TOP-21-012

TOP-21-012

# Measurement of the differential production cross section as a function of
the jet mass and extraction of the top quark mass in hadronic decays of
boosted top quarks

###### Abstract

A measurement of the jet mass distribution in hadronic decays of Lorentz-
boosted top quarks is presented. The measurement is performed in the
lepton+jets channel of top quark pair production () events, where the lepton
is an electron or muon. The products of the hadronic top quark decay are
reconstructed using a single large-radius jet with transverse momentum greater
than 400. The data were collected with the CMS detector at the LHC in proton-
proton collisions and correspond to an integrated luminosity of 138. The
differential production cross section as a function of the jet mass is
unfolded to the particle level and is used to extract the top quark mass. The
jet mass scale is calibrated using the hadronic boson decay within the large-
radius jet. The uncertainties in the modelling of the final state radiation
are reduced by studying angular correlations in the jet substructure. These
developments lead to a significant increase in precision, and a top quark mass
of $173.06\pm 0.84\GeV$.

##  0.1 Introduction

The top quark is the most massive elementary particle discovered so far [1,
2]. Because of its high mass $m_{\PQt}$ and its large Yukawa coupling it plays
a crucial role in the electroweak sector of the standard model (SM) of
particle physics. Precise measurements of $m_{\PQt}$ allow for stringent tests
of the validity of the SM [3, 4, 5] and place constraints on the stability of
the electroweak vacuum [6, 7, 8].

Direct measurements of $m_{\PQt}$ using the top quark decay products have
already achieved a precision of about 0.5 [9, 10, 11, 12, 13, 14, 15]. In
these measurements, observables with high sensitivity to the value of
$m_{\PQt}$ are constructed. Measured distributions in these observables are
compared to detector level simulations to extract the value of $m_{\PQt}$ that
fits the data best. The predictions rely on a precise modelling of the parton
shower and hadronisation process, which cannot be calculated from first
principles, and are thus subject to corresponding systematic uncertainties. In
addition, uncertainties of the size 0.5–1exist in the translation of
$m_{\PQt}$ extracted from event generators to a value of $m_{\PQt}$ in a well-
defined renormalisation scheme [16, 17], as used in precise analytic
calculations in quantum field theory.

A different approach is the determination of $m_{\PQt}$ from cross section
measurements corrected for detector effects. To facilitate a direct comparison
to analytic calculations from first principles, these measurements have to be
corrected to the parton level, which represents the pair before its decay. The
corrections applied need to include effects from the top quark decay and the
hadronisation of its colour-charged decay products. The inclusive cross
section of top quark pair () production can be measured precisely and has been
used to extract a value of the top quark pole mass by a comparison to fixed-
order calculations in perturbative quantum chromodynamics (QCD). Such
measurements have been carried out by the [18, 19], ATLAS [20, 21, 22], and
CMS [23, 24, 25] Collaborations. These measurements of the total cross section
are sensitive to various sources of uncertainties, which can not be
constrained in situ during the extraction of $m_{\PQt}$, resulting in a
precision of about 2. Differential cross section measurements can also be used
for measuring $m_{\PQt}$ [26, 27, 28, 29]. A multi-differential cross section
measurement has been performed by the CMS Collaboration, achieving an
uncertainty of 0.8in the top quark pole mass [30]. The shape of the measured
distributions close to the production threshold is sensitive to the value of
$m_{\PQt}$, and a more precise result is achieved compared to the inclusive
cross section measurements.

An alternative method which combines the advantages of the two approaches is
the determination of $m_{\PQt}$ from a measurement of the jet mass
$m_{\text{jet}}$ in events with Lorentz-boosted top quarks [31, 32, 33]. At
high energies, the decay products of top quarks are Lorentz boosted and merge
into a single large-radius jet. The peak position of the distribution in
$m_{\text{jet}}$ is sensitive to $m_{\PQt}$ and allows for a precise
measurement of $m_{\PQt}$ [34]. The unfolding of the data to the level of
stable particles will allow for a comparison to analytic calculations in
perturbative QCD, once these become available. This enables a measurement of
the top quark pole mass from the shape of a distribution at the particle
level. Presently, analytic calculations for $m_{\text{jet}}$ are restricted to
top quark transverse momenta $\pt>750\GeV$ [34], a requirement which results
in too few events in data for a differential cross section measurement using
the current CERN LHC data sets. Previous measurements by the CMS Collaboration
using proton-proton ($\Pp\Pp$) collision data at $\sqrt{s}=8\TeV$ [35] and 13
[36] with a top quark $\pt>400\GeV$, have reached an uncertainty of 2.5, where
$m_{\PQt}$ has been determined using event generators. The results are
compatible with those obtained from production at lower energy scales. In this
article, we present a measurement of the differential cross section for
production as a function of the large-radius jet mass with significantly
improved statistical and systematic uncertainties. The measurement is used to
determine $m_{\PQt}$ using event generators at next-to-leading order (NLO)
precision in QCD. The approach is complementary to measurements close to
threshold production with fully resolved final state objects. This provides a
precise test of the validity of the approximations made in event generators
and the corresponding systematic uncertainties.

In the lepton+jets channel of production, the final state is obtained from one
top quark decaying to a quark and leptons,
$\PQt\to\PQb\PW\to\PQb\Pell\PGn_{\\!\ell}$, and the second decaying
hadronically, $\PQt\to\PQb\PW\to\PQb\cPq\cPaq^{\prime}$. Here, the term lepton
denotes an electron or muon. This final state combines the advantages of a
clear signature from the leptonic boson decay, with a small background from
events with jets from light-flavour quarks and gluons. The large branching
fraction for the lepton+jets channel also results in large event samples. In
addition, in case of production with high top quark , the hadronic decay
allows the full reconstruction of the top quark decay within a single large-
radius jet with $\pt>400\GeV$, provided that the decay products are produced
within the detector acceptance. The lepton serves as a means to select events,
and the mass of the large-radius jet in the opposite hemisphere of the event
is the measurable for this analysis. The lepton is not necessarily isolated,
because the large Lorentz boost can result in particles from the fragmentation
of the quark to be produced inside of the isolation cone around the lepton.
The analysis strategy follows the one from the previous measurement at 13
[36].

In this article, we analyse 13$\Pp\Pp$ collision data, recorded in the years
2016 to 2018 and corresponding to an integrated luminosity of 138. Besides the
improved statistical precision, the leading systematic uncertainties are
reduced by using a dedicated calibration of the jet mass scale (JMS) and a
detailed study of the effects from final state radiation (FSR) in large-radius
jets.

In the previous measurements of $m_{\text{jet}}$ in boosted events [35, 36],
the uncertainties in the jet energy scale (JES) have been propagated to
$m_{\text{jet}}$. For these the JES uncertainties are the leading experimental
systematic uncertainties. While the JES, and therefore the jet momentum, can
be determined precisely using the balance or the MPF (missing transverse
momentum projection fraction) methods [37, 38], these methods do not
necessarily provide the most precise calibrations for $m_{\text{jet}}$. In
this article, we calibrate the JMS by reconstructing the boson mass from two
subjets within the large-radius jet. A fit to data in the peak region of the
jet mass results in a JMS with smaller uncertainties.

The FSR is modelled by the parton showers in the event generators, which are
matched to the simulation of the hard process. The value of the strong
coupling used in the FSR shower, evaluated at the mass of the boson,
$\alpS^{\text{FSR}}(m_{\PZ}^{2})$, is an important parameter that affects the
amount of FSR. Changes in its value can cause large differences in the
substructure of large-radius jets. Observables probing the angular
distributions of the energy density within a jet, such as $N$-subjettiness
[39, 40] ratios, are very sensitive to the amount of FSR in the simulation. In
this article, we measure distributions in $N$-subjettiness ratios calculated
for large-radius jets, and use these to constrain the value of
$\alpS^{\text{FSR}}(m_{\PZ}^{2})$ used in the modelling of FSR. This leads to
smaller uncertainties in $m_{\text{jet}}$ from the FSR modelling compared to
the usual variations of the scale $\mu$ in $\alpS^{\text{FSR}}(\mu^{2})$ [41,
36, 42].

Tabulated results are provided in the HEPData record for this analysis [43].

##  0.2 The CMS detector

The central feature of the CMS detector is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. 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
central barrel and two endcap sections, reside within the solenoid volume.
Forward calorimeters extend the pseudorapidity ($\eta$) coverage provided by
the barrel and endcap detectors. Muons are detected in gas-ionisation 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, can be found in Ref. [44]. Between the 2016 and 2017 data taking runs,
the CMS pixel detector was upgraded with additional layers in the barrel and
endcap regions of the CMS detector. Details about the changes can be found in
Ref. [45].

Events of interest are selected using a two-tiered trigger system. The first
level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed latency of about 4 [46]. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimised for fast processing, and reduces the
event rate to around 1kHz before data storage [47].

##  0.3 Data and simulated samples

The measurement is performed in the lepton+jets final state of production. The
event selection is based on the presence of a single lepton which uses the
data selected by single-lepton triggers [47, 46]. Muon candidates are required
to have $\pt>50\GeV$ and $\abs{\eta}<2.4$, without any requirement on the
isolation of the muon. In the electron channel, we use a combination of
triggers. The first trigger requires electron candidates with $\abs{\eta}<2.5$
that are isolated and have a minimum of 27, 35, or 32for the years 2016, 2017,
and 2018, respectively. A second trigger selects electron candidates with
$\pt>120\GeV$, without an isolation requirement. In addition a single-photon
trigger is used for selecting electrons without a track requirement. This
trigger selects photon candidates with a minimum of 175in 2016, and 200in 2017
and 2018. The photon trigger ensures a stable selection efficiency for
electrons at high because selection criteria applied to clusters in the ECAL
are less strict than those used by the electron trigger. In the offline
analysis, muons and electrons are selected with $\abs{\eta}<2.4$ and
$\pt>55\GeV$, ensuring that selected events are in the plateau region of the
trigger efficiency. After this selection, the average efficiency of the muon
trigger is 91, 90, and 91% for 2016, 2017, and 2018, respectively. The
combination of the three electron and photon triggers provides high efficiency
over the full range in considered in this analysis, which is comparable to
that obtained using the muon triggers. For lepton $\pt<120\GeV$, the top quark
decay is less collimated and the jet does not overlap with the lepton
isolation cone. In this case, the event selection efficiency is greater than
90% for triggers with an isolation requirement. For $\pt>120\GeV$, the
nonisolated electron trigger has an average efficiency of 95%, increasing to
nearly 100% for $\pt>200\GeV$, where the high efficiency is calculated in
combination with the photon trigger. The total data set corresponds to an
integrated luminosity of 138, with 36.3 [48], 41.5 [49], and 59.7 [50]
recorded in the years 2016, 2017, and 2018, respectively.

For each of the three years of data taking, the processes relevant for this
analysis are simulated individually using a Monte Carlo (MC) simulation
technique and they are normalised to the integrated luminosity of each year.
The process is simulated at NLO using the v2 [51, 52, 53, 54, 55, 56]
generator with a top quark mass of 172.5. We adjust the total cross section to
831.8pb, obtained from a prediction at next-to-NLO (NNLO) precision in QCD,
including resummation of next-to-next-to-leading logarithmic soft gluon terms,
using the computer program 2.0 [57]. We simulate additional samples with
$m_{\PQt}=169.5$, 171.5, 173.5, and 175.5, which are used for studying the
dependence of the measured cross section on the value of $m_{\PQt}$ used in
simulation, and for the extraction of $m_{\PQt}$. The background contribution
from electroweak single production is generated at NLO using , and the
$\PW\text{+jets}$ background is generated at leading order (LO) using v2.2.2
[58, 59]. The cross section for single production in association with a boson
is adjusted to approximate NNLO calculations taken from Refs. [60, 61]. The
single top quark $s$\- and $t$-channel cross sections are adjusted to
predictions at NLO precision obtained with hathor v2.1 [62]. Events from
Drell–Yan (DY) production with additional jets are simulated at LO using and
normalised to the NLO cross section [63]. The production of two heavy gauge
bosons with additional jets, and events in which jets are produced only
through QCD interactions are simulated at LO using the event generator in
version 8.212 [64] for the simulation of 2016 data and version 8.230 for 2017
and 2018. The diboson and QCD multijet samples are referred to as “other SM”
backgrounds in the following. The NNPDF3.0 [65] parton distribution functions
(PDFs) are used for 2016 simulations and the NNPDF3.1 [66] PDFs are used for
2017 and 2018 simulations.

In all processes, the hadronisation, parton showers, and multiple parton
interactions are simulated with . In samples simulated with , the matrix
element calculation is matched to the parton showers using the FxFx [67] and
MLM [68] algorithms for NLO and LO, respectively. In the simulation of 2016
data, 8.212 is used with the underlying event (UE) tune CUETP8M2T4 [69] for
the simulation of and single top quark production in the $t$ channel. In this
tune, $\alpS^{\text{FSR}}(m_{\PZ}^{2})=0.1365$ is used for the simulation of
FSR. All other simulated samples in 2016 use the CUETP8M1 [70, 41] tune. For
the 2017 and 2018 data, 8.230 is used with the CP5 [69] tune. Here, a value of
$\alpS^{\text{FSR}}(m_{\PZ}^{2})=0.118$ is used. The detector response is
simulated with the package [71, 72].

Additional inelastic $\Pp\Pp$ collision events are simulated using and
superimposed on simulated events to model the effect of additional $\Pp\Pp$
collisions within the same or adjacent bunch crossings (pileup). We use a
total inelastic cross section of 69.2mb [73] to estimate the expected number
of $\Pp\Pp$ interactions per bunch crossing and correct the simulation to
match the corresponding distribution to that observed in data.

##  0.4 Event reconstruction

The particle-flow (PF) algorithm [74] aims to reconstruct and identify each
individual particle in an event, using an optimised combination of information
from the various elements of the CMS detector. The candidate vertex with the
largest sum of the square of the transverse momenta $\pt^{2}$ of the physics
objects is taken to be the leading primary $\Pp\Pp$ interaction vertex. The
physics objects are the jets, clustered using the anti-jet finding algorithm
[75, 76] with a distance parameter of $R=0.4$ with tracks assigned to
candidate vertices as inputs, and the associated missing transverse momentum,
taken as the negative vector sum of the of those jets. More details are given
in Section 9.4.1 of Ref. [77].

Muons are reconstructed from tracks in the inner tracker and hits in the muon
system using the PF algorithm. The muon momentum is obtained from the
curvature of the corresponding track [78]. For electron reconstruction,
clusters in the ECAL are connected to tracks in the inner tracker. The
electron energy is determined by a combination of the electron momentum at the
primary interaction vertex as determined by the tracker, the energy of the
corresponding cluster in the ECAL, and the sum of all bremsstrahlung photons
spatially compatible with originating from the electron track [79]. The energy
of photons is directly obtained from the ECAL measurement [79]. Both muons and
electrons have to pass tight quality criteria developed by the CMS
Collaboration to ensure a proper reconstruction [78, 79]. The energy of
charged hadrons is determined from a combination of their momentum measured in
the tracker and the matching ECAL and HCAL energy deposits. Finally, the
energy of neutral hadrons is obtained from the corresponding corrected ECAL
and HCAL energy [74].

Jets are reconstructed from PF candidates using the anti- [75] or the XCone
(eXclusive Cone) [80] algorithm as implemented in the software package [76].
Two sets of anti-jets are obtained using distance parameters of $R=0.4$ (AK4
jets) and 0.8 (AK8 jets). In the jet clustering procedure, charged PF
candidates are excluded if they are associated with pileup vertices. While AK4
jets are used mostly for the identification of jets in this analysis, AK8 jets
are used to study the influence of FSR on the jet substructure as described in
Section 0.8. For the XCone jets, a specialized two-step clustering procedure
[81] is used. Being an exclusive algorithm, XCone always returns a requested
number of jets. This feature of the algorithm can be leveraged to efficiently
reconstruct the boosted final state. At first, the XCone algorithm is run
finding exactly two large-radius jets with a distance parameter of $R=1.2$.
This step takes all PF candidates, after removing charged particles assigned
to a pileup vertex, as an input and aims to reconstruct the two top quark
decays of the process in separate jets. As a second step, all PF candidates
clustered into a large-radius XCone jet are input to the XCone algorithm
again, which is now required to find three XCone subjets, $N_{\text{sub}}=3$,
with a distance parameter $R_{\text{sub}}=0.4$. The second step aims to
reconstruct the three-prong decay $\PQt\to\PQb\PW\to\PQb\cPq\cPaq^{\prime}$
while minimising the effects of uncorrelated soft radiation or additional
energy deposits from pileup. The final XCone jets are then defined as the sum
of the four-momenta of their respective subjets. In this way, all particles
not clustered into the three subjets are removed from the large-radius XCone
jets, similar to the trimming algorithm [82]. The jet mass is calculated from
the sum of the four-momenta of all particles clustered into the subjets. Since
no lepton selection has been applied at this stage, the XCone reconstruction
will also reconstruct $\PQt\to\PQb\PW\to\PQb\Pell\PGn_{\\!\ell}$ with three
subjets. We have verified that the difference from the more natural choice of
two XCone subjets for the reconstruction of the leptonic decay does not
significantly affect the identification of the leptonic XCone jet and the
event reconstruction. The XCone jet with larger angular distance to the
identified single lepton is selected as the measurement jet and is labelled
“XCone jet” in the following. The XCone jet closer to the lepton is referred
to as “second XCone jet”. Here, the angular distance between two objects is
defined as
${\Delta}R=\sqrt{\smash[b]{({\Delta}\eta)^{2}+({\Delta}\phi)^{2}}}$, where
$\phi$ is the azimuthal angle in radians. The four-momenta of identified
leptons are subtracted from AK4 jets and XCone subjets if they are within
${\Delta}R<0.4$ of the respective (sub)jet.

Jet energy corrections (JECs) [38] derived for AK4 jets are applied to AK4
jets, as well as to XCone subjets in this analysis. These JECs include
corrections for contributions from pileup, as derived for AK4 jets clustered
after removing all charged particles assigned to a pileup vertex. Jet energies
in simulated events are smeared to match the jet energy resolution (JER)
observed in data. The XCone subjets are corrected with the same procedure as
in Ref. [36], where an additional XCone correction is derived because of
residual differences to AK4 jets. The correction is obtained from simulated
samples of in the all-jets channel and parametrised as a function of the XCone
subjet and $\abs{\eta}$. The XCone jet mass is calibrated as described in
Section 0.7. The JMS correction is applied to the four-momentum of the jet
such that it changes only the mass but leaves the three-momentum unaltered.

##  0.5 Particle-level phase space

The measurement of $m_{\text{jet}}$ is carried out at the particle level in
the fiducial region defined below. The particle level is defined by the set of
all stable particles, with a lifetime longer than $10^{-8}\unit{s}$ as
provided by the event simulation. We develop an unfolding procedure to correct
the data for detector and pileup effects. This procedure provides a
measurement at the particle level.

Figure 1: Distribution in $m_{\text{jet}}$ at the particle level after the
selection of the fiducial region in the lepton+jets channel of , simulated
with . The contributions from fully merged events (blue solid) and not merged
events (red dashed) are displayed, as well as the sum of the two (black
solid).

The fiducial region at the particle level is defined such that similar
requirements can be used on data at the detector level, which helps to keep
the corrections small in the unfolding step. In order to select the
lepton+jets channel of the process, exactly one prompt electron or muon with
$\pt>60\GeV$ originating from the decay of a boson must be present. Decays to
leptons contribute a small background. They are not selected and are treated
as background in this analysis. The two-step XCone clustering procedure is
performed similarly to the one at the reconstruction level, as explained in
Section 0.4, with all stable particles except for neutrinos as input. Decays
of boosted top quarks must have an XCone jet with $\pt>400\GeV$. All three
XCone subjets have to satisfy $\pt>30\GeV$ and $\abs{\eta}<2.5$. The
requirement on $\abs{\eta}$ ensures that the XCone jet is reconstructed within
the geometric acceptance of the detector. The second XCone jet has to have
$\pt>10\GeV$ after the lepton four-momentum has been subtracted. This
requirement rejects pathological cases, where the second XCone jet does not
contain the subjet from the decay. We find that 6.7% of all events are
rejected by this requirement. The XCone-jet mass $m_{\text{jet}}$ has to be
larger than the invariant mass of the sum of the second XCone jet and the
selected lepton. Since the neutrino from the leptonic decay is not
reconstructed, this requirement is always fulfilled if all decay products of
the hadronic decay are reconstructed within the XCone jet, referred to as
“fully merged events”. This criterion helps to select fully merged decays
without introducing a bias on the measurement XCone jet, which would be the
case with additional requirements on its substructure. It removes about 32.6%
of the events at the particle level, where a large fraction of the removed
events consists of not fully merged events. Figure 1 shows the distribution in
$m_{\text{jet}}$ at the particle level after the selection of the fiducial
region. The distribution has a narrow peak, with the maximum close to
$m_{\PQt}$. Contributions from the UE and FSR lead to a shift of the peak
towards higher values. In the peak region, the contribution of fully merged
top quark decays is about 87%. Contributions from events that are not fully
merged dominate the regions to the left and right of the peak. Typically, in
these events the top quark has only been partially reconstructed within the
XCone jet, or the XCone jet originates from radiation not associated with the
system. With respect to the measurement at 8 [35], which used Cambridge–Aachen
jets [83, 84] with $R=1.2$ and no grooming, the width of the distribution in
the peak region is reduced by a factor of two. This improvement is achieved by
the two-step XCone clustering procedure which acts as a grooming algorithm
[33], removing all particles in the XCone jet not clustered into the three
subjets.

##  0.6 Event selection

At the detector level, the event selection aims to include a similar phase
space as selected at the particle level. Events must contain a single muon or
single electron with $\pt>60\GeV$ and $\abs{\eta}<2.4$. Leptons with
$55<\pt<60\GeV$ and $\abs{\eta}<2.4$ are used to construct a sideband region
when unfolding the data, as described in Section 0.9. Electrons with
$\pt<120\GeV$ must pass an isolation requirement [79], where the isolation is
defined as the sum of charged hadrons and neutral particles in a cone with
radius $R=0.3$ around the electron. The isolation variable is corrected to
mitigate the contribution from pileup. Electron candidates with $\pt>120\GeV$
and muons are rejected if there is an AK4 jet within ${\Delta}R<0.4$ and
$p_{\text{T}}^{\text{rel}}<40\GeV$, where $p_{\text{T}}^{\text{rel}}$ is the
component of the lepton momentum orthogonal to the AK4-jet axis. The last
criterion has high efficiency of selecting highly boosted
$\PQt\to\PQb\PW(\to\Pell\PGn_{\\!\ell})$ decays, where the lepton would not
have passed an isolation requirement because of the angular proximity of the
jet, while rejecting QCD multijet events [85, 86].

In order to suppress non-backgrounds, at least one AK4 jet is required to be
tagged using the DeepJet algorithm [87, 88]. The candidate jets are required
to have $\pt>30\GeV$ and $\abs{\eta}<2.4$, and must pass a selection on the
DeepJet discriminator value corresponding to a misidentification rate of 0.1%
for light-flavour quark and gluon jets, and an efficiency of 68%.

Figure 2: Distributions in the reconstructed XCone jet (left) and
$m_{\text{jet}}$ (right), after the full event selection. The vertical bars on
the markers show the statistical uncertainty. The hatched regions show the
total uncertainty in the simulation, including the statistical and
experimental systematic uncertainties. The lower panels show the ratio of the
data to the simulation. The uncertainty bands include the experimental
systematic uncertainties and statistical uncertainties in the simulation. In
the ratios, the statistical (light grey) and total (dark grey) uncertainties
are shown separately.

Figure 3: Distributions in reconstructed of the -leading XCone subjet (upper
left), second XCone subjet (upper right) and third XCone subjet (lower). The
vertical bars on the markers show the statistical uncertainty. The hatched
regions show the total uncertainty in the simulation, including the
statistical and experimental systematic uncertainties. The lower panels show
the ratio of the data to the simulation. The uncertainty bands include the
experimental systematic uncertainties and statistical uncertainties in the
simulation. In the ratios, the statistical (light grey) and total (dark grey)
uncertainties are shown separately.

In addition, the magnitude of the negative vector sum of the transverse
momenta of the PF candidates in an event [89], , has to be larger than 50. The
energy scale corrections applied to AK4 jets are propagated to . This
requirement suppresses the contribution of multijet backgrounds from the
production of light-flavour quarks and gluons.

The XCone jet is required to have $\pt>400\GeV$ and all three subjets must
have $\pt>30\GeV$ and $\abs{\eta}<2.5$. The second XCone jet has to have
$\pt>10\GeV$ and the invariant mass of the system containing the second XCone
jet and the lepton must not surpass $m_{\text{jet}}$.

Figure 2 shows the XCone jet (left) and $m_{\text{jet}}$ (right) spectra at
the detector level. Here, data from all three years and both lepton flavours
are combined. For the sake of comparing the shapes of these distributions, the
simulation has been scaled down such that the number of simulated events
matches the number of events observed in the data. The distribution in shows
the characteristic falling behaviour above the 400threshold, while the
distribution in $m_{\text{jet}}$ shows a narrow peak close to $m_{\PQt}$. We
find reasonable agreement between data and simulation in the and
$m_{\text{jet}}$ distributions when we use the JECs, and the XCone and JMS
corrections described in Section 0.7. For above 900, we observe that the
simulation predicts more events than observed in data, a feature which has
been reported previously in differential cross section measurements when
comparing to NLO calculations [90, 91, 92]. Figure 3 shows the distributions
in of the XCone subjets. Because of the XCone-jet selection with
$\pt>400\GeV$, the first subjet has a most probable of about 250, and the
second subjet has a value of about 150. The remaining subjet features a
falling distribution, starting from the minimum value of 30.

##  0.7 Calibration of the jet mass scale

The experimental precision in the measurement of $m_{\text{jet}}$ in boosted
top quark decays is limited by the calibration of the jet four-momentum. In
our previous analysis [36], the uncertainty in the JES was propagated to
$m_{\text{jet}}$ and resulted in the dominant experimental systematic
uncertainty. In this article, we measure the JMS using the invariant mass of
the two XCone subjets originating from the hadronic boson decay. With this
additional measurement, the uncertainty in the JES affects the jet three-
momentum, while the uncertainty in the JMS affects $m_{\text{jet}}$. The JMS
calibration is crucial for the improvement in the overall precision of this
measurement.

Figure 4: Peak region of the reconstructed boson mass in the four regions
$\pt^{\PW}<300\GeV$ and $r_{\pt}<0.7$ (upper left), $\pt^{\PW}<300\GeV$ and
$r_{\pt}>0.7$ (upper right), $\pt^{\PW}>300\GeV$ and $r_{\pt}<0.7$ (lower
left), and $\pt^{\PW}>300\GeV$ and $r_{\pt}>0.7$ (lower right). The
background-subtracted data and the simulation are normalised to unit area. For
illustration, the simulation is also shown with the JEC and XCone correction
factors varied by one standard deviation. The lower panels show the ratios to
the nominal simulation.

For the JMS calibration, the same selection as for the measurement is applied.
The boson decay is reconstructed using two of the three XCone subjets from the
XCone jet initiated by the hadronic top quark decay. We identify the XCone
subjet originating from the fragmentation of the quark using the DeepJet
algorithm on AK4 jets. First, the AK4 jet with the largest value of the
DeepJet discriminant among those with angular distance ${\Delta}R<1.2$ to the
XCone jet is selected. In a second step, the XCone subjet with the smallest
${\Delta}R$ to the selected -tagged AK4 jet is assigned to originate from the
quark. This XCone subjet is rejected, and the measurement of the JMS is
performed using the invariant mass of the other two XCone subjets. Data from
the two lepton flavours and three different years are combined for the JMS
calibration.

The JMS in simulation is adjusted by introducing two factors, $f^{\text{JEC}}$
and $f^{\text{XCone}}$, that vary the jet energy scale in the AK4 JECs and the
additional XCone-jet corrections, respectively. The factors are constructed
such that values of 0, ${+}1$ and ${-}1$ represent the nominal correction, and
the up and down shifts by one standard deviation, respectively. With these two
factors, the squared XCone jet mass becomes

$m_{\text{jet}}^{2}=\left(\sum_{i=1}^{3}p_{i}\left(c_{\text{JEC}}(p_{\text{T},i},\eta_{i})+f^{\text{JEC}}\sigma_{\text{JEC}}(p_{\text{T},i},\eta_{i})\right)\left(c_{\text{XC}}(p_{\text{T},i},\eta_{i})+f^{\text{XCone}}\sigma_{\text{XC}}(p_{\text{T},i},\eta_{i})\right)\right)^{2},$
(1)

where $p_{i}$ are the three subjet four-momenta before the application of the
JEC and XCone corrections, $c_{\text{JEC}}(p_{\text{T},i},\eta_{i})$ and
$c_{\text{XC}}(p_{\text{T},i},\eta_{i})$ denote the JEC and XCone corrections,
respectively, and $\sigma_{\text{JEC}}(p_{\text{T},i},\eta_{i})$ and
$\sigma_{\text{XC}}(p_{\text{T},i},\eta_{i})$ are the uncertainties in these
corrections. The JES and XCone corrections and the corresponding uncertainties
depend on the and $\eta$ of the uncorrected subjet four-momentum. The
additional corrections proportional to $f^{\text{JEC}}$ and $f^{\text{XCone}}$
allow $m_{\text{jet}}$ to float, while retaining the shape and functional form
of the JEC and XCone uncertainties in and $\eta$. This JMS correction is
constructed to change only the XCone jet mass but not the three-momentum that
is calibrated with established methods. The decoupling of the JMS correction
from the three-momentum calibration allows the JMS correction to target
effects which change only the jet mass and not the three-momentum, like
splitting and merging of calorimeter clusters.

The measurement is performed in four regions that are defined in the two-
dimensional plane of the of the reconstructed boson, $\pt^{\PW}$, and the
ratio $r_{\pt}=\pt^{\text{s}_{1}}/\pt^{\PW}$, defined as the ratio of the
carried by the highest XCone subjet $\text{s}_{1}$ to $\pt^{\PW}$. These
regions are constructed to reduce correlations between $f^{\text{JEC}}$ and
$f^{\text{XCone}}$, because these factors can cancel each other in an
inclusive measurement of the JMS. We find an improvement by a factor of 1.6 in
the obtained precision of $f^{\text{JEC}}$ and $f^{\text{XCone}}$ when using
these four regions, compared to a calibration using the inclusive $m_{\PW}$
distribution. Because of the different size of $\sigma_{\text{JEC}}(\pt,\eta)$
and $\sigma_{\text{XC}}(\pt,\eta)$ in subjet and $\eta$, the effects of
$f^{\text{JEC}}$ and $f^{\text{XCone}}$ are different in the four regions
defined by $\pt^{\PW}$ and $r_{\pt}$, such that these two factors can be
determined simultaneously. Figure 4 shows the four distributions in the
reconstructed boson mass $m_{\PW}$ in the vicinity of their peaks in the four
regions, defined by $\pt^{\PW}$ larger or smaller than 300and $r_{\pt}$ larger
or smaller than 0.7. We consider only regions around the peak position with
bins populated by more than 100 events in the background-subtracted data for a
bin width of 1. This requirement leads to the following $m_{\PW}$ ranges in
the four regions: 70–105for $\pt^{\PW}>300\GeV$ and $r_{\pt}>0.7$; 75–104for
$\pt^{\PW}>300\GeV$ and $r_{\pt}<0.7$; 62–98for $\pt^{\PW}<300\GeV$ and
$r_{\pt}>0.7$; and 63–101for $\pt^{\PW}<300\GeV$ and $r_{\pt}<0.7$. These
ranges are used to exclude tails in the $m_{\PW}$ distributions, which
originate from a wrong assignment of subjets to the reconstructed boson. In
total, 138 bins are used in the JMS calibration. The distributions of
background-subtracted data and signal have been normalised to unit area and
are given in arbitrary units (a.u.), such that only shapes are considered and
the total yield does not affect the measurement. The simulation is shown for
the different variations in the jet corrections, parametrised by
$f^{\text{JEC}}$ and $f^{\text{XCone}}$. The peak in $m_{\PW}$ is shifted in
the four regions by 0.42–0.61and by 0.17–0.25for the $f^{\text{JEC}}$ and
$f^{\text{XCone}}$ variations, respectively.

In each bin $i$ of the $m_{\PW}$ distribution, a linear prediction $g_{i}$ as
a function of $f^{\text{JEC}}$ and $f^{\text{XCone}}$ is defined,

$g_{i}(f^{\text{JEC}},f^{\text{XCone}})=a_{i}+b_{i}f^{\text{JEC}}+c_{i}f^{\text{XCone}},$
(2)

with the free parameters $a_{i}$, $b_{i}$, and $c_{i}$. The free parameters
are obtained from a fit to simulation in the
$f^{\text{JEC}}$-$f^{\text{XCone}}$ plane in each bin $i$. We have verified
that a linear fit in both factors describes the dependence of $m_{\PW}$ on
$f^{\text{JEC}}$ and $f^{\text{XCone}}$ sufficiently well, with a fit quality
matching the expectation of statistical fluctuations only.

To verify that the statistical uncertainties in the simulation do not bias the
result, we have performed a test where we increased the bin size in the four
$m_{\PW}$ distributions by a factor of three, to 3. This results in 47 bins
and reduces the fluctuations in the four $m_{\PW}$ distributions. We find that
the linear parametrisations of Eq. (2) provide a good description of the
variations in $f^{\text{JEC}}$ and $f^{\text{XCone}}$. Performing the JMS
calibration with these larger bins and reduced statistical uncertainties in
$g_{i}$ gives a similar result with respect to the nominal fit with 138 bins.
The reduced information in the fit with 47 bins results in an increased
correlation between $f^{\text{XCone}}$ and $f^{\text{JEC}}$ compared to the
nominal fit.

Figure 5: The two-dimensional $\chi^{2}$ as a function of $f^{\text{JEC}}$ and
$f^{\text{XCone}}$, obtained from a comparison of background-subtracted data
with the predictions from production in the reconstructed $m_{\PW}$
distributions. The minimum is indicated by a black cross, and the borders of
the 68 and 95% intervals are shown by the light and dark red ellipses,
respectively.

The factors $f^{\text{JEC}}$ and $f^{\text{XCone}}$ are obtained from a fit to
the data, where a two-dimensional $\chi^{2}$ function is constructed,

$\chi^{2}=d^{T}V^{-1}d.$ (3)

The vector $d$ is built from the differences between the predictions
$g_{i}(f^{\text{JEC}},f^{\text{XCone}})$ and the background-subtracted data in
each bin $i$ of all four regions in $\pt^{\PW}$ and $r_{\pt}$. The covariance
matrix $V$ includes the statistical uncertainty in data, also considering
correlations from the normalisation to unit area, and the uncertainties in the
functions $g_{i}$ from the fit to simulation. The latter are estimated from
the statistical uncertainty of the simulated sample. We also include the
leading systematic uncertainties, namely the JER uncertainty, modelling
uncertainties from the simulation, and uncertainties from the background
subtraction. These uncertainties are treated as fully correlated across all
bins as well as the four regions. We find that the statistical uncertainties
are the dominant uncertainties in the calibration of the JMS, followed by the
JER uncertainties. All other uncertainties are small in comparison.

Figure 5 shows the evaluated two-dimensional $\chi^{2}$, as a function of
$f^{\text{JEC}}$ and $f^{\text{XCone}}$. The minimum of the $\chi^{2}$
function lies within the one-standard deviation intervals of the correction
factors. The global minimum has a value of $\chi^{2}=130$ for 132 degrees of
freedom. We find the best-fit values $f^{\text{JEC}}=0.60\pm 0.24$ and
$f^{\text{XCone}}=-0.06\pm 0.57$ with a linear correlation coefficient of
${-}0.66$. The JMS uncertainty obtained from the two-dimensional 68%
confidence level () interval is reduced compared to the variations of
$f^{\text{JEC}}$ and $f^{\text{XCone}}$ in the intervals between ${-}1$ and
${+}1$. In order to construct variations of one standard deviation in one
dimension for the evaluation of systematic uncertainties, the endpoints of the
minor axis are chosen. These result in the largest shift in the
$m_{\text{jet}}$ distribution, because along the minor axis both factors
$f^{\text{JEC}}$ and $f^{\text{XCone}}$ shift the value of $m_{\text{jet}}$ in
the same direction. Changes of $f^{\text{JEC}}$ and $f^{\text{XCone}}$ along
the major axis result in shifts in opposite directions, which cancel to a
large part. The extracted value pairs in $(f^{\text{JEC}},f^{\text{XCone}})$,
with the nominal value pair of $(0.60,-0.06)$, are $(0.78,0.01)$ and
$(0.42,-0.13)$, which are used in the determination of systematic
uncertainties. These pairs of values are referred to as JMS correction in the
following, with the corresponding uncertainties. We have verified that
variations of $m_{\PQt}$ in the simulation do not alter this result.
Additionally, we have tested that the results obtained from the electron and
muon channels are compatible. The final results of the $m_{\text{jet}}$
measurement agree within the uncertainties if the JMS calibration is carried
out in the electron channel and applied to the muon channel, and vice versa.

Figure 6: Jet mass distribution of hadronic decays of the boson, reconstructed
from two XCone subjets. The vertical bars on the markers show the statistical
uncertainty. The hatched regions show the total uncertainty in the simulation,
including the statistical and experimental systematic uncertainties. The lower
panel shows the ratio of the data to the simulation. The uncertainty bands
include the experimental systematic uncertainties and statistical
uncertainties in the simulation. The statistical (light grey) and total (dark
grey) uncertainties are shown separately in the ratio.

Figure 7: Mean values of the $m_{\text{jet}}$ distribution for and boson
decays, as a function of the number of primary vertices $N_{\text{PV}}$
(left). Data (markers) are compared with simulation (filled areas). The
vertical bars and size of the filled areas show the statistical uncertainties
in the calculation of the mean values. Jet mass resolution in simulation as a
function of particle-level XCone-jet , given for different intervals in the
number of primary vertices (right). The vertical bars indicate the statistical
uncertainties and the horizontal bars indicate the bin width.

Figure 6 shows the reconstructed $m_{\PW}$ distribution after applying the JMS
correction. The data are well described by the simulation over the full
distribution in $m_{\text{jet}}$. The mean values of $m_{\text{jet}}$ for the
reconstructed top quark and boson masses are shown as a function of the number
of primary vertices in Fig. 7 (left). The values for the top quark mass are
obtained using all three XCone subjets, while the boson mass is calculated
from the two subjets not matched to the -tagged AK4 jet. The mean values of
$m_{\text{jet}}$ are larger than the parameters $m_{\PQt}$ and $m_{\PW}$ used
in the simulation by about 4 and 2, respectively, because of contributions
from the UE and pileup interactions. The slope of the mean value of
$m_{\text{jet}}$ as a function of the number of pileup interactions is small,
indicating that the XCone reconstruction and calibration remove most of the
contributions from pileup. The mean values and the slopes are well described
by the simulation. The achieved resolution in $m_{\text{jet}}$ is displayed in
Fig. 7 (right). We calculate the resolution as the width parameter of a
Gaussian function, fitted to distributions in
$m_{\text{jet}}^{\text{rec}}/m_{\text{jet}}^{\text{gen}}$, where
$m_{\text{jet}}^{\text{rec}}$ denotes the reconstructed value of
$m_{\text{jet}}$ at the detector level and $m_{\text{jet}}^{\text{gen}}$ is
the jet mass at the particle level. The achieved resolution is below 8% over
the full range in . For an inclusive selection in the number of primary
vertices, the mass resolution improves from 7.7% at $\pt=400\GeV$ to 7% for
$\pt>800\GeV$. For a selection with less than 10 primary vertices, the
resolution is about one percentage point better than for a selection with more
than 20 primary vertices.

##  0.8 Studies of the final state radiation

The uncertainty in the modelling of FSR was the dominant model uncertainty in
the previous $m_{\text{jet}}$ measurement at 13 [36]. There, the energy scale
parameter $\mu$, which enters into the definition of the strong coupling
$\alpS^{\text{FSR}}(\mu^{2})$, was changed by factors of 0.5 and 2 in the FSR
simulation to estimate this uncertainty. This is equivalent to changing the
value of the effective strong coupling at the mass of the boson from
$\alpS^{\text{FSR}}(m_{\PZ}^{2})=0.1365$, as used in the parton shower and UE
event tune CUETP8M2T4 for the simulation of 2016 data, to values of 0.1556 and
0.1217, respectively. While the data are well described using the central
value, we find that the large uncertainty variations do not describe the data
in the fiducial region of this measurement. For the simulation of 2017 and
2018 data, the CP5 tune is used with $\alpS^{\text{FSR}}(m_{\PZ}^{2})=0.118$,
which is not the optimal choice for the modelling of jet substructure
observables in production, where a larger value is preferred [69]. To remedy
this situation, we perform a study of the FSR modelling and find the value of
$\alpS^{\text{FSR}}(m_{\PZ}^{2})$ that fits the data best. The study is
performed separately for the two samples with different tunes, namely for the
year 2016, and for the combination of the years 2017 and 2018. The
uncertainties in $\alpS^{\text{FSR}}(m_{\PZ}^{2})$ from this study are
propagated to the FSR uncertainty in the $m_{\text{jet}}$ measurement.

As a starting point, we modify the energy scale in the FSR simulation by a
factor $f_{\text{FSR}}$. With this definition, the FSR modelling uncertainty
as used in the previous measurement is obtained by setting
$f_{\text{FSR}}=0.5$ and 2. The prediction becomes a function of
$f_{\text{FSR}}$, which we use to determine the best fit value of
$f_{\text{FSR}}$ through a comparison of distributions between data and
simulation in the $N$-subjettiness ratio $\tau_{32}=\tau_{3}/\tau_{2}$ [39,
40]. The distributions in $\tau_{32}$ are sensitive to the angular
distribution of the energy density inside jets and are thus well suited for
determining $f_{\text{FSR}}$.

We use the same event selection as used for the $m_{\text{jet}}$ measurement,
described in Section 0.6. Instead of XCone jets, we use AK8 jets to study the
$\tau_{32}$ distributions. These have a higher sensitivity to effects from
FSR, because AK8 jets are obtained without jet grooming, unlike the XCone jets
clustered with the two-step procedure. The AK8 jet that is within
${\Delta}R<0.8$ of the XCone jet is selected, provided it has
$m_{\text{jet}}>140\GeV$. This requirement on $m_{\text{jet}}$ ensures that
only jets including all particles from the hadronic decay are accepted.

Figure 8: The normalised distributions in $\tau_{32}$ for AK8 jets with
$m_{\text{jet}}>140\GeV$ from the hadronic decay of boosted top quarks. Shown
are distributions for 2016 (left) and the combination of 2017 and 2018
(right). The background-subtracted data are compared to simulations with the
UE tunes CUETP8M2T4 for 2016 and CP5 for the combination of 2017 and 2018, and
different values of $f_{\text{FSR}}$ are shown as well. The lower panels show
the ratio to the simulation with $f_{\text{FSR}}=1$.

Figure 8 shows the normalised distributions in $\tau_{32}$ for 2016 (left),
and the combination of 2017 and 2018 (right). In both cases, larger values of
$f_{\text{FSR}}$ shift the distributions to lower values in $\tau_{32}$, and
smaller values of $f_{\text{FSR}}$ lead to a larger average value of
$\tau_{32}$. This is compatible with the expectation of less radiation for
larger values of $f_{\text{FSR}}$, corresponding to smaller values of
$\alpS^{\text{FSR}}$. Without additional radiation, $\tau_{32}$ becomes small
and compatible with a three-prong decay. If radiation is added to the jet, the
value of $\tau_{3}$ increases, and shifts the average $\tau_{32}$ to larger
values.

The sensitivity of the $\tau_{32}$ distribution to FSR can be used to
determine the value of $f_{\text{FSR}}$ that is most compatible with the data.
We construct predictions $g_{i}(f_{\text{FSR}})$ in each bin $i$ of the
normalised $\tau_{32}$ distributions,

$g_{i}(f_{\text{FSR}})=a_{i}+b_{i}\log{f_{\text{FSR}}^{-2}}+c_{i}f_{\text{FSR}}^{-2},$
(4)

with the free parameters $a_{i}$, $b_{i}$, and $c_{i}$. The functional form of
$g_{i}$ is inspired by the logarithmic dependence of $\alpS^{\text{FSR}}$ on
the square of the modified energy scale $(f_{\text{FSR}}\mu)^{2}$. The values
of the free parameters are determined in a fit to simulation, sampled at the
points $f_{\text{FSR}}\in\\{\frac{1}{2},1,2\\}$ in 2016 and
$f_{\text{FSR}}\in\\{\frac{1}{4},\frac{1}{2},\frac{1}{\sqrt{2}},1,\sqrt{2},2,4\\}$
in 2017 and 2018.

The compatibility with the data is tested with a $\chi^{2}$ function,
equivalent to the definition in Eq. (3). The vector of differences is built
from the normalised background-subtracted data, and the predictions
$g_{i}(f_{\text{FSR}})$. The uncertainties taken into account by the
covariance matrix include statistical uncertainties from data with
correlations from the normalisation, and systematic uncertainties in the JECs
and in the predictions $g_{i}(f_{\text{FSR}})$. The latter are conservatively
estimated by using the largest statistical uncertainty in a given bin $i$ from
any of the points obtained from the simulated samples with different values of
$f_{\text{FSR}}$. This choice was made because the point with
$f_{\text{FSR}}=4$ has the smallest statistical precision due to the presence
of a large spread of weights in the simulation. The statistical uncertainty in
data is the dominant uncertainty in this measurement.

The best fit value of $f_{\text{FSR}}$ is obtained by minimising the
$\chi^{2}$ function. Uncertainties corresponding to one standard deviation are
evaluated at $\chi^{2}_{\text{min}}+1$. We obtain the best fit values
$f_{\text{FSR}}=0.97\pm 0.07$ for 2016, and $f_{\text{FSR}}=0.33\pm 0.02$ for
the combined data of 2017 and 2018. The uncertainties in $f_{\text{FSR}}$ take
into account statistical and leading systematic sources, where the latter are
dominated by changes of the modelling in simulation, as described in Section
0.10. The modelling uncertainties included are uncertainties in the initial
state radiation (ISR), the colour reconnection model, the underlying event
tune, and the matching between matrix element and the parton shower.
Experimental uncertainties considered are uncertainties in the JECs, the
additional XCone-jet corrections, and JMS. We have found that the $\tau_{32}$
distributions obtained with different values of $m_{\PQt}$ are compatible
within the statistical precision of the simulated samples, and therefore we do
not consider changes of $m_{\PQt}$ in this study. We find that the statistical
uncertainties from data and the limited size of the simulated samples
constitute the largest source of uncertainty in the determination of
$f_{\text{FSR}}$.

The best fit values of $f_{\text{FSR}}$ can be translated to values of
$\alpS^{\text{FSR}}(m_{\PZ}^{2})$. This gives
$\alpS^{\text{FSR}}(m_{\PZ}^{2})=0.1373_{-0.0018}^{+0.0017}$ for 2016 and
$\alpS^{\text{FSR}}(m_{\PZ}^{2})=0.1416_{-0.0018}^{+0.0019}$ for the
combination of 2017 and 2018, evaluated using five active flavours in the
four-loop evolution of [93]. We note that these values do not represent a
generally valid measurement of $\alpS^{\text{FSR}}$, which would need a
different treatment of theory uncertainties from missing higher orders, but
the results can be used to calibrate the two different tunes used for the
simulation with +. In fact, the two values are compatible and much closer to
each other than the values used in the CUETP8M2T4 and CP5 tunes. The
uncertainty for 2016 is comparable to the one from the combination of 2017 and
2018, which constitutes a larger data set, because the latter is dominated by
statistical uncertainties in the simulation originating from a large spread of
weights used to obtain the samples with changes in $f_{\text{FSR}}$. The data
are well described by the nominal simulation in 2016, but prefer a larger
value of $\alpS^{\text{FSR}}$ in the 2017 and 2018 simulations. We have
checked that the 2017 and 2018 data are equally well or better described by
the adjusted simulations with $f_{\text{FSR}}=0.33$ in all distributions
relevant for this analysis. The change in the 2016 simulation is
insubstantial, with changes in distributions that are consistent with the
statistical uncertainties of the simulated sample. We have verified that
extracting $f_{\text{FSR}}$ from different intervals in $m_{\text{jet}}$ and
leads to compatible results, validating the calibration of the FSR modelling
in the full fiducial region of this measurement.

##  0.9 Unfolding

The data are unfolded to the particle level using regularised unfolding as
implemented in the TUnfold [94] framework. We have chosen the curvature
regularisation condition, such that the second derivative of the unfolded
result is regularised. This option introduces the smallest model dependencies
in this measurement. The optimal regularisation strength is found by
minimising the average global correlation coefficient in the output bins [95].
In addition to the measurement phase space defined in Section 0.6, five
sideband regions are constructed by loosening the most important selection
steps. These regions include events where the XCone jet has $350<\pt<400\GeV$,
the lepton has $55<\pt<60\GeV$, at least one of the XCone subjets has
$10<\pt<30\GeV$, $m_{\text{jet}}$ is less than the invariant mass of the sum
of the second XCone jet and lepton, and the AK4 jet passes a -tagging
requirement with a misidentification rate of 1%, but not the tight requirement
with 0.1%. Additionally, the measurement region and the region with XCone jet
$350<\pt<400\GeV$ are divided into bins of . The two bins in the peak region
of $m_{\text{jet}}$ with bin boundaries at 152, 172 and 192are split into four
bins in the unfolding, but merged afterwards to avoid large bin-to-bin
correlations. The splitting into regions of $\pt^{\text{jet}}$ and the
subdivision of $m_{\text{jet}}$ bins result in a reduced dependence on the
modelling parameters in the simulation and help to reduce the corresponding
uncertainties. In addition, this procedure ensures that the most important
migrations between the detector and particle levels into and out of the
fiducial region of the measurement are included in the unfolding and not
purely estimated from simulation. In total, the response matrix includes 200
bins at the detector level and 72 bins at the particle level.

We unfold the three years individually in order to check for a potential bias
originating from the different tunes in the simulation that is used to
construct the response matrix. With the dedicated calibration of the FSR
parameter in the simulation, all three years are compatible and agree within
one standard deviation. We have also ensured that unfolding the electron and
muon channels separately leads to a consistent result. For the final
measurement, all data and simulated samples are combined before the unfolding.

##  0.10 Uncertainties

Several sources of statistical and systematic uncertainties are considered in
the measurement of $m_{\text{jet}}$. These are split into four categories:
statistical, experimental, model, and theory uncertainties.

Statistical uncertainties are defined as the uncertainties due to the finite
statistical precision of the data. With respect to the previous measurement
[36], the statistical precision is increased by including data from 2017 and
2018, which increases the size of the data set by a factor of almost four. The
statistical uncertainties are propagated through the unfolding process using
Gaussian error propagation.

Experimental uncertainties encompass uncertainties in correction factors that
are connected to the calibration of physics objects. These include the JECs
[38], JER, additional XCone-jet corrections, JMS, as well as the factors
correcting for the efficiencies in the trigger selection [47], lepton
identification [78, 79], and tagging [96]. The JMS correction has been
obtained by calibrating $m_{\text{jet}}$ in the reconstructed $m_{\PW}$, which
is dominated by XCone subjets originating from light-flavour quarks. To
account for a possible difference in the detector response to XCone subjets
originating from the fragmentation of quarks, an additional flavour
uncertainty [38] is applied to XCone subjets matched to AK4 -tagged jets (JMS
flavour uncertainty), where the matching is identical to the procedure
outlined in Section 0.7. This JMS flavour uncertainty is obtained from the
response difference of jets in and [97, 98]. In addition, it is studied in a
+-jet sample where the jet response can be studied in data [38]. The
uncertainties in the reweighting of the pileup profile are considered. The
experimental uncertainties are calculated by changing the corrections up and
down by one standard deviation, and the difference with respect to the nominal
response matrix is then propagated to the unfolded distribution. The
uncertainty in the measurement of the integrated luminosity is estimated to be
1.6% [48, 49, 50] and is assigned to the unfolded distribution directly.
Statistical uncertainties from the limited size of the simulated samples,
denoted by “MC stat”, are included in the experimental uncertainties. The
simulated samples for 2017 and 2018 increase the statistical precision of the
unfolding compared to the previous measurement with 2016 data only, because of
the higher statistical precision in the response matrix, which is obtained
using simulated events. Simulated background processes are used to estimate
the amount of background events and are subtracted from data. The
corresponding statistical uncertainties in the background samples are much
smaller than the uncertainties in the cross sections of these processes, which
are 19% for $\PW\text{+jets}$ production, 23% for single top quark production
and 100% for other SM backgrounds [99, 100, 101, 102, 103, 104]. The
statistical uncertainties from the limited size of the MC samples are found to
be a factor of more than three smaller compared to the ones from data.

Model uncertainties arise from the choice of parameters in the event
simulation. These parameters include the factorisation and renormalisation
scales $\mu_{\text{F}}$ and $\mu_{\text{R}}$, the top quark mass, the colour
reconnection, the UE tune, and the choice of PDFs. Uncertainties in the parton
shower are estimated by changing the energy scales for the ISR and the FSR,
and varying the parameter that controls the matching between matrix element
and parton shower ($h_{\text{damp}}$) [69]. These variations cover all
observed differences between data and simulation in distributions relevant for
this measurement. The uncertainty in the fragmentation of the quark has been
estimated by changing its distribution in the + simulation. It was found to
have a negligible effect.

We do not consider an additional uncertainty from a comparison to an
alternative parton shower simulation, as for example implemented in the event
generator. Simulated events using + [97] version 7.1 with tune CH3 [105] do
not describe the data as well as events produced with +. Furthermore, an
uncertainty derived from the difference between these simulations would result
in an overestimation of the parton shower uncertainty and in a double counting
of uncertainty sources. Instead, accounting for the different sources of
parton shower uncertainties (ISR, FSR, $h_{\text{damp}}$) provides a means to
trace the relevant modelling uncertainties for this measurement. All model
parameters are varied within their uncertainties and the corresponding
uncertainties in the $m_{\text{jet}}$ measurement are estimated as described
in the following.

The values of $\mu_{\text{F}}$, $\mu_{\text{R}}$, and the ISR scales are
varied by factors from 0.5 to 2. The parameter $h_{\text{damp}}$ and the UE
tune are varied within their uncertainties [69]. For $\mu_{\text{F}}$ and
$\mu_{\text{R}}$, there are eight possible combinations to vary the scales. We
find that the simultaneous up and down variations of both scales have the
largest effects. In order to estimate the uncertainty in the $\mu_{\text{F}}$
and $\mu_{\text{R}}$ scales, we thus only consider simultaneous shifts of
$\mu_{\text{F}}$ and $\mu_{\text{R}}$. In order to estimate the uncertainty in
the colour reconnection model, three different models [106, 107, 108] are
considered as variations. The uncertainty due to the choice of PDFs has been
found to be negligible in the last measurements of $m_{\text{jet}}$ [35, 36]
because $m_{\text{jet}}$ in fully merged top quark decays is sensitive to the
decay of the top quark, but not to the dynamics of its production. Therefore,
we do not follow the recommendation for estimating PDF uncertainties using
different PDF sets [109], but we estimate the PDF uncertainty by using 100
variations of the NNPDF sets versions 3.0 [65] and 3.1 [66].

For all model variations, the simulated $m_{\text{jet}}$ distribution at the
detector level is unfolded to the particle level using the same setup as for
data. Differences between the true distribution at the particle level and the
unfolded simulation with model variations indicate a potential bias in the
unfolding setup and are treated as uncertainties. For uncertainties in the ISR
scale, the $\mu_{\text{F}}$ and $\mu_{\text{R}}$ scales, the $h_{\text{damp}}$
parameter, and the UE tune the average bias of the up and down variations is
calculated in each bin and taken as an uncertainty. In the case of the colour
reconnection model, the impact of a change in the model is calculated by
taking the difference in the mean of $m_{\text{jet}}$ between the true
distribution at the particle level and the unfolded distribution. The model
with the largest difference is chosen, and we take the resulting bias as the
uncertainty from the colour reconnection model.

Figure 9: Relative experimental (left) and model (right) uncertainties in the
measurement of $m_{\text{jet}}$. Various sources are displayed as coloured
lines and compared to the total experimental or model uncertainty,
respectively. The uncertainty sources are calculated as the square root of the
diagonal entries from the respective covariance matrix, and do not include
bin-to-bin correlations.

The uncertainty due to the choice of $m_{\PQt}$ in the simulation used to
unfold the data is calculated using samples with different values of
$m_{\PQt}$. The difference between the unfolded distribution and the true
particle-level distribution is parametrised in each bin of the unfolded
distribution. We use a linear function with $m_{\PQt}$ as its argument to
describe the difference. The parameters of this function are obtained using
the samples with $m_{\PQt}=169.5$, 171.5, 173.5, and 175.5. The uncertainty is
then evaluated from the linear function at $m_{\PQt}=172.5\pm 1\GeV$. This
procedure has the advantage of being less susceptible to statistical
fluctuations in the individual samples, therefore resulting in a more reliable
estimate of this uncertainty. The interval of ${\pm}1\GeV$ has been found to
be sufficient, because larger variations do not agree with the data at the
detector level.

We use the same method to calculate the uncertainty in the modelling of FSR.
The simulated samples with different choices of $f_{\text{FSR}}$ are unfolded,
and the differences between the true distribution at the particle level and
the unfolded distributions are parametrised as a function of $f_{\text{FSR}}$
in each bin. The uncertainty is obtained by evaluating the parametrisation at
the values obtained in the studies described in Section 0.8.

Figure 9 summarises the experimental and model uncertainties in the
measurement of $m_{\text{jet}}$. The largest experimental uncertainties arise
from the JES and JER corrections. In the $m_{\text{jet}}$ peak region (the
third and fourth bins) the largest sources of model uncertainties are from the
UE tune, the $h_{\text{damp}}$ parameter, and the choice of $m_{\PQt}$. In the
first two bins, the limited statistical precision of the samples with model
variations, in combination with a smaller number of observed events than in
the peak region, leads to statistical fluctuations in the estimation of model
uncertainties. This results in large uncertainties from $h_{\text{damp}}$ and
the colour reconnection models in the first and second bins of the
measurement, respectively. Because the sensitivity to $m_{\PQt}$ of the
$m_{\text{jet}}$ measurement comes from the peak region, these uncertainties
have a minor effect on the determination of $m_{\PQt}$.

Figure 10: Relative experimental (left) and model (right) uncertainties after
normalising the measurement to the total cross section. Various sources are
displayed as coloured lines and compared to the total experimental or model
uncertainty, respectively. The uncertainty sources are calculated as the
square root of the diagonal entries from the respective covariance matrix, and
do not include bin-to-bin correlations.

When normalising the unfolded distribution, systematic uncertainties cancel
fully or partially. For example, the uncertainty in the integrated luminosity
cancels completely as it affects all bins by an equal amount. The uncertainty
component in the JEC that changes only the three-vector predominantly changes
the XCone jet , and thus affects the selection efficiency of the measurement.
This uncertainty cancels to a large part when normalising the measurement and
becomes negligible. The uncertainties in the normalised measurement are
summarized in Fig. 10. In the peak region, the dominant experimental
uncertainties originate from JER and JMS corrections. The dominant model
uncertainties are the same as for the absolute cross section measurement.

Theory uncertainties are those uncertainties that apply to predictions at the
particle level. The scales for FSR, ISR, as well as $\mu_{\text{F}}$ and
$\mu_{\text{R}}$ are varied by factors of 0.5 and 2. The UE tune and the
$h_{\text{damp}}$ parameter are varied within their uncertainties. All three
models of colour reconnection are used to calculate the corresponding
uncertainty. For each source, the uncertainty in each bin is estimated by the
largest difference to the nominal prediction at the particle level.

##  0.11 Results and determination of the top quark mass

Figure 11: Differential production cross section as a function of
$m_{\text{jet}}$ compared to predictions obtained with : absolute (left) and
normalised (right). For the normalised measurement, the data are compared to
predictions with different $m_{\PQt}$. The vertical bars represent the total
uncertainties, and the statistical uncertainties are shown by short horizontal
bars. The long horizontal bars reflect the bin widths. Theoretical
uncertainties in the prediction are indicated by the bands. The lower panels
show the ratio of the theoretical prediction to data.

The three different years, as well as the electron and muon channels, are
combined before the unfolding, but are also processed individually to validate
their consistency. Figure 11 (left) shows the differential cross section in
the fiducial region as a function of $m_{\text{jet}}$, measured in data and
compared to simulation. The production cross section in the fiducial region is
measured to be $581\pm 8\stat\pm 46\,\text{(exp)}\pm
19\,\text{(model)}\unit{fb}$. This can be compared to the prediction from the
simulation, $690\pm 59\unit{fb}$. The smaller value of the measured cross
section compared to the prediction from at NLO has been observed in other
analyses for top quark $\pt>400\GeV$ [90, 91, 92], where NNLO calculations
describe the shape of the top quark distribution better.

We determine the value of $m_{\PQt}$ from the normalised differential
production cross section as a function of $m_{\text{jet}}$. This enables a
measurement using the shape of the $m_{\text{jet}}$ distribution without
sensitivity to uncertainties in the normalisation. Figure 11 (right) shows the
normalised measurement compared to predictions from with different values of
$m_{\PQt}$. In order to extract $m_{\PQt}$, a fit is performed based on
$\chi_{m}^{2}=d_{m}^{T}V_{m}^{-1}d_{m}$, where $d_{m}$ is the vector of
differences between the measured normalised differential cross section and the
simulation with different values of $m_{\PQt}$. Four of the five bins in
$m_{\text{jet}}$ are used in the calculation of $d_{m}$, because of the
normalisation of the measurement. The covariance matrix $V_{m}$ contains all
statistical, experimental, model, and theory uncertainties. We use the Linear
Template Fit [110] package to parametrise the cross section as a function of
$m_{\PQt}$ and obtain the best fit value with the corresponding uncertainties
analytically.

Figure 12: Correlations between the bins in the unfolding before (left) and
after (right) normalising the distribution to the total cross section. Boxes
with crosses indicate negative values of the correlation coefficient.

The bin-to-bin correlations in the measurement calculated from $V_{m}$,
including statistical, experimental, and model contributions, are displayed in
Fig. 12. Negative correlations between neighbouring bins originate from
migrations at the detector level, which have been corrected for by the
unfolding and result in anticorrelated statistical uncertainties. The
systematic variations that shift the peak of the $m_{\text{jet}}$
distribution, for example the JMS, also contribute to the negative
correlations.

In order to validate that the determination of $m_{\PQt}$ is unbiased, we
perform the $m_{\PQt}$ measurement using simulated samples with various values
of $m_{\PQt}$. The obtained value of $m_{\PQt}$ is compared to the true value
in Fig. 13. In this comparison, all extracted values agree with the respective
true values of $m_{\PQt}$, demonstrating the validity of the mass extraction.

Figure 13: Extracted top quark mass from simulation compared to the true
value. The vertical error bars show the total uncertainty in the extraction of
$m_{\PQt}$.

Performing the extraction on collision data and considering all sources of
uncertainties, we extract $m_{\PQt}$ using the +simulation,

$\displaystyle m_{\PQt}$ $\displaystyle=173.06\pm 0.24\stat\pm
0.61\,\text{(exp)}\pm 0.47\,\text{(model)}\pm 0.23\thy\GeV$
$\displaystyle=173.06\pm 0.84\GeV.$

With respect to the previous CMS measurement at 13 [36], this corresponds to
an improvement by more than a factor of three in terms of precision. This
measurement from boosted top quark production has an uncertainty comparable
with the most precise $m_{\PQt}$ extractions from fully resolved final states
[9, 10, 11, 12, 13, 14, 15].

When unfolding the 2016, 2017 and 2018 data separately and extracting
$m_{\PQt}$ from these three independent measurements, we find agreement
between the extracted values of $m_{\PQt}$ to better than one standard
deviation. All three values are compatible with the combined value to better
than one half standard deviation. We find the same when unfolding the electron
and muon channels separately.

Total and individual uncertainties in the extraction of $m_{\PQt}$ from the
normalised differential cross section. The uncertainties are grouped into
experimental, model, theory, and statistical uncertainties. Uncertainties from
the choice of the PDF, tagging, the luminosity measurement, and the lepton
triggers, identification and reconstruction are smaller than 0.01and are not
listed.  Source Uncertainty [] Jet energy resolution 0.38 Jet mass scale 0.37
Jet mass scale flavour 0.26 MC stat 0.09 Pileup 0.08 Jet energy scale 0.07
Additional XCone corrections 0.01 Backgrounds 0.01 Experimental total 0.61
Choice of $m_{\PQt}$ 0.41 Colour reconnection 0.17 $h_{\text{damp}}$ 0.09
Underlying event tune 0.09 $\mu_{\text{F}}$, $\mu_{\text{R}}$ scales 0.08 ISR
0.02 FSR 0.02 Model total 0.47 Underlying event tune 0.13 FSR 0.11
$\mu_{\text{F}}$, $\mu_{\text{R}}$ scales 0.10 Colour reconnection 0.09
$h_{\text{damp}}$ 0.04 ISR 0.04 Theory total 0.23 Statistical 0.24 Total 0.84

The individual sources of uncertainty and their impact on the mass extraction
are detailed in Table 0.11. The dominant experimental uncertainties are
connected to the calibration of the JER, the JMS calibration, and the JMS
flavour uncertainty, also visible in Fig. 10. The dominant modelling
uncertainties arise from the choice of the $m_{\PQt}$ and $h_{\text{damp}}$
parameters in the simulation. Compared to the previous measurement, the
dedicated measurement of the JMS leads to an uncertainty reduced by a factor
of 5 in the jet calibration. By constraining the simulation of FSR with data,
this previously dominant model uncertainty becomes small. The use of about
four times the data, corresponding to an integrated luminosity of 138, leads
to a reduction in the statistical uncertainty by a factor of 2.

The improvements described in this article result in a considerable gain in
precision, allowing for a determination of $m_{\PQt}$ from production at high
with an uncertainty comparable to the one achieved in measurements close to
the production threshold with fully resolved final state objects. The
measurement also provides important information on the modelling of the jet
mass in decays of boosted top quarks, which is the most important substructure
variable for the identification of large-radius jets [111].

##  0.12 Conclusions

A measurement of the differential top quark pair () production cross section
as a function of the jet mass $m_{\text{jet}}$ in hadronic decays of boosted
top quarks has been presented. The normalised distribution in $m_{\text{jet}}$
is sensitive to the top quark mass $m_{\PQt}$, which is measured to be
$173.06\pm 0.84\GeV$. This value is compatible with earlier precision
measurements in fully resolved final states [11, 14, 15]. With respect to an
earlier CMS analysis [36], the precision is improved by a factor of more than
three. This has been achieved by a dedicated calibration of the jet mass
scale, a study of the effects of final state radiation inside large-radius
jets, and about 4 times more data. With these improvements, the uncertainty in
the extraction of $m_{\PQt}$ at high top quark boosts becomes comparable to
direct measurements close to the production threshold. The sources of the
leading systematic uncertainties are very different, highlighting the
complementarity of this measurement. In addition, the study of boosted top
quarks offers the possibility to directly compare the distribution in
$m_{\text{jet}}$ to analytic calculations [34]. When these calculations become
available, the unfolded $m_{\text{jet}}$ distribution can be used to measure
the top quark pole mass directly. The precisely measured differential cross
section as a function of $m_{\text{jet}}$ represents an important step towards
understanding and resolving the ambiguities between the top quark mass
extracted from a direct reconstruction of $m_{\PQt}$, and the top quark pole
mass.

###### 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 centres and personnel of the Worldwide LHC Computing Grid and other
centres 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 programme 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).

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##  .13 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, F.M.
Pitters, J. Schieck20000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784, D.
Schwarz0000-0002-3821-7331, 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, S. Moortgat0000-0002-6612-3420, A.
Morton0000-0002-9919-3492, D. Müller0000-0002-1752-4527, A.R.
Sahasransu0000-0003-1505-1743, S. Tavernier0000-0002-6792-9522, W. Van
Doninck, 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, M.
Niedziela0000-0001-5745-2567, C. Rendón, C. Roskas0000-0002-6469-959X, 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, F. Torres Da Silva De Araujo80000-0002-4785-3057, 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.
Javaid90009-0007-2757-4054, M. Mittal0000-0002-6833-8521, L.
Yuan0000-0002-6719-5397

Department of Physics, Tsinghua University, Beijing, China M.
Ahmad0000-0001-9933-995X, G. Bauer10, Z. Hu0000-0001-8209-4343, S.
Lezki0000-0002-6909-774X, K. Yi10,11

Institute of High Energy Physics, Beijing, China G.M.
Chen90000-0002-2629-5420, H.S. Chen90000-0001-8672-8227, M.
Chen90000-0003-0489-9669, F. Iemmi0000-0001-5911-4051, C.H. Jiang, A.
Kapoor0000-0002-1844-1504, H. Kou0000-0003-4927-243X, H.
Liao0000-0002-0124-6999, Z.-A. Liu120000-0002-2896-1386, 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, C. Chen, 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

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, D.
Majumder0000-0002-7578-0027, S. Mishra0000-0002-3510-4833, M.
Roguljic0000-0001-5311-3007, A. Starodumov130000-0001-9570-9255, T.
Susa0000-0001-7430-2552

University of Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K.
Christoforou0000-0003-2205-1100, M. Kolosova0000-0002-5838-2158, 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 H.
Abdalla140000-0002-4177-7209, Y. Assran15,16

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

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

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

Helsinki Institute of Physics, Helsinki, Finland S.
Bharthuar0000-0001-5871-9622, E. Brücken0000-0001-6066-8756, F.
Garcia0000-0002-4023-7964, J. Havukainen0000-0003-2898-6900, M.S.
Kim0000-0003-0392-8691, R. Kinnunen, T. Lampén0000-0002-8398-4249, K. Lassila-
Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén0009-0002-4847-8882, M. Lotti, L. Martikainen0000-0003-1609-3515, M.
Myllymäki0000-0003-0510-3810, J. Ott0000-0001-9337-5722, 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

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, P.
Jarry0000-0002-1343-8189, 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-Navarro170000-0002-9481-5168, 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.
Agram180000-0001-7476-0158, J. Andrea0000-0002-8298-7560, D.
Apparu0009-0004-1837-0496, D. Bloch0000-0002-4535-5273, G.
Bourgatte0009-0005-7044-8104, J.-M. Brom0000-0003-0249-3622, E.C.
Chabert0000-0003-2797-7690, C. Collard0000-0002-5230-8387, D. Darej, U.
Goerlach0000-0001-8955-1666, C. Grimault, A.-C. Le Bihan0000-0002-8545-0187,
P. Van Hove0000-0002-2431-3381

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S.
Beauceron0000-0002-8036-9267, B. Blancon0000-0001-9022-1509, G.
Boudoul0009-0002-9897-8439, A. Carle, N. Chanon0000-0002-2939-5646, J.
Choi0000-0002-6024-0992, D. Contardo0000-0001-6768-7466, P.
Depasse0000-0001-7556-2743, C. Dozen190000-0002-4301-634X, 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. Toriashvili200000-0003-1655-6874, Z. Tsamalaidze130000-0001-5377-3558

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, 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
Ahmad210000-0003-1491-0446, 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, P. Asmuss,
S. Baxter0009-0008-4191-6716, M. Bayatmakou0009-0002-9905-0667, O.
Behnke0000-0002-4238-0991, A. Bermúdez Martínez0000-0001-8822-4727, S.
Bhattacharya0000-0002-3197-0048, A.A. Bin Anuar0000-0002-2988-9830, F.
Blekman220000-0002-7366-7098, K. Borras230000-0003-1111-249X, 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, L. Didukh0000-0003-4900-5227, G. Eckerlin, D.
Eckstein0000-0002-7366-6562, L.I. Estevez Banos0000-0001-6195-3102, O.
Filatov0000-0001-9850-6170, E. Gallo220000-0001-7200-5175, 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. Jafari240000-0001-7327-1870, N.Z.
Jomhari0000-0001-9127-7408, B. Kaech0000-0002-1194-2306, A.
Kasem230000-0002-6753-7254, 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.
Lipka250000-0002-8427-3748, W. Lohmann260000-0002-8705-0857, 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, A.
Raspereza0000-0003-2167-498X, B. Ribeiro Lopes0000-0003-0823-447X, J.
Rübenach, A. Saggio0000-0002-7385-3317, A. Saibel0000-0002-9932-7622, M.
Savitskyi0000-0002-9952-9267, M. Scham27,230000-0001-9494-2151, V. Scheurer,
S. Schnake230000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger220000-0001-6699-6662, 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. Wiens230000-0002-4423-4461, 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, 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, R.
Caspart0000-0002-5502-9412, 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.
Hartmann280000-0001-8989-8387, M. Horzela0000-0002-3190-7962, U.
Husemann0000-0002-6198-8388, M. Klute0000-0002-0869-5631, R.
Koppenhöfer0000-0002-6256-5715, 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, M. Schnepf, D. Seith, 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, 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. Gadallah290000-0002-8305-6661, S.
Lökös300000-0002-4447-4836, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, A.J.
Rádl310000-0001-8810-0388, O. Surányi0000-0002-4684-495X, G.I.
Veres0000-0002-5440-4356

Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók320000-0002-4440-2701, G. Bencze, C. Hajdu0000-0002-7193-800X, D.
Horvath33,340000-0003-0091-477X, 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. Karancsi320000-0003-0802-7665, J.
Molnar, Z. Szillasi, D. Teyssier0000-0002-5259-7983

Institute of Physics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari350000-0003-0498-4265

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo310000-0002-9110-9663, F. Nemes310000-0002-1451-6484, 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.
Dhingra360000-0002-7200-6204, 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. Singh370000-0001-9029-2462, 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, 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. Barman380000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, D. Bhowmik, S. Dutta0000-0001-9650-8121, S.
Dutta, B. Gomber390000-0002-4446-0258, M. Maity38, 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, P.
Kalbhor0000-0002-5892-3743, J.R. Komaragiri400000-0002-9344-6655, D.
Kumar400000-0002-6636-5331, A. Muhammad0000-0002-7535-7149, L.
Panwar400000-0003-2461-4907, R. Pradhan0000-0001-7000-6510, P.R.
Pujahari0000-0002-0994-7212, A. Sharma0000-0002-0688-923X, A.K.
Sikdar0000-0002-5437-5217, P.C. Tiwari400000-0002-3667-3843, S.
Verma0000-0003-1163-6955

Bhabha Atomic Research Centre, Mumbai, India K. Naskar410000-0003-0638-4378

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, R. Chudasama0009-0007-8848-6146, 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.
Bahinipati420000-0002-3744-5332, C. Kar0000-0002-6407-6974, P.
Mal0000-0002-0870-8420, T. Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu430000-0003-4671-815X, A. Nayak430000-0002-7716-4981, P.
Saha0000-0002-7013-8094, S.K. Swain, D. Vats430009-0007-8224-4664

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.
Bakhshiansohi440000-0001-5741-3357, E. Khazaie0000-0001-9810-7743, M.
Zeinali450000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S.
Chenarani460000-0002-1425-076X, 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,470000-0001-6808-1335, C.
Arutaa,b0000-0001-9524-3264, A. Colaleoa0000-0002-0711-6319, D.
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