HIN-23-001

HIN-23-001

# Girth and groomed radius of jets recoiling against isolated photons in lead-
lead and proton-proton collisions at $\sqrtsNN=5.02\TeV$

The CMS Collaboration

###### Abstract

This Letter presents the first measurements of the groomed jet radius
$R_{\mathrm{g}}$ and the jet girth $g$ in events with an isolated photon
recoiling against a jet in lead-lead (PbPb) and proton-proton ($\Pp\Pp$)
collisions at the LHC at a nucleon-nucleon center-of-mass energy of 5.02. The
observables $R_{\mathrm{g}}$ and $g$ provide a quantitative measure of how
narrow or broad a jet is. The analysis uses PbPb and $\Pp\Pp$ data samples
with integrated luminosities of $1.7\nbinv$ and $301\pbinv$, respectively,
collected with the CMS experiment in 2018 and 2017. Events are required to
have a photon with transverse momentum $\pt^{\PGg}>100\GeV$ and at least one
jet back-to-back in azimuth with respect to the photon and with transverse
momentum $\pt^{\text{jet}}$ such that $\pt^{\text{jet}}/\pt^{\PGg}>0.4$. The
measured $R_{\mathrm{g}}$ and $g$ distributions are unfolded to the particle
level, which facilitates the comparison between the PbPb and $\Pp\Pp$ results
and with theoretical predictions. It is found that jets with
$\pt^{\text{jet}}/\pt^{\PGg}>0.8$, , those that closely balance the photon
$\pt^{\PGg}$, are narrower in PbPb than in $\Pp\Pp$ collisions. Relaxing the
selection to include jets with $\pt^{\text{jet}}/\pt^{\PGg}>0.4$ reduces the
narrowing of the angular structure of jets in PbPb relative to the $\Pp\Pp$
reference. This shows that selection bias effects associated with jet energy
loss play an important role in the interpretation of jet substructure
measurements.

##  0.1 Introduction

The quark-gluon plasma (QGP) is a high-temperature, strongly interacting,
phase of nuclear matter that is produced in ultrarelativistic heavy ion
collisions [1, 2]. The fragmentation of highly energetic jets, initiated by
high-virtuality partons produced in the early stages of the collision, is
intertwined with the time evolution of this hot and dense matter. The
interactions between the jet and this medium happen over a wide range of
energy scales, from the high virtuality of the initial parton of up to hundred
to the medium temperature of hundreds of . These interactions are responsible
for partonic energy loss in the QGP medium, a phenomenon generally termed as
“jet quenching.” These interactions are also expected to modify the jet
radiation pattern in lead-lead (PbPb) collisions when compared with proton-
proton ($\Pp\Pp$) collisions. Jets produced in $\Pp\Pp$ collisions, which are
unaffected by the medium, are used as a reference for “vacuum radiation.” Jets
are clustered with a finite radius, and because of the energy redistribution
that occurs from the interaction of the jet shower with the medium, there is
an effective energy loss with respect to their expected energy if the jet
shower had developed in vacuum. The comparison of observables sensitive to
medium-induced jet modifications with theoretical model predictions aims to
characterize the microscopic structure of the QGP and its abilities to
transport matter and energy [3, 4, 5, 6].

The dominant interaction mechanism between the partons created in the parton
branching process and the QGP at short distance scales is expected to be
medium-induced gluon radiation, leading to a characteristic broadening of the
parton shower [7, 8, 9]. The existence of a minimum separation is a
consequence of a characteristic physical scale. This “medium resolution
length” [10, 11] is the minimum angular separation between two partons or
subjects that can be resolved by the medium as independent color charges [12].
The additional medium interactions experienced by a jet with larger number of
resolved constituents is expected to lead to stronger quenching. The
importance of the medium resolution length in the overall medium-induced
modification of the jet has not been conclusively established experimentally,
although some models expect it to be the dominant effect.

Another aspect of the jet-medium interaction is that the QGP is found to
behave as a strongly-coupled liquid when probed at energy scales that are of
the order of its temperature. When the QGP is probed at energy scales much
larger than its temperature, the quasiparticle degrees of freedom of the QGP
are expected to emerge due to asymptotic freedom [13, 14]. It is expected that
jet substructure may give access to these properties.

Experimental results from heavy ion collisions provide evidence for medium-
induced jet modifications [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27]; recent reviews can be found in Refs. [28, 29]. One of the observables
that has been used to study the modifications of the radiation pattern of the
jet is the groomed jet radius, $R_{\mathrm{g}}$, defined as the
rapidity–azimuth ($y$–$\phi$) distance between the two subjets obtained using
the soft-drop grooming algorithm [30, 31], described in Section 0.3. With
soft-drop grooming, a jet may be split into two hard subjets that can be used
as proxies for the two most energetic partons in the jet shower evolution
[32]. In $\Pp\Pp$ collisions, jet grooming is used to remove soft and wide-
angle radiation, allowing for a description in the framework of perturbation
theory. In PbPb collisions, in addition to the removal of soft- and wide-angle
radiation as in $\Pp\Pp$ collisions, jet grooming has been proposed as a
possible path to study medium-induced jet modifications using properties such
as the hardness and broadness of the jet radiation pattern [33, 34]. With
$R_{\mathrm{g}}$, we inspect the angular distribution of hard intrajet
branchings in search of signatures interpretable in terms of medium-induced
effects. These signatures include, in particular, the possible manifestation
of the medium resolution length [33, 34, 35, 36].

Measurements of $R_{\mathrm{g}}$ at the RHIC and LHC show a narrower jet
substructure in heavy ion collisions compared with $\Pp\Pp$ collisions for
jets reconstructed with the same jet transverse momentum [37, 38, 26, 27].
This narrowing of the hard intrajet angular distributions measured via
$R_{\mathrm{g}}$ could be a manifestation of the medium resolution length of
the QGP. However, before investigating such a connection, one should assess a
potential selection bias that emerges when comparing PbPb and $\Pp\Pp$ jets
with the same reconstructed [39, 40, 41]. Since jet energy loss may fluctuate
jet-by-jet and the jet spectrum is steeply falling, a given jet interval is
preferentially populated by jets that are less quenched. The jet shower in
heavy ion collisions is expected to factorize into an early vacuum shower,
created before the medium formation, followed by a medium-modified showering
process [35]. Selecting jets with large $R_{\mathrm{g}}$ values might
preferentially isolate early vacuum radiation patterns that are broad in angle
and thus interact more frequently with the QGP, which leads to stronger jet
energy loss and to migrations to lower jet momenta, as illustrated
schematically in Fig. 1. Thus, to interpret the modifications of the radiation
pattern of the jet in terms of the emergence of other physical scales, such as
the medium resolution length, selection bias effects must be understood first.

Figure 1: Schematic diagram of the potential selection bias due to jet energy
loss that may occur when selecting jets based on the their . Broader
structures are expected to be more quenched (red line, thicker arrow), whereas
narrower structures are expected to be quenched less (blue line, thinner
arrow). Combined with the steeply falling jet spectrum, this can lead to a
preferential selection of narrow jets in a given jet interval, as indicated by
the vertical rectangular box. The dashed curve represents the jet spectrum in
the absence of medium-induced jet modifications.

Prompt photons produced in quark-gluon Compton scattering and quark-antiquark
annihilation processes, in which their momentum is balanced by a recoiling
jet, provide an ideal topology to study medium-induced jet modifications. A
high-momentum photon does not interact strongly with the QGP [42, 43, 44, 45,
46], so its can be used as a proxy for the of the recoiling parton that
initiates the jet shower. By selecting on the photon , selection biases
associated with jet energy loss should be reduced. Prompt photons can be
studied using photon isolation techniques [47, 48]. Isolated photon+jet events
have been used to quantify the jet energy loss via the measurement of the
momentum imbalance, defined as the ratio of the of the highest-jet and the
photon momentum $x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}$ [17, 24,
49]. The results show a stronger momentum imbalance in PbPb collisions than in
$\Pp\Pp$ collisions due to the jet energy loss that occurs in the former.
Fragmentation functions have been measured in isolated photon+jet events in
PbPb collisions, showing a suppression of hard jet constituents and an
enhancement of soft constituents relative to $\Pp\Pp$ collisions [50].

In this Letter, we compare for the first time the groomed jet radius
$R_{\mathrm{g}}$ of jets recoiling against isolated photons, hereafter
referred to as “photon-tagged jets”, in PbPb and $\Pp\Pp$ collisions. In
addition, we measure the girth $g$ of these jets, described in detail in
Section 0.3, which is a -weighted measure of how distant the constituents of
the jet are with respect to its axis [51, 52, 53, 22]. Photons are required to
have transverse momenta $\pt^{\PGg}>100\GeV$. The momentum imbalance
$x_{\PGg\mathrm{j}}$ is used as a proxy for jet energy loss in this study. We
consider two categories of events: $x_{\PGg\mathrm{j}}>0.4$ and
$x_{\PGg\mathrm{j}}>0.8$. A population of jets with varying degrees of medium
modifications is included in the $x_{\PGg\mathrm{j}}>0.4$ sample,
corresponding to a more inclusive jet selection. The selection with
$x_{\PGg\mathrm{j}}>0.8$ is less inclusive; it selects jets that experience,
on average, weaker energy loss. A comparison of these two categories is used
to demonstrate the effect of a strong selection bias on the substructure of
the jet. Tabulated results are provided in the HepData record for this
analysis [54].

##  0.2 Experimental setup and event reconstruction

The analysis uses PbPb and $\Pp\Pp$ data collected by the CMS experiment in
2018 and 2017, respectively. The PbPb and $\Pp\Pp$ data samples, both at a
nucleon-nucleon center-of-mass energy of 5.02, correspond to integrated
luminosities of $1.70\pm 0.03\nbinv$ and $301\pm 6\pbinv$, respectively [55,
56].

The CMS apparatus [57, 58] is a multipurpose, nearly hermetic detector,
designed to trigger on [59, 60] and identify electrons, muons, photons, and
(charged and neutral) hadrons [61, 62, 63]. A global “particle-flow” (PF)
algorithm [64] aims to reconstruct all individual particles in an event,
combining information provided by the all-silicon inner tracker and by the
crystal electromagnetic (ECAL) and brass-scintillator hadron calorimeters
(HCAL), operating inside a 3.8T superconducting solenoid, with data from the
gas-ionization muon detectors embedded in the flux-return yoke outside the
solenoid. The reconstructed particles are used to build leptons, jets, and
missing transverse momentum [65, 66, 67].

Events of interest are selected using a two-tiered trigger system. The first
level (L1), composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 30kHz for
PbPb collisions and 100kHz for $\Pp\Pp$ collisions [59]. The second level,
known as the high-level trigger (HLT), consists of a farm of processors
running a version of the full event reconstruction software optimized for fast
processing, and reduces the event rate to around 1kHz before data storage
[60].

The events are required to have a primary vertex reconstructed within 15cm of
the nominal interaction point along the beam direction, and within 0.15cm in
the transverse plane. The additional collisions per bunch crossing have a
negligible effect on the measurement.

The collision centrality in heavy ion collisions is expressed as a percentile
of the total inelastic hadronic cross section, following the Glauber
methodology [68], and is determined based on the transverse energy deposited
in both hadron forward (HF) calorimeters [15]. The events with close to 0%
centrality, known as “central” events, have the largest overlap of the two
colliding nuclei, whereas glancing collisions between the two nuclei are
referred to as “peripheral” events. Hadronic events are selected by requiring
at least two towers with an energy larger than 4in each of the HF
calorimeters. In order to focus on medium modification effects, we restrict
the analysis to the 30% most central events, corresponding to PbPb collisions
with relatively small impact parameter.

Jets are clustered from the list of PF candidates using the anti-algorithm
[69] with a distance parameter $R=0.2$. The anti-distance parameter of $R=0.2$
is a proxy for a circular jet radius of $R=0.2$ in the $y$–$\phi$ plane. The
value of $R=0.2$, which represents a smaller jet size than the standard
$R=0.4$ value used at the LHC, is chosen to mitigate the sensitivity to
uncorrelated background contributions at the level of jets and their
substructure [34], as discussed in Section 0.3. The four-momentum of the jet
is determined using the vector sum of all particle momenta in the jet. For
this analysis, jets are required to have $\pt^{\text{jet}}>40\GeV$ and
pseudorapidity $\abs{\eta}<2$. In PbPb collisions, the constituents of the jet
are corrected for the underlying event (UE) contribution using the constituent
subtraction method [70], an approach that removes or corrects jet constituents
based on the average UE density. In PbPb collisions, the UE comprises the
multiple nucleon-nucleon interactions occurring in the same collision. To
estimate the average UE density per event, a dedicated iterative procedure
[71] was employed. Due to region-to-region fluctuations of the UE, residual
background fluctuations smear the jet and its internal structure [72], which
are corrected with unfolding, as described in Section 0.4. The jets are
corrected for the detector response with jet energy corrections derived from
independent PbPb and $\Pp\Pp$ simulations along with additional corrections
for the imperfect modeling of the detector response [73]. In addition to the
jet energy correction, the momentum resolution of jets is larger in data
compared with simulation. Corrections to the simulation to account for the
momentum resolution differences are derived from dijet balancing studies
performed in $\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$ in 2017 and 2018 [66].
To incorporate this effect, a Gaussian smearing is applied to the detector-
level jet values in simulation to match the resolution in data. This smeared
mapping is then applied in the unfolding procedure, as described in Section
0.4.

Photons in PbPb and $\Pp\Pp$ collisions are reconstructed using the algorithms
described in Ref. [61]. Reconstruction, identification, and energy correction
algorithms have been optimized to perform in the extreme conditions of high UE
activity in central PbPb collisions [61].

The 8.230 [74] event generator with tune CP5 [75] is used to calculate Monte
Carlo (MC) corrections and for making comparisons with the data. For $\Pp\Pp$
simulations, a second sample is generated at leading-order (LO) by 7.2.2 [76,
77, 78] with the CH3 tune [79] to assess systematic uncertainties related to
the modeling of the parton shower, hadronization, multiparton interactions,
and beam-beam remnant interactions. All generated samples are passed through a
detailed simulation of the CMS detector using [80]. In $\Pp\Pp$ collisions,
the UE consists of multiparton interactions and those of beam-beam remnants.
In PbPb collisions, the UE encompasses, in addition, the multiple nucleon-
nucleon interactions occurring in the same PbPb collision in addition to the
hard scattering. Thus, for the PbPb simulation, 8 CP5 events are embedded into
a heavy ion UE produced with v1.9 [81], which is tuned to reproduce the global
event properties of PbPb collisions.

##  0.3 Analysis method

The groomed jet radius $R_{\mathrm{g}}$ is the $y$–$\phi$ distance between the
two subjets found with the soft-drop grooming algorithm. This algorithm
consists of first reclustering the jet constituents obtained from the anti-jet
[69] using the Cambridge–Aachen algorithm, which is a pairwise clustering
algorithm that clusters particles (and subjets thereafter) that are closer in
rapidity and azimuth, imposing angular ordering [82, 83]. Then, the
Cambridge–Aachen clustering history is undone iteratively, always undoing the
hardest subjet at each step of the iteration, until the first pair of subjets
that satisfies the condition $z>z_{\text{cut}}\theta^{\beta}$ is found. Here,
$z$ corresponds to the momentum fraction
$z=\pt^{\mathrm{sub}}/(\pt^{\mathrm{sub}}+\pt^{\mathrm{lead}})$ where
$\pt^{\mathrm{lead}}$ ($\pt^{\mathrm{sub}}$) is the momentum of the harder
(softer) subjet in the declustering step and $\theta$ the distance in
$y$–$\phi$ between the harder and softer subjets in the declustering
procedure, which corresponds to $R_{\mathrm{g}}$ for the pair of subjets
obtained by the soft-drop algorithm. We use the parameters
$z_{\text{cut}}=0.2$ and $\beta=0$ for soft-drop grooming, which allows us to
better control the large UE background of PbPb collisions [84]. These soft-
drop grooming parameter values were also used by the ALICE and ATLAS
Collaborations [37, 38]. All the PF constituents of the original anti-jet are
used in the Cambridge–Aachen clustering without an explicit selection
requirement on their .

In addition to $R_{\mathrm{g}}$, we measure the jet girth $g$ [51, 52, 53,
22], which is defined as the sum of the product of the momentum fraction of
the jet constituents and their distance relative to the anti-jet axis, namely:

$g=\frac{1}{{\pt^{\text{jet}}}}\sum_{i}\pt^{i}\Delta R_{i,\text{jet}},$ (1)

where $\Delta R_{i,\text{jet}}=\sqrt{\smash[b]{(\Delta
y_{i,\text{jet}})^{2}+(\Delta\varphi_{i,\text{jet}})^{2}}}$ is the distance in
$y$–$\phi$ of the $i$-th jet constituent with respect to the anti-jet axis.
Rapidity $y$ is chosen over pseudorapidity $\eta$ for $\Delta
R_{i,\text{jet}}$ and for $R_{\mathrm{g}}$, since rapidity differences are
invariant under longitudinal Lorentz boosts. For detector-related corrections,
where the geometry of the detector matters, we use $\eta$. The observables $g$
and $R_{\mathrm{g}}$ are derived in different ways starting from the
constituents of the anti-jet. Since $g$ does not rely on organizing radiation
into clusters of particles, as $R_{\mathrm{g}}$ does, and since it uses all
hadrons in the jet, it potentially highlights different aspects of the jet
fragmentation in vacuum and in the medium.

This analysis requires the presence of at least one isolated photon with
$\pt^{\PGg}>100\GeV$ and $\abs{\eta_{\PGg}}<1.44$ and the presence of at least
one anti-jet with $R=0.2$ and $\abs{\eta_{\text{jet}}}<2$ carrying at least 40
or 80% of $\pt^{\PGg}$ ($x_{\PGg\mathrm{j}}>0.4$ or 0.8). The highest-isolated
photon is used in the analysis. The $x_{\PGg\mathrm{j}}>0.4$ requirement is
applied so that jets have a minimum of 40, whereas the
$x_{\PGg\mathrm{j}}>0.8$ selection is motivated by the shift caused by out-of-
cone radiation for jets produced in $\Pp\Pp$ collisions [85]. The isolated
photon and the jets have a separation in azimuthal angle defined by
$\Delta\varphi_{\PGg j}>\frac{2}{3}\pi$. The selection requirements are such
that the reconstructed jet primarily originates from the hard scattering, and
reduces the likelihood that the jet originates from the large UE fluctuations
of more central PbPb collisions. We verified that the
$\Delta\phi_{\PGg\mathrm{j}}$ distributions in PbPb and $\Pp\Pp$ collisions
are compatible within the statistical uncertainties, which indicates that the
residual combinatorial jet background from the UE in PbPb collisions is
negligible within the statistical sensitivity of the measurement. In turn,
this enables the application of unfolding corrections to the particle level
that are robust with respect to detector smearing and the large UE background
effects on jet . Overall, these requirements represent a balance between
managing the large UE activity and preserving our ability to reach lower
values of $x_{\PGg\mathrm{j}}$ using hard jets.

The substructure observables are measured using the highest jet recoiling
against the selected isolated photon. The UE in PbPb collisions can create
spurious structures at the level of the jet substructure. This is mitigated
with the use of a small distance parameter $R=0.2$, since the contribution of
particles from the UE scales with $R^{2}$ [34]. In addition, soft-drop
grooming with $z_{\text{cut}}=0.2$ is used to further suppress these
contributions [34]. The reason is that subjets created by the large UE tend to
have a more asymmetric momentum balance compared with the expectation from
parton branchings. For $\pt^{\PGg}>100\GeV$, triggers are fully efficient both
in PbPb and $\Pp\Pp$ systems. No additional correction to account for residual
trigger inefficiency is applied.

For parton energy loss studies in heavy ion collisions, a selection of prompt
photons is desired since they provide a more reliable proxy for the momentum
of the recoil parton that initiated the jet shower. To suppress the
contribution from nonprompt photons, a cut-based approach is followed, where
the selection is optimized using binary trees to maximize the signal
significance squared [86]. The signals at the particle level are prompt
photons with isolation criteria such that the scalar sum of all the particles
around the photon within a radius of 0.4 in units of $\eta$–$\phi$ must be
less than 5. Residual inefficiencies for the signal are accounted for in the
corrections described in Section 0.4. The detector-level variables used in the
optimization of the photon selection are the fraction of hadronic energy
around the photon candidate $H/E$, the shower shape variable
$\sigma_{\eta\eta}$, and the isolation variable $\mathcal{I}$ [61]. The
fraction $H/E$ is calculated as the ratio of HCAL over ECAL energy within an
$\eta$–$\phi$ distance of 0.15 units with respect to the photon candidate. The
$\sigma_{\eta\eta}$ variable quantifies the lateral energy spread in the ECAL
cluster, which is typically wider for photons from neutral-hadron decays than
for prompt photons. The isolation variable $\mathcal{I}$ is given by the sum
of transverse energies in ECAL and HCAL and the transverse momenta of all
tracks with $\pt>2\GeV$ within an $\eta$–$\phi$ distance of 0.4 with respect
to the photon candidate, which is corrected by subtracting the estimated
average energy from the UE [42]. The $\mathcal{I}$ variable can have negative
values as a result of the aforementioned UE subtraction. In PbPb ($\Pp\Pp$)
events, we select isolated photons with $\mathcal{I}<2.1\GeV$ ($-0.1\GeV$),
which corresponds to a particle level $\mathcal{I}$ value of less than 5for
generated photons.

The contribution of photon-tagged jets originating from the decays of neutral
hadrons is taken into account via signal photon purity of the cut-based
selected sample. The photon purity estimation is obtained by performing a
template fit of the shower shape distribution where the signal template is MC-
based and the background template is built with nonisolated
($10<\mathcal{I}<20\GeV$) photons in the data [42]. The region
$10<\mathcal{I}<20\GeV$ is depleted in signal contributions from prompt
photons according to simulation studies. An alternative matrix (“ABCD”) method
is used to estimate the photon purity, which relies on data and is independent
of the template fit method. The ABCD method consists of dividing the
photon+jet data sample into four mutually exclusive regions using a two-
dimensional plane with the variables $\sigma_{\eta\eta}$ and $\mathcal{I}$. We
assume that the selection efficiencies of $\sigma_{\eta\eta}$ are independent
of $\mathcal{I}$ for the background, which is supported by simulation studies.
Three of the four regions are dominated by the background (the B, C and D
regions), while the fourth region (A) is a mixture of signal and background.
The regions A, B, C, and D are determined using the same selection
requirements on $\sigma_{\eta\eta}$ and $\mathcal{I}$ as for the template
fits. According to simulation studies, it is expected that the amount of
remaining prompt photon events in the B, C, D regions is negligible with
respect to the background. The signal inefficiency from the ABCD or template-
fit methods, which is at the per mille-level, is taken into account as part of
the corrections described in Section 0.4. The normalization of the background
calculated with the ABCD method is different from the one obtained with the
template fit by a few percent for PbPb and $\Pp\Pp$. The estimated photon
purities have values of $0.93\pm 0.02$ and $0.77\pm 0.01$ for PbPb and
$\Pp\Pp$ collisions, respectively, where the central value corresponds to the
photon purity obtained with the template fit method and the uncertainties
correspond to the symmetrized difference of the photon purity obtained with
the alternative ABCD method.

The number of reconstructed events with $x_{\PGg\mathrm{j}}>0.4$ (0.8) is 4717
(1940) in PbPb collisions, and is 20636 (10796) in $\Pp\Pp$ collisions.
According to simulations using LO matrix elements for the hard scattering, the
sample of photons used in this analysis is dominated by prompt photons from
quark-gluon Compton scattering and quark-antiquark annihilation processes.
Bremsstrahlung photons contribution about 15%, primarily in the
$x_{\PGg\mathrm{j}}>1$ region [87].

##  0.4 Unfolding and systematic uncertainties

To facilitate comparisons with theoretical predictions and with other
experiments, we unfold the detector-level distributions to the level of stable
particles. Two unfolding corrections are performed: one set for
($x_{\PGg\mathrm{j}}$, $R_{\mathrm{g}}$) and the other for
$(x_{\PGg\mathrm{j}},g)$. The corrections to the particle level account for
bin-to-bin migration effects, background, and acceptance and efficiency
corrections. To quantitatively describe the migration effects, a
multidimensional response matrix is calculated using isolated photon+jet
events at particle and detector levels. Both isolated photons and recoiling
jets at particle and detector levels are matched by proximity in $\eta$–$\phi$
space. The migrations in $x_{\PGg\mathrm{j}}$ are dominated by the smearing of
the jet . The unfolded distributions are normalized to the number of jets
whose substructure is analyzed and that pass the given $x_{\PGg\mathrm{j}}$
selection, $N_{\text{jet}}$. In the case of soft-drop groomed jets, this
includes the jets that fail the soft drop condition.

Due to the sensitivity to statistical fluctuations in the detector-level
distribution, we use regularized unfolding. We use D’Agostini iterative
unfolding with early stopping [88], as implemented in the RooUnfold package
[89]. Unfolding corrects the full jet (via $x_{\PGg\mathrm{j}}$) and the jet
substructure variables. Bin-by-bin matching purity and efficiency corrections
are applied before and after the unfolding, respectively. The matching purity
correction accounts for the fraction of detector-level jets that are not
assigned to a generator level jet, whereas the efficiency correction accounts
for the fraction of particle level jets that are not associated with a
detector-level jet. The purity and efficiency corrections are of a few percent
and nearly independent of $R_{\mathrm{g}}$ and $g$. The binning choice ensures
a sufficiently large number of counts per bin for stable unfolding
corrections. The number of iterations, which plays the role of the
regularization parameter in D’Agostini unfolding, is such that the unfolded
solution folded back to detector level is statistically compatible with the
input distribution, the measured one.

Theoretical and experimental uncertainties are propagated in the unfolding
procedure through variations of the response matrix, as well as the purity,
and efficiency corrections. The following sources of systematic uncertainties
are considered:

Physics model dependence: Regularized unfolding introduces a bias towards a MC
generator-level spectrum in the unfolded solution to reduce the sensitivity to
statistical fluctuations. To assess this bias, we use other assumptions for
the prior spectrum to cover a reasonable range of possibilities. In addition,
this results in a change in the detector migration matrix itself that is used
for the unfolding corrections.

In $\Pp\Pp$ collisions, the nominal MC sample used for unfolding is 8 with the
CP5 tune. We use 7.1.4 [90, 91] simulated events as an alternative MC sample,
using the CH3 tune [79]. The 7 generator has an angular-ordered shower to
account for color coherence effects, distinct from the -ordered shower of 8. 7
uses the cluster fragmentation model, different from the string model used in
8. We take the difference of the unfolding determined with these variations
relative to the nominal unfolded results as the respective systematic
uncertainty. The resulting uncertainty is symmetrized bin by bin.

For PbPb collisions, we considered a variation of the 8 CP5 events with a
modification of its quark-gluon jet fraction, which is used as a proxy of
medium-modification effects. It is expected that medium-induced jet
modifications in general leads to a larger number of particles and momentum
broadening. One way of assessing this effect in the corrections is by
increasing the fraction of gluon jets. Quark and gluon jets have different
shapes in $R_{\mathrm{g}}$ and $g$; quark jets tend to be narrower than gluon
jets on average. The modification of the quark-gluon jet fraction in
simulation is done by fitting the jet substructure observables at the detector
level using a template fit with quark and gluon jet templates from simulated 8
CP5 events. The template fitting yields a sample of MC events with a larger
fraction of gluon-initiated jets (from 45% to about 65%) for
$x_{\PGg\mathrm{j}}>0.4$ while reducing the fraction of gluon-initiated jets
(from 25% to about 12%) for $x_{\PGg\mathrm{j}}>0.8$ compared with the
generator level yields, which we use to reweigh the nominal 8 CP5 sample. The
unfolding procedure is repeated with this variation of the 8 CP5 sample using
reweighted events according to the quark/gluon jet fraction found in the
template fit. The symmetrized difference with respect to the nominal result is
used as the respective systematic uncertainty.

Regularization bias uncertainty: The optimal number of iterations in
D’Agostini unfolding has a dependence on the particle level prior spectrum
used for regularization. To quantify the uncertainty associated with the prior
spectrum choice, we use the optimal number of iterations obtained with MC
sample variations described in the previous paragraph (quark/gluon fraction
changes for PbPb, 7 for $\Pp\Pp$) and compare it with the results obtained
with the nominal number of iterations.

PF candidate energy scale uncertainty: To assess the impact of the individual
energy calibration uncertainties of the PF candidates that are used for the
jet substructure, we scale the four-momenta of the charged hadron and photon
PF candidates by $\pm 1$% and of neutral hadron PF candidates by $\pm 3$% at
the detector level in simulation [92]. The PF energy scale variations are done
in an uncorrelated way, , an up or down shift in the four-momenta for each PF
candidate species at a time, such that six different variations of PF energy
scale shifts are evaluated. The unfolding corrections are repeated for each
variation, and the difference with respect to the nominal unfolding result is
evaluated. This has a different effect in $R_{\mathrm{g}}$ and $g$. For
$R_{\mathrm{g}}$, it changes the fraction of subjets that previously failed
(or passed) the soft-drop grooming condition, and it mildly modifies the
substructure distribution itself via smearing of the subjet . For $g$, since
it is defined as the sum of the products of of the constituents and their
$y$–$\phi$ distance to the jet axis, the shift has a direct effect on its
shape and thus the effect is stronger than for $R_{\mathrm{g}}$.

Jet energy scale (JES) uncertainties: The uncertainty in the JES is evaluated
from dijet and balancing methods [66]. The uncertainty in the JES in $\Pp\Pp$
collisions is around 3–4%, increasing as a function of $\abs{\eta}$. In PbPb
collisions, the uncertainty is around 4%, except for the barrel-endcap
transition region ($1.3<\abs{\eta}<1.6$), where it can become as large as 10%.
An additional source of uncertainty in the JES is considered in PbPb
collisions to take into account the differences in the particle mixture in
simulation and data due to jet energy loss [17]. We construct an alternative
set of unfolding corrections by varying the JES within its uncertainties. The
JES uncertainty does not affect the jet substructure itself, since it is
related to the calibration of the full jet. However, it can lead to migration
effects via the $x_{\PGg\mathrm{j}}>0.4$ (0.8) threshold used in our
selection.

Jet energy resolution uncertainties: The uncertainty in the jet energy
resolution is evaluated from a dijet balancing method [66]. In $\Pp\Pp$
collisions, the uncertainty in the resolution is in the range of 2–4% in the
barrel region, but is larger in the endcap and transition regions, where it
varies in the range of 10–20%, depending on $\eta$. To propagate the
uncertainty from jet resolution, the data-to-simulation resolution scale
factors are varied according to their uncertainties and the unfolding
corrections are repeated. In PbPb collisions, there is an additional
contribution to the jet energy resolution uncertainty because of the modeling
of the UE in simulation with . This uncertainty is evaluated by comparing the
energy in randomly-distributed cones in data and simulation. The difference in
data and simulation on the energy distributions obtained using the random-cone
method is used to estimate the effect on the jet resolution. To estimate this
uncertainty, we shift the centrality interval in simulation. To account for
these data-to-simulation differences, in practice the centrality in simulation
is shifted by 4.5% for the nominal results, i.e, 0–30% central events in data
correspond to 4.5–34.5% in simulation. For the respective uncertainty, we
consider the intervals of 3–33% and 6–36% for the down and up variations,
respectively.

Photon background subtraction uncertainty: Nominally, the normalization of the
photon background from neutral hadron decays is determined using a template
fit of the $\sigma_{\eta\eta}$ distribution using simulated signal photon+jet
events and the shower shape distribution in data in a region dominated by
background (sideband in $\mathcal{I}$). As an alternative method, we calculate
the normalization of the background using control samples in data via the ABCD
method, as explained in Section 0.3. The unfolding is repeated after this
neutral-hadron photon decay background subtraction variation and the
corresponding difference with respect to the yields extracted with the
template fit is considered as the systematic uncertainty, which is
symmetrized.

Response matrix statistical uncertainties: We consider the statistical
uncertainties in the MC sample used to construct the response matrix as an
additional systematic uncertainty, which is propagated in the unfolding
procedure at each iteration.

The uncertainties are added in quadrature bin-by-bin for the final combination
of systematic uncertainties. The uncertainties are considered to be bin-to-bin
fully correlated, except for the statistical uncertainties of the unfolded
distributions and the response matrix statistical uncertainties, whose
covariance matrices are determined directly as part of the unfolding procedure
RooUnfold. The relative bin-by-bin uncertainties are shown in Table 0.4 for
$x_{\PGg\mathrm{j}}>0.4$ and in Table 0.4 for $x_{\PGg\mathrm{j}}>0.8$ in both
$\Pp\Pp$ and PbPb collisions. The dominant contribution to the measurement
uncertainty is the model dependence used in the corrections, with the PF
energy scale uncertainty being the subleading one. The uncertainties grow at
large values of $g$ and $R_{\mathrm{g}}$. Statistical uncertainties are
commensurate with the systematic uncertainties for the PbPb measurement,
whereas they are smaller than the systematic ones for $\Pp\Pp$. Since the
radiation pattern is modified in nontrivial ways in the case of PbPb
collisions and since the PbPb and $\Pp\Pp$ data were collected in different
years, we consider the systematic uncertainties to be uncorrelated between the
PbPb and $\Pp\Pp$ measurements.

Summary of bin-by-bin percentual relative uncertainties for
$x_{\PGg\mathrm{j}}>0.4$.  Uncertainty source $g$ $R_{\mathrm{g}}$ $\Pp\Pp$
PbPb $\Pp\Pp$ PbPb Physics model dependence 1.3–7.5 1.2–2.5 0.2–5.3 0.9–5.4
Regularization bias 0.1–0.7 $\lesssim$0.1–1.2 $\lesssim$0.1 $\lesssim$0.1
Photon PF energy scale 0.4–1.5 1.8–5.1 0.2–0.6 0.1–2.9 Charged hadron PF
energy scale 0.6–4.1 0.5–5.3 $\lesssim$0.1–0.5 $\lesssim$0.1–1.6 Neutral
hadron PF energy scale 0.1–1.5 0.2–5.3 0.1–0.5 0.3–2.4 JES 0.2–3.3 0.2–2.6
0.1–1.5 0.3–3.3 JER $\lesssim$0.1–2.0 $\lesssim$0.1–3.7 $\lesssim$0.1–0.2
0.1–1.8 Centrality 0.6–4.0 $\lesssim$0.1–2.4 Photon background subtraction
0.1–0.3 $\lesssim$0.1 $\lesssim$0.1–0.2 $\lesssim$0.1 Response matrix
statistical 1.0–2.9 1.4–4.5 0.9–2.2 1.4–3.6 Total systematic 2.2–9.8 2.7–10.7
1.3–6.0 2.7–8.5 Total statistical 1.4–3.5 3.5–7.6 1.4–2.5 3.6–6.4

Summary of bin-by-bin percentual relative uncertainties for
$x_{\PGg\mathrm{j}}>0.8$.  Uncertainty source $g$ $R_{\mathrm{g}}$ $\Pp\Pp$
PbPb $\Pp\Pp$ PbPb Physics model dependence 0.3–8.9 0.1–7.3 0.6–3.0 0.1–5.7
Regularization bias 0.3–0.9 0.1–2.0 $\lesssim$0.1–1.6 0.6–2.9 Photon PF energy
scale 0.1–1.3 0.1–4.4 0.1–1.4 0.2–0.9 Charged hadron PF energy scale 0.5–3.7
0.1–8.6 $\lesssim$0.1–2.5 0.1–1.3 Neutral hadron PF energy scale 0.0–2.4
0.2–7.5 0.1–1.9 0.1–3.7 JES 0.7–4.8 1.5–7.8 0.0–5.3 2.0–6.8 JER
$\lesssim$0.1–1.0 0.2–3.0 $\lesssim$0.1–2.1 0.3–3.0 Centrality 0.4–5.9 0.1–2.5
Photon background subtraction $\lesssim$0.1–0.1 $\lesssim$0.1–0.1 0.1–0.1
$\lesssim$0.1–0.1 Response matrix statistical 1.0–3.8 1.5–5.9 1.0–3.2 1.1–4.0
Total systematic 1.6–11.7 2.5–14.8 2.7–7.5 2.6–10.6 Total statistical 1.4–5.1
4.2–14.6 1.6–3.9 3.6–11.6

##  0.5 Results

The unfolded distributions of $g$ and $R_{\mathrm{g}}$ of photon-tagged jets
in $\Pp\Pp$ collisions are shown in Figs. 2 and 3 for $x_{\PGg\mathrm{j}}>0.4$
and $x_{\PGg\mathrm{j}}>0.8$, respectively. In $\Pp\Pp$ collisions, one can
use the $x_{\PGg\mathrm{j}}>0.4$ and 0.8 categories to better understand out-
of-cone radiation effects, which lead to an average shift in jet [85]. On
average, gluon-initiated jets experience a larger shift from out-of-cone
radiation than quark-initiated jets [85], so selecting events based on
$x_{\PGg\mathrm{j}}>0.4$ and 0.8 leads to different parton flavor
compositions.

The $\Pp\Pp$ measurement is compared with MC simulation results from 8.230
CP5, 8.303 with the vincia shower [93], 8.303 with the dire shower [94], 7.2.2
with an angular-ordered shower using tune CH3, and 7.2.0 with a dipole shower
[90, 91]. The dire shower implements a -ordered color dipole shower, where the
radiator–spectator particle pairs evolve simultaneously, and it includes
higher-order corrections, such as triple-collinear or double-soft parton
emissions. The version of vincia in 8.303 uses the antenna sector shower
formalism [95, 93, 96]. The branching kernels, known as antenna functions,
treat coherent sums of parton pairs without requiring a separation into
radiators and spectators. Both vincia and dire have their respective set of
tuning parameters in 8. The dipole parton shower available in 7 uses the
Catani–Seymour dipole factorization formalism [97]. We also compare the
“hybrid” model predictions for vacuum radiation with the data [98], which
consists of 8 generator predictions with the Monash tune as a baseline [99],
but without multiple parton interactions, a slightly larger cutoff for the
final-state radiation, and with the PDF set NNPDF2.3 QCD+QED LO [77]. All the
predictions presented here use matrix elements at LO in perturbation theory
for the hard scattering.

Differences between $\Pp\Pp$ unfolded distributions and the MC simulated
predictions are of the order of 20% in the bulk of the distributions. The
largest discrepancies are at small and large values of $g$ and at large values
of $R_{\mathrm{g}}$. This indicates that the jets measured in data are, on
average, broader than the jets in simulation. The 8 CP5 predictions describe
the $g$ distribution for both $x_{\PGg\mathrm{j}}>0.4$ and 0.8 selections
within the uncertainties, but not $R_{\mathrm{g}}$. The hybrid model
predictions for $\Pp\Pp$ have similar trends as 8 CP5. The best global
description is provided by the 7 CH3 generator for both
$x_{\PGg\mathrm{j}}>0.4$ and 0.8 categories in the $g$ and $R_{\mathrm{g}}$
variables, whereas the 7 generator with a dipole shower tends to have the
largest differences with the data. Recent measurements of jet substructure in
$\PZ{+}\text{jet}$ and dijet events in $\Pp\Pp$ collisions [92] suggest that
the jet substructure modeling depends on details of the quark and gluon jet
composition of the sample. Photon+jet events are expected to have a larger
quark jet fraction relative to inclusive jets for a similar jet domain. Thus,
the present measurement can help constrain the modeling of quark jet
fragmentation in simulations, which can be useful to improve the modeling of
the vacuum shower baseline used in predictions for medium-induced jet
modification.

Figure 2: Unfolded distributions of jet girth $g$ (left) and groomed jet
radius $R_{\mathrm{g}}$ (right) of photon-tagged jets in $\Pp\Pp$ collisions
for $x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}>0.4$. The upper
panels show the comparison of the observable in $\Pp\Pp$ collisions and
predictions from simulated events. The lower panels show the corresponding
ratios of the MC calculations and data. The bands represent the total
uncertainties, whereas the vertical bars represent the statistical
uncertainties.

Figure 3: Unfolded distributions of jet girth $g$ (left) and groomed jet
radius $R_{\mathrm{g}}$ (right) of photon-tagged jets in $\Pp\Pp$ collisions
for $x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}>0.8$. The upper
panels show the comparison of the observable in $\Pp\Pp$ collisions and
predictions from simulated events. The lower panels show the corresponding
ratios of the MC calculations and data. The bands represent the total
experimental uncertainties, whereas the vertical bars represent the
statistical uncertainties.

Figure 4: Unfolded distributions of jet girth $g$ (left) and groomed jet
radius $R_{\mathrm{g}}$ (right) of photon-tagged jets in PbPb and $\Pp\Pp$
collisions for $x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}>0.4$
(selecting both more and less quenched jets). The bands represent the total
uncertainties, whereas the vertical bars represent the statistical
uncertainties.

Figure 5: Unfolded distributions of jet girth $g$ (left) and groomed jet
radius $R_{\mathrm{g}}$ (right) of photon-tagged jets in PbPb and $\Pp\Pp$
collisions for $x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}>0.8$
(selecting less quenched jets). The bands represent the total uncertainties,
whereas the vertical bars represent the statistical uncertainties.

The unfolded distributions of $g$ and $R_{\mathrm{g}}$ for photon-tagged jets
in PbPb collisions compared with those of $\Pp\Pp$ collisions are shown in
Figs. 4 and 5 for $x_{\PGg\mathrm{j}}>0.4$ and 0.8. The ratios of the unfolded
distributions of PbPb to $\Pp\Pp$ are shown in Fig. 6 for
$x_{\PGg\mathrm{j}}>0.4$ and in Fig. 7 for $x_{\PGg\mathrm{j}}>0.8$. The
ratios of PbPb to $\Pp\Pp$ distributions are used to identify potential
medium-induced modifications of the jet shower. For photon-tagged jets with
$x_{\PGg\mathrm{j}}>0.4$ in Fig. 4, which corresponds to a more inclusive jet
selection where both quenched and less quenched jets are selected, we observe
no narrowing of the angular structure of jets produced in PbPb collisions
relative to the distributions in $\Pp\Pp$ collisions within the experimental
uncertainties. For $g$, there are hints of a broadening of the substructure of
the jet in PbPb collisions at large values of $g$, whereas $R_{\mathrm{g}}$ is
consistent with $\Pp\Pp$ within the experimental uncertainties. On the other
hand, by selecting less quenched jets via $x_{\PGg\mathrm{j}}>0.8$ in Fig. 5,
which is a less inclusive jet selection, we observe a narrowing of the angular
structure of jets in PbPb collisions when compared with $\Pp\Pp$ collisions,
contrary to the trend observed in events selected with
$x_{\PGg\mathrm{j}}>0.4$. Figures 6 and 7 compare the measurements of the
ratio of the unfolded distributions of PbPb to $\Pp\Pp$ with the hybrid model
[98]. The model is a “hybrid” approach of weak and strong coupling
approximations to describe medium-induced jet modifications. It includes the
modeling of energy loss due to the strong coupling between the partons and the
medium, large-angle deflections of partons transversing the QGP, referred to
as “Moliére elastic scatterings” [13, 14], and the nonperturbative
backreaction of the medium [100], also known as medium response. These effects
are denoted as “elastic” and “wake” in Figs. 6 and 7, and the absence of such
effects as “no elastic” or “no wake.” In the model, the medium resolution
length is controlled by the parameter $L_{\text{res}}$ [101], which
corresponds to the minimum transverse length between two color-connected
partons for the medium to resolve them separately. Three values of
$L_{\text{res}}$ are considered: $L_{\text{res}}=0$, $2/(\pi T),$ and
$\infty$, where $T$ is the temperature of the medium in the model. The
$L_{\text{res}}=0$ value corresponds to the incoherence limit where all the
radiators are resolved by the medium. The $L_{\text{res}}=2/(\pi T)$
corresponds to the expectation that $L_{\text{res}}$ should be of the same
order of magnitude as the Debye screening length [101] and it represents an
intermediate scenario where only a fraction of the radiators interact with the
medium. The $L_{\text{res}}=\infty$ value corresponds to the full coherence
limit, where radiators are not resolved individually and the jet interacts
with the medium as a single color charge. In the case of elastic scatterings
with the medium, the present model accounts for them for $L_{\text{res}}=0$.

Some conclusions can be drawn from the comparison of these theory predictions
from the hybrid model to the experimental data. First, the contribution of the
wake is negligible, as shown in Figs. 6 and 7, because of the small $R$
parameter used in the measurement. Second, there is no consistent choice of
parameters that can describe both the $x_{\PGg\mathrm{j}}>0.4$ and 0.8
categories simultaneously. For $x_{\PGg\mathrm{j}}>0.4$ in Fig. 6, the
incoherence case $L_{\text{res}}=0$ overestimates the narrowing, while the
model describes the data better when the Molière elastic scatterings are
added, which broaden the jet, or when the limiting case of
$L_{\text{res}}=\infty$ is considered. The $x_{\PGg\mathrm{j}}>0.4$ data
sample alone cannot be used to separate the contribution of color coherence
and elastic scatterings, but when used together with the unfolded
distributions of $x_{\PGg\mathrm{j}}>0.8$, one can better separate the
consequences of such effects, as shown in Fig. 7. The incoherence limit
$L_{\text{res}}=0$ is favored in the $x_{\PGg\mathrm{j}}>0.8$ category, and
both the effects of elastic scatterings and the effect of nonzero
$L_{\text{res}}$ values result in a slight overestimation of the normalized
yield at large $R_{\mathrm{g}}$ values. For $g$ in Figs. 6 and 7, the results
of the model comparison to the data are qualitatively the same as for
$R_{\mathrm{g}}$, with an underestimation of the PbPb-to-$\Pp\Pp$ ratio at
large values of $g$ from all setups for $x_{\PGg\mathrm{j}}>0.4$. Since the
categories $x_{\PGg\mathrm{j}}>0.4$ and 0.8 enable the selection of different
degrees of jet energy loss, the present measurement has sensitivity on how the
different physical mechanisms affect the radiation pattern of the jet as well
as the jet energy loss. Thus, the present measurement can be used to optimize
the model parameter choice together with existing measurements that are
sensitive to other aspects of medium-induced jet modifications.

Figure 6: Ratio of the normalized yields of PbPb to $\Pp\Pp$ data for jet
girth $g$ (left) and groomed jet radius $R_{\mathrm{g}}$ (right) of photon-
tagged jets in PbPb and $\Pp\Pp$ collisions for
$x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}>0.4$ (selecting both more
and less quenched jets). The data are compared with the hybrid model
predictions for $L_{\text{res}}=0$ (upper) and for nonzero values of
$L_{\text{res}}$ without elastic scattering (lower). The bands around the data
points represent the total experimental uncertainties, whereas the vertical
bars represent the statistical uncertainties. The uncertainties in the PbPb-
to-$\Pp\Pp$ ratio have been obtained assuming the PbPb and $\Pp\Pp$
measurements are uncorrelated. The bands around the theory predictions
represent the statistical uncertainties of the prediction.

Figure 7: Ratio of the normalized yields of PbPb to $\Pp\Pp$ for jet girth $g$
(left) and groomed jet radius $R_{\mathrm{g}}$ (right) of photon-tagged jets
in PbPb and $\Pp\Pp$ collisions for
$x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}>0.8$ (selecting less
quenched jets). The data are compared with the hybrid predictions for
$L_{\text{res}}=0$ (upper) and nonzero values of $L_{\text{res}}$ without
elastic scatterings (lower). The bands around the data points represent the
total experimental uncertainties, whereas the vertical bars represent the
statistical uncertainties. The uncertainties in the PbPb-to-$\Pp\Pp$ ratio
have been obtained assuming the PbPb and $\Pp\Pp$ measurements are
uncorrelated. The bands around the theory predictions represent the
statistical uncertainties of the prediction.

The measurement using photon-tagged jets is complementary to previous
measurements in inclusive jet events. In inclusive jet measurements, the
comparison between jets in PbPb and $\Pp\Pp$ collisions is done at the same
reconstructed jet , but due to the jet energy loss that occurs in PbPb
collisions, the comparison is not done for the same initial parton energies.
In contrast to the trends observed by the ALICE and ATLAS Collaborations for
$R_{\mathrm{g}}$ or $g$ in inclusive jet events [37, 38], we do not observe a
narrowing of the substructure of jets in $R_{\mathrm{g}}$ or $g$ within the
experimental uncertainties when selecting jets with $x_{\PGg\mathrm{j}}>0.4$
and $\pt^{\PGg}>100\GeV$. One possible explanation is that, after reducing the
selection bias effect due to jet energy loss, the sample of events being
analyzed in photon-tagged events consists not only of narrow unquenched jets
but also of broader, and thus more quenched, jets. By selecting less quenched
jets via $x_{\PGg\mathrm{j}}>0.8$, we observe an effective narrowing in the
angular substructure of jets produced in PbPb collisions, qualitatively
similar to the distributions measured by the ALICE and ATLAS Collaborations
[37, 38]. We verified that the conclusions of the study are the same even
after imposing a requirement on $x_{\PGg\mathrm{j}}<1$ to reduce the
contribution of bremsstrahlung photons.

##  0.6 Summary

In summary, we report the first measurements girth $g$ and the groomed jet
radius $R_{\mathrm{g}}$ of jets recoiling against isolated photons in lead-
lead (PbPb) and proton-proton ($\Pp\Pp$) collisions. The analysis uses PbPb
and $\Pp\Pp$ collision data, both at a nucleon-nucleon center-of-mass energy
of 5.02. The distributions are unfolded to the particle level in order to
facilitate comparisons between experiments and with theoretical predictions.

The transverse momentum of isolated photons ($\pt^{\PGg}$) can be used as a
proxy for the of the high-virtuality parton that initiates the shower of the
recoiling jet. This enables the disentanglement of the potential modification
of the momentum and angular substructure of jets due to the interactions with
the medium from the selection bias effects that can originate from jet energy
loss. This is done using the transverse momentum imbalance, defined as the
ratio of the hardest recoil jet ($\pt^{\text{jet}}$) and $\pt^{\PGg}$,
$x_{\PGg\mathrm{j}}\equiv\pt^{\text{jet}}/\pt^{\PGg}$. It is found that jets
with $\pt^{\text{jet}}/\pt^{\PGg}>0.8$, , those that closely balance the
photon $\pt^{\PGg}$, are narrower in PbPb than in $\Pp\Pp$ collisions.
Relaxing the selection to include jets with $\pt^{\text{jet}}/\pt^{\PGg}>0.4$
reduces the narrowing of the angular structure of jets in PbPb relative to the
$\Pp\Pp$ reference. These observations suggest that selection bias effects
play an important role in the interpretation of the modification of the
angular scales of jets in terms of medium-induced effects. The measured
distributions are compared with calculations based on a hybrid strong and weak
coupling model to describe medium-induced jet modifications. According to
model predictions, the $R_{\mathrm{g}}$ and $g$ distributions are not very
sensitive to medium response effects or to variations of the medium resolution
length. However, changes in the modeling of Molière elastic scatterings have
an effect of 10–40% at large values of $g$ and $R_{\mathrm{g}}$. This shows
the ability of the data to constrain the impact of Molière scatterings in a
way that is effectively factorized from the effects of the wake and the medium
resolution length.

Medium-induced jet modifications are commonly assessed by comparing jets and
their substructure at the same reconstructed in PbPb and $\Pp\Pp$ collisions,
which in the former case corresponds to the momentum of the jet after its
interactions with the quark gluon plasma. These interactions are expected to
broaden the jet and reduce its energy. Thus, in an inclusive jet measurement,
when comparing populations of jets in PbPb and pp within the same measured jet
window, a selection bias can lead to an effective narrowing of the angular
structure of jets in PbPb relative to $\Pp\Pp$. One possibility is that the
population of jets that were initially broader (hence, more strongly quenched
jets) has migrated to lower jet energies, whereas the population of narrower
jets (less strongly quenched jets) remains. Thus, events with high-jets
recoiling against energetic isolated photons can be used to better constrain
genuine medium modifications of the jet shower, complementing measurements in
inclusive jet production.

###### Acknowledgements.

We thank Daniel Pablos and Krishna Rajagopal for providing their theoretical
calculations of the groomed jet radius and jet girth adapted with the
selection requirements used in this analysis. We congratulate our colleagues
in the CERN accelerator departments for the excellent performance of the LHC
and thank the technical and administrative staffs at CERN and at other CMS
institutes for their contributions to the success of the CMS effort. In
addition, we gratefully acknowledge the computing centers and personnel of the
Worldwide LHC Computing Grid and other centers for delivering so effectively
the computing infrastructure essential to our analyses. Finally, we
acknowledge the enduring support for the construction and operation of the
LHC, the CMS detector, and the supporting computing infrastructure provided by
the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS
and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and
BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia);
MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and
MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and
CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI
(Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES
and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain);
MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI
and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093,
101115353, 101002207, and COST Action CA16108 (European Union); the Leventis
Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt
Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian
Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans
l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor
Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and
FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n.
30820817; the Beijing Municipal Science & Technology Commission, No.
Z191100007219010 and Fundamental Research Funds for the Central Universities
(China); the Ministry of Education, Youth and Sports (MEYS) of the Czech
Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985
(Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s
Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under
project number 400140256 - GRK2497; the Hellenic Foundation for Research and
Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of
Sciences, the New National Excellence Program - ÚNKP, the NKFIH research
grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K
147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council
of Science and Industrial Research, India; ICSC – National Research Center for
High Performance Computing, Big Data and Quantum Computing and FAIR – Future
Artificial Intelligence Research, funded by the NextGenerationEU program
(Italy); the Latvian Council of Science; the Ministry of Education and
Science, project no. 2022/WK/14, and the National Science Center, contracts
Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a
Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National
Priorities Research Program by Qatar National Research Fund;
MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del
Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd
Century Project Advancement Project, and the National Science, Research and
Innovation Fund via the Program Management Unit for Human Resources &
Institutional Development, Research and Innovation, grant B37G660013
(Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro
Corporation; the Welch Foundation, contract C-1845; and the Weston Havens
Foundation (USA).

## References

  * [1] W. Busza, K. Rajagopal, and W. van der Schee, “Heavy ion collisions: the big picture and the big questions”,  Ann. Rev. Nucl. Part. Sci. 68 (2018) 339, 10.1146/annurev-nucl-101917-020852, arXiv:1802.04801.
  * [2] A. Adams et al., “Strongly correlated quantum fluids: ultracold quantum gases, quantum chromodynamic plasmas, and holographic duality”,  New J. Phys. 14 (2012) 115009, 10.1088/1367-2630/14/11/115009, arXiv:1205.5180.
  * [3] J. D. Bjorken, “Highly relativistic nucleus-nucleus collisions: the central rapidity region”,  Phys. Rev. D 27 (1983) 140, 10.1103/PhysRevD.27.140.
  * [4] G.-Y. Qin and X.-N. Wang, “Jet quenching in high-energy heavy ion collisions”,  Int. J. Mod. Phys. E 24 (2015) 1530014, 10.1142/S0218301315300143, arXiv:1511.00790.
  * [5] J.-P. Blaizot and Y. Mehtar-Tani, “Jet structure in heavy ion collisions”,  Int. J. Mod. Phys. E 24 (2015) 1530012, 10.1142/S021830131530012X, arXiv:1503.05958.
  * [6] J. Casalderrey-Solana and C. A. Salgado, “Introductory lectures on jet quenching in heavy ion collisions”,  Acta Phys. Polon. B 38 (2007) 3731, arXiv:0712.3443.
  * [7] M. Gyulassy and M. Plumer, “Jet quenching in dense matter”,  Phys. Lett. B 243 (1990) 432, 10.1016/0370-2693(90)91409-5.
  * [8] R. Baier et al., “Radiative energy loss of high-energy quarks and gluons in a finite volume quark-gluon plasma”,  Nucl. Phys. B 483 (1997) 291, 10.1016/S0550-3213(96)00553-6, arXiv:hep-ph/9607355.
  * [9] R. Baier et al., “Radiative energy loss and broadening of high-energy partons in nuclei”,  Nucl. Phys. B 484 (1997) 265, 10.1016/S0550-3213(96)00581-0, arXiv:hep-ph/9608322.
  * [10] J. Casalderrey-Solana and E. Iancu, “Interference effects in medium-induced gluon radiation”,  JHEP 08 (2011) 015, 10.1007/JHEP08(2011)015, arXiv:1105.1760.
  * [11] Y. Mehtar-Tani, C. A. Salgado, and K. Tywoniuk, “Anti-angular ordering of gluon radiation in QCD media”,  Phys. Rev. Lett. 106 (2011) 122002, 10.1103/PhysRevLett.106.122002, arXiv:1009.2965.
  * [12] J. Casalderrey-Solana, Y. Mehtar-Tani, C. A. Salgado, and K. Tywoniuk, “New picture of jet quenching dictated by color coherence”,  Phys. Lett. B 725 (2013) 357, 10.1016/j.physletb.2013.07.046, arXiv:1210.7765.
  * [13] F. D’Eramo, M. Lekaveckas, H. Liu, and K. Rajagopal, “Momentum broadening in weakly coupled quark-gluon plasma (with a view to finding the quasiparticles within liquid quark-gluon plasma)”,  JHEP 05 (2013) 031, 10.1007/JHEP05(2013)031, arXiv:1211.1922.
  * [14] F. D’Eramo, K. Rajagopal, and Y. Yin, “Molière scattering in quark-gluon plasma: finding point-like scatterers in a liquid”,  JHEP 01 (2019) 172, 10.1007/JHEP01(2019)172, arXiv:1808.03250.
  * [15] CMS Collaboration, “Observation and studies of jet quenching in PbPb collisions at nucleon-nucleon center-of-mass energy $\sqrtsNN=2.76\TeV$”,  Phys. Rev. C 84 (2011) 024906, 10.1103/PhysRevC.84.024906, arXiv:1102.1957.
  * [16] CMS Collaboration, “First measurement of large area jet transverse momentum spectra in heavy ion collisions”,  JHEP 05 (2021) 284, 10.1007/JHEP05(2021)284, arXiv:2102.13080.
  * [17] CMS Collaboration, “Study of jet quenching with isolated-photon+jet correlations in PbPb and pp collisions at $\sqrt{s_{{}_{\mathrm{NN}}}}=$ 5.02 TeV”,  Phys. Lett. B 785 (2018) 14, 10.1016/j.physletb.2018.07.061, arXiv:1711.09738.
  * [18] CMS Collaboration, “Measurement of the Splitting Function in $pp$ and Pb-Pb Collisions at $\sqrt{s_{{}_{\mathrm{NN}}}}=$ 5.02 TeV”,  Phys. Rev. Lett. 120 (2018) 142302, 10.1103/PhysRevLett.120.142302, arXiv:1708.09429.
  * [19] ALICE Collaboration, “Measurement of jet quenching with semi-inclusive hadron-jet distributions in central PbPb collisions at $\sqrtsNN=2.76\TeV$”,  JHEP 09 (2015) 170, 10.1007/JHEP09(2015)170, arXiv:1506.03984.
  * [20] ALICE Collaboration, “Measurements of inclusive jet spectra in $\Pp\Pp$ and central PbPb collisions at $\sqrtsNN=5.02\TeV$”,  Phys. Rev. C 101 (2020) 034911, 10.1103/PhysRevC.101.034911, arXiv:1909.09718.
  * [21] ALICE Collaboration, “Exploration of jet substructure using iterative declustering in $\Pp\Pp$ and PbPb collisions at LHC energies”,  Phys. Lett. B 802 (2020) 135227, 10.1016/j.physletb.2020.135227, arXiv:1905.02512.
  * [22] ALICE Collaboration, “Medium modification of the shape of small-radius jets in central PbPb collisions at $\sqrtsNN=2.76\TeV$”,  JHEP 10 (2018) 139, 10.1007/JHEP10(2018)139, arXiv:1807.06854.
  * [23] ATLAS Collaboration, “Measurement of jet fragmentation in PbPb and $\Pp\Pp$ collisions at $\sqrtsNN=5.02\TeV$ with the ATLAS detector”,  Phys. Rev. C 98 (2018) 024908, 10.1103/PhysRevC.98.024908, arXiv:1805.05424.
  * [24] ATLAS Collaboration, “Measurement of photon-jet transverse momentum correlations in 5.02PbPb and $\Pp\Pp$ collisions with ATLAS”,  Phys. Lett. B 789 (2019) 167, 10.1016/j.physletb.2018.12.023, arXiv:1809.07280.
  * [25] ATLAS Collaboration, “Comparison of fragmentation functions for jets dominated by light quarks and gluons from $\Pp\Pp$ and PbPb collisions in ATLAS”,  Phys. Rev. Lett. 123 (2019) 042001, 10.1103/PhysRevLett.123.042001, arXiv:1902.10007.
  * [26] STAR Collaboration, “Measurement of groomed jet substructure observables in $\Pp\Pp$ collisions at $\sqrt{s}=200\GeV$ with STAR”,  Phys. Lett. B 811 (2020) 135846, 10.1016/j.physletb.2020.135846, arXiv:2003.02114.
  * [27] STAR Collaboration, “Differential measurements of jet substructure and partonic energy loss in AuAu collisions at $\sqrtsNN=200\GeV$”,  Phys. Rev. C 105 (2022) 044906, 10.1103/PhysRevC.105.044906, arXiv:2109.09793.
  * [28] L. Apolinário, Y.-J. Lee, and M. Winn, “Heavy quarks and jets as probes of the QGP”,  Prog. Part. Nucl. Phys. 127 (2022) 103990, 10.1016/j.ppnp.2022.103990, arXiv:2203.16352.
  * [29] L. Cunqueiro and A. M. Sickles, “Studying the QGP with jets at the LHC and RHIC”,  Prog. Part. Nucl. Phys. 124 (2022) 103940, 10.1016/j.ppnp.2022.103940, arXiv:2110.14490.
  * [30] J. M. Butterworth, A. R. Davison, M. Rubin, and G. P. Salam, “Jet substructure as a new Higgs search channel at the LHC”,  Phys. Rev. Lett. 100 (2008) 242001, 10.1103/PhysRevLett.100.242001, arXiv:0802.2470.
  * [31] A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft drop”,  JHEP 05 (2014) 146, 10.1007/JHEP05(2014)146, arXiv:1402.2657.
  * [32] A. J. Larkoski, D. Neill, and J. Thaler, “Jet shapes with the broadening axis”,  JHEP 04 (2014) 017, 10.1007/JHEP04(2014)017, arXiv:1401.2158.
  * [33] Y. Mehtar-Tani and K. Tywoniuk, “Groomed jets in heavy ion collisions: sensitivity to medium-induced bremsstrahlung”,  JHEP 04 (2017) 125, 10.1007/JHEP04(2017)125, arXiv:1610.08930.
  * [34] H. A. Andrews et al., “Novel tools and observables for jet physics in heavy ion collisions”,  J. Phys. G 47 (2020) 065102, 10.1088/1361-6471/ab7cbc, arXiv:1808.03689.
  * [35] P. Caucal, E. Iancu, A. H. Mueller, and G. Soyez, “Vacuum-like jet fragmentation in a dense QCD medium”,  Phys. Rev. Lett. 120 (2018) 232001, 10.1103/PhysRevLett.120.232001, arXiv:1801.09703.
  * [36] P. Caucal, A. Soto-Ontoso, and A. Takacs, “Dynamically groomed jet radius in heavy ion collisions”,  Phys. Rev. D 105 (2022) 114046, 10.1103/PhysRevD.105.114046, arXiv:2111.14768.
  * [37] ALICE Collaboration, “Measurement of the groomed jet radius and momentum splitting fraction in $\Pp\Pp$ and PbPb collisions at $\sqrtsNN=5.02\TeV$”,  Phys. Rev. Lett. 128 (2022) 102001, 10.1103/PhysRevLett.128.102001, arXiv:2107.12984.
  * [38] ATLAS Collaboration, “Measurement of substructure-dependent jet suppression in PbPb collisions at 5.02 with the ATLAS detector”,  Phys. Rev. C 107 (2023) 054909, 10.1103/PhysRevC.107.054909, arXiv:2211.11470.
  * [39] J. Brewer, J. G. Milhano, and J. Thaler, “Sorting out quenched jets”,  Phys. Rev. Lett. 122 (2019) 222301, 10.1103/PhysRevLett.122.222301, arXiv:1812.05111.
  * [40] Y.-L. Du, D. Pablos, and K. Tywoniuk, “Deep learning jet modifications in heavy ion collisions”,  JHEP 21 (2020) 206, 10.1007/JHEP03(2021)206, arXiv:2012.07797.
  * [41] J. Brewer, Q. Brodsky, and K. Rajagopal, “Disentangling jet modification in jet simulations and in +jet data”,  JHEP 02 (2022) 175, 10.1007/JHEP02(2022)175, arXiv:2110.13159.
  * [42] CMS Collaboration, “The production of isolated photons in PbPb and $\Pp\Pp$ collisions at $\sqrtsNN=5.02\TeV$”,  JHEP 07 (2020) 116, 10.1007/JHEP07(2020)116, arXiv:2003.12797.
  * [43] ATLAS Collaboration, “Centrality, rapidity and transverse momentum dependence of isolated prompt photon production in lead-lead collisions at $\sqrtsNN=2.76\TeV$ measured with the ATLAS detector”,  Phys. Rev. C 93 (2016) 034914, 10.1103/PhysRevC.93.034914, arXiv:1506.08552.
  * [44] ALICE Collaboration, “Direct photon production in PbPb collisions at $\sqrtsNN=2.76\TeV$”,  Phys. Lett. B 754 (2016) 235, 10.1016/j.physletb.2016.01.020, arXiv:1509.07324.
  * [45] STAR Collaboration, “Direct virtual photon production in AuAu collisions at $\sqrtsNN=200\GeV$”,  Phys. Lett. B 770 (2017) 451, 10.1016/j.physletb.2017.04.050, arXiv:1607.01447.
  * [46] PHENIX Collaboration, “Centrality dependence of direct photon production in $\sqrtsNN=200\GeV$ AuAu collisions”,  Phys. Rev. Lett. 94 (2005) 232301, 10.1103/PhysRevLett.94.232301, arXiv:nucl-ex/0503003.
  * [47] CMS Collaboration, “Measurement of the differential cross section for isolated prompt photon production in $\Pp\Pp$ collisions at 7 ”,  Phys. Rev. D 84 (2011) 052011, 10.1103/PhysRevD.84.052011, arXiv:1108.2044.
  * [48] T. Becher, S. Favrod, and X. Xu, “QCD anatomy of photon isolation”,  JHEP 01 (2023) 005, 10.1007/JHEP01(2023)005, arXiv:2208.01554.
  * [49] CMS Collaboration, “Jet Shapes of Isolated Photon-Tagged Jets in Pb-Pb and pp Collisions at $\sqrt{s_{\mathrm{NN}}}=$ 5.02 TeV”,  Phys. Rev. Lett. 122 (2019) 152001, 10.1103/PhysRevLett.122.152001, arXiv:1809.08602.
  * [50] CMS Collaboration, “Observation of medium-induced modifications of jet fragmentation in PbPb collisions at $\sqrtsNN=5.02\TeV$ using isolated photon-tagged jets”,  Phys. Rev. Lett. 121 (2018) 242301, 10.1103/PhysRevLett.121.242301, arXiv:1801.04895.
  * [51] W. T. Giele, E. W. N. Glover, and D. A. Kosower, “Jet investigations using the radial moment”,  Phys. Rev. D 57 (1998) 1878, 10.1103/PhysRevD.57.1878, arXiv:hep-ph/9706210.
  * [52] R. Kunnawalkam Elayavalli and K. C. Zapp, “Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions”,  JHEP 07 (2017) 141, 10.1007/JHEP07(2017)141, arXiv:1707.01539.
  * [53] R.-Z. Wan et al., “Jet shape modification at LHC energies by JEWEL”,  Chin. Phys. C 43 (2019) 054110, 10.1088/1674-1137/43/5/054110, arXiv:1812.10062.
  * [54] “HEPData record for this analysis”, 2024.  10.17182/hepdata.151507.
  * [55] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}=13\TeV$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019.  https://cds.cern.ch/record/2676164.
  * [56] CMS Collaboration, “Luminosity measurement in proton-proton collisions at 5.02 in 2017 at CMS”, CMS Physics Analysis Summary CMS-PAS-LUM-19-001, 2021.  http://cds.cern.ch/record/2765655.
  * [57] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [58] CMS Collaboration, “Development of the CMS detector for the CERN LHC Run 3”, 2023. arXiv:2309.05466. Submitted to JINST.
  * [59] CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s}=13\TeV$”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [60] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [61] CMS Collaboration, “Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC”,  JINST 16 (2021) P05014, 10.1088/1748-0221/16/05/P05014, arXiv:2012.06888.
  * [62] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13\TeV$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [63] CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,  JINST 9 (2014) P10009, 10.1088/1748-0221/9/10/P10009, arXiv:1405.6569.
  * [64] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [65] CMS Collaboration, “Performance of reconstruction and identification of $\PGt$ leptons decaying to hadrons and $\PGn_{\PGt}$ in $\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$”,  JINST 13 (2018) P10005, 10.1088/1748-0221/13/10/P10005, arXiv:1809.02816.
  * [66] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in $\Pp\Pp$ collisions at 8 ”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [67] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=13\TeV$ using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [68] C. Loizides, J. Kamin, and D. d’Enterria, “Improved Monte Carlo Glauber predictions at present and future nuclear colliders”,  Phys. Rev. C 97 (2018) 054910, 10.1103/PhysRevC.97.054910, arXiv:1710.07098. [Erratum: Phys.Rev.C 99 (2019) 019901].
  * [69] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [70] P. Berta, M. Spousta, D. W. Miller, and R. Leitner, “Particle-level pileup subtraction for jets and jet shapes”,  JHEP 06 (2014) 092, 10.1007/JHEP06(2014)092, arXiv:1403.3108.
  * [71] O. Kodolova, I. Vardanyan, A. Nikitenko, and A. Oulianov, “The performance of the jet identification and reconstruction in heavy ions collisions with CMS detector”,  Eur. Phys. J. C 50 (2007) 117, 10.1140/epjc/s10052-007-0223-9.
  * [72] ALICE Collaboration, “Measurement of event background fluctuations for charged particle jet reconstruction in PbPb collisions at $\sqrtsNN=2.76\TeV$”,  JHEP 03 (2012) 053, 10.1007/JHEP03(2012)053, arXiv:1201.2423.
  * [73] CMS Collaboration, “Determination of jet energy calibration and transverse momentum resolution in CMS”,  JINST 6 (2011) P11002, 10.1088/1748-0221/6/11/P11002, arXiv:1107.4277.
  * [74] T. Sjöstrand et al., “An introduction to 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [75] CMS Collaboration, “Extraction and validation of a new set of CMS 8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [76] S. Gieseke, P. Stephens, and B. Webber, “New formalism for QCD parton showers”,  JHEP 12 (2003) 045, 10.1088/1126-6708/2003/12/045, arXiv:hep-ph/0310083.
  * [77] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [78] B. R. Webber, “A QCD model for jet fragmentation including soft gluon interference”,  Nucl. Phys. B 238 (1984) 492, 10.1016/0550-3213(84)90333-X.
  * [79] CMS Collaboration, “Development and validation of HERWIG 7 tunes from CMS underlying-event measurements”,  Eur. Phys. J. C 81 (2021) 312, 10.1140/epjc/s10052-021-08949-5, arXiv:2011.03422.
  * [80] Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [81] I. P. Lokhtin and A. M. Snigirev, “A model of jet quenching in ultrarelativistic heavy ion collisions and high-hadron spectra at RHIC”,  Eur. Phys. J. C 45 (2006) 211, 10.1140/epjc/s2005-02426-3, arXiv:hep-ph/0506189.
  * [82] Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”,  JHEP 08 (1997) 001, 10.1088/1126-6708/1997/08/001, arXiv:hep-ph/9707323.
  * [83] M. Wobisch and T. Wengler, “Hadronization corrections to jet cross-sections in deep inelastic scattering”, in  Workshop on Monte Carlo Generators for HERA Physics (Plenary Starting Meeting), p. 270.  1998\.  arXiv:hep-ph/9907280.
  * [84] J. Mulligan and M. Ploskon, “Identifying groomed jet splittings in heavy ion collisions”,  Phys. Rev. C 102 (2020) 044913, 10.1103/PhysRevC.102.044913, arXiv:2006.01812.
  * [85] M. Dasgupta, L. Magnea, and G. P. Salam, “Nonperturbative QCD effects in jets at hadron colliders”,  JHEP 02 (2008) 055, 10.1088/1126-6708/2008/02/055, arXiv:0712.3014.
  * [86] H. Voss, A. Höcker, J. Stelzer, and F. Tegenfeldt, “TMVA, the toolkit for multivariate data analysis with ROOT”, in  XIth International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT), p. 40.  2007\.  arXiv:physics/0703039.  [PoS(ACAT)040]. 10.22323/1.050.0040.
  * [87] D. d’Enterria and J. Rojo, “Quantitative constraints on the gluon distribution function in the proton from collider isolated-photon data”,  Nucl. Phys. B 860 (2012) 311, 10.1016/j.nuclphysb.2012.03.003, arXiv:1202.1762.
  * [88] G. D’Agostini, “A multidimensional unfolding method based on Bayes’ theorem”,  Nucl. Instrum. Meth. A 362 (1995) 487, 10.1016/0168-9002(95)00274-X.
  * [89] T. Adye, “Unfolding algorithms and tests using RooUnfold”, in  PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, p. 313.  2011\.  arXiv:1105.1160.  10.5170/CERN-2011-006.313.
  * [90] J. Bellm et al., “7.0/3.0 release note”,  Eur. Phys. J. C 76 (2016) 196, 10.1140/epjc/s10052-016-4018-8, arXiv:1512.01178.
  * [91] J. Bellm et al., “7.2 release note”,  Eur. Phys. J. C 80 (2020) 452, 10.1140/epjc/s10052-020-8011-x, arXiv:1912.06509.
  * [92] CMS Collaboration, “Study of quark and gluon jet substructure in +jet and dijet events from $\Pp\Pp$ collisions”,  JHEP 01 (2022) 188, 10.1007/JHEP01(2022)188, arXiv:2109.03340.
  * [93] W. T. Giele, D. A. Kosower, and P. Z. Skands, “A simple shower and matching algorithm”,  Phys. Rev. D 78 (2008) 014026, 10.1103/PhysRevD.78.014026, arXiv:0707.3652.
  * [94] S. Höche and S. Prestel, “The midpoint between dipole and parton showers”,  Eur. Phys. J. C 75 (2015) 461, 10.1140/epjc/s10052-015-3684-2, arXiv:1506.05057.
  * [95] A. Gehrmann-De Ridder, T. Gehrmann, and E. W. N. Glover, “Antenna subtraction at NNLO”,  JHEP 09 (2005) 056, 10.1088/1126-6708/2005/09/056, arXiv:hep-ph/0505111.
  * [96] H. Brooks, C. T. Preuss, and P. Skands, “Sector showers for hadron collisions”,  JHEP 07 (2020) 032, 10.1007/JHEP07(2020)032, arXiv:2003.00702.
  * [97] S. Catani and M. H. Seymour, “A general algorithm for calculating jet cross sections in NLO QCD”,  Nucl. Phys. B 485 (1997) 291, 10.1016/S0550-3213(96)00589-5, arXiv:hep-ph/9605323. [Erratum: 10.1016/S0550-3213(98)81022-5].
  * [98] J. Casalderrey-Solana et al., “A hybrid strong/weak coupling approach to jet quenching”,  JHEP 10 (2014) 019, 10.1007/JHEP09(2015)175, arXiv:1405.3864. [Erratum: JHEP 09 (2015) 175].
  * [99] P. Skands, S. Carrazza, and J. Rojo, “Tuning 8.1: the Monash 2013 tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y, arXiv:1404.5630.
  * [100] J. Casalderrey-Solana et al., “Jet wake from linearized hydrodynamics”,  JHEP 05 (2021) 230, 10.1007/JHEP05(2021)230, arXiv:2010.01140.
  * [101] Z. Hulcher, D. Pablos, and K. Rajagopal, “Resolution effects in the hybrid strong/weak coupling model”,  JHEP 03 (2018) 010, 10.1007/JHEP03(2018)010, arXiv:1707.05245.

##  .7 The CMS Collaboration

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

Institut für Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341,
J.W. Andrejkovic, T. Bergauer0000-0002-5786-0293, S.
Chatterjee0000-0003-2660-0349, K. Damanakis0000-0001-5389-2872, M.
Dragicevic0000-0003-1967-6783, P.S. Hussain0000-0002-4825-5278, M.
Jeitler20000-0002-5141-9560, N. Krammer0000-0002-0548-0985, A.
Li0000-0002-4547-116X, D. Liko0000-0002-3380-473X, I.
Mikulec0000-0003-0385-2746, J. Schieck20000-0002-1058-8093, R.
Schöfbeck0000-0002-2332-8784, D. Schwarz0000-0002-3821-7331, M.
Sonawane0000-0003-0510-7010, S. Templ0000-0003-3137-5692, W.
Waltenberger0000-0002-6215-7228, C.-E. Wulz20000-0001-9226-5812

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

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

Université Libre de Bruxelles, Bruxelles, Belgium B.
Clerbaux0000-0001-8547-8211, A.K. Das, G. De Lentdecker0000-0001-5124-7693, H.
Evard0009-0005-5039-1462, L. Favart0000-0003-1645-7454, P.
Gianneios0009-0003-7233-0738, D. Hohov0000-0002-4760-1597, J.
Jaramillo0000-0003-3885-6608, A. Khalilzadeh, F.A. Khan0009-0002-2039-277X, K.
Lee0000-0003-0808-4184, M. Mahdavikhorrami0000-0002-8265-3595, A.
Malara0000-0001-8645-9282, S. Paredes0000-0001-8487-9603, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden0009-0000-7725-7945, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496

Ghent University, Ghent, Belgium M. De Coen0000-0002-5854-7442, D.
Dobur0000-0003-0012-4866, Y. Hong0000-0003-4752-2458, J.
Knolle0000-0002-4781-5704, L. Lambrecht0000-0001-9108-1560, G. Mestdach, K.
Mota Amarilo0000-0003-1707-3348, C. Rendón, A. Samalan, K.
Skovpen0000-0002-1160-0621, N. Van Den Bossche0000-0003-2973-4991, J. van der
Linden0000-0002-7174-781X, L. Wezenbeek0000-0001-6952-891X

Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke0000-0003-0252-3609, A. Bethani0000-0002-8150-7043, G.
Bruno0000-0001-8857-8197, C. Caputo0000-0001-7522-4808, C.
Delaere0000-0001-8707-6021, I.S. Donertas0000-0001-7485-412X, A.
Giammanco0000-0001-9640-8294, Sa. Jain0000-0001-5078-3689, V. Lemaitre, J.
Lidrych0000-0003-1439-0196, P. Mastrapasqua0000-0002-2043-2367, T.T.
Tran0000-0003-3060-350X, S. Wertz0000-0002-8645-3670

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil G.A.
Alves0000-0002-8369-1446, E. Coelho0000-0001-6114-9907, C.
Hensel0000-0001-8874-7624, T. Menezes De Oliveira0009-0009-4729-8354, A.
Moraes0000-0002-5157-5686, P. Rebello Teles0000-0001-9029-8506, M. Soeiro

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, M. Alves Gallo Pereira0000-0003-4296-7028, M.
Barroso Ferreira Filho0000-0003-3904-0571, H. Brandao
Malbouisson0000-0002-1326-318X, W. Carvalho0000-0003-0738-6615, J.
Chinellato5, E.M. Da Costa0000-0002-5016-6434, G.G. Da
Silveira60000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680, S.
Fonseca De Souza0000-0001-7830-0837, R. Gomes De Souza, J.
Martins70000-0002-2120-2782, C. Mora Herrera0000-0003-3915-3170, L.
Mundim0000-0001-9964-7805, H. Nogima0000-0001-7705-1066, J.P.
Pinheiro0000-0002-3233-8247, A. Santoro0000-0002-0568-665X, A.
Sznajder0000-0001-6998-1108, M. Thiel0000-0001-7139-7963, A. Vilela
Pereira0000-0003-3177-4626

Universidade Estadual Paulista, Universidade Federal do ABC, São Paulo, Brazil
C.A. Bernardes60000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
P.G. Mercadante0000-0001-8333-4302, S.F. Novaes0000-0003-0471-8549, B.
Orzari0000-0003-4232-4743, Sandra S. Padula0000-0003-3071-0559

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

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

Instituto De Alta Investigación, Universidad de Tarapacá, Casilla 7 D, Arica,
Chile S. Keshri0000-0003-3280-2350, S. Thakur0000-0002-1647-0360

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

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

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

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

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

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

Nanjing Normal University, Nanjing, China G. Bauer14

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

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

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

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

University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia D. Giljanovic0009-0005-6792-6881, N.
Godinovic0000-0002-4674-9450, D. Lelas0000-0002-8269-5760, A.
Sculac0000-0001-7938-7559

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

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

University of Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K.
Christoforou0000-0003-2205-1100, A. Hadjiagapiou, S.
Konstantinou0000-0003-0408-7636, J. Mousa0000-0002-2978-2718, C. Nicolaou, 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 A.A.
Abdelalim17,180000-0002-2056-7894, S. Elgammal19

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy0000-0003-4681-0079, Y. Mohammed0000-0001-8399-3017

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

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

Helsinki Institute of Physics, Helsinki, Finland S.
Bharthuar0000-0001-5871-9622, E. Brücken0000-0001-6066-8756, F.
Garcia0000-0002-4023-7964, K.T.S. Kallonen0000-0001-9769-7163, R. Kinnunen, T.
Lampén0000-0002-8398-4249, K. Lassila-Perini0000-0002-5502-1795, S.
Lehti0000-0003-1370-5598, T. Lindén0009-0002-4847-8882, L.
Martikainen0000-0003-1609-3515, M. Myllymäki0000-0003-0510-3810, M.m.
Rantanen0000-0002-6764-0016, H. Siikonen0000-0003-2039-5874, E.
Tuominen0000-0002-7073-7767, J. Tuominiemi0000-0003-0386-8633

Lappeenranta-Lahti University of Technology, Lappeenranta, Finland P.
Luukka0000-0003-2340-4641, H. Petrow0000-0002-1133-5485

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France M.
Besancon0000-0003-3278-3671, F. Couderc0000-0003-2040-4099, M.
Dejardin0009-0008-2784-615X, D. Denegri, J.L. Faure, F.
Ferri0000-0002-9860-101X, S. Ganjour0000-0003-3090-9744, P.
Gras0000-0002-3932-5967, G. Hamel de Monchenault0000-0002-3872-3592, V.
Lohezic0009-0008-7976-851X, J. Malcles0000-0002-5388-5565, L.
Portales0000-0002-9860-9185, J. Rander, A. Rosowsky0000-0001-7803-6650, M.Ö.
Sahin0000-0001-6402-4050, A. Savoy-Navarro200000-0002-9481-5168, P.
Simkina0000-0002-9813-372X, M. Titov0000-0002-1119-6614, M.
Tornago0000-0001-6768-1056

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut
Polytechnique de Paris, Palaiseau, France F. Beaudette0000-0002-1194-8556, A.
Buchot Perraguin0000-0002-8597-647X, P. Busson0000-0001-6027-4511, A.
Cappati0000-0003-4386-0564, C. Charlot0000-0002-4087-8155, M.
Chiusi0000-0002-1097-7304, F. Damas0000-0001-6793-4359, O.
Davignon0000-0001-8710-992X, A. De Wit0000-0002-5291-1661, I.T.
Ehle0000-0003-3350-5606, B.A. Fontana Santos Alves0000-0001-9752-0624, S.
Ghosh0009-0006-5692-5688, A. Gilbert0000-0001-7560-5790, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi0009-0008-2093-8131, B.
Harikrishnan0000-0003-0174-4020, L. Kalipoliti0000-0002-5705-5059, G.
Liu0000-0001-7002-0937, J. Motta0000-0003-0985-913X, M.
Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, R.
Salerno0000-0003-3735-2707, J.B. Sauvan0000-0001-5187-3571, Y.
Sirois0000-0001-5381-4807, A. Tarabini0000-0001-7098-5317, E.
Vernazza0000-0003-4957-2782, A. Zabi0000-0002-7214-0673, A.
Zghiche0000-0002-1178-1450

Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram210000-0001-7476-0158, J. Andrea0000-0002-8298-7560, D.
Apparu0009-0004-1837-0496, D. Bloch0000-0002-4535-5273, J.-M.
Brom0000-0003-0249-3622, E.C. Chabert0000-0003-2797-7690, C.
Collard0000-0002-5230-8387, S. Falke0000-0002-0264-1632, U.
Goerlach0000-0001-8955-1666, C. Grimault, R. Haeberle0009-0007-5007-6723,
A.-C. Le Bihan0000-0002-8545-0187, M. Meena0000-0003-4536-3967, G.
Saha0000-0002-6125-1941, M.A. Sessini0000-0003-2097-7065, P. Van
Hove0000-0002-2431-3381

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S.
Beauceron0000-0002-8036-9267, B. Blancon0000-0001-9022-1509, G.
Boudoul0009-0002-9897-8439, N. Chanon0000-0002-2939-5646, J.
Choi0000-0002-6024-0992, D. Contardo0000-0001-6768-7466, P.
Depasse0000-0001-7556-2743, C. Dozen220000-0002-4301-634X, H. El Mamouni, J.
Fay0000-0001-5790-1780, S. Gascon0000-0002-7204-1624, M.
Gouzevitch0000-0002-5524-880X, C. Greenberg, G. Grenier0000-0002-1976-5877, B.
Ille0000-0002-8679-3878, I.B. Laktineh, M. Lethuillier0000-0001-6185-2045, L.
Mirabito, S. Perries, A. Purohit0000-0003-0881-612X, M. Vander
Donckt0000-0002-9253-8611, P. Verdier0000-0003-3090-2948, J.
Xiao0000-0002-7860-3958

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

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

RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany S.
Diekmann0009-0004-8867-0881, A. Dodonova0000-0002-5115-8487, N.
Eich0000-0001-9494-4317, D. Eliseev0000-0001-5844-8156, F.
Engelke0000-0002-9288-8144, J. Erdmann0000-0002-8073-2740, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B.
Fischer0000-0002-3900-3482, T. Hebbeker0000-0002-9736-266X, K.
Hoepfner0000-0002-2008-8148, F. Ivone0000-0002-2388-5548, A.
Jung0000-0002-2511-1490, M.y. Lee0000-0002-4430-1695, F.
Mausolf0000-0003-2479-8419, M. Merschmeyer0000-0003-2081-7141, A.
Meyer0000-0001-9598-6623, S. Mukherjee0000-0001-6341-9982, D.
Noll0000-0002-0176-2360, F. Nowotny, A. Pozdnyakov0000-0003-3478-9081, Y.
Rath, W. Redjeb0000-0001-9794-8292, F. Rehm, H. Reithler0000-0003-4409-702X,
U. Sarkar0000-0002-9892-4601, V. Sarkisovi0000-0001-9430-5419, A.
Schmidt0000-0003-2711-8984, A. Sharma0000-0002-5295-1460, J.L.
Spah0000-0002-5215-3258, A. Stein0000-0003-0713-811X, F. Torres Da Silva De
Araujo230000-0002-4785-3057, S. Wiedenbeck0000-0002-4692-9304, S. Zaleski

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany C.
Dziwok0000-0001-9806-0244, G. Flügge0000-0003-3681-9272, W. Haj
Ahmad240000-0003-1491-0446, T. Kress0000-0002-2702-8201, A.
Nowack0000-0002-3522-5926, O. Pooth0000-0001-6445-6160, A.
Stahl0000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A.
Zotz0000-0002-1320-1712

Deutsches Elektronen-Synchrotron, Hamburg, Germany H. Aarup
Petersen0009-0005-6482-7466, M. Aldaya Martin0000-0003-1533-0945, J.
Alimena0000-0001-6030-3191, S. Amoroso, Y. An0000-0003-1299-1879, S.
Baxter0009-0008-4191-6716, M. Bayatmakou0009-0002-9905-0667, H. Becerril
Gonzalez0000-0001-5387-712X, O. Behnke0000-0002-4238-0991, A.
Belvedere0000-0002-2802-8203, S. Bhattacharya0000-0002-3197-0048, F.
Blekman250000-0002-7366-7098, K. Borras260000-0003-1111-249X, A.
Campbell0000-0003-4439-5748, A. Cardini0000-0003-1803-0999, C. Cheng, F.
Colombina0009-0008-7130-100X, S. Consuegra Rodríguez0000-0002-1383-1837, G.
Correia Silva0000-0001-6232-3591, M. De Silva0000-0002-5804-6226, G. Eckerlin,
D. Eckstein0000-0002-7366-6562, L.I. Estevez Banos0000-0001-6195-3102, O.
Filatov0000-0001-9850-6170, E. Gallo250000-0001-7200-5175, A.
Geiser0000-0003-0355-102X, A. Giraldi0000-0003-4423-2631, V.
Guglielmi0000-0003-3240-7393, M. Guthoff0000-0002-3974-589X, A.
Hinzmann0000-0002-2633-4696, A. Jafari270000-0001-7327-1870, L.
Jeppe0000-0002-1029-0318, N.Z. Jomhari0000-0001-9127-7408, B.
Kaech0000-0002-1194-2306, M. Kasemann0000-0002-0429-2448, C.
Kleinwort0000-0002-9017-9504, R. Kogler0000-0002-5336-4399, M.
Komm0000-0002-7669-4294, D. Krücker0000-0003-1610-8844, W. Lange, D. Leyva
Pernia0009-0009-8755-3698, K. Lipka280000-0002-8427-3748, W.
Lohmann290000-0002-8705-0857, F. Lorkowski0000-0003-2677-3805, R.
Mankel0000-0003-2375-1563, I.-A. Melzer-Pellmann0000-0001-7707-919X, M.
Mendizabal Morentin0000-0002-6506-5177, A.B. Meyer0000-0001-8532-2356, G.
Milella0000-0002-2047-951X, A. Mussgiller0000-0002-8331-8166, L.P.
Nair0000-0002-2351-9265, A. Nürnberg0000-0002-7876-3134, Y. Otarid, J.
Park0000-0002-4683-6669, D. Pérez Adán0000-0003-3416-0726, E.
Ranken0000-0001-7472-5029, A. Raspereza0000-0003-2167-498X, D.
Rastorguev0000-0001-6409-7794, B. Ribeiro Lopes0000-0003-0823-447X, J.
Rübenach, A. Saggio0000-0002-7385-3317, M. Scham30,260000-0001-9494-2151, S.
Schnake260000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger250000-0001-6699-6662, D. Selivanova0000-0002-7031-9434, K.
Sharko0000-0002-7614-5236, M. Shchedrolosiev0000-0003-3510-2093, R.E. Sosa
Ricardo0000-0002-2240-6699, D. Stafford, F. Vazzoler0000-0001-8111-9318, A.
Ventura Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, Q.
Wang0000-0003-1014-8677, Y. Wen0000-0002-8724-9604, K. Wichmann, L.
Wiens260000-0002-4423-4461, C. Wissing0000-0002-5090-8004, Y.
Yang0009-0009-3430-0558, A. Zimermmane Castro Santos0000-0001-9302-3102

University of Hamburg, Hamburg, Germany A. Albrecht0000-0001-6004-6180, S.
Albrecht0000-0002-5960-6803, M. Antonello0000-0001-9094-482X, S.
Bein0000-0001-9387-7407, L. Benato0000-0001-5135-7489, S. Bollweg, M.
Bonanomi0000-0003-3629-6264, P. Connor0000-0003-2500-1061, K. El
Morabit0000-0001-5886-220X, Y. Fischer0000-0002-3184-1457, E.
Garutti0000-0003-0634-5539, A. Grohsjean0000-0003-0748-8494, J.
Haller0000-0001-9347-7657, H.R. Jabusch0000-0003-2444-1014, G.
Kasieczka0000-0003-3457-2755, P. Keicher, R. Klanner0000-0002-7004-9227, W.
Korcari0000-0001-8017-5502, T. Kramer0000-0002-7004-0214, V.
Kutzner0000-0003-1985-3807, F. Labe0000-0002-1870-9443, J.
Lange0000-0001-7513-6330, A. Lobanov0000-0002-5376-0877, C.
Matthies0000-0001-7379-4540, 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, B. Raciti0009-0005-5995-6685, M.
Rieger0000-0003-0797-2606, D. Savoiu0000-0001-6794-7475, J.
Schindler0009-0006-6551-0660, P. Schleper0000-0001-5628-6827, M.
Schröder0000-0001-8058-9828, J. Schwandt0000-0002-0052-597X, M.
Sommerhalder0000-0001-5746-7371, H. Stadie0000-0002-0513-8119, G.
Steinbrück0000-0002-8355-2761, A. Tews, M. Wolf0000-0003-3002-2430

Karlsruher Institut fuer Technologie, Karlsruhe, Germany S.
Brommer0000-0001-8988-2035, M. Burkart, E. Butz0000-0002-2403-5801, T.
Chwalek0000-0002-8009-3723, A. Dierlamm0000-0001-7804-9902, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, A.
Gottmann0000-0001-6696-349X, F. Hartmann310000-0001-8989-8387, R.
Hofsaess0009-0008-4575-5729, M. Horzela0000-0002-3190-7962, U.
Husemann0000-0002-6198-8388, J. Kieseler0000-0003-1644-7678, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, J.M.
Lawhorn0000-0002-8597-9259, M. Link, A. Lintuluoto0000-0002-0726-1452, B.
Maier0000-0001-5270-7540, S. Maier0000-0001-9828-9778, S.
Mitra0000-0002-3060-2278, M. Mormile0000-0003-0456-7250, Th.
Müller0000-0003-4337-0098, M. Neukum, M. Oh0000-0003-2618-9203, E.
Pfeffer0009-0009-1748-974X, M. Presilla0000-0003-2808-7315, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, B.
Regnery0000-0003-1539-923X, N. Shadskiy0000-0001-9894-2095, I.
Shvetsov0000-0002-7069-9019, H.J. Simonis0000-0002-7467-2980, M.
Toms0000-0002-7703-3973, N. Trevisani0000-0002-5223-9342, R.F. Von
Cube0000-0002-6237-5209, M. Wassmer0000-0002-0408-2811, S.
Wieland0000-0003-3887-5358, F. Wittig, R. Wolf0000-0001-9456-383X, X.
Zuo0000-0002-0029-493X

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

National and Kapodistrian University of Athens, Athens, Greece P.
Kontaxakis0000-0002-4860-5979, G. Melachroinos, Z.
Painesis0000-0001-5061-7031, A. Panagiotou, I. Papavergou0000-0002-7992-2686,
I. Paraskevas0000-0002-2375-5401, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K.
Vellidis0000-0001-5680-8357, I. Zisopoulos0000-0001-5212-4353

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

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

HUN-REN Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók320000-0002-4440-2701, C. Hajdu0000-0002-7193-800X, D.
Horvath33,340000-0003-0091-477X, K. Márton, A.J. Rádl350000-0001-8810-0388, F.
Sikler0000-0001-9608-3901, V. Veszpremi0000-0001-9783-0315

MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd
University, Budapest, Hungary M. Csanád0000-0002-3154-6925, K.
Farkas0000-0003-1740-6974, M.M.A. Gadallah360000-0002-8305-6661, Á.
Kadlecsik0000-0001-5559-0106, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, G.I.
Veres0000-0002-5440-4356

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

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

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo370000-0002-9110-9663, F. Nemes370000-0002-1451-6484, T.
Novak0000-0001-6253-4356

Panjab University, Chandigarh, India J. Babbar0000-0002-4080-4156, S.
Bansal0000-0003-1992-0336, S.B. Beri, V. Bhatnagar0000-0002-8392-9610, G.
Chaudhary0000-0003-0168-3336, S. Chauhan0000-0001-6974-4129, N.
Dhingra380000-0002-7200-6204, A. Kaur0000-0002-1640-9180, A.
Kaur0000-0003-3609-4777, H. Kaur0000-0002-8659-7092, M.
Kaur0000-0002-3440-2767, S. Kumar0000-0001-9212-9108, K.
Sandeep0000-0002-3220-3668, T. Sheokand, J.B. Singh0000-0001-9029-2462, A.
Singla0000-0003-2550-139X

University of Delhi, Delhi, India A. Ahmed0000-0002-4500-8853, A.
Bhardwaj0000-0002-7544-3258, A. Chhetri0000-0001-7495-1923, B.C.
Choudhary0000-0001-5029-1887, A. Kumar0000-0003-3407-4094, A.
Kumar0000-0002-5180-6595, M. Naimuddin0000-0003-4542-386X, K.
Ranjan0000-0002-5540-3750, S. Saumya0000-0001-7842-9518

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S.
Baradia0000-0001-9860-7262, S. Barman390000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, S. Dutta0000-0001-9650-8121, S. Dutta, S.
Sarkar

Indian Institute of Technology Madras, Madras, India M.M.
Ameen0000-0002-1909-9843, P.K. Behera0000-0002-1527-2266, S.C.
Behera0000-0002-0798-2727, S. Chatterjee0000-0003-0185-9872, P.
Jana0000-0001-5310-5170, P. Kalbhor0000-0002-5892-3743, J.R.
Komaragiri400000-0002-9344-6655, D. Kumar400000-0002-6636-5331, P.R.
Pujahari0000-0002-0994-7212, N.R. Saha0000-0002-7954-7898, A.
Sharma0000-0002-0688-923X, A.K. Sikdar0000-0002-5437-5217, S.
Verma0000-0003-1163-6955

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

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

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

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

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

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S.
Chenarani480000-0002-1425-076X, 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,170000-0001-6808-1335, A.
Colaleoa,b0000-0002-0711-6319, D. Creanzaa,c0000-0001-6153-3044, B.
D’Anzia,b0000-0002-9361-3142, N. De Filippisa,c0000-0002-0625-6811, M. De
Palmaa,b0000-0001-8240-1913, A. Di Florioa,c0000-0003-3719-8041, W.
Elmetenaweea,b,170000-0001-7069-0252, L. Fiorea0000-0002-9470-1320, G.
Iasellia,c0000-0003-2546-5341, M. Loukaa,b, G. Maggia,c0000-0001-5391-7689, M.
Maggia0000-0002-8431-3922, I. Margjekaa,b0000-0002-3198-3025, V.
Mastrapasquaa,b0000-0002-9082-5924, S. Mya,b0000-0002-9938-2680, S.
Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b0000-0003-3279-6114, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, R.
Radognaa0000-0002-1094-5038, G. Ramirez-Sancheza,c0000-0001-7804-5514, D.
Ramosa0000-0002-7165-1017, A. Ranieria0000-0001-7912-4062, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, Ü.
Sözbilira0000-0001-6833-3758, A. Stamerraa0000-0003-1434-1968, R.
Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631, A.
Zazaa,b0000-0002-0969-7284

INFN Sezione di Bolognaa, Università di Bolognab, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia0000-0001-8679-4443, R.
Campaninia,b0000-0002-2744-0597, P. Capiluppia,b0000-0003-4485-1897, A.
Castroa,b0000-0003-2527-0456, F.R. Cavalloa0000-0002-0326-7515, M.
Cuffiania,b0000-0003-2510-5039, G.M. Dallavallea0000-0002-8614-0420, T.
Diotalevia,b0000-0003-0780-8785, F. Fabbria0000-0002-8446-9660, D.
Fasanellaa,b0000-0002-2926-2691, P. Giacomellia0000-0002-6368-7220, L.
Giommia,b0000-0003-3539-4313, C. Grandia0000-0001-5998-3070, L.
Guiduccia,b0000-0002-6013-8293, S. Lo Meoa,490000-0003-3249-9208, L.
Lunertia,b0000-0002-8932-0283, S. Marcellinia0000-0002-1233-8100, G.
Masettia0000-0002-6377-800X, F.L. Navarriaa,b0000-0001-7961-4889, A.
Perrottaa0000-0002-7996-7139, F. Primaveraa,b0000-0001-6253-8656, A.M.
Rossia,b0000-0002-5973-1305, T. Rovellia,b0000-0002-9746-4842, G.P.
Sirolia,b0000-0002-3528-4125

INFN Sezione di Cataniaa, Università di Cataniab, Catania, Italy S.
Costaa,b,500000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,500000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153

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

INFN Laboratori Nazionali di Frascati, Frascati, Italy L.
Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124, S.
Meola510000-0002-8233-7277, D. Piccolo0000-0001-5404-543X

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

INFN Sezione di Milano-Bicoccaa, Università di Milano-Bicoccab, Milano, Italy
A. Benagliaa0000-0003-1124-8450, G. Boldrinia,b0000-0001-5490-605X, F.
Brivioa0000-0001-9523-6451, F. Cetorellia0000-0002-3061-1553, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, R.
Gerosaa,b0000-0001-8359-3734, A. Ghezzia,b0000-0002-8184-7953, P.
Govonia,b0000-0002-0227-1301, L. Guzzia0000-0002-3086-8260, M.T.
Lucchinia,b0000-0002-7497-7450, M. Malbertia0000-0001-6794-8419, S.
Malvezzia0000-0002-0218-4910, A. Massironia0000-0002-0782-0883, D.
Menascea0000-0002-9918-1686, L. Moronia0000-0002-8387-762X, M.
Paganonia,b0000-0003-2461-275X, S. Palluottoa,b0009-0009-1025-6337, D.
Pedrinia0000-0003-2414-4175, B.S. Pinolinia, G. Pizzatia,b, S.
Ragazzia,b0000-0001-8219-2074, T. Tabarelli de Fatisa,b0000-0001-6262-4685, D.
Zuoloa0000-0003-3072-1020

INFN Sezione di Napolia, Università di Napoli ’Federico II’b, Napoli, Italy;
Università della Basilicatac, Potenza, Italy; Scuola Superiore Meridionale
(SSM)d, Napoli, Italy S. Buontempoa0000-0001-9526-556X, A.
Cagnottaa,b0000-0002-8801-9894, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, F. Fabozzia,c0000-0001-9821-4151, A.O.M.
Iorioa,b0000-0002-3798-1135, L. Listaa,b,520000-0001-6471-5492, P.
Paoluccia,310000-0002-8773-4781, B. Rossia0000-0002-0807-8772, C.
Sciaccaa,b0000-0002-8412-4072

INFN Sezione di Padovaa, Università di Padovab, Padova, Italy; Università di
Trentoc, Trento, Italy R. Ardinoa0000-0001-8348-2962, P.
Azzia0000-0002-3129-828X, N. Bacchettaa,530000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, G.
Bortolatoa,b, A. Bragagnoloa,b0000-0003-3474-2099, A.C.M.
Bullaa0000-0001-5924-4286, R. Carlina,b0000-0001-7915-1650, P.
Checchiaa0000-0002-8312-1531, T. Dorigoa0000-0002-1659-8727, F.
Gasparinia,b0000-0002-1315-563X, U. Gasparinia,b0000-0002-7253-2669, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, F.
Marinia0000-0002-2374-6433, A.T. Meneguzzoa,b0000-0002-5861-8140, M.
Migliorinia,b0000-0002-5441-7755, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b0000-0003-3466-7500, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, A. Triossia,b0000-0001-5140-9154, S.
Venturaa0000-0002-8938-2193, H. Yarara,b, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,540000-0002-0820-0483, C. Aimèa,b0000-0003-0449-4717, A.
Braghieria0000-0002-9606-5604, S. Calzaferria0000-0002-1162-2505, D.
Fiorinaa0000-0002-7104-257X, P. Montagnaa,b0000-0001-9647-9420, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia,b0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778

INFN Sezione di Perugiaa, Università di Perugiab, Perugia, Italy S.
Ajmala,b0000-0002-2726-2858, G.M. Bileia0000-0002-4159-9123, D.
Ciangottinia,b0000-0002-0843-4108, L. Fanòa,b0000-0002-9007-629X, M.
Magherinia,b0000-0003-4108-3925, G. Mantovania,b, V.
Mariania,b0000-0001-7108-8116, M. Menichellia0000-0002-9004-735X, F.
Moscatellia,550000-0002-7676-3106, A. Rossia,b0000-0002-2031-2955, A.
Santocchiaa,b0000-0002-9770-2249, D. Spigaa0000-0002-2991-6384, T.
Tedeschia,b0000-0002-7125-2905

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia N. Bin Norjoharuddeen0000-0002-8818-7476, I.
Yusuff560000-0003-2786-0732, Z. Zolkapli

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

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala0000-0002-8294-8692, H. Castilla-Valdez0009-0005-9590-9958, H. Crotte
Ledesma, E. De La Cruz-Burelo0000-0002-7469-6974, I. Heredia-De La
Cruz570000-0002-8133-6467, R. Lopez-Fernandez0000-0002-2389-4831, C.A.
Mondragon Herrera, A. Sánchez Hernández0000-0001-9548-0358

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

University of Colombo, Colombo, Sri Lanka M.K. Jayananda0000-0002-7577-310X,
B. Kailasapathy580000-0003-2424-1303, D.U.J. Sonnadara0000-0001-7862-2537,
D.D.C. Wickramarathna0000-0002-6941-8478

University of Ruhuna, Department of Physics, Matara, Sri Lanka W.G.D.
Dharmaratna590000-0002-6366-837X, K. Liyanage0000-0002-3792-7665, N.
Perera0000-0002-4747-9106, N. Wickramage0000-0001-7760-3537

CERN, European Organization for Nuclear Research, Geneva, Switzerland D.
Abbaneo0000-0001-9416-1742, C. Amendola0000-0002-4359-836X, E.
Auffray0000-0001-8540-1097, G. Auzinger0000-0001-7077-8262, J. Baechler, D.
Barney0000-0002-4927-4921, A. Bermúdez Martínez0000-0001-8822-4727, M.
Bianco0000-0002-8336-3282, B. Bilin0000-0003-1439-7128, A.A. Bin
Anuar0000-0002-2988-9830, A. Bocci0000-0002-6515-5666, C.
Botta0000-0002-8072-795X, E. Brondolin0000-0001-5420-586X, C.
Caillol0000-0002-5642-3040, G. Cerminara0000-0002-2897-5753, N.
Chernyavskaya0000-0002-2264-2229, D. d’Enterria0000-0002-5754-4303, A.
Dabrowski0000-0003-2570-9676, A. David0000-0001-5854-7699, A. De
Roeck0000-0002-9228-5271, M.M. Defranchis0000-0001-9573-3714, M.
Deile0000-0001-5085-7270, M. Dobson0009-0007-5021-3230, L.
Forthomme0000-0002-3302-336X, G. Franzoni0000-0001-9179-4253, W.
Funk0000-0003-0422-6739, S. Giani, D. Gigi, K. Gill0009-0001-9331-5145, F.
Glege0000-0002-4526-2149, L. Gouskos0000-0002-9547-7471, M.
Haranko0000-0002-9376-9235, J. Hegeman0000-0002-2938-2263, B. Huber, V.
Innocente0000-0003-3209-2088, T. James0000-0002-3727-0202, P.
Janot0000-0001-7339-4272, O. Kaluzinska0009-0001-9010-8028, S.
Laurila0000-0001-7507-8636, P. Lecoq0000-0002-3198-0115, E.
Leutgeb0000-0003-4838-3306, C. Lourenço0000-0003-0885-6711, L.
Malgeri0000-0002-0113-7389, M. Mannelli0000-0003-3748-8946, A.C.
Marini0000-0003-2351-0487, M. Matthewman, F. Meijers0000-0002-6530-3657, S.
Mersi0000-0003-2155-6692, E. Meschi0000-0003-4502-6151, V.
Milosevic0000-0002-1173-0696, F. Monti0000-0001-5846-3655, F.
Moortgat0000-0001-7199-0046, M. Mulders0000-0001-7432-6634, I.
Neutelings0009-0002-6473-1403, S. Orfanelli, F. Pantaleo0000-0003-3266-4357,
G. Petrucciani0000-0003-0889-4726, A. Pfeiffer0000-0001-5328-448X, M.
Pierini0000-0003-1939-4268, D. Piparo0009-0006-6958-3111, H.
Qu0000-0002-0250-8655, D. Rabady0000-0001-9239-0605, M.
Rovere0000-0001-8048-1622, H. Sakulin0000-0003-2181-7258, S.
Scarfi0009-0006-8689-3576, C. Schwick, M. Selvaggi0000-0002-5144-9655, A.
Sharma0000-0002-9860-1650, K. Shchelina0000-0003-3742-0693, P.
Silva0000-0002-5725-041X, P. Sphicas600000-0002-5456-5977, A.G. Stahl
Leiton0000-0002-5397-252X, A. Steen0009-0006-4366-3463, S.
Summers0000-0003-4244-2061, D. Treille0009-0005-5952-9843, P.
Tropea0000-0003-1899-2266, A. Tsirou, D. Walter0000-0001-8584-9705, J.
Wanczyk610000-0002-8562-1863, J. Wang, S. Wuchterl0000-0001-9955-9258, P.
Zehetner0009-0002-0555-4697, P. Zejdl0000-0001-9554-7815, W.D. Zeuner

Paul Scherrer Institut, Villigen, Switzerland T.
Bevilacqua620000-0001-9791-2353, L. Caminada620000-0001-5677-6033, A.
Ebrahimi0000-0003-4472-867X, W. Erdmann0000-0001-9964-249X, R.
Horisberger0000-0002-5594-1321, Q. Ingram0000-0002-9576-055X, H.C.
Kaestli0000-0003-1979-7331, D. Kotlinski0000-0001-5333-4918, C.
Lange0000-0002-3632-3157, M. Missiroli620000-0002-1780-1344, L.
Noehte620000-0001-6125-7203, T. Rohe0009-0005-6188-7754

ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich,
Switzerland T.K. Aarrestad0000-0002-7671-243X, K.
Androsov610000-0003-2694-6542, M. Backhaus0000-0002-5888-2304, G. Bonomelli,
A. Calandri0000-0001-7774-0099, C. Cazzaniga0000-0003-0001-7657, K.
Datta0000-0002-6674-0015, A. De Cosa0000-0003-2533-2856, G.
Dissertori0000-0002-4549-2569, M. Dittmar, M. Donegà0000-0001-9830-0412, F.
Eble0009-0002-0638-3447, M. Galli0000-0002-9408-4756, K.
Gedia0009-0006-0914-7684, F. Glessgen0000-0001-5309-1960, C.
Grab0000-0002-6182-3380, N. Härringer0000-0002-7217-4750, T.G. Harte, D.
Hits0000-0002-3135-6427, W. Lustermann0000-0003-4970-2217, A.-M.
Lyon0009-0004-1393-6577, R.A. Manzoni0000-0002-7584-5038, M.
Marchegiani0000-0002-0389-8640, L. Marchese0000-0001-6627-8716, C. Martin
Perez0000-0003-1581-6152, A. Mascellani610000-0001-6362-5356, F. Nessi-
Tedaldi0000-0002-4721-7966, F. Pauss0000-0002-3752-4639, V.
Perovic0009-0002-8559-0531, S. Pigazzini0000-0002-8046-4344, C.