search regions, weighted by the yields
in the respective bins. For the asymmetric uncertainties, the upper (lower)
entry is the uncertainty due to the upward (downward) variation, which can be
in the same direction as a result of taking the weighted average. In the
header row, the top squark and LSP masses in are indicated in parentheses. The
uncertainty values shown here are prior to the maximum likelihood fit
described in Sec. 0.8.  Uncertainty source $x=0.5$ $\PSQtDo(300)$
$\PSGczDo(100)$ $x=0.5$ $\PSQtDo(500)$ $\PSGczDo(350)$ $x=0.5$ $\PSQtDo(800)$
$\PSGczDo(300)$ $x=0.5$ $\PSQtDo(1000)$ $\PSGczDo(1)$ $\ttbar$ Single
(DY+jets) \+ Other SM Misid.  Signal cross-section $\pm$6.9% $\pm$7.5%
$\pm$9.5% $\pm$11% FastSim resolution $\pm$1.6% $\pm$1.6% $\pm$0.3 $\pm$0.1%
FastSim/ $\pm$0.7% $\pm$0.7% $\pm$0.9% $\pm$1.3% $\PGm$ FastSim/ $\pm$1.7%
$\pm$1.4% $\pm$2.9% $\pm$3.1% JER +0.6% +0.3% $<$0.1% +0.1% +4.2% +0.1% -0.1%
-0.5% $<$0.1% $<$0.1% -1.5% -0.4% 2018 $\mTii$ uncertainty $<$0.1% $<$0.1%
$<$0.1% $<$0.1% JES +0.1% +0.2% $<$0.1% +0.1% +4.7% +0.4% -0.3% -0.5% $<$0.1%
-0.1% -3.0% -0.4% $\mu_{\text{R}}$ and $\mu_{\text{F}}$ scales 0.5% +0.8%
+0.2% +0.2% +4.0% +4.9% -0.5% -0.8% -0.3% -0.3% -5.1% -5.1% Id-iso +3.2% +3.2%
+3.2% +3.2% +3.1% +3.1% 3.1% +1.6% -3.9% -3.8% -4.1% -4.1% -3.8% -3.9% -3.6%
-1.3% Pileup +1.1% +0.2% +0.5 +0.7% +0.7% +0.3% -1.1% -0.2% -0.5 -0.7% -0.7%
-0.3% unclustered energy $<$0.1% $<$0.1% +0.1% $<$0.1% +5.0% 0.2% $<$0.1% 0.1%
$<$0.1% -0.1% -3.2% -0.3% Background normalization $\pm$15% b tagging $<$0.1%
$\pm$0.1% $\pm$0.14% $\pm$0.4% $\pm$5.3% $\pm$0.7% Luminosity $\pm$2.1%
$\pm$2.1% $\pm$2.1% $\pm$2.1% $\pm$2.1% 2017 $\ptmiss$ uncertainty $<$0.1%
$<$0.1% $<$0.1% $<$0.1% energy scale -0.6% -0.05% -0.3% $<$0.1% +0.1% +0.1%
+2.5% +0.1% -0.1% -0.6% -0.1% $<$0.1% -0.1% -0.1% -3.8% -0.1% Trigger $<$0.1%
$<$0.1% $<$0.1% $<$0.1% $<$0.1% $<$0.1% $<$0.1% $<$0.1% $\ttbar$ \+ single SF
$\pm$3.8% $\pm$3.9% misid. rate (parton flavor) $\pm$30% Non-+jets background
modeling in $R$ $\pm$10%

##  0.8 Results

We present the observed and expected yields along with their uncertainties in
all 15 search bins in Tables 0.8, 0.8, and 0.8 for the $\tauh\tauh$,
$\Pe\tauh$, and $\PGm\tauh$ categories, respectively. Figure 8 shows the
observed data in all search bins compared with the signal and background
predictions. As expected, the dominant contributions in the sensitive signal
bins are from and misidentified backgrounds. In cases where the background
prediction of a process in a given bin is negligible, the statistical
uncertainty is modeled by a gamma distribution [93] in the likelihood function
used for the statistical interpretation, and the Poissonian upper limit at 68%
confidence level () is shown as a positive uncertainty in the Tables 0.8, 0.8
and 0.8. The number of events observed in data is found to be consistent with
the SM background prediction.

The test statistic used for the interpretation of the result is the profile
likelihood ratio
$q_{\mu}=-2\ln{(\mathcal{L}_{\mu}/\mathcal{L}_{\text{max}})}$, where
$\mathcal{L}_{\mu}$ is the maximum likelihood for a fixed signal strength
modifier $\mu$, and $\mathcal{L}_{\text{max}}$ is the global maximum of the
likelihood [93]. The systematic uncertainties discussed in Section 0.7 are
modeled by log-normal distributions [93] in the likelihood function. We set
upper limits on signal production at 95% using a modified frequentist approach
with a criterion [94, 95] that is implemented through an asymptotic
approximation of the test statistic [96]. In this calculation all the
background and signal uncertainties are incorporated as nuisance parameters
and profiled in the maximum likelihood fit [93].

Final results are obtained by simultaneously fitting all the SR bins in the
$\tauh\tauh$, $\Pe\tauh$, and $\PGm\tauh$ categories from the 2016, 2017, and
2018 data sets. The contributions from , single top quark, DY+jets, and
misidentified candidates are modeled separately in the fit, whereas the rest
of the minor SM backgrounds are treated as a single component. The uncertainty
in the integrated luminosity is treated as partially correlated between the
three data sets. The systematic uncertainties due to JES, factorization and
renormalization scales, misidentification rate measurement, and FastSim
correction are assumed to be correlated, and the rest of the uncertainties are
treated as uncorrelated among the three data sets. All sources of systematic
uncertainties that are common to the $\tauh\tauh$ and $\Pell\tauh$ categories,
are assumed to be correlated among the categories.

The observed and expected exclusion limits are presented in the plane of the
top squark and LSP masses, in Fig. 9. Top squark masses up to $1150\GeV$ are
excluded for a nearly massless LSP, and LSP masses up to $450\GeV$ are
excluded for a top squark mass of $900\GeV$. The exclusion limits are not very
sensitive to the choice of the $\PSGt_{1}$ mass parameter $x$ because of the
complementary nature of the signal diagrams, as discussed in Section 0.3.

The final limits are generally driven by the yields in the $\tauh\tauh$
category because of its higher signal-to-background ratio compared with the
$\Pell\tauh$ category. The most sensitive search bin for the higher top squark
masses (${\approx}$) is bin 15, which is the highest , and bin. The observed
$\tauh\tauh$ yield in this bin is greater than the total background
prediction, resulting in the observed limit being lower than the expected one
by approximately one standard deviation in that region of the
${m_{\PSQtDo}}$-$m_{\PSGczDo}$ plane. The total background predictions in bins
9 and 10 of the $\tauh\tauh$ category and bin 9 of the $\Pe\tauh$ category are
greater than the observed yields by about 1–2 standard deviations. These
correspond to the highest and intermediate bins. However, these bins are not
among the most sensitive ones and hence do not affect the final limits to any
appreciable degree. The limits become weaker with decreasing $\Delta
m=m_{\PSQtDo}-m_{\PSGczDo}$, corresponding to a parameter space with final-
state particles having lower momentum.

Predicted background yields along with uncertainties for the $\tauh\tauh$
category in the 15 search bins, as defined in Fig. 6. The number of events
observed in data is also shown. The first uncertainty value listed is
statistical and the second is systematic. The uncertainties smaller than 0.05
are listed as 0.0. The background yields and uncertainties shown here are
prior to the maximum likelihood fit described in Sec. 0.8.  SR $\ttbar$ \+
single t DY+jets Other SM Misid.  Total bkg. Data 1 $407^{+9+36}_{-9-36}$
$120^{+14+29}_{-14-30}$ $3.5^{+1.4+1.0}_{-1.4-1.1}$ $612^{+44+154}_{-44-155}$
$1142^{+47+164}_{-47-165}$ $1255$ 2 $568^{+11+49}_{-11-51}$
$94^{+10+24}_{-10-18}$ $10^{+3+2}_{-3-2}$ $239^{+27+90}_{-27-92}$
$911^{+31+111}_{-31-111}$ $882$ 3 $51^{+3+4}_{-3-4}$ $13^{+3+3}_{-3-2}$
$2.8^{+0.6+0.6}_{-0.6-0.8}$ $28^{+8+25}_{-8-13}$ $95^{+9+26}_{-9-14}$ $94$ 4
$48^{+3+4}_{-3-4}$ $6.8^{+2.1+2.4}_{-2.1-2.1}$ $1.4^{+0.7+0.4}_{-0.7-0.4}$
$15^{+6+17}_{-6-8}$ $71^{+7+18}_{-7-9}$ $67$ 5 $23^{+2+2}_{-2-2}$
$3.5^{+5.1+0.9}_{-1.7-0.7}$ $2.3^{+1.1+0.5}_{-1.1-0.5}$
$4.6^{+4.2+8.3}_{-4.2-2.6}$ $33^{+7+9}_{-5-4}$ $46$ 6 $116^{+5+12}_{-5-12}$
$13^{+3+3}_{-3-6}$ $1.5^{+1.2+0.4}_{-1.2-0.7}$ $194^{+21+74}_{-21-69}$
$324^{+22+75}_{-22-71}$ $277$ 7 $129^{+5+13}_{-5-14}$
$9.7^{+2.9+5.0}_{-2.9-5.2}$ $1.5^{+1.4+0.4}_{-1.4-0.4}$ $81^{+15+25}_{-15-27}$
$221^{+16+30}_{-16-31}$ $219$ 8 $7.2^{+1.2+0.8}_{-1.2-0.7}$
$0.8^{+4.1+0.2}_{-0.4-0.4}$ $0.4^{+0.1+0.1}_{-0.1-0.1}$ $14^{+4+14}_{-4-7}$
$22^{+6+14}_{-4-7}$ $17$ 9 $7.4^{+1.2+0.7}_{-1.2-0.7}$
$0.0^{+3.5+0.0}_{-0.0-0.0}$ $0.2^{+0.2+0.1}_{-0.2-0.1}$
$6.7^{+2.3+7.3}_{-2.3-3.4}$ $14^{+4+7}_{-3-3}$ $7$ 10
$1.4^{+0.6+0.1}_{-0.6-0.4}$ $0.4^{+6.0+0.1}_{-0.4-0.1}$
$1.0^{+0.8+0.5}_{-0.8-0.4}$ $4.9^{+1.3+5.3}_{-1.3-2.6}$
$7.7^{+6.2+5.4}_{-1.6-2.7}$ $2$ 11 $8.8^{+1.4+1.0}_{-1.4-1.2}$
$0.7^{+5.4+0.2}_{-0.7-0.7}$ $0.5^{+0.3+0.2}_{-0.3-0.2}$ $17^{+7+10}_{-7-6}$
$27^{+9+10}_{-7-7}$ $30$ 12 $9.8^{+1.5+1.7}_{-1.5-1.2}$
$6.4^{+2.3+4.8}_{-2.3-2.0}$ $0.7^{+0.2+0.2}_{-0.2-0.2}$ $35^{+7+22}_{-7-15}$
$52^{+7+22}_{-7-15}$ $37$ 13 $1.3^{+0.6+0.2}_{-0.5-0.4}$
$2.1^{+5.0+1.0}_{-1.3-0.8}$ $0.3^{+0.1+0.1}_{-0.1-0.1}$
$6.7^{+3.1+5.5}_{-3.1-3.2}$ $10^{+6+6}_{-3-3}$ $14$ 14
$0.5^{+0.7+0.1}_{-0.5-0.1}$ $<3.5$ $0.2^{+0.1+0.0}_{-0.1-0.0}$
$2.4^{+1.9+3.7}_{-0.6-1.3}$ $3.1^{+4.0+3.7}_{-0.8-1.3}$ $2$ 15
$1.1^{+0.6+0.2}_{-0.6-0.2}$ $<3.5$ $0.1^{+0.0+0.0}_{-0.0-0.0}$
$0.7^{+2.6+0.6}_{-0.5-0.4}$ $1.9^{+4.4+0.7}_{-0.8-0.4}$ $4$ Total
$1380^{+17+123}_{-17-126}$ $270^{+23+66}_{-19-59}$ $26^{+4+6}_{-4-6}$
$1261^{+59+461}_{-59-403}$ $2937^{+66+482}_{-65-427}$ $2953$

Predicted background yields along with uncertainties for the $\Pe\tauh$
category in the 15 search bins, as defined in Fig. 6. The number of events
observed in data is also shown. The first uncertainty value listed is
statistical and the second is systematic. The uncertainties smaller than 0.05
are listed as 0.0. The background yields and uncertainties shown here are
prior to the maximum likelihood fit described in Sec. 0.8.  SR $\ttbar$ Single
(DY+jets) Misid.  Total bkg. Data +Other SM 1 $11574^{+50+634}_{-50-651}$
$1210^{+15+62}_{-15-66}$ $793^{+34+74}_{-34-90}$ $2646^{+29+849}_{-29-848}$
$16222^{+69+1064}_{-68-1075}$ $15744$ 2 $12239^{+50+568}_{-50-630}$
$799^{+12+50}_{-12-50}$ $717^{+26+45}_{-26-55}$ $2619^{+30+846}_{-30-845}$
$16374^{+65+1021}_{-65-1057}$ $15605$ 3 $1151^{+15+57}_{-15-63}$
$90^{+4+11}_{-4-9}$ $84^{+7+8}_{-7-6}$ $277^{+10+91}_{-10-94}$
$1601^{+20+108}_{-20-114}$ $1524$ 4 $779^{+13+43}_{-13-46}$
$123^{+5+10}_{-5-9}$ $55^{+6+4}_{-6-8}$ $92^{+6+31}_{-6-32}$
$1048^{+16+54}_{-16-57}$ $1039$ 5 $381^{+8+34}_{-8-35}$ $65^{+4+9}_{-4-7}$
$30^{+5+4}_{-5-3}$ $39^{+5+18}_{-5-22}$ $514^{+11+40}_{-11-43}$ $520$ 6
$6984^{+40+335}_{-40-368}$ $774^{+12+38}_{-12-43}$ $78^{+11+35}_{-11-8}$
$1989^{+24+635}_{-24-635}$ $9825^{+49+720}_{-49-735}$ $9372$ 7
$4822^{+32+251}_{-32-285}$ $290^{+7+18}_{-7-19}$ $52^{+6+19}_{-6-6}$
$1395^{+21+447}_{-21-447}$ $6559^{+39+513}_{-38.9-530}$ $6222$ 8
$287^{+8+23}_{-8-24}$ $18^{+2+2}_{-2-2}$ $9.2^{+1.9+6.5}_{-1.9-1.1}$
$104^{+6+34}_{-6-34}$ $418^{+10+41}_{-10-42}$ $435$ 9 $251^{+7+17}_{-7-18}$
$27^{+2+2}_{-2-2}$ $3.2^{+1.3+2.8}_{-1.3-0.6}$ $62^{+4+20}_{-4-20}$
$343^{+9+26}_{-9-27}$ $303$ 10 $70^{+4+8}_{-4-9}$ $12^{+1+1}_{-1-1}$
$1.1^{+0.3+0.3}_{-0.3-0.3}$ $17^{+2.6+5.7}_{-2.6-6.1}$
$99^{+4.8+10}_{-4.8-11}$ $95$ 11 $800^{+14+41}_{-14-44}$ $87^{+4+5}_{-4-6}$
$5.9^{+2.1+1.2}_{-2.1-2.0}$ $257^{+8+82}_{-8-83}$ $1150^{+17+94}_{-17-94}$
$1131$ 12 $575^{+11+35}_{-11-43}$ $37^{+3+3}_{-3-3}$
$6.4^{+2.1+8.1}_{-2.1-0.8}$ $254^{+8+81}_{-8-82}$ $873^{+14+89}_{-14-92}$
$921$ 13 $44^{+3+6}_{-3-6}$ $5.7^{+1.1+1.0}_{-1.1-0.7}$
$6.8^{+2.8+0.9}_{-2.8-3.3}$ $40^{+3+13}_{-3-13}$ $97^{+5+14}_{-5-14}$ $114$ 14
$24^{+2+4}_{-2-4}$ $2.6^{+0.7+0.3}_{-0.7-0.3}$ $2.7^{+1.2+0.6}_{-1.2-0.9}$
$13^{+2+4.2}_{-2-4.4}$ $42^{+3+5.9}_{-3-6.1}$ $49$ 15
$5.8^{+0.9+1.8}_{-0.9-1.7}$ $1.5^{+0.6+0.2}_{-0.6-0.2}$
$0.3^{+0.1+0.1}_{-0.1-0.1}$ $9.5^{+1.6+3.4}_{-1.6-3.3}$ $17^{+2+3.9}_{-2-3.7}$
$17$ Total $39985^{+92+2006}_{-92-2176}$ $3543^{+26+211}_{-26-217}$
$1844^{+46+171}_{-46-170}$ $9811^{+56+3152}_{-56-3154}$
$55183^{+120+3745}_{-120-3841}$ $53122$

Predicted background yields along with uncertainties for the $\PGm\tauh$
category in the 15 search bins, as defined in Fig. 6. The number of events
observed in data is also shown. The first uncertainty value listed is
statistical and the second is systematic. The uncertainties smaller than 0.05
are listed as 0.0. The background yields and uncertainties shown here are
prior to the maximum likelihood fit described in Sec. 0.8.  SR $\ttbar$ Single
(DY+jets) Misid.  Total bkg. Data +Other SM 1 $20947^{+70+1147}_{-70-1178}$
$2152^{+21+109}_{-21-118}$ $2340^{+61+338}_{-61-299}$
$5391^{+41+1726}_{-41-1724}$ $30801^{+104+2102}_{-104-1212}$ $29475$ 2
$18973^{+65+876}_{-65-972}$ $1206^{+16+75}_{-16-76}$ $1359^{+37+92}_{-37-97}$
$4340^{+38+1397}_{-38-1398}$ $25861^{+85+1654}_{-85-1707}$ $25055$ 3
$1624^{+18+80}_{-18-90}$ $126^{+5+14}_{-5-12}$ $151^{+10+14}_{-10-14}$
$424^{+12+139}_{-12-144}$ $2323^{+25+162}_{-25-170}$ $2273$ 4
$1258^{+17+70}_{-17-75}$ $182^{+6+14}_{-6-13}$ $98^{+11+8}_{-11-27}$
$163^{+8+55}_{-8-56}$ $1700^{+23+91}_{-23-99}$ $1678$ 5
$579^{+10+52}_{-10-54}$ $95^{+4+13}_{-4-10}$ $45^{+6+4}_{-6-5}$
$47^{+6+24}_{-6-30}$ $764^{+14+59}_{-14-63}$ $800$ 6
$13094^{+56+633}_{-56-692}$ $1358^{+17+68}_{-17-75}$ $193^{+13+55}_{-13-28}$
$4132^{+34+1317}_{-34-1317}$ $18752^{+69+1464}_{-69-1490}$ $18412$ 7
$7754^{+42+409}_{-42-459}$ $453^{+10+30}_{-10-32}$ $85^{+8+29}_{-8-7}$
$2398^{+27+768}_{-27-768}$ $10685^{+51+871}_{-51-896}$ $10441$ 8
$444^{+10+36}_{-10-37}$ $33.4^{+3+4}_{-3-3.2}$ $17^{+3+8}_{-3-1.8}$
$172^{+7+55}_{-7-56}$ $666^{+13+66}_{-13-68}$ $638$ 9 $414^{+10+29}_{-10-31}$
$44.5^{+3.0+3.5}_{-3.0-3.5}$ $7.0^{+3.0+1.5}_{-3.0-2.4}$ $88^{+6+29}_{-6-29}$
$554^{+12+41}_{-12-43}$ $565$ 10 $107^{+5+13}_{-5-13}$ $16^{+2+2}_{-2-2}$
$1.9^{+1.0+2.9}_{-1.0-0.5}$ $24^{+3+8}_{-3-9}$ $149^{+6+16}_{-6-16}$ $132$ 11
$1332^{+18+67}_{-18-78}$ $153^{+6+9}_{-6-10}$ $12^{+4+6}_{-4-1}$
$435^{+11.1+139}_{-11-140}$ $1931^{+22+155}_{-22-161}$ $2027$ 12
$905^{+15+56}_{-15-62}$ $59^{+4+4}_{-4-5}$ $29^{+5+8}_{-5-3}$
$391^{+10+124}_{-10-126}$ $1383^{+19+137}_{-19-140}$ $1333$ 13
$70^{+4+9}_{-4-9}$ $6.7^{+2.0+0.6}_{-2.0-3.0}$ $5.9^{+1.1+0.6}_{-1.1-0.6}$
$46^{+4+15}_{-4-15}$ $128^{+6+17}_{-6-18}$ $111$ 14 $39^{+3+6}_{-3-6}$
$3.1^{+0.9+0.2}_{-0.9-0.2}$ $2.3^{+0.7+0.2}_{-0.7-0.3}$ $25^{+3+8}_{-3-8}$
$70^{+4+10}_{-4-10}$ $69$ 15 $8.1^{+1.2+2.5}_{-1.2-2.5}$
$2.7^{+0.8+0.4}_{-0.8-0.3}$ $0.8^{+0.2+0.2}_{-0.2-0.2}$
$8.3^{+1.5+2.6}_{-1.5-2.6}$ $20^{+2+4}_{-2-4}$ $18$ Total
$67548^{+125+3395}_{-125-3676}$ $5890^{+35+350}_{-35-360}$
$4348^{+75+510}_{-75-449}$ $18083^{+75+5794}_{-75-5798}$
$95870^{+167+6743}_{-167-6889}$ $93072$

Figure 8: Event yields in the 15 search bins as defined in Fig. 6, for the
$\tauh\tauh$ (upper), $\Pe\tauh$ (lower left), and $\PGm\tauh$ (lower right)
categories . The yields for the background processes are stacked, and those
expected for a few representative sets of model parameter values are overlaid:
$x=0.5$ and [$m_{\PSQtDo}$, $m_{\PSGczDo}$] = [300, 100], [500, 350], [800,
300], and [1000, 1] . The and bin definitions are shown in . The lower panel
indicates the ratio of the observed number of events to the total predicted
number of background events in each bin. The shaded bands indicate the
statistical and systematic uncertainties in the background, added in
quadrature. The predicted yields and uncertainties shown here are prior to the
maximum likelihood fit described in Sec. 0.8.

Figure 9:  Exclusion limits at 95% for the pair production of top squarks
decaying to $\PGt_{\Pell}\tauh$ or $\tauh\tauh$ final states, displayed in the
${m_{\PSQtDo}}$-$m_{\PSGczDo}$ plane for $x=0.25$ (upper left), 0.5 (upper
right) and 0.75 (lower), as described in Eq. (1). Branching fractions are
denoted by $\mathit{B}$. The color axis represents the observed upper limit in
the cross section, while the black (red) lines represent the observed
(expected) upper mass limits. The signal cross sections are evaluated using
NNLO$+$NLL calculations. The solid lines represent the central values. The
dashed red lines indicate the region containing 68% of the distribution of
limits expected under the background-only hypothesis. The dashed black lines
show the change in the observed limit due to variation of the signal cross
sections within their theoretical uncertainties.

##  0.9 Summary

Top squark pair production in final states with two tau leptons has been
explored in data collected by the CMS detector during 2016, 2017, and 2018,
corresponding to an integrated luminosity of 138. This search improves upon
the previous publication [36] by analyzing the entirety of the Run 2 data,
adding the $\Pe\tauh$ and $\PGm\tauh$ final states, and utilizing improved
algorithms for identifying hadronically decaying tau leptons and quark jets.
The dominant standard model backgrounds originate from top quark pair and
single top quark production and processes where jets were misidentified as
decays. Control regions in data are used to estimate these backgrounds,
whereas other backgrounds are estimated using simulation. The simulated
objects (leptons, jets, etc.) are corrected using scale factors to account for
differences between their performance in simulation and collision data. No
significant excess is observed, and exclusion limits on the top squark and
lightest neutralino masses are set at 95% confidence level within the
framework of simplified models where the top squark decays via a chargino to
final states including tau leptons. A branching fraction of 50% is assumed for
each of the two considered decay modes of the chargino,
$\PSGcpDo\to{\HepSusyParticle{\PGt}{1}{+}}\Xspace\PGnGt$ and
$\PSGcpDo\to\PGtp\PSGnGt$. These decay modes are motivated by high-and
higgsino-like scenarios where decays to tau leptons are enhanced. In such
models, top squark masses are excluded up to about 1150for a lightest
supersymmetric particle (LSP) of mass 1, while LSP masses up to 450are
excluded for a top squark mass of 900. These are the most stringent exclusion
limits to date for the signal models considered in this study.

###### 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 c enters and personnel of the Worldwide LHC Computing Grid and other
c enters 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: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI
(Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC
(Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR
(Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and
NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie program and the European Research Council and Horizon 2020 Grant,
contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST
Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288
(Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s
Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under
project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New
National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K
124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460,
K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council
of Science and Industrial Research, India; 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 B05F650021
(Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro
Corporation; the Welch Foundation, contract C-1845; and the Weston Havens
Foundation (USA).

## References

  * [1] P. Ramond, “Dual theory for free fermions”,  Phys. Rev. D 3 (1971) 2415, 10.1103/PhysRevD.3.2415.
  * [2] Yu. A. Golfand and E. P. Likhtman, “Extension of the algebra of Poincaré group generators and violation of p invariance”,  JETP Lett. 13 (1971) 323, 10.1142/9789814542340_0001.
  * [3] A. Neveu and J. H. Schwarz, “Factorizable dual model of pions”,  Nucl. Phys. B 31 (1971) 86, 10.1016/0550-3213(71)90448-2.
  * [4] J. Wess and B. Zumino, “A Lagrangian model invariant under supergauge transformations”,  Phys. Lett. B 49 (1974) 52, 10.1016/0370-2693(74)90578-4.
  * [5] P. Fayet, “Supergauge invariant extension of the Higgs mechanism and a model for the electron and its neutrino”,  Nucl. Phys. B 90 (1975) 104, 10.1016/0550-3213(75)90636-7.
  * [6] G. ’t Hooft, “Naturalness, chiral symmetry, and spontaneous chiral symmetry breaking”,  NATO Sci. Ser. B 59 (1980) 135, 10.1007/978-1-4684-7571-5_9.
  * [7] R. K. Kaul and P. Majumdar, “Cancellation of quadratically divergent mass corrections in globally supersymmetric spontaneously broken gauge theories”,  Nucl. Phys. B 199 (1982) 36, 10.1016/0550-3213(82)90565-X.
  * [8] H. P. Nilles, “Supersymmetry, supergravity and particle physics”,  Phys. Rept. 110 (1984) 1, 10.1016/0370-1573(84)90008-5.
  * [9] S. P. Martin, “A supersymmetry primer”, 1997. arXiv:hep-ph/9709356.
  * [10] G. R. Farrar and P. Fayet, “Phenomenology of the production, decay, and detection of new hadronic states associated with supersymmetry”,  Phys. Lett. B 76 (1978) 575, 10.1016/0370-2693(78)90858-4.
  * [11] E. Witten, “Dynamical breaking of supersymmetry”,  Nucl. Phys. B 188 (1981) 513, 10.1016/0550-3213(81)90006-7.
  * [12] S. Dimopoulos and H. Georgi, “Softly broken supersymmetry and su(5)”,  Nucl. Phys. B 193 (1981) 150, 10.1016/0550-3213(81)90522-8.
  * [13] N. Sakai, “Naturalness in supersymmetric guts”,  Z. Phys. C 11 (1981) 153, 10.1007/BF01573998.
  * [14] L. J. Hall, D. Pinner, and J. T. Ruderman, “A natural SUSY Higgs near 126 GeV”,  JHEP 04 (2012) 131, 10.1007/JHEP04(2012)131, arXiv:1112.2703.
  * [15] A. Arbey et al., “Implications of a 125 GeV Higgs for supersymmetric models”,  Phys. Lett. B 708 (2012) 162, 10.1016/j.physletb.2012.01.053, arXiv:1112.3028.
  * [16] H. Baer et al., “Collider phenomenology for supersymmetry with large tan$\beta$”,  Phys. Rev. Lett. 79 (1997) 986, 10.1103/PhysRevLett.79.986, arXiv:hep-ph/9704457. [Erratum: 10.1103/PhysRevLett.80.642].
  * [17] M. Guchait and D. P. Roy, “Using $\tau$ polarization as a distinctive SUGRA signature at LHC”,  Phys. Lett. B 541 (2002) 356, 10.1016/S0370-2693(02)02269-4, arXiv:hep-ph/0205015.
  * [18] Particle Data Group, “Review of particle physics”,  PTEP 2022 (2022) 083C01, 10.1093/ptep/ptac097.
  * [19] J. Alwall, P. Schuster, and N. Toro, “Simplified models for a first characterization of new physics at the LHC”,  Phys. Rev. D 79 (2009) 075020, 10.1103/PhysRevD.79.075020, arXiv:0810.3921.
  * [20] LHC New Physics Working Group, “Simplified models for LHC new physics searches”,  J. Phys. G 39 (2012) 105005, 10.1088/0954-3899/39/10/105005, arXiv:1105.2838.
  * [21] CMS Collaboration, “Search for top squark pair production in pp collisions at $\sqrt{s}=13$ TeV using single lepton events”,  JHEP 10 (2017) 019, 10.1007/JHEP10(2017)019, arXiv:1706.04402.
  * [22] CMS Collaboration, “Search for top squarks and dark matter particles in opposite-charge dilepton final states at $\sqrt{s}=$ 13 TeV”,  Phys. Rev. D 97 (2018) 032009, 10.1103/PhysRevD.97.032009, arXiv:1711.00752.
  * [23] CMS Collaboration, “Search for top-squark pair production in the single-lepton final state in pp collisions at $\sqrt{s}$ = 8 TeV”,  Eur. Phys. J. C 73 (2013) 2677, 10.1140/epjc/s10052-013-2677-2, arXiv:1308.1586.
  * [24] CMS Collaboration, “Search for direct pair production of scalar top quarks in the single- and dilepton channels in proton-proton collisions at $\sqrt{s}=8$ TeV”,  JHEP 07 (2016) 027, 10.1007/JHEP07(2016)027, arXiv:1602.03169. [Erratum: 10.1007/JHEP09(2016)056].
  * [25] CMS Collaboration, “Search for top squark pair production in compressed-mass-spectrum scenarios in proton-proton collisions at $\sqrt{s}$ = 8 TeV using the $\alpha_{t}$ variable”,  Phys. Lett. B 767 (2017) 403, 10.1016/j.physletb.2017.02.007, arXiv:1605.08993.
  * [26] CMS Collaboration, “Searches for pair production of third-generation squarks in $\sqrt{s}=13$ TeV pp collisions”,  Eur. Phys. J. C 77 (2017) 327, 10.1140/epjc/s10052-017-4853-2, arXiv:1612.03877.
  * [27] CMS Collaboration, “Search for direct production of supersymmetric partners of the top quark in the all-jets final state in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JHEP 10 (2017) 005, 10.1007/JHEP10(2017)005, arXiv:1707.03316.
  * [28] CMS Collaboration, “Search for supersymmetry in proton-proton collisions at 13 TeV using identified top quarks”,  Phys. Rev. D 97 (2018) 012007, 10.1103/PhysRevD.97.012007, arXiv:1710.11188.
  * [29] ATLAS Collaboration, “Search for direct top squark pair production in final states with two leptons in $\sqrt{s}=13$ TeV pp collisions with the atlas detector”,  Eur. Phys. J. C 77 (2017) 898, 10.1140/epjc/s10052-017-5445-x, arXiv:1708.03247.
  * [30] ATLAS Collaboration, “ATLAS run 1 searches for direct pair production of third-generation squarks at the large hadron collider”,  Eur. Phys. J. C 75 (2015) 510, 10.1140/epjc/s10052-015-3726-9, arXiv:1506.08616. [Erratum: 10.1140/epjc/s10052-016-3935-x].
  * [31] ATLAS Collaboration, “Search for top squark pair production in final states with one isolated lepton, jets, and missing transverse momentum in $\sqrt{s}=$ 8 TeV pp collisions with the ATLAS detector”,  JHEP 11 (2014) 118, 10.1007/JHEP11(2014)118, arXiv:1407.0583.
  * [32] ATLAS Collaboration, “Search for direct top-squark pair production in final states with two leptons in pp collisions at $\sqrt{s}=$ 8 TeV with the ATLAS detector”,  JHEP 06 (2014) 124, 10.1007/JHEP06(2014)124, arXiv:1403.4853.
  * [33] ATLAS Collaboration, “Search for top squarks in final states with one isolated lepton, jets, and missing transverse momentum in $\sqrt{s}=13$ TeV pp collisions with the ATLAS detector”,  Phys. Rev. D 94 (2016) 052009, 10.1103/PhysRevD.94.052009, arXiv:1606.03903.
  * [34] ATLAS Collaboration, “Search for top squarks decaying to tau sleptons in pp collisions at $\sqrt{s}=13\text{ }\text{ }\text{Te}\mathrm{V}$ with the ATLAS detector”,  Phys. Rev. D 98 (2018) 032008, 10.1103/PhysRevD.98.032008, arXiv:1803.10178.
  * [35] ATLAS Collaboration, “Search for new phenomena in $pp$ collisions in final states with tau leptons, b-jets, and missing transverse momentum with the ATLAS detector”,  Phys. Rev. D 104 (2021) 112005, 10.1103/PhysRevD.104.112005, arXiv:2108.07665.
  * [36] CMS Collaboration, “Search for top squark pair production in a final state with two tau leptons in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 02 (2020) 015, 10.1007/JHEP02(2020)015, arXiv:1910.12932.
  * [37] HEPData record for this analysis, 2023.  10.17182/hepdata.138986.
  * [38] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [39] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [40] CMS Collaboration, “Performance of the CMS level-1 trigger in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [41] C. Oleari, “The POWHEG-BOX”,  Nucl. Phys. B Proc. Suppl. 205-206 (2010) 36, 10.1016/j.nuclphysbps.2010.08.016, arXiv:1007.3893.
  * [42] 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.
  * [43] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [44] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [45] S. Frixione, P. Nason, and G. Ridolfi, “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.
  * [46] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: $s$\- and $t$-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: JHEP 02, 011 (2010)].
  * [47] 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.
  * [48] Y. Li and F. Petriello, “Combining QCD and electroweak corrections to dilepton production in FEWZ”,  Phys. Rev. D 86 (2012) 094034, 10.1103/PhysRevD.86.094034, arXiv:1208.5967.
  * [49] T. Sjöstrand et al., “An Introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [50] 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.
  * [51] CMS Collaboration, “Investigations of the impact of the parton shower tuning in pythia8 in the modelling of $\mathrm{t\overline{t}}$ at $\sqrt{s}=8$ and 13 TeV”, CMS Physics Analysis Summary CMS-PAS-TOP-16-021, 2016.
  * [52] CMS Collaboration, “Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [53] GEANT4 Collaboration, “ – a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [54] W. Beenakker, R. Höpker, M. Spira, and P. M. Zerwas, “Squark and gluino production at hadron colliders”,  Nucl. Phys. B 492 (1997) 51, 10.1016/S0550-3213(97)00084-9, arXiv:hep-ph/9610490.
  * [55] A. Kulesza and L. Motyka, “Threshold resummation for squark-antisquark and gluino-pair production at the LHC”,  Phys. Rev. Lett. 102 (2009) 111802, 10.1103/PhysRevLett.102.111802, arXiv:0807.2405.
  * [56] A. Kulesza and L. Motyka, “Soft gluon resummation for the production of gluino-gluino and squark-antisquark pairs at the LHC”,  Phys. Rev. D 80 (2009) 095004, 10.1103/PhysRevD.80.095004, arXiv:0905.4749.
  * [57] W. Beenakker et al., “Soft-gluon resummation for squark and gluino hadroproduction”,  JHEP 12 (2009) 041, 10.1088/1126-6708/2009/12/041, arXiv:0909.4418.
  * [58] W. Beenakker et al., “Squark and gluino hadroproduction”,  Int. J. Mod. Phys. A 26 (2011) 2637, 10.1142/S0217751X11053560, arXiv:1105.1110.
  * [59] A. Giammanco, “The fast simulation of the CMS experiment”,  J. Phys. Conf. Ser. 513 (2014) 022012, 10.1088/1742-6596/513/2/022012.
  * [60] 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.
  * [61] 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.
  * [62] 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.
  * [63] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [64] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [65] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2017.
  * [66] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [67] CMS Collaboration, “Performance of the DeepJet b tagging algorithm using 41.9/fb of data from proton-proton collisions at 13 TeV with Phase-I CMS detector”, CMS Detector Performance Note CMS-DP-2018-058, 2018.
  * [68] E. Bols et al., “Jet flavour classification using DeepJet”,  JINST 15 (2020) P12012, 10.1088/1748-0221/15/12/P12012, arXiv:2008.10519.
  * [69] 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.
  * [70] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8$ TeV”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [71] 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.
  * [72] CMS Collaboration, “Performance of reconstruction and identification of $\uptau$ leptons decaying to hadrons and v${}_{\uptau}$ in pp collisions at $\sqrt{s}=13$ TeV”,  JINST 13 (2018) P10005, 10.1088/1748-0221/13/10/p10005, arXiv:1809.02816.
  * [73] CMS Collaboration, “Identification of hadronic tau lepton decays using a deep neural network”,  JINST 17 (2022) P07023, 10.1088/1748-0221/17/07/P07023, arXiv:2201.08458.
  * [74] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=$ 13 TeV using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [75] C. G. Lester and D. J. Summers, “Measuring masses of semiinvisibly decaying particles pair produced at hadron colliders”,  Phys. Lett. B 463 (1999) 99, 10.1016/S0370-2693(99)00945-4, arXiv:hep-ph/9906349.
  * [76] A. Barr, C. Lester, and P. Stephens, “: The truth behind the glamour”,  J. Phys. G 29 (2003) 2343, 10.1088/0954-3899/29/10/304, arXiv:hep-ph/0304226.
  * [77] A. J. Barr and C. Gwenlan, “The race for supersymmetry: Using for discovery”,  Phys. Rev. D 80 (2009) 074007, 10.1103/PhysRevD.80.074007, arXiv:0907.2713.
  * [78] CMS Collaboration, “Search for direct pair production of supersymmetric partners to the $\tau$ lepton in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Eur. Phys. J. C 80 (2020) 189, 10.1140/epjc/s10052-020-7739-7, arXiv:1907.13179.
  * [79] CMS Collaboration, “Search for heavy neutrinos and third-generation leptoquarks in hadronic states of two $\tau$ leptons and two jets in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2019) 170, 10.1007/JHEP03(2019)170, arXiv:1811.00806.
  * [80] CMS Collaboration, “Search for supersymmetry in events with a $\tau$ lepton pair and missing transverse momentum in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 11 (2018) 151, 10.1007/JHEP11(2018)151, arXiv:1807.02048.
  * [81] CMS Collaboration, “Search for direct pair production of supersymmetric partners to the $\tau$ lepton in the all-hadronic final state at $\sqrt{s}=13~{}\mathrm{TeV}$”, technical report, 2021\.
  * [82] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$ TeV”,  JHEP 07 (2018) 161, 10.1007/JHEP07(2018)161, arXiv:1802.02613.
  * [83] A. Kalogeropoulos and J. Alwall, “The SysCalc code: A tool to derive theoretical systematic uncertainties”, 2018. arXiv:1801.08401.
  * [84] 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.
  * [85] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018.
  * [86] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019.
  * [87] CMS Collaboration, “Measurement of the differential Drell-Yan cross section in proton-proton collisions at $\sqrt{\mathrm{s}}$ = 13 TeV”,  JHEP 12 (2019) 059, 10.1007/JHEP12(2019)059, arXiv:1812.10529.
  * [88] CMS Collaboration, “Measurements of $\mathrm{t\overline{t}}$ differential cross sections in proton-proton collisions at $\sqrt{s}=$ 13 TeV using events containing two leptons”,  JHEP 02 (2019) 149, 10.1007/JHEP02(2019)149, arXiv:1811.06625.
  * [89] ATLAS Collaboration, “Measurement of the $\mathrm{W^{+}W^{-}}$ production cross section in pp collisions at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV with the ATLAS experiment”,  Phys. Lett. B 773 (2017) 354, 10.1016/j.physletb.2017.08.047, arXiv:1702.04519.
  * [90] CMS Collaboration, “Measurement of top quark pair production in association with a Z boson in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2020) 056, 10.1007/JHEP03(2020)056, arXiv:1907.11270.
  * [91] CMS Collaboration, “Measurements of the pp $\to$ WZ inclusive and differential production cross section and constraints on charged anomalous triple gauge couplings at $\sqrt{s}=$ 13 TeV”,  JHEP 04 (2019) 122, 10.1007/JHEP04(2019)122, arXiv:1901.03428.
  * [92] CMS Collaboration, “Measurement of the differential cross sections for the associated production of a $w$ boson and jets in proton-proton collisions at $\sqrt{s}=13$ TeV”,  Phys. Rev. D 96 (2017) 072005, 10.1103/PhysRevD.96.072005, arXiv:1707.05979.
  * [93] ATLAS and CMS Collaborations, and the 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.
  * [94] 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.
  * [95] A. L. Read, “Presentation of search results: the technique”,  J. Phys. G 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [96] 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].

##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Tumasyan10009-0000-0684-6742

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

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

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

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

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

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

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

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

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

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

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

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

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

Department of Physics, Tsinghua University, Beijing, China M.
Ahmad0000-0001-9933-995X, G. Bauer9, Z. Hu0000-0001-8209-4343, S.
Lezki0000-0002-6909-774X, K. Yi9,10

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

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

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

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

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

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

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

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

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

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

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

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

Charles University, Prague, Czech Republic M. Finger0000-0002-7828-9970, M.
Finger Jr.0000-0003-3155-2484, A. Kveton0000-0001-8197-1914

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

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

Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt S. Elgammal13, A.
Ellithi Kamel14

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy0000-0003-4681-0079, M.A. Mahmoud0000-0001-8692-5458

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

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

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

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

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

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

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

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

Georgian Technical University, Tbilisi, Georgia I. Lomidze0009-0002-3901-2765,
T. Toriashvili180000-0003-1655-6874, Z. Tsamalaidze120000-0001-5377-3558

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

RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany S.
Diekmann0009-0004-8867-0881, A. Dodonova0000-0002-5115-8487, N.
Eich0000-0001-9494-4317, D. Eliseev0000-0001-5844-8156, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, D.
Fasanella0000-0002-2926-2691, B. Fischer0000-0002-3900-3482, T.
Hebbeker0000-0002-9736-266X, K. Hoepfner0000-0002-2008-8148, F.
Ivone0000-0002-2388-5548, M.y. Lee0000-0002-4430-1695, L. Mastrolorenzo, M.
Merschmeyer0000-0003-2081-7141, A. Meyer0000-0001-9598-6623, S.
Mondal0000-0003-0153-7590, S. Mukherjee0000-0001-6341-9982, D.
Noll0000-0002-0176-2360, A. Novak0000-0002-0389-5896, F. Nowotny, A.
Pozdnyakov0000-0003-3478-9081, Y. Rath, W. Redjeb0000-0001-9794-8292, H.
Reithler0000-0003-4409-702X, A. Schmidt0000-0003-2711-8984, S.C. Schuler, A.
Sharma0000-0002-5295-1460, A. Stein0000-0003-0713-811X, F. Torres Da Silva De
Araujo190000-0002-4785-3057, L. Vigilante, S. Wiedenbeck0000-0002-4692-9304,
S. Zaleski

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany C.
Dziwok0000-0001-9806-0244, G. Flügge0000-0003-3681-9272, W. Haj
Ahmad200000-0003-1491-0446, O. Hlushchenko, T. Kress0000-0002-2702-8201, A.
Nowack0000-0002-3522-5926, O. Pooth0000-0001-6445-6160, A.
Stahl0000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A.
Zotz0000-0002-1320-1712

Deutsches Elektronen-Synchrotron, Hamburg, Germany H. Aarup
Petersen0009-0005-6482-7466, M. Aldaya Martin0000-0003-1533-0945, J.
Alimena0000-0001-6030-3191, P. Asmuss, S. Baxter0009-0008-4191-6716, M.
Bayatmakou0009-0002-9905-0667, H. Becerril Gonzalez0000-0001-5387-712X, O.
Behnke0000-0002-4238-0991, S. Bhattacharya0000-0002-3197-0048, F.
Blekman210000-0002-7366-7098, K. Borras220000-0003-1111-249X, D.
Brunner0000-0001-9518-0435, A. Campbell0000-0003-4439-5748, A.
Cardini0000-0003-1803-0999, C. Cheng, F. Colombina, S. Consuegra
Rodríguez0000-0002-1383-1837, G. Correia Silva0000-0001-6232-3591, M. De
Silva0000-0002-5804-6226, G. Eckerlin, D. Eckstein0000-0002-7366-6562, L.I.
Estevez Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E.
Gallo210000-0001-7200-5175, A. Geiser0000-0003-0355-102X, A.
Giraldi0000-0003-4423-2631, G. Greau, A. Grohsjean0000-0003-0748-8494, V.
Guglielmi0000-0003-3240-7393, M. Guthoff0000-0002-3974-589X, A.
Jafari230000-0001-7327-1870, N.Z. Jomhari0000-0001-9127-7408, B.
Kaech0000-0002-1194-2306, M. Kasemann0000-0002-0429-2448, H.
Kaveh0000-0002-3273-5859, C. Kleinwort0000-0002-9017-9504, R.
Kogler0000-0002-5336-4399, M. Komm0000-0002-7669-4294, D.
Krücker0000-0003-1610-8844, W. Lange, D. Leyva Pernia0009-0009-8755-3698, K.
Lipka240000-0002-8427-3748, W. Lohmann250000-0002-8705-0857, R.
Mankel0000-0003-2375-1563, I.-A. Melzer-Pellmann0000-0001-7707-919X, M.
Mendizabal Morentin0000-0002-6506-5177, J. Metwally, A.B.
Meyer0000-0001-8532-2356, G. Milella0000-0002-2047-951X, M.
Mormile0000-0003-0456-7250, A. Mussgiller0000-0002-8331-8166, A.
Nürnberg0000-0002-7876-3134, Y. Otarid, D. Pérez Adán0000-0003-3416-0726, E.
Ranken0000-0001-7472-5029, A. Raspereza0000-0003-2167-498X, B. Ribeiro
Lopes0000-0003-0823-447X, J. Rübenach, A. Saggio0000-0002-7385-3317, M.
Savitskyi0000-0002-9952-9267, M. Scham26,220000-0001-9494-2151, V. Scheurer,
S. Schnake220000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger210000-0001-6699-6662, M. Shchedrolosiev0000-0003-3510-2093,
R.E. Sosa Ricardo0000-0002-2240-6699, D. Stafford, N.
Tonon${}^{\textrm{\textdagger}}$0000-0003-4301-2688, M. Van De
Klundert0000-0001-8596-2812, F. Vazzoler0000-0001-8111-9318, A. Ventura
Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, D.
Walter0000-0001-8584-9705, Q. Wang0000-0003-1014-8677, Y.
Wen0000-0002-8724-9604, K. Wichmann, L. Wiens220000-0002-4423-4461, C.
Wissing0000-0002-5090-8004, S. Wuchterl0000-0001-9955-9258, Y.
Yang0009-0009-3430-0558, A. Zimermmane Castro Santos0000-0001-9302-3102

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

Karlsruher Institut fuer Technologie, Karlsruhe, Germany S.
Brommer0000-0001-8988-2035, M. Burkart, E. Butz0000-0002-2403-5801, T.
Chwalek0000-0002-8009-3723, A. Dierlamm0000-0001-7804-9902, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, J.O. Gosewisch,
A. Gottmann0000-0001-6696-349X, F. Hartmann270000-0001-8989-8387, M.
Horzela0000-0002-3190-7962, U. Husemann0000-0002-6198-8388, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, M. Link, A.
Lintuluoto0000-0002-0726-1452, S. Maier0000-0001-9828-9778, S.
Mitra0000-0002-3060-2278, Th. Müller0000-0003-4337-0098, M. Neukum, M.
Oh0000-0003-2618-9203, G. Quast0000-0002-4021-4260, K.
Rabbertz0000-0001-7040-9846, J. Rauser, I. Shvetsov0000-0002-7069-9019, H.J.
Simonis0000-0002-7467-2980, N. Trevisani0000-0002-5223-9342, R.
Ulrich0000-0002-2535-402X, J. van der Linden0000-0002-7174-781X, R.F. Von
Cube0000-0002-6237-5209, M. Wassmer0000-0002-0408-2811, S.
Wieland0000-0003-3887-5358, R. Wolf0000-0001-9456-383X, S.
Wozniewski0000-0001-8563-0412, S. Wunsch, X. Zuo0000-0002-0029-493X

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

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

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

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

MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd
University, Budapest, Hungary M. Csanád0000-0002-3154-6925, K.
Farkas0000-0003-1740-6974, M.M.A. Gadallah280000-0002-8305-6661, S.
Lökös290000-0002-4447-4836, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, A.J.
Rádl300000-0001-8810-0388, O. Surányi0000-0002-4684-495X, G.I.
Veres0000-0002-5440-4356

Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók310000-0002-4440-2701, G. Bencze, C. Hajdu0000-0002-7193-800X, D.
Horvath32,330000-0003-0091-477X, F. Sikler0000-0001-9608-3901, V.
Veszpremi0000-0001-9783-0315

Institute of Nuclear Research ATOMKI, Debrecen, Hungary N.
Beni0000-0002-3185-7889, S. Czellar, J. Karancsi310000-0003-0802-7665, J.
Molnar, Z. Szillasi, D. Teyssier0000-0002-5259-7983

Institute of Physics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari340000-0003-0498-4265, G. Zilizi0000-0002-0480-0000

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo300000-0002-9110-9663, F. Nemes300000-0002-1451-6484, T.
Novak0000-0001-6253-4356

Panjab University, Chandigarh, India J. Babbar0000-0002-4080-4156, S.
Bansal0000-0003-1992-0336, S.B. Beri, V. Bhatnagar0000-0002-8392-9610, G.
Chaudhary0000-0003-0168-3336, S. Chauhan0000-0001-6974-4129, N.
Dhingra350000-0002-7200-6204, R. Gupta, A. Kaur0000-0002-1640-9180, A.
Kaur0000-0003-3609-4777, H. Kaur0000-0002-8659-7092, M.
Kaur0000-0002-3440-2767, S. Kumar0000-0001-9212-9108, P.
Kumari0000-0002-6623-8586, M. Meena0000-0003-4536-3967, K.
Sandeep0000-0002-3220-3668, T. Sheokand, J.B. Singh360000-0001-9029-2462, A.
Singla0000-0003-2550-139X, A. K. Virdi0000-0002-0866-8932

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

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S.
Baradia0000-0001-9860-7262, S. Barman370000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, D. Bhowmik, S. Dutta0000-0001-9650-8121, S.
Dutta, B. Gomber380000-0002-4446-0258, M. Maity37, P.
Palit0000-0002-1948-029X, G. Saha0000-0002-6125-1941, B.
Sahu0000-0002-8073-5140, S. Sarkar

Indian Institute of Technology Madras, Madras, India P.K.
Behera0000-0002-1527-2266, S.C. Behera0000-0002-0798-2727, S.
Chatterjee0000-0003-0185-9872, P. Kalbhor0000-0002-5892-3743, J.R.
Komaragiri390000-0002-9344-6655, D. Kumar390000-0002-6636-5331, A.
Muhammad0000-0002-7535-7149, L. Panwar390000-0003-2461-4907, R.
Pradhan0000-0001-7000-6510, P.R. Pujahari0000-0002-0994-7212, N.R.
Saha0000-0002-7954-7898, A. Sharma0000-0002-0688-923X, A.K.
Sikdar0000-0002-5437-5217, S. Verma0000-0003-1163-6955

Bhabha Atomic Research Centre, Mumbai, India K. Naskar400000-0003-0638-4378

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

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

National Institute of Science Education and Research, An OCC of Homi Bhabha
National Institute, Bhubaneswar, Odisha, India S.
Bahinipati410000-0002-3744-5332, A.K. Das, C. Kar0000-0002-6407-6974, P.
Mal0000-0002-0870-8420, T. Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu420000-0003-4671-815X, A. Nayak420000-0002-7716-4981, P.
Saha0000-0002-7013-8094, S.K. Swain, D. Vats420009-0007-8224-4664

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

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi430000-0001-5741-3357, E. Khazaie0000-0001-9810-7743, M.
Zeinali440000-0001-8367-6257

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

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

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

INFN Sezione di Bolognaa, Università di Bolognab, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia0000-0001-8679-4443, L.
Brigliadoria, R. Campaninia,b0000-0002-2744-0597, P.
Capiluppia,b0000-0003-4485-1897, A. Castroa,b0000-0003-2527-0456, F.R.
Cavalloa0000-0002-0326-7515, M. Cuffiania,b0000-0003-2510-5039, G.M.
Dallavallea0000-0002-8614-0420, T. Diotalevia,b0000-0003-0780-8785, F.
Fabbria0000-0002-8446-9660, A. Fanfania,b0000-0003-2256-4117, P.
Giacomellia0000-0002-6368-7220, L. Giommia,b0000-0003-3539-4313, C.
Grandia0000-0001-5998-3070, L. Guiduccia,b0000-0002-6013-8293, S. Lo
Meoa,470000-0003-3249-9208, L. Lunertia,b0000-0002-8932-0283, S.
Marcellinia0000-0002-1233-8100, G. Masettia0000-0002-6377-800X, F.L.
Navarriaa,b0000-0001-7961-4889, A. Perrottaa0000-0002-7996-7139, F.
Primaveraa,b0000-0001-6253-8656, A.M. Rossia,b0000-0002-5973-1305, T.
Rovellia,b0000-0002-9746-4842, G.P. Sirolia,b0000-0002-3528-4125

INFN Sezione di Cataniaa, Università di Cataniab, Catania, Italy S.
Costaa,b,480000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, 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 G.
Barbaglia0000-0002-1738-8676, G. Bardellia,b0000-0002-4662-3305, B.
Camaiania,b0000-0002-6396-622X, A. Cassesea0000-0003-3010-4516, R.
Ceccarellia,b0000-0003-3232-9380, V. Ciullia,b0000-0003-1947-3396, C.
Civininia0000-0002-4952-3799, R. D’Alessandroa,b0000-0001-7997-0306, E.
Focardia,b0000-0002-3763-5267, G. Latinoa,b0000-0002-4098-3502, P.
Lenzia,b0000-0002-6927-8807, M. Lizzoa,b0000-0001-7297-2624, M.
Meschinia0000-0002-9161-3990, S. Paolettia0000-0003-3592-9509, 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 M.
Bozzoa,b0000-0002-1715-0457, P. Chatagnona0000-0002-4705-9582, F.
Ferroa0000-0002-7663-0805, E. Robuttia0000-0001-9038-4500, S.
Tosia,b0000-0002-7275-9193

INFN Sezione di Milano-Bicoccaa, Università di Milano-Bicoccab, Milano, Italy
A. Benagliaa0000-0003-1124-8450, G. Boldrinia0000-0001-5490-605X, F.
Brivioa,b0000-0001-9523-6451, 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, A.
Ghezzia,b0000-0002-8184-7953, P. Govonia,b0000-0002-0227-1301, L.
Guzzia,b0000-0002-3086-8260, 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, D.
Pedrinia0000-0003-2414-4175, B.S. Pinolinia, S. Ragazzia,b0000-0001-8219-2074,
N. Redaellia0000-0002-0098-2716, T. Tabarelli de Fatisa,b0000-0001-6262-4685,
D. Zuoloa,b0000-0003-3072-1020

INFN Sezione di Napolia, Università di Napoli ’Federico II’b, Napoli, Italy;
Università della Basilicatac, Potenza, Italy; Università G. Marconid, Roma,
Italy S. Buontempoa0000-0001-9526-556X, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, A. De Iorioa,b0000-0002-9258-1345, F.
Fabozzia,c0000-0001-9821-4151, A.O.M. Iorioa,b0000-0002-3798-1135, L.
Listaa,b,500000-0001-6471-5492, P. Paoluccia,270000-0002-8773-4781, B.
Rossia0000-0002-0807-8772, C. Sciaccaa,b0000-0002-8412-4072

INFN Sezione di Padovaa, Università di Padovab, Padova, Italy; Università di
Trentoc, Trento, Italy P. Azzia0000-0002-3129-828X, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, R. Carlina,b0000-0001-7915-1650, P.
Checchiaa0000-0002-8312-1531, T. Dorigoa0000-0002-1659-8727, F.
Fanzagoa0000-0003-0336-5729, U. Gasparinia,b0000-0002-7253-2669, F.
Gonellaa0000-0001-7348-5932, G. Grossoa, L. Layera,51, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, A.T.
Meneguzzoa,b0000-0002-5861-8140, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b0000-0003-3466-7500, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, H. Yarara,b, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,520000-0002-0820-0483, C. Aimèa,b0000-0003-0449-4717, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b0000-0002-1162-2505, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b0000-0001-9647-9420, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778

INFN Sezione di Perugiaa, Università di Perugiab, Perugia, Italy P.
Asenova,530000-0003-2379-9903, G.M. Bileia0000-0002-4159-9123, D.
Ciangottinia,b0000-0002-0843-4108, L. Fanòa,b0000-0002-9007-629X, M.
Magherinia,b0000-0003-4108-3925, G. Mantovania,b, V.
Mariania,b0000-0001-7108-8116, M. Menichellia0000-0002-9004-735X, F.
Moscatellia,530000-0002-7676-3106, A. Piccinellia,b0000-0003-0386-0527, M.
Presillaa,b0000-0003-2808-7315, A. Rossia,b0000-0002-2031-2955, A.
Santocchiaa,b0000-0002-9770-2249, D. Spigaa0000-0002-2991-6384, T.
Tedeschia,b0000-0002-7125-2905

INFN Sezione di Pisaa, Università di Pisab, Scuola Normale Superiore di Pisac,
Pisa, Italy; Università di Sienad, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, V.
Bertacchia,c0000-0001-9971-1176, R. Bhattacharyaa0000-0002-7575-8639, L.
Bianchinia,b0000-0002-6598-6865, T. Boccalia0000-0002-9930-9299, E.
Bossinia,b0000-0002-2303-2588, D. Bruschinia,c0000-0001-7248-2967, R.
Castaldia0000-0003-0146-845X, M.A. Cioccia,b0000-0003-0002-5462, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, S.
Donatoa0000-0001-7646-4977, A. Giassia0000-0001-9428-2296, F.
Ligabuea,c0000-0002-1549-7107, D. Matos Figueiredoa0000-0003-2514-6930, A.
Messineoa,b0000-0001-7551-5613, M. Musicha,b0000-0001-7938-5684, F.
Pallaa0000-0002-6361-438X, S. Paroliaa0000-0002-9566-2490, G. Ramirez-
Sancheza,c0000-0001-7804-5514, A. Rizzia,b0000-0002-4543-2718, G.