Search for diphoton resonances in the 66 to 110 GeV mass range using pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

Abstract

A bstract
A search is performed for light, spin-0 bosons decaying into two photons in the 66 to 110 GeV mass range, using 140 fb − 1 of proton-proton collisions at s \sqrt{s} s = 13 TeV produced by the Large Hadron Collider and collected by the ATLAS detector. Multivariate analysis techniques are used to define event categories that improve the sensitivity to new resonances beyond the Standard Model. A model-independent search for a generic spin-0 particle and a model-dependent search for an additional low-mass Higgs boson are performed in the diphoton invariant mass spectrum. No significant excess is observed in either search. Mass-dependent upper limits at the 95% confidence level are set in the model-independent scenario on the fiducial cross-section times branching ratio into two photons in the range of 8 fb to 53 fb. Similarly, in the model-dependent scenario upper limits are set on the total cross-section times branching ratio into two photons as a function of the Higgs boson mass in the range of 19 fb to 102 fb.

Full text

JHEP01(2025)053
Published for SISSA by Springer
Received: July 11, 2024
Accepted: November 10, 2024
Published: January 8, 2025
Search for diphoton resonances in the 66 to 110 GeV
mass range using
pp
collisions at
√s
= 13 TeV with the
ATLAS detector
The ATLAS collaboration
E-mail: atlas.publications@cern.ch
Abstract: A search is performed for light, spin-0 bosons decaying into two photons in
the 66 to 110 GeV mass range, using 140
fb−1
of proton-proton collisions at
√s
= 13 TeV
produced by the Large Hadron Collider and collected by the ATLAS detector. Multivariate
analysis techniques are used to define event categories that improve the sensitivity to new
resonances beyond the Standard Model. A model-independent search for a generic spin-0
particle and a model-dependent search for an additional low-mass Higgs boson are performed
in the diphoton invariant mass spectrum. No significant excess is observed in either search.
Mass-dependent upper limits at the 95% confidence level are set in the model-independent
scenario on the fiducial cross-section times branching ratio into two photons in the range of
8 fb to 53 fb. Similarly, in the model-dependent scenario upper limits are set on the total
cross-section times branching ratio into two photons as a function of the Higgs boson mass
in the range of 19 fb to 102 fb.
Keywords: Beyond Standard Model, Hadron-Hadron Scattering, Higgs Physics
ArXiv ePrint: 2407.07546
Open Access, Copyright CERN,
for the benefit of the ATLAS Collaboration.
Article funded by SCOAP3.
https://doi.org/10.1007/JHEP01(2025)053

JHEP01(2025)053
Contents
1 Introduction 1
2 ATLAS detector 3
3 Data and simulated event samples 3
4 Event reconstruction 5
5 Event selection 6
6 Event categorisation 7
7 Signal model 10
8 Background estimate 11
9 Results 13
10 Conclusion 17
The ATLAS collaboration 26
1 Introduction
This paper presents a search for an additional light, spin-0 boson with an invariant mass
ranging from 66
GeV to
110
GeV
decaying into two photons, a signature that can arise in many
theories of beyond the Standard Model (SM) physics. These include two-Higgs-doublet models
(2HDMs) [
1
,
2
] and next-to-2HDMs (N2HDMs) [
3
], next-to-minimal supersymmetric models
(NMSSM) [
4
], and models of supersymmetry that introduce pseudo-Nambu-Goldstone bosons
(R-axions) through symmetry breaking [
5
]. The introduction of an additional spin-0 boson can
also be used to explain features observed in other experimental measurements. For example,
the excess of
GeV
-scale gamma rays from the galactic centre can be explained if the additional
spin-0 boson is a Higgs boson that acts as a scalar partner of dark matter [
6
]. Alternatively,
if the additional spin-0 boson is an axion, even a weak coupling with the Higgs sector allows
electroweak baryogenesis to explain the observed baryon asymmetry of the universe [
7
].
This analysis uses the full LHC proton-proton (
pp
) collision data sample at a centre-
of-mass-energy of 13
TeV
collected by the ATLAS detector (see section 2) during the years
2015–2018, corresponding to an integrated luminosity of 140
fb−1
at a centre-of-mass energy
of 13
TeV
. Both a model-independent search for a spin-0 particle (
X
) and a model-dependent
search for an additional low-mass Higgs boson (
H
, assuming the production-mode cross-
section times branching ratio into two photons as predicted by the SM at a given mass
mH
) are performed. In both cases, the assumption of a narrow-width resonance (NWA) is
– 1 –

JHEP01(2025)053
made and interference effects between the signal and background processes are neglected.
An additional search for larger-width signals is also performed in the model-independent
search and considers ratios of decay widths ΓXto the mass mXof the spin-0 particle up to
2
.
5 %. Additional signal widths are not considered for the model-dependent analysis due to
the narrow decay width of SM-like Higgs bosons in the mass range considered here [
8
].
Events that contain at least two photons (see section 3) are analysed for evidence of
resonances in the diphoton invariant mass distribution. The diphoton final state provides a
clean experimental signature due to the excellent invariant mass resolution of the ATLAS
detector. In addition to the kinematic requirements, identification and isolation selections
are applied to photons to reduce the impact of jet backgrounds and to ensure a high signal
sensitivity (see section 4). The event selection, described in section 5, uses photon transverse
energy
ET1
selections that depend on the diphoton invariant mass
mγγ
to suppress sculpting
of the invariant mass distribution by the trigger selection. A gradient boosted decision tree
(BDT) is additionally used for photon–electron discrimination.
Multiple event categories (see section 6) are defined to maximise signal sensitivity. The
model-independent search for a spin-0 boson,
X
, splits the data into three categories based
on whether the photons interact with nuclei in the detector that cause them to convert into a
pair of electrons or not. Alternatively, the model-dependent search for an additional low-mass
Higgs boson,
H
, employs a BDT to define three additional categories within each of the
three photon-conversion categories, resulting in a total of nine categories. This additional
categorisation is only used for the model-dependent result due to the SM-like production-mode
cross-sections assumed for the signal sample used to train the BDT.
The resonant
mγγ
signal distribution is modelled using analytic functions whose parame-
ters are determined using Monte Carlo (MC) simulation, as described in section 7. There are
two main components of the background: 1. the non-resonant
γγ
,
γ
j, and
jj
processes that
are henceforth referred to as the continuum background, where “j” refers to a jet misidentified
as a photon, and 2. the resonant Drell-Yan (DY) dielectron processes (mainly
Z→ee
events)
in which both the electrons are misidentified as photons. The
mγγ
distributions of both the
background components are described by analytic functions determined from MC simulation
and data-driven background estimations, further described in section 8. The uncertainty in
the continuum background due to limited data and MC simulated events is reduced using a
Gaussian Process regression [
9
]. The DY background affects prompt photons that convert to
two electrons (converted photons) much more than those that do not (unconverted photons),
hence a significant gain is obtained by splitting the analysis into separate conversion categories.
The final background
mγγ
shape parameters, background yield, and potential signal
yield are obtained from a fit to the diphoton invariant mass distribution in data. The
mγγ
region 62
GeV to
120
GeV
is chosen to minimise the systematic uncertainty in the background
model. A search for hypothetical signal peaks in the range of 66
GeV to
110
GeV
is performed,
1
ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the
centre of the detector and the
z
-axis along the beam pipe. The
x
-axis points from the IP to the centre of
the LHC ring, and the
y
-axis points upwards. Polar coordinates (
r, ϕ
)are used in the transverse plane,
ϕ
being the azimuthal angle around the
z
-axis. The pseudorapidity is defined in terms of the polar angle
θ
as
η
=
−ln tan
(
θ/
2) and is equal to the rapidity
y
=
1
2ln E+pzc
E−pzc
in the relativistic limit. Angular distance is
measured in units of ∆R≡p(∆y)2+ (∆ϕ)2.
– 2 –

JHEP01(2025)053
which ensures that there is enough data to constrain the background model both above and
below the signal peak. The resulting
p
-value scans are presented in section 9. Since no
significant excess is observed, limits are set on the cross-section times branching ratio within
a fiducial volume defined at particle-level in the model-independent search for
X
and on the
cross-section times branching ratio in the model-dependent search for
H
.
A previous search by the ATLAS Collaboration, using 20
.
3
fb−1
of data at 8
TeV
[
10
],
found no significant excesses. Previous searches by the CMS Collaboration, using 19
.
7
fb−1
of data at 8
TeV
combined with 35
.
9
fb−1
of data at 13
TeV
[
11
], and later with 132
.
2
fb−1
of
data at 13
TeV
[
12
], observed a maximal excess with a local (global) significance relative to
the SM prediction of
2.8σ
(
1.3σ
) at 95
.
3
GeV
and
2.9σ
(
1.3σ
) at 95
.
4
GeV
, respectively.
2 ATLAS detector
The ATLAS experiment [
13
] at the LHC is a multipurpose particle detector with a forward-
backward symmetric cylindrical geometry and a near 4
π
coverage in solid angle. It consists
of an inner tracking detector surrounded by a thin superconducting solenoid providing a 2 T
axial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer.
The inner tracking detector covers the pseudorapidity range
|η|<
2
.
5. It consists of silicon
pixel, silicon microstrip, and transition radiation tracking detectors. Lead/liquid-argon
(LAr) sampling calorimeters provide electromagnetic (EM) energy measurements with high
granularity within the region
|η|<
3
.
2. A steel/scintillator-tile hadronic calorimeter covers the
central pseudorapidity range (
|η|<
1
.
7). The endcap and forward regions are instrumented
with LAr calorimeters for EM and hadronic energy measurements up to
|η|
= 4
.
9. The muon
spectrometer surrounds the calorimeters and is based on three large superconducting air-core
toroidal magnets with eight coils each. The field integral of the toroids ranges between
2
.
0and 6
.
0 T m across most of the detector. The muon spectrometer includes a system of
precision tracking chambers up to
|η|
= 2
.
7and fast detectors for triggering up to
|η|
= 2
.
4.
The luminosity is measured mainly by the LUCID–2 [14] detector, which is located close to
the beampipe. A two-level trigger system is used to select events [
15
]. The first-level trigger
is implemented in hardware and uses a subset of the detector information to accept events at
a rate below 100
kHz
. This is followed by a software-based trigger that reduces the accepted
event rate to 1
kHz
on average depending on the data-taking conditions. A software suite [
16
]
is used in data simulation, in the reconstruction and analysis of real and simulated data, in
detector operations, and in the trigger and data acquisition systems of the experiment.
3 Data and simulated event samples
The ATLAS detector was used to collect
√s
= 13
TeV pp
collisions from the 2015–2018
LHC running periods, corresponding to an integrated luminosity of 139
.
5(12)
fb−1
[
17
] after
data-quality requirements [
18
]. The uncertainty in the integrated luminosity is obtained
using the LUCID-2 detector [
14
] for the primary luminosity measurements, complemented
by measurements using the inner detector and calorimeters. The data were recorded using
diphoton triggers that required two EM clusters with transverse energies
ET
above a certain
threshold and satisfying identification criteria based on variables describing the shape of the
– 3 –

JHEP01(2025)053
EM showers in the calorimeter (hereafter called shower shapes) [
19
]. In the 2015 and the first
portion of 2016 data taking, the
ET
threshold was 20
GeV
, while in the remainder of 2016
data taking
ET>
22
GeV
was required. During 2017 and 2018 data taking, the
ET
threshold
was reverted to 20
GeV
, however an additional requirement on the sum of transverse energy
around the photon candidate was applied [
20
].
Simulated event samples are used to study signal and background processes, and to
determine the analytic functions used to model both. The search itself is performed by using
the data to determine the parameters of the analytic functions (see section 8). Interference
effects between the resonant signal and all background processes are expected to be small
for the signal widths considered here [
21
] and are neglected.
Background events containing two photons with associated jets were simulated with the
SHERPA 2.2.4 [
22
,
23
] event generator. Matrix elements were calculated with up to three
partons at next-to-leading order (NLO) in quantum chromodynamics (QCD) [
24
], and merged
with the SHERPA parton shower [
25
] according to the ME+PS@NLO prescription [
26
]. The
CT10 parton distribution function (PDF) set [
27
] was used in conjunction with dedicated
parton shower tuning developed by the SHERPA authors. Events containing
Z
bosons
decaying into electron pairs were generated at NLO in QCD using POWHEG BOX v2 [
28
,
29
]
interfaced to the PYTHIA 8.186 [
30
] parton shower model and the CT10 PDF set was used.
The AZNLO set of tuned parameters for the underlying event [
31
] was used, with the CTEQ[
6L1] PDF set [
32
]. Additional events containing
Z
bosons were also simulated with the
SHERPA 2.2.1 event generator, for comparison with POWHEG BOX v2.
To better study electrons reconstructed as photons, single-electron and single-photon
MC event samples were simulated with a pile-up profile corresponding to the Run 2 data
sample. These single-particle samples were generated with transverse energy distributions
covering the range from 5
GeV
to 3
TeV
.
The signal samples assume a SM Higgs boson at different mass values, and were generated
at NLO in QCD using POWHEG BOX v2 interfaced to the PYTHIA 8.186 parton shower
model using the AZNLO set of tuned parameters, for the gluon–gluon fusion (ggF) and
vector-boson fusion (VBF) production modes. Samples were also simulated with the PYTHIA
8.186 event generator using the A14 set of tuned parameters [
33
], assuming the production of
a Higgs boson in association with a
W
boson (WH),
Z
boson (ZH) or top-quark pair (t
¯
tH).
Simulated samples were produced for fixed values of the mass of the assumed resonance,
spanning the range 60
GeV to
120
GeV
. Generally, the model-independent search for
X
uses the ggF sample as the nominal signal model with an uncertainty calculated by taking
the envelope created by the other production modes, while the model-dependent search
for a low-mass
H
combines all production modes for a given mass point assuming SM-like
cross-sections. All assume a narrow-width resonance of 4
MeV
that is negligible compared
with the experimental resolution, which ranges from 0
.
9
GeV to
2
.
2
GeV
(see section 7).
The effects of additional
pp
interactions in the same or neighboring bunch crossings
(pile-up) were modeled by overlaying soft QCD processes simulated with PYTHIA 8.186
using the A2 set of tuned parameters [
34
] and the MSTW2008LO PDF set [
35
]. The
simulated events are weighted to reproduce the distribution of average number of individual
pp
interactions per bunch crossing and the distribution of the primary vertex
z
-position
– 4 –

JHEP01(2025)053
observed in data (pile-up reweighting). All generated events were propagated through a
detailed simulation of the ATLAS detector [
36
] based on GEANT [
37
]. The fast detector
simulation used for background events containing two photons used a parameterisation of
the performance of the calorimeters [
38
].
4 Event reconstruction
The event reconstruction is similar to the one described in ref. [
39
]. Photon candidates are
reconstructed from topological clusters of energy deposited in the EM calorimeter (calorimeter
clusters), and from charged-particle tracks and conversion vertices reconstructed in the ID.
Photon clusters without any corresponding track in the ID are considered unconverted.
Two opposite-charge tracks that form a vertex consistent with a massless particle and that
both match to calorimeter clusters are considered converted photons; single-track vertices —
essentially those without hits in the innermost ID layers — that match to a calorimeter cluster
are also considered as converted photons [
40
]. Photon conversion fractions vary from 20 % to
65 %, depending on
η
[
40
]. Photons and electrons are required to fulfil identification criteria
based on the shower shapes in the EM calorimeter [
40
]. Residual differences between the
average values of the shower-shape variables measured in data and simulation using
Z→ℓℓγ
events are corrected by shifting the shower-shape distributions in the simulation [
41
]. The
photon identification efficiency is determined as a function of
ET
and
|η|
and the efficiency
increases with
ET
from 70% at 20
GeV
to 90% at 50
GeV
[
19
].
Photons are required to be in the high-precision EM calorimeter within the pseudorapidity
interval
|η|<
2
.
37, excluding the transition region 1
.
37
<|η|<
1
.
52 between the barrel and
end-cap calorimeters. Corrections to the energy of photon clusters is based on a multivariate
regression algorithm optimised on simulated samples and scale factors derived extracted
from data samples [
42
]. The two candidates with the highest transverse energies, both
satisfying
ET>
22
GeV
, are retained. This transverse energy requirement is slightly higher
than the trigger threshold, for most of the data-taking periods, to mitigate the trigger
efficiency turn-on effect. A subsequent requirement on the ratio of photon
ET
to the diphoton
mass (see section 5) further raises the energy of selected photons. Primary vertices are
reconstructed using at least two good-quality tracks with
pT>
500
MeV
[
43
]. The photon
candidates are used to select the diphoton vertex using a neural-network algorithm based
on charged-particle tracks and primary vertex information, as well as the direction of the
two photons measured in the calorimeters and ID [
44
]. Once the diphoton vertex is selected,
the direction of the two photon candidates is re-computed relative to this updated primary
vertex. This recomputation improves the
ET
measurement of each photon candidate through
its dependence on the photon candidate’s direction in
η
. The updated energy measurement
improves the diphoton invariant mass resolution by about 8% for inclusive Higgs boson
production relative to the default primary vertex selection [
43
].
Electron candidates — used for studying the DY background process — are reconstructed
by matching tracks in the inner detector with clusters of energy deposits in the EM calorimeter
formed with the same algorithm as in the photon reconstruction [
40
]. The tracks are required
to be consistent with the diphoton vertex using their longitudinal (
z0
) and transverse (
d0
)
impact parameters. Electrons must also satisfy the same
ET
and
η
selection criteria as photons.
– 5 –

JHEP01(2025)053
To improve the rejection of jets misidentified as photons, the candidates are required to be
isolated using both the calorimeter and tracking detector information. The calorimeter-based
isolation variable
Eiso
T
is defined as the scalar sum of the
ET
over positive-energy topological
clusters [
45
] within a radius ∆
R
= 0
.
2around the photon candidate, excluding the photon
energy and correcting for pile-up and underlying-event contributions [
46
–
48
]. For both the
candidates this variable is required to be below 0
.
065
×ET
, where
ET
is the transverse
energy of the photon. The track-based isolation variable
piso
T
is based on charged-particle
tracks, and is defined as the scalar sum of the transverse momenta
pT
over tracks within
a radius ∆
R
= 0
.
2around the photon candidate. Only tracks with
pT>
1
GeV
that are
consistent with originating from the diphoton production vertex and that are not associated
with a photon conversion vertex are used. For both the candidates this variable is required
to be below 0
.
05
×ET
. Small differences between the average value of
Eiso
T
between data
and simulation are corrected in the simulation. The photon isolation efficiency — i.e., the
fraction of photons fulfilling the identification requirement that also satisfy the isolation
requirement — is determined using simulated samples and increases with
mγγ
from 80 %
at 62
GeV
to 90 % at 120
GeV
.
5 Event selection
Only events containing at least one primary vertex candidate are considered. The two selected
photon candidates are used to define the diphoton invariant mass,
mγγ
, and only events
in the mass range 62
GeV to
120
GeV
are included in the analysis. To avoid distortions in
the diphoton invariant mass spectrum due to the kinematic turn-on effects from the trigger
selection, each photon is required to satisfy
ET/mγγ >
22
/
58
≈
0
.
38. This particular value is
chosen to maximise the signal efficiency while allowing the mass range 62
GeV to
120
GeV
to
be described more easily using monotonically decreasing analytic functions (see section 8).
Following these selections, two significant background components are identified:
γγ
,
γ
j
and
jj
continuum backgrounds coming from QCD production, and
ee
pairs coming from DY
production. Here, the
γ
jcomponent includes events where either the leading or sub-leading
object is a jet misidentified as a photon, and the
jj
component includes events where both
the objects are jets misidentified as photons. The relative contribution of the continuum
backgrounds varies by
mγγ
and conversion category, with roughly 75 % due to
γγ
,20 % due
to
γ
j, and 5 % due to
jj
processes (see section 8). The contributions of events containing
SM Higgs bosons or
W
and
Z
bosons produced in association with a photon are estimated
by using MC simulated event samples and are found to be negligible.
To further reduce the number of background
ee
events, in particular the events where
a topological cluster is ambiguously reconstructed as both an electron and a photon [
40
], a
boosted decision tree (BDT) is developed using LightGBM [
49
]. Photon candidates from
single-photon MC samples are considered as signal in the training, while photon candidates
from the single-electron MC samples are considered as background when training the BDT.
The model is trained using kinematic information related to the converted photon and detailed
tracking information related to the electron and conversion of the photon candidate. The full
list of variables considered for this classifier are chosen to avoid introducing shapes in the
background distribution that are difficult to model with analytic functions (see section 8).
– 6 –

JHEP01(2025)053
(a) (b)
Figure 1. (a) Distribution of electron–photon ambiguity BDT scores constructed by taking the
minimum score of the two photon candidates in simulated ggF
mX
= 100
GeV
signal events (solid
line),
Z/γ∗→ee
events (dashed line), and the continuum
γγ
background events (dotted line).
The distributions are scaled independently for illustrative purposes. (b) Efficiency versus minimum
requirement on the ambiguity BDT score, shown for the same samples. Only events in which both
photon candidates are converted are shown.
The output of this classifier (henceforth referred to as the ambiguity BDT ) is a score on the
interval [0,1], where scores closer to 0 indicate an electron-like object and a score nearer to 1
indicate a photon-like object. Unconverted and non-ambiguous photons are assigned a score
of 1 and are not further classified. The score of the ambiguity BDT is then evaluated for each
of the two photon candidates. In figure 1, the minimum score between the two candidates and
the selection efficiency for the signal and background processes are shown, both as a function
of ambiguity BDT score in events with two converted photon candidates and thus most likely
to suffer from
ee
backgrounds. Requiring both the ambiguity BDT scores to be above 0.2
results in a signal selection efficiency above 93 % and a reduction of
ee
backgrounds between
65 % to 90 %, where the largest reduction is obtained for events with two converted photons.
6 Event categorisation
As the
ee
backgrounds predominantly impact events where both photons convert to a pair
of electrons (see section 4), including distinct conversion categories results in a significant
increase in sensitivity to new physics. After the event selection from section 5is applied,
events are categorized into those where: 1. both photons remain unconverted (UU), 2. either
one of the two photons convert (UC), or 3. both photons convert (CC).
For the model-dependent result, in addition to the conversion categories, events are
separated between the continuum background and low-mass Higgs boson processes by another
BDT, henceforth referred to as the category BDT. The training of this BDT is performed using
the adaptive boosting (AdaBoost) algorithm [
50
], with a boosting parameter of 0.5, designed
with the TMVA toolkit [
51
]. SM-like assumptions on the production-mode cross-sections
are used for the model-dependent result. Eight input variables are considered and are given
here ranked in order of variable importance in the training: the cosine of the difference in
– 7 –

JHEP01(2025)053
Category Selection Requirement
Model-Independent categories
UU 2 unconverted photons
UC 1 converted photon and 1 unconverted photon
CC 2 converted photons
Model-Dependent categories
UU1 UU and category BDT score < −0.2
UU3 UU and category BDT score [−0.2,0)
UU3 UU and category BDT score >= 0
UC1 UC and category BDT score < −0.2
UC2 UC and category BDT score [0.2,0)
UC3 UC and category BDT score >= 0
CC1 CC and category BDT score < −0.2
CC3 CC and category BDT score [−0.2,0)
CC3 CC and category BDT score >= 0
Table 1. The selection requirements and names of each category in the model-independent and
model-dependent analyses.
azimuthal angle between the two photons, the ratio
ET/mγγ
of each photon, the
η
of the
each photon, the minimum of the two ambiguity BDT scores, and the ambiguity BDT scores
of each of the two photons. Photon candidates from simulated samples with
mH
=60, 80,
100, and 120
GeV
—where the different masses and production modes are weighted assuming
the SM-like cross-sections for a Higgs boson at the specified
mH
— are considered as signal
(S) and simulated diphoton events are considered as background (B) when training this
BDT. For simplicity, the photon candidates from all three conversion categories are used in
the BDT training. The resulting category BDT score is shown in figure 2, for signal and
background processes. The category BDT score is used to define three sub-categories within
each of the three conversion categories, labelled as 1
to
3in increasing order of expected signal
compared to background. The nine categories are defined based on the combined conversion
and BDT category as UU1, UU2, UU3, UC1, UC2, UC3, CC1, CC2, and CC3. The number
of categories and the corresponding boundaries are optimised based on the expected signal-
to-background significance, quantified by
S/√B
, while requiring that there are a sufficient
number of simulated events for determining the background model. Each sub-category is
required to contain at least 20% of the total number of diphoton background events in a given
photon conversion category. While small differences in the optimal BDT score boundaries are
found for different categories, for simplicity, the same boundaries in BDT score of
−
0
.
2and 0
are used for all events. A list of each category and its requirements can be found in table 1.
The category BDT classification is found not to cause significant distortions of the
diphoton invariant mass distribution for any category. The expected signal and background
yields for a signal with
mH
= 90
GeV
are given in table 2.
– 8 –

JHEP01(2025)053
Figure 2. Distributions of the category BDT scores for the merged SM-like Higgs boson considering
all production modes (ggF, VBF, t
¯
tH, WH, ZH), the diphoton (
γγ
) and reducible backgrounds
(
γ
j, jj) continuum, and the simulated
Z→ee
background prediction. The reducible background
components are derived from dedicated data control regions where the photon identification and
isolation requirements are inverted. The merged signal contains signals generated for
mH
=60, 80,
100, and 120
GeV
and each MC sample is weighted according to the SM-like Higgs boson cross-section.
Photons from all three conversion categories are used in the BDT training. The merged signal and
backgrounds are separately normalised to unity. The vertical lines and arrows at category BDT scores
of
−
0
.
2and 0 define the categorisation used in this analysis. Events with category BDT scores below
−
0
.
2are in BDT 1, events with category BDT scores between
−
0
.
2and 0 are in BDT 2, and events
with category BDT scores above 0 are in BDT 3.
SM-like Higgs boson (mH= 90 GeV) Background
BDT Category Total ggF VBF WH ZH t¯
tH Total DY
[%] [%] [%] [%] [%] [GeV−1] [GeV−1]
1 741 97.1 1.2 1.0 0.6 0.1 18877 2179
2 942 93.4 2.9 2.1 1.2 0.4 14014 713
3 1187 72.4 13.5 6.7 4.0 3.4 6522 294
Total 2870 85.7 6.8 3.7 2.2 1.6 39413 3186
Table 2. The expected number of signal events, fractions of each Higgs boson production mode, and
the number of background events per
GeV
at
mγγ
= 90
GeV
for each BDT category, and for all three
photon conversion categories together. The per
GeV
binning corresponds to approximately 1
σ
of the
mass resolution at
mγγ
= 90
GeV
. The background events are extracted from the background-only
fit to the data and the “Total” category includes the number of Drell-Yan events (DY) that are
also shown separately. The BDT categories are defined as follows: events with category BDT scores
below
−
0
.
2are in BDT category 1, events with category BDT scores between
−
0
.
2and 0 are in BDT
category 2, and events with category BDT scores above 0 are in BDT category 3.
– 9 –

JHEP01(2025)053
(a) (b)
Figure 3. Simulated diphoton invariant mass distribution of a narrow-width signal particle
H
of mass
80
GeV
(points) in the (a) UU3 and (b) CC1 categories, overlaid with the DSCB function resulting
from the signal model parameterisation (line). The error bars on the simulated data points indicate
the statistical uncertainties. An arbitrary normalisation is used for illustration purposes.
7 Signal model
The
mγγ
distribution of the signal, assumed to have a narrow decay width relative to the
mass resolution, is modelled using a double-sided Crystal Ball (DSCB) function, composed
of a Gaussian core with power-law tails [
52
,
53
]. Each parameter is determined in a fit to
the fixed-mass simulated samples, and is parameterised as a linear function of the resonance
mass separately for each conversion category. For the model-independent search for a spin-0
scalar
X
only the ggF production mode is considered; for the model-dependent search for
a low-mass
H
all production modes for a given mass value are combined assuming SM-like
cross-sections [
54
]. The width of the Gaussian core, which is entirely determined by detector
resolution, ranges from 0
.
9
GeV to
2
.
2
GeV
depending on the resonance mass and analysis
category. Good agreement between the signal model fit and the simulated mγγ distribution
is found, with reduced chi-square values generally between 0.9 and 1.4. Examples fits are
illustrated in figure 3for the UU3 and CC1 categories.
In addition to the narrow width signals considered above, a search for signals with
larger widths is also performed in the model-independent analysis. The large width signal
distributions for specific values of the mass and width are derived by convolving the detector
resolution with the predicted line shape defined in the MC simulation of the ggF process.
The detector resolution is modelled using the DSCB function described above and the signal
line shapes are comprised of a Breit-Wigner (BW) function of width Γ
X
combined with a
mass-dependent gluon–gluon luminosity functional form. The large width signal hypotheses
considered in this analysis cover the range 0 %
≤
Γ
X/mX≤
2
.
5 %. To ensure that sufficient
side bands are available for the largest width signals, only signal masses in the range 75
GeV
to
105
GeV
are considered for the large-width analysis.
– 10 –

JHEP01(2025)053
8 Background estimate
The two main components of the background, the continuum and the resonant DY, are
estimated separately in each category. In both cases, a data-driven approach is used to
describe the normalisation and shape of their
mγγ
distributions. The continuum background is
fitted on data, with the normalisation and function parameters free. For the DY background,
both the shape and normalisation are fitted, but the corresponding parameters are constrained
using information from
Z→ee
decays, as described below.
The continuum background is predominately composed of events from
γγ
but also
γ
jand
jj
production processes. To evaluate its composition, the two-dimensional side bands method
described in ref. [
55
] is used to simultaneously extract the fraction of
γγ
,
γ
j, and
jj
events
(including fake photons coming from DY electrons). The inputs to this method are the signal
efficiencies of the identification and isolation requirements, as well as the number of events in
sixteen categories, defined by whether each candidate passes or fails the photon identification
and isolation criteria (four categories for each photon). The signal leakage in the background
control regions — those that fail the photon identification or isolation requirements — is
evaluated with simulation. A system of equations predicting the number of events in each
category is solved with a
χ2
minimisation process, extracting the background decomposition
estimate shown in table 3. The diphoton purity increases with the invariant mass
mγγ
and
varies from 60 % to 70 % across the mass range 62
GeV to
120
GeV
with an uncertainty of 1 %
to 5 % depending on the category. The uncertainties in this purity measurement arise from:
statistical uncertainty in the data and simulated MC samples; the alternate definitions of the
identification criteria that define the control regions; the dependence of the signal leakage
evaluation in the control regions on the event generator; the modelling of the isolation variable
and shower-shape distributions; and possible correlations between the isolation variables
and the inverted identification criteria [
55
].
The continuum background
mγγ
distribution in each category is described by an analytic
function whose form is determined using the method described in ref. [
56
]. The bias related
to the choice of analytic function is estimated as the fitted spurious signal [
44
] yield extracted
using a signal-plus-background fit to a background-only template. The background-only
template is built using simulated samples for the
γγ
component and a data control sample
for the
γ
jand
jj
components, mixed according to the fractions determined by the 2x2D
side band method presented above. To minimise the effects of statistical fluctuations in the
background-only template due to finite MC simulation statistics, the templates are smoothed
with a Gaussian Process regression [
9
]. This analysis uses a similar methodology to that used
in ref. [
57
], with an update to use the Gibbs kernel [
58
] to allow for an adaptive length scale
hyperparameter. The following functions are considered: Bernstein polynomials [
59
] of order
five to seven, and exponentials of second, third and fourth order polynomials. A function is
only considered for modelling the background in a given analysis category if the maximal
spurious signal from a fit to the background template is found to be less than 50% of the
statistical uncertainty in the fitted signal yield (from the background distributions normalised
to the same statistics as the data) over the mass range 66
GeV to
110
GeV
. In the cases
where two or more functions satisfy this requirement, the function with the fewest degrees of
freedom is chosen to model the continuum background in that analysis category. For most
– 11 –

JHEP01(2025)053
Category UU UC CC
Model Independent
Ndata 1356130 1104590 243984
fγγ [%] 74.4±1.3 69.2±3.3 61.6±3.7
fγj[%] 19.9±1.2 23.7±1.9 28.1±2.2
fjj [%] 5.3±0.5 6.0±0.7 7.3±1.7
fDY [%] 0.46±0.03 1.1±0.1 3.1±0.4
BDT Category 1
Ndata 592403 494228 112603
fγγ [%] 71.5±2.0 67.0±3.4 57.3±3.8
fγj[%] 20.9±1.5 24.1±2.0 29.9±3.1
fjj [%] 6.8±0.6 7.2±0.9 8.2±0.9
fDY [%] 0.7±0.1 1.7±0.2 4.7±0.6
BDT Category 2
Ndata 508548 401646 86275
fγγ [%] 74.7±1.4 69.7±4.1 64.5±3.8
fγj[%] 20.2±1.3 24.1±2.7 26.7±2.6
fjj [%] 4.8±0.5 5.5±0.6 7.0±1.2
fDY [%] 0.29±0.02 0.69±0.01 1.8±0.2
BDT Category 3
Ndata 255179 208716 45106
fγγ [%] 80.4±1.0 73.5±2.8 66.6±5.1
fγj[%] 16.7±1.2 21.9±1.8 26.2±3.3
fjj [%] 2.7±0.3 4.0±0.9 5.5±1.4
fDY [%] 0.19±0.02 0.56±0.06 1.7±0.2
Table 3. The number of data events (
Ndata
), the expected fraction of
γγ
,
γ
j,
jj
events determined
with the two dimensional side band method, and the fraction of Drell-Yan events in each category.
The uncertainties in the fractions of
γγ
,
γ
j,
jj
arise from the statistical uncertainty varying the
identification requirements. The BDT categories are defined as follows: events with category BDT
scores below
−
0
.
2are in BDT Category 1, events with category BDT scores between
−
0
.
2and 0 are
in BDT Category 2, and events with category BDT scores above 0 are in BDT Category 3.
categories considered in this analysis, the result is an exponential of a third- or fourth-order
polynomial. The exception is the UC category in the model independent analysis, which is
modelled using a Bernstein polynomial of order six. The corresponding modelling uncertainty
is derived by fitting the local maxima of the absolute spurious signal as a function of diphoton
mass with an exponential of a second-order polynomial in each category.
The DY background is modelled using a DSCB function, with parameters determined
by fitting a data-driven
mγγ
template. At first order, this template is derived using an
mee
distribution from
Z→ee
events in data. Electrons are required to pass the same
ET
and
η
requirements as photons, as well as identification requirements based on shower shape
variables; no isolation requirement is applied. Because electrons misidentified as photons
– 12 –

JHEP01(2025)053
generally lose a large amount of energy due to bremsstrahlung, a Smirnov transformation [
60
]
derived from simulation is used to correct the
mee
template shape to the
mγγ
shape expected
from electrons faking a photon. The normalisation of the resulting
mγγ
template is computed
from
e→γ
fake rates obtained from the same data used to derive the template, as described
in ref. [
10
], and is shown in table 3. Since electrons reconstructed as unconverted photons
are more affected by bremsstrahlung than those reconstructed as converted photons, the
UU events are more shifted to lower invariant masses than CC events. To account for
these differences, the Smirnov transformation and normalisation are derived separately for
each analysis category. Variations arising from limited MC sample sizes, variations of the
Z
-boson mass window and background subtraction used when deriving fake rates, results
using different MC event generators, and results using different detector geometries are
considered as uncertainties.
9 Results
For the model-independent result, the measurement of the signal production cross-section
times branching ratio, σfid × B, is performed in a fiducial region. The fiducial region, which
closely matches the selection requirements of the reconstructed photons, is constructed in
order to reduce the dependence of the measurement on the chosen theoretical model. The
fiducial region is defined at particle level as: two photons with
ET>
22
GeV
,
|η|<
2
.
37
excluding 1
.
37
<|η|<
1
.
52, passing the isolation requirement
Eiso
T<
0
.
065
ET
+ 1
GeV
, and
passing
ET/mγγ >
22
/
58. Here,
Eiso
T
is defined as the magnitude of the vector sum of
pT
for all particles with a lifetime longer than 10
ps
(except neutrinos and muons) within a
radius of ∆
R
= 0
.
2around the photon. This isolation requirement is chosen to reproduce the
detector-level selection. The particle-level fiducial cross-section includes a signal efficiency
correction factor
CX
through:
σfid × B =NS
CXL,with CX=Ndet
MC
Nfid
MC
,(9.1)
where
NS
is the fitted number of signal events in data,
L
is the integrated luminosity,
Ndet
MC
is the number of simulated signal events passing the detector-level selection criteria, and
Nfid
MC
is the number of simulated signal events passing the particle-level selection. Since a
generic spin-0 scalar
X
is targeted, only the ggF production mode is used for the nominal
correction factor, and the
CX
factor values range from 0
.
46
to
0
.
69 as a function of
mX
.
The envelope of the
CX
values from the five production modes is taken as an uncertainty.
This model dependence uncertainty is determined using simulated samples of
X
with several
production modes: ggF, VBF, VH, and t
¯
tH.
Several experimental uncertainties directly impact the signal yields and signal shape
and are accounted for in the fit using nuisance parameters constrained by Gaussian penalty
terms in the likelihood function. The largest effect on the signal yield in the model-dependent
analysis comes from the uncertainty in the pile-up reweighting procedure. This uncertainty
is estimated by changing the nominal rescaling factor and evaluating the change in the
signal efficiency. Uncertainties in the signal yield arising from uncertainties in the luminosity
determination, the trigger efficiency, and photon identification and isolation efficiencies are also
– 13 –

JHEP01(2025)053
considered. Uncertainties in the signal mass scale and resolution are assessed by propagating
the photon energy calibration uncertainties onto the MC signal samples and constructing
a new signal template. These shifted signal templates are then refit using the signal model
parameterisation given in section 7and the deviations from the central mass and resolution
values are taken as an uncertainty. These uncertainties in the signal mass scale (resolution)
are in the range
±
0
.
3 % to
±
0
.
5 % (
±
3 % to
±
10 %), with the larger uncertainties occurring at
higher values of
mγγ
. A systematic uncertainty is derived from the variation of the detector
material description in the simulation, resulting in migrations across conversion categories.
An uncertainty is also assessed on the electron-photon ambiguity BDT efficiency that accounts
for differences in performance between data and the MC simulation selection efficiency in a
control region outside of the mass range considered in this analysis. The absolute differences
in efficiencies between data and MC simulation are found to be 0
.
1 %,1
.
2 %, and 2
.
9 % for
the UU, UC, and CC categories, respectively. Since no attempt was made to remove the
γ
j
and
jj
components from the diphoton data samples — accounting for around the 25 % of the
data sample in the mass range 110
GeV to
120
GeV
—these differences are expected to be
conservative estimates. The envelope of these uncertainties results in an uncertainty of 0
.
7 %
on the signal selection efficiency. The variations on the DY background result in uncertainties
in the peak position, the peak width, and the normalisation which are derived separately for
each conversion category. The magnitude of these uncertainty components is shown in table 4.
For the model-dependent result, the total signal production cross-section times branching
ratio,
σH× B
, is computed as:
σH× B =NS
AHCHL,(9.2)
where
NS
is the number of signal events fit in data,
CH
is the signal efficiency factor for
a SM-like Higgs boson, and
AH
is an acceptance factor defined as the probability of a
generated event to be selected in the fiducial volume at the particle level. The
AH
and
CH
factors are parameterised using the weighted average of the different Higgs boson production
modes assuming the SM-like cross-section for each production mode. The acceptance times
correction factor varies from 0
.
13
to
0
.
2depending on the mass of the SM-like Higgs boson
mH
. Since the model-dependent result assumes SM-like production-mode cross-sections when
constructing
CH
, the uncertainty from taking the envelope of the production modes — as done
when deriving
CX
in the model-independent results — is not relevant. After accounting for
the differences between the analyses, the model-dependent result provides an approximately
30 % to 60 % stronger limit than the model-independent result.
The expected fraction of signal events for the model-independent (model-dependent)
analysis in each of the three conversion (nine) categories is parameterised as a function
of
mX
(
mH
). The migration of signal events between different BDT categories in the
model-dependent analysis is found to be negligible.
The number of signal and background events is measured with an extended maximum-
likelihood simultaneous binned fit to the
mγγ
spectra. This fit is performed in the three
conversion categories for the model-independent analysis and in nine categories (three BDT
categories for each conversion category) in the model-dependent analysis. Scans over different
– 14 –

JHEP01(2025)053
mX
and
mH
hypotheses are performed in 0.1
GeV
steps over the mass range 66
GeV to
110
GeV
.
The signal-plus-background model has the form:
NS·fS(mγγ ) + NSS ·fS(mγ γ ) + NB·fB(mγγ ) + NDY ·fDY(mγ γ ),(9.3)
where
fS
is the signal model described in section 7,
NS
(
NB
) is the fitted number of signal
(continuum background) events,
NSS
is the number of spurious signal events,
fB
(
fDY
) is the
continuum (Drell-Yan) background model described in section 8, and
NDY
is the number
of DY background events. The systematic uncertainties are included in the likelihood via
nuisance parameters, and constrained by Gaussian or log-normal penalty terms.
A background-only fit of the data is shown in figure 4for the three model-independent
conversion categories and in figure 5for the three model-dependent BDT 3 categories. The
parameters of the background model in each category are determined during a simultaneous fit
to all categories. A goodness-of-fit test is performed in each category and returns a probability
between 5% and 89% depending on the category. As expected, the DY contribution is most
prominent in the CC categories (see table 3).
The compatibility of the observed diphoton mass spectra with the background-only
hypothesis, for a given resonance mass, is determined with a local
p
-value based on the
profile-likelihood-ratio-test statistic [
61
] as detailed in ref. [
53
]. The observed and expected
95 % confidence level (CL) exclusion limits on the production cross-section times branching
ratio are evaluated using a modified frequentist approach
CLs
[
62
] with the asymptotic
approximation to the test-statistic distribution [
61
].
The result of the model-independent
p
-value scan is shown in figure 6(a). No significant
excess with respect to the background-only hypothesis is observed. The largest localised
deviation for the model-independent search is observed for a mass of 71.8
GeV
, corresponding
to a local significance of 2.2
σ
.
For the narrow-width model-independent result, an upper limit at the 95 % CL is set
on
σfid × B
from 8 fb to 53 fb, as shown in figure 6(b).
The model-independent analysis considers additional larger width signal hypotheses, and
the
p
-value scan is expanded to include signal hypotheses with Γ
X/mX
in the range 0 % to
2
.
5 %. The result of this
p
-value scan is shown in figure 7. Due to the truncated range in
mX
, the most significant excess occurs at 85
.
2
GeV
for the narrow-width model hypothesis
and corresponds to a local significance of 1.7σ. In the absence of a significant excess, upper
limits are set on
σfid × B
as a function of Γ
X/mX
and are illustrated in figure 8.
The result of the model-dependent
p
-value scan is shown in figure 9(a) and the largest
deviation is observed for a mass of 95
.
4
GeV
, corresponding to a local significance of 1.7
σ
.
An upper limit at the 95 % CL is set on
σH×B
from 19
fb
to 100
fb
for the model-dependent
result, as shown in figure 9(b). The limited number of
pp
collisions recorded is the dominant
uncertainty impacting this result. The model-dependent result can be compared to a similar
result from the CMS Collaboration, which sets an observed upper limit ranging from 15
fb
to 73
fb
in the mass range 70
GeV to
110
GeV
[
12
]. The largest deviation observed by CMS
is for a mass of 95
.
4
GeV
, corresponding to a local significance of 2.9
σ
.
– 15 –

JHEP01(2025)053
(a) (b)
(c)
Figure 4. Background-only fit to the data (black markers) as a function of the diphoton invariant
mass
mγγ
for the model-independent conversion categories: (a) UU, (b) UC, and (c) CC. The solid
lines show the sum of the Drell-Yan and the continuum background components and the dashed
lines show only the continuum background components. The difference between the data and the
total background component divided by the statistical uncertainty of the data,
σdata
, is shown at the
bottom panel separately for each category. The green (yellow) bands denote the total uncertainty in
the background model at one (two) standard deviation.
Despite using the same
pp
collision events, the results between the model-dependent and
model-independent searches in this analysis are expected to show differences due to the model
assumptions and additional categories used in the model-dependent analysis. An example that
illustrates these differences is the mild excess of events that appears around
mH
=77
GeV
in
the model-dependent analysis. This mild excess is localised in two high-significance categories,
UU3 and UC3, which contain about 20 % of all UU and UC events. In the low-significance
categories that comprise the bulk of the UU and UC events, a deficit of events is observed,
which matches that observed in the model-independent analysis.
– 16 –

JHEP01(2025)053
(a) (b)
(c)
Figure 5. Background-only fit to the data (black markers) as a function of the diphoton invariant
mass mγγ for each of the model-dependent BDT 3 categories: (a) UU3, (b) UC3, and (c) CC3. The
solid lines show the sum of the Drell-Yan and the continuum background components and the dashed
lines show only the continuum background components. The difference between the data and the
total background component divided by the uncertainty, with
σdata
denoting only the statistical error
of the data, is shown at the bottom panel separately for each category. The green (yellow) bands
denote the total uncertainty in the background model at one (two) standard deviation.
10 Conclusion
Searches for new narrow-width resonances, either a generic spin-0 particle or an additional
low-mass Higgs boson, are performed in the diphoton invariant mass spectra ranging from
66
GeV
to 110
GeV
, using 140
fb−1
of
pp
collision data collected at
√s
= 13
TeV
with the
ATLAS detector at the Large Hadron Collider. The dominant uncertainties arise from the
limited number of
pp
collisions collected and the spurious signal uncertainty due to the
choice of analytic functions to model the continuum background. Both a model-independent
search for a spin-0 particle (
X
) and a model-dependent search for an additional low-mass
Higgs boson (
H
, assuming the SM production-mode times branching ratio to two photons
cross-sections) are performed. No significant excess above the SM background expectation is
observed, and 95 % CL upper limits on the cross-section times branching ratio are set for each
– 17 –

JHEP01(2025)053
Source Uncertainty [%] Remarks
Signal yield
Luminosity ±0.83
Electron-photon ambiguity BDT efficiency ±0.7
Trigger efficiency ±1.0– 1.5 mX-dependent
Photon identification efficiency ±1.8– 3.0 mX-dependent
Photon isolation efficiency ±1.6–2.4 mX-dependent
Photon energy scale ±0.1– 0.3 mX-dependent
Photon energy resolution ±0.1–0.15 mX-dependent
Pile-up ±1.6– 5.0 mX-dependent
Production mode ±4.3– 29 mX-dependent (model-independent only)
Signal modelling
Photon energy scale ±0.3– 0.5 mX- and category-dependent
Photon energy resolution ±3– 10 mX- and category-dependent
Migration between categories
Material −2.0/+1.0/+4.1category-dependent
DY background modelling
Peak position ±0.1–0.2category-dependent
Peak width ±1.9–3.5category-dependent
Normalisation ±7.1–13 category-dependent
Continuum background (model-dependent)
Spurious signal, NWA 9–171 events, (10% – 50%) mX- and category-dependent
Continuum background (model-independent)
Spurious signal, NWA 37 –310 events, (20% – 50%) mX- and category-dependent
Spurious signal, ΓX/mX= 1.0% 65 –539 events, (20% – 50%) mX- and category-dependent
Spurious signal, ΓX/mX= 2.5% 92 –879 events, (20% – 50%) mX- and category-dependent
Table 4. Summary of the systematic uncertainties considered in this analysis. In general, the values
correspond to the uncertainties associated to the fit nuisance parameters. The DY uncertainty is
the percent error on the nominal peak position, peak width, and normalisation. The spurious signal
uncertainty is expressed as a number of events and relative to the expected statistical uncertainty
(
δS
) of a fitted signal. The “Remarks” column indicates specific information about the systematic
uncertainty, including whether or not the uncertainty varies as a function of resonance mass or analysis
category.
search. In the model-independent analysis, the observed 95 % CL upper limits on the fiducial
cross-section times branching ratio for a generic spin-0 signal are in the range 8
fb
to 53
fb
for
new resonances with masses 66
< mX<
110
GeV
. For the model-dependent analysis, the
observed upper limits on the production cross-section times branching ratio to two photons
for a SM-like Higgs boson range from 19
fb
to 102
fb
in the same mass range.
Acknowledgments
We thank CERN for the very successful operation of the LHC and its injectors, as well
as the support staff at CERN and at our institutions worldwide without whom ATLAS
could not be operated efficiently.
The crucial computing support from all WLCG partners is acknowledged gratefully, in
particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF
(Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF
– 18 –

JHEP01(2025)053
70 80 90 100 110
[GeV]
X
m
3−
10
2−
10
1−
10
1
Local p-value
σ0
σ1
σ2
σ3
ATLAS
-1
= 13 TeV, 140 fbs
= NWAΓ, γγ→X
Observed
(a)
70 75 80 85 90 95 100 105 110
[GeV]
X
m
0
20
40
60
80
100
120
) [fb]
γγ → B(X×
fid
X
σ
ATLAS
-1
= 13 TeV, 140 fbs
= NWAΓ, γγ →X
All limits at 95% CL
Observed limit
Expected limit
σ 1±Expected
σ 2±Expected
(b)
Figure 6. (a) Compatibility of the data, in the model-independent search, in terms of local
p
-value
(solid line), with the background-only hypothesis as a function of the assumed NWA signal mass
mX
. The dotted-dashed lines correspond to the standard deviation quantification
σ
. (b) 95% CL
upper limits on the fiducial cross-section times branching ratio
B
(
X→γγ
)as a function of NWA
mX
, where the solid (dashed) line corresponds to the observed (expected) limit and the green (yellow)
band corresponds to one (two) standard deviation from the expectation.
75 80 85 90 95 100 105
[GeV]
X
m
0
0.5
1
1.5
2
2.5
[%]
X
/m
X
Γ
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
]σ Local significance [
ATLAS
-1
= 13 TeV, 140 fbs
Figure 7. Compatibility of the data with the background-only hypothesis, using the local
p0
quantified
in units of standard deviations,
σ
, as a function of the assumed signal mass
mX
and of the relative
width ΓX/mXfor the model-independent search.
(Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities
worldwide and large non-WLCG resource providers. Major contributors of computing
resources are listed in ref. [
63
].
We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC,
Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil;
NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China;
Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-
CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany;
GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN,
Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway;
– 19 –

JHEP01(2025)053
75 80 85 90 95 100 105
[GeV]
X
m
0
0.5
1
1.5
2
2.5
[%]
X
/m
X
Γ
0
10
20
30
40
50
60
) [fb]γγ→ B(X×
fid
σ95% CL upper limit on
ATLAS
-1
= 13 TeV, 140 fbs Expected limit
(a)
75 80 85 90 95 100 105
[GeV]
X
m
0
0.5
1
1.5
2
2.5
[%]
X
/m
X
Γ
0
10
20
30
40
50
60
) [fb]γγ→ B(X×
fid
σ95% CL upper limit on
ATLAS
-1
= 13 TeV, 140 fbs Observed limit
(b)
Figure 8. (a) Expected and (b) observed limits on the fiducial cross-section times branching ratio
B
(
X→γγ
)computed using the asymptotic approximation as a function of the assumed signal mass
mXand relative width ΓX/mXfor the model-independent scalar resonance search.
(a) (b)
Figure 9. (a) Compatibility of the data with the background-only hypothesis as quantified by the
local
p
-value (solid line) as a function of the assumed signal mass
mH
, for the model-dependent search.
The dotted-dashed lines correspond to the standard deviation quantification
σ
. (b) 95% CL upper
limits on the total cross-section times branching ratio
B
(
H→γγ
)as a function of
mH
, where the solid
(dashed) line corresponds to the observed (expected) limit and the green (yellow) band corresponds to
one (two) standard deviation from the expectation.
MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia;
ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg
Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC,
Taipei; TENMAK, Türkiye; STFC/UKRI, United Kingdom; DOE and NSF, United States
of America.
Individual groups and members have received support from BCKDF, CANARIE, CRC
and DRAC, Canada; CERN-CZ, PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic;
COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Skłodowska-Curie
Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex
– 20 –

JHEP01(2025)053
and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia
programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA,
Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La
Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO
and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden;
The Royal Society and Leverhulme Trust, United Kingdom.
In addition, individual members wish to acknowledge support from CERN: European
Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investi-
gación y Desarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812,
FONDECYT 1230987); China: National Natural Science Foundation of China (NSFC -
12175119, NSFC 12275265, NSFC-12075060); Czech Republic: PRIMUS Research Programme
(PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC - 101002463); European
Union: European Research Council (ERC - 948254), Horizon 2020 Framework Programme
(MUCCA - CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research
(FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big
Data and Quantum Computing (ICSC, NextGenerationEU), Marie Sklodowska-Curie Actions
(EU H2020 MSC IF GRANT NO 101033496); France: Agence Nationale de la Recherche (ANR-
20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002), Investisse-
ments d’Avenir Idex (ANR-11-LABX-0012), Investissements d’Avenir Labex (ANR-11-LABX-
0012); Germany: Baden-Württemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme),
Deutsche Forschungsgemeinschaft (DFG - 469666862, DFG - CR 312/5-1); Italy: Istituto
Nazionale di Fisica Nucleare (FELLINI G.A. n. 754496, ICSC, NextGenerationEU); Japan:
Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI
JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227); Netherlands:
Netherlands Organisation for Scientific Research (NWO Veni 2020 - VI.Veni.202.179); Nor-
way: Research Council of Norway (RCN-314472); Poland: Polish National Agency for
Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Cen-
tre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-
2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187); Slove-
nia: Slovenian Research Agency (ARIS grant J1-3010); Spain: BBVA Foundation (LEO22-1-
603), Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), La Caixa Bank-
ing Foundation (LCF/BQ/PI20/11760025), Ministry of Science and Innovation (MCIN &
NextGenEU PCI2022-135018-2, MICIN & FEDER PID2021-125273NB, RYC2019-028510-I,
RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Pro-
grammes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden:
Swedish Research Council (VR 2018-00482, VR 2022-03845, VR 2022-04683, VR grant
2021-03651), Knut and Alice Wallenberg Foundation (KAW 2017.0100, KAW 2018.0157,
KAW 2018.0458, KAW 2019.0447); Switzerland: Swiss National Science Foundation (SNSF
- PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-
004); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515),
Neubauer Family Foundation.
Data Availability Statement. This article has no associated data or the data will not
be deposited.
– 21 –

JHEP01(2025)053
Code Availability Statement. This article has no associated code or the code will not
be deposited.
Open Access. This article is distributed under the terms of the Creative Commons Attri-
bution License (CC-BY4.0), which permits any use, distribution and reproduction in any
medium, provided the original author(s) and source are credited.
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The ATLAS collaboration
G. Aad
102
, B. Abbott
120
, K. Abeling
55
, N.J. Abicht
49
, S.H. Abidi
29
, A. Aboulhorma
35e
,
H. Abramowicz 152 , H. Abreu 151, Y. Abulaiti 117 , B.S. Acharya 69a,69b,m ,
C. Adam Bourdarios
4
, L. Adamczyk
86a
, S.V. Addepalli
26
, M.J. Addison
101
, J. Adelman
115
,
A. Adiguzel 21c, T. Adye 134, A.A. Affolder 136 , Y. Afik 39, M.N. Agaras 13 ,
J. Agarwala 73a,73b, A. Aggarwal 100 , C. Agheorghiesei 27c, A. Ahmad 36 , F. Ahmadov 38,aa ,
W.S. Ahmed
104
, S. Ahuja
95
, X. Ai
62e
, G. Aielli
76a,76b
, A. Aikot
163
, M. Ait Tamlihat
35e
,
B. Aitbenchikh 35a, I. Aizenberg 169, M. Akbiyik 100 , T.P.A. Åkesson 98, A.V. Akimov 37 ,
D. Akiyama 168 , N.N. Akolkar 24 , S. Aktas 21a, K. Al Khoury 41 , G.L. Alberghi 23b,
J. Albert 165 , P. Albicocco 53 , G.L. Albouy 60 , S. Alderweireldt 52 , Z.L. Alegria 121,
M. Aleksa 36, I.N. Aleksandrov 38, C. Alexa 27b, T. Alexopoulos 10, F. Alfonsi 23b,
M. Algren 56, M. Alhroob 120 , B. Ali 132 , H.M.J. Ali 91, S. Ali 149 , S.W. Alibocus 92 ,
M. Aliev 33c, G. Alimonti 71a, W. Alkakhi 55 , C. Allaire 66, B.M.M. Allbrooke 147 ,
J.F. Allen 52, C.A. Allendes Flores 137f, P.P. Allport 20, A. Aloisio 72a,72b, F. Alonso 90 ,
C. Alpigiani 139, M. Alvarez Estevez 99 , A. Alvarez Fernandez 100 , M. Alves Cardoso 56,
M.G. Alviggi 72a,72b, M. Aly 101, Y. Amaral Coutinho 83b, A. Ambler 104 , C. Amelung36,
M. Amerl 101, C.G. Ames 109 , D. Amidei 106, S.P. Amor Dos Santos 130a, K.R. Amos 163 ,
V. Ananiev 125, C. Anastopoulos 140 , T. Andeen 11 , J.K. Anders 36, S.Y. Andrean 47a,47b,
A. Andreazza 71a,71b, S. Angelidakis 9, A. Angerami 41,ad, A.V. Anisenkov 37, A. Annovi 74a,
C. Antel 56 , M.T. Anthony 140 , E. Antipov 146 , M. Antonelli 53, F. Anulli 75a, M. Aoki 84,
T. Aoki 154 , J.A. Aparisi Pozo 163 , M.A. Aparo 147 , L. Aperio Bella 48, C. Appelt 18,
A. Apyan 26 , N. Aranzabal 36 , S.J. Arbiol Val 87 , C. Arcangeletti 53, A.T.H. Arce 51 ,
E. Arena
92
, J-F. Arguin
108
, S. Argyropoulos
54
, J.-H. Arling
48
, O. Arnaez
4
, H. Arnold
114
,
G. Artoni 75a,75b, H. Asada 111, K. Asai 118 , S. Asai 154, N.A. Asbah 61 , J. Assahsah 35d,
K. Assamagan 29, R. Astalos 28a, S. Atashi 159, R.J. Atkin 33a, M. Atkinson162, H. Atmani35f,
P.A. Atmasiddha 128, K. Augsten 132, S. Auricchio 72a,72b, A.D. Auriol 20 , V.A. Austrup 101,
G. Avolio 36 , K. Axiotis 56, G. Azuelos 108,ah , D. Babal 28b, H. Bachacou 135,
K. Bachas 153,q , A. Bachiu 34, F. Backman 47a,47b, A. Badea 61 , T.M. Baer 106,
P. Bagnaia 75a,75b, M. Bahmani 18, D. Bahner 54 , A.J. Bailey 163 , V.R. Bailey 162,
J.T. Baines 134, L. Baines 94 , O.K. Baker 172 , E. Bakos 15, D. Bakshi Gupta 8,
V. Balakrishnan 120, R. Balasubramanian 114 , E.M. Baldin 37, P. Balek 86a,
E. Ballabene
23b,23a
, F. Balli
135
, L.M. Baltes
63a
, W.K. Balunas
32
, J. Balz
100
, E. Banas
87
,
M. Bandieramonte 129 , A. Bandyopadhyay 24, S. Bansal 24 , L. Barak 152 , M. Barakat 48 ,
E.L. Barberio 105 , D. Barberis 57b,57a, M. Barbero 102 , M.Z. Barel 114, K.N. Barends 33a,
T. Barillari 110, M-S. Barisits 36 , T. Barklow 144 , P. Baron 122 , D.A. Baron Moreno 101,
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S. Barsov 37 , F. Bartels 63a, R. Bartoldus 144, A.E. Barton 91 , P. Bartos 28a, A. Basan 100 ,
M. Baselga 49, A. Bassalat 66,b , M.J. Basso 156a, C.R. Basson 101, R.L. Bates 59 ,
S. Batlamous35e, J.R. Batley 32, B. Batool 142, M. Battaglia 136 , D. Battulga 18,
M. Bauce 75a,75b, M. Bauer 36, P. Bauer 24 , L.T. Bazzano Hurrell 30, J.B. Beacham 51 ,
T. Beau 127, J.Y. Beaucamp 90 , P.H. Beauchemin 158, P. Bechtle 24 , H.P. Beck 19,p ,
K. Becker 167 , A.J. Beddall 82, V.A. Bednyakov 38, C.P. Bee 146 , L.J. Beemster 15 ,
T.A. Beermann 36, M. Begalli 83d, M. Begel 29 , A. Behera 146, J.K. Behr 48 , J.F. Beirer 36 ,
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JHEP01(2025)053
F. Beisiegel 24, M. Belfkir 116b, G. Bella 152 , L. Bellagamba 23b, A. Bellerive 34 , P. Bellos 20 ,
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S. Bentvelsen 114 , L. Beresford 48, M. Beretta 53 , E. Bergeaas Kuutmann 161, N. Berger 4,
B. Bergmann 132, J. Beringer 17a, G. Bernardi 5, C. Bernius 144 , F.U. Bernlochner 24,
F. Bernon 36,102, A. Berrocal Guardia 13 , T. Berry 95 , P. Berta 133, A. Berthold 50 ,
I.A. Bertram 91, S. Bethke 110, A. Betti 75a,75b, A.J. Bevan 94 , N.K. Bhalla 54,
M. Bhamjee 33c, S. Bhatta 146, D.S. Bhattacharya 166, P. Bhattarai 144 , K.D. Bhide 54,
V.S. Bhopatkar 121, R.M. Bianchi 129 , G. Bianco 23b,23a, O. Biebel 109, R. Bielski 123 ,
M. Biglietti 77a, M. Bindi 55, A. Bingul 21b, C. Bini 75a,75b, A. Biondini 92 ,
C.J. Birch-sykes 101 , G.A. Bird 32,134, M. Birman 169 , M. Biros 133, S. Biryukov 147,
T. Bisanz 49, E. Bisceglie 43b,43a, J.P. Biswal 134 , D. Biswas 142 , A. Bitadze 101,
K. Bjørke 125 , I. Bloch 48, A. Blue 59 , U. Blumenschein 94, J. Blumenthal 100 ,
G.J. Bobbink 114, V.S. Bobrovnikov 37 , M. Boehler 54 , B. Boehm 166 , D. Bogavac 36,
A.G. Bogdanchikov
37
, C. Bohm
47a
, V. Boisvert
95
, P. Bokan
36
, T. Bold
86a
, M. Bomben
5
,
M. Bona 94, M. Boonekamp 135 , C.D. Booth 95 , A.G. Borbély 59 , I.S. Bordulev 37,
H.M. Borecka-Bielska
108
, G. Borissov
91
, D. Bortoletto
126
, D. Boscherini
23b
, M. Bosman
13
,
J.D. Bossio Sola 36, K. Bouaouda 35a, N. Bouchhar 163, J. Boudreau 129 ,
E.V. Bouhova-Thacker 91 , D. Boumediene 40, R. Bouquet 165 , A. Boveia 119 , J. Boyd 36,
D. Boye 29 , I.R. Boyko 38, J. Bracinik 20 , N. Brahimi 62d, G. Brandt 171 , O. Brandt 32,
F. Braren 48, B. Brau 103 , J.E. Brau 123, R. Brener 169 , L. Brenner 114 , R. Brenner 161 ,
S. Bressler 169, D. Britton 59 , D. Britzger 110, I. Brock 24 , R. Brock 107, G. Brooijmans 41 ,
W.K. Brooks 137f, E. Brost 29 , L.M. Brown 165 , L.E. Bruce 61, T.L. Bruckler 126 ,
P.A. Bruckman de Renstrom 87, B. Brüers 48 , A. Bruni 23b, G. Bruni 23b, M. Bruschi 23b,
N. Bruscino 75a,75b, T. Buanes 16, Q. Buat 139 , D. Buchin 110 , A.G. Buckley 59,
O. Bulekov 37 , B.A. Bullard 144 , S. Burdin 92 , C.D. Burgard 49, A.M. Burger 40 ,
B. Burghgrave 8, O. Burlayenko 54 , J.T.P. Burr 32, C.D. Burton 11 , J.C. Burzynski 143 ,
E.L. Busch 41 , V. Büscher 100 , P.J. Bussey 59 , J.M. Butler 25, C.M. Buttar 59 ,
J.M. Butterworth 96 , W. Buttinger 134 , C.J. Buxo Vazquez 107, A.R. Buzykaev 37 ,
S. Cabrera Urbán 163, L. Cadamuro 66, D. Caforio 58 , H. Cai 129 , Y. Cai 14a,14e, Y. Cai 14c,
V.M.M. Cairo 36, O. Cakir 3a, N. Calace 36 , P. Calafiura 17a, G. Calderini 127,
P. Calfayan 68 , G. Callea 59, L.P. Caloba83b, D. Calvet 40 , S. Calvet 40 , M. Calvetti 74a,74b,
R. Camacho Toro 127, S. Camarda 36 , D. Camarero Munoz 26, P. Camarri 76a,76b,
M.T. Camerlingo 72a,72b, D. Cameron 36, C. Camincher 165 , M. Campanelli 96 ,
A. Camplani 42, V. Canale 72a,72b, A. Canesse 104 , J. Cantero 163 , Y. Cao 162 ,
F. Capocasa 26 , M. Capua 43b,43a, A. Carbone 71a,71b, R. Cardarelli 76a, J.C.J. Cardenas 8,
F. Cardillo 163, G. Carducci 43b,43a, T. Carli 36 , G. Carlino 72a, J.I. Carlotto 13,
B.T. Carlson 129,r, E.M. Carlson 165,156a, L. Carminati 71a,71b, A. Carnelli 135 ,
M. Carnesale 75a,75b, S. Caron 113, E. Carquin 137f, S. Carrá 71a, G. Carratta 23b,23a,
J.W.S. Carter 155, T.M. Carter 52 , M.P. Casado 13,i, M. Caspar 48 , F.L. Castillo 4,
L. Castillo Garcia 13, V. Castillo Gimenez 163 , N.F. Castro 130a,130e, A. Catinaccio 36,
J.R. Catmore 125, T. Cavaliere 4, V. Cavaliere 29 , N. Cavalli 23b,23a, V. Cavasinni 74a,74b,
Y.C. Cekmecelioglu 48, E. Celebi 21a, F. Celli 126 , M.S. Centonze 70a,70b, V. Cepaitis 56 ,
K. Cerny 122 , A.S. Cerqueira 83a, A. Cerri 147, L. Cerrito 76a,76b, F. Cerutti 17a,
– 27 –

JHEP01(2025)053
B. Cervato 142 , A. Cervelli 23b, G. Cesarini 53 , S.A. Cetin 82, D. Chakraborty 115 ,
J. Chan 17a, W.Y. Chan 154, J.D. Chapman 32 , E. Chapon 135 , B. Chargeishvili 150b,
D.G. Charlton 20, M. Chatterjee 19 , C. Chauhan 133, Y. Che 14c, S. Chekanov 6,
S.V. Chekulaev
156a
, G.A. Chelkov
38,a
, A. Chen
106
, B. Chen
152
, B. Chen
165
, H. Chen
14c
,
H. Chen 29, J. Chen 62c, J. Chen 143 , M. Chen 126, S. Chen 154 , S.J. Chen 14c,
X. Chen
62c,135
, X. Chen
14b,ag
, Y. Chen
62a
, C.L. Cheng
170
, H.C. Cheng
64a
, S. Cheong
144
,
A. Cheplakov 38 , E. Cheremushkina 48 , E. Cherepanova 114, R. Cherkaoui El Moursli 35e,
E. Cheu 7, K. Cheung 65, L. Chevalier 135, V. Chiarella 53 , G. Chiarelli 74a, N. Chiedde 102,
G. Chiodini 70a, A.S. Chisholm 20 , A. Chitan 27b, M. Chitishvili 163 , M.V. Chizhov 38,s ,
K. Choi 11, A.R. Chomont 75a,75b, Y. Chou 139, E.Y.S. Chow 113 , T. Chowdhury 33g,
K.L. Chu 169 , M.C. Chu 64a, X. Chu 14a,14e, J. Chudoba 131, J.J. Chwastowski 87 ,
D. Cieri 110, K.M. Ciesla 86a, V. Cindro 93 , A. Ciocio 17a, F. Cirotto 72a,72b,
Z.H. Citron
169,k
, M. Citterio
71a
, D.A. Ciubotaru
27b
, A. Clark
56
, P.J. Clark
52
, C. Clarry
155
,
J.M. Clavijo Columbie 48 , S.E. Clawson 48 , C. Clement 47a,47b, J. Clercx 48 , Y. Coadou 102 ,
M. Cobal 69a,69c, A. Coccaro 57b, R.F. Coelho Barrue 130a, R. Coelho Lopes De Sa 103,
S. Coelli 71a, B. Cole 41 , J. Collot 60 , P. Conde Muiño 130a,130g, M.P. Connell 33c,
S.H. Connell 33c, I.A. Connelly 59, E.I. Conroy 126 , F. Conventi 72a,ai, H.G. Cooke 20 ,
A.M. Cooper-Sarkar 126 , A. Cordeiro Oudot Choi 127 , L.D. Corpe 40, M. Corradi 75a,75b,
F. Corriveau 104,y , A. Cortes-Gonzalez 18 , M.J. Costa 163, F. Costanza 4, D. Costanzo 140 ,
B.M. Cote 119, G. Cowan 95 , K. Cranmer 170 , D. Cremonini 23b,23a, S. Crépé-Renaudin 60 ,
F. Crescioli 127, M. Cristinziani 142 , M. Cristoforetti 78a,78b, V. Croft 114, J.E. Crosby 121,
G. Crosetti 43b,43a, A. Cueto 99, T. Cuhadar Donszelmann 159 , H. Cui 14a,14e, Z. Cui 7,
W.R. Cunningham 59, F. Curcio 43b,43a, P. Czodrowski 36 , M.M. Czurylo 63b,
M.J. Da Cunha Sargedas De Sousa 57b,57a, J.V. Da Fonseca Pinto 83b, C. Da Via 101,
W. Dabrowski
86a
, T. Dado
49
, S. Dahbi
33g
, T. Dai
106
, D. Dal Santo
19
, C. Dallapiccola
103
,
M. Dam
42
, G. D’amen
29
, V. D’Amico
109
, J. Damp
100
, J.R. Dandoy
34
, M. Danninger
143
,
V. Dao 36, G. Darbo 57b, S. Darmora 6, S.J. Das 29,ak , S. D’Auria 71a,71b, C. David 33a,
T. Davidek 133 , B. Davis-Purcell 34, I. Dawson 94, H.A. Day-hall 132 , K. De 8,
R. De Asmundis 72a, N. De Biase 48 , S. De Castro 23b,23a, N. De Groot 113 , P. de Jong 114,
H. De la Torre 115, A. De Maria 14c, A. De Salvo 75a, U. De Sanctis 76a,76b,
F. De Santis 70a,70b, A. De Santo 147 , J.B. De Vivie De Regie 60, D.V. Dedovich38,
J. Degens 114, A.M. Deiana 44 , F. Del Corso 23b,23a, J. Del Peso 99 , F. Del Rio 63a,
L. Delagrange
127
, F. Deliot
135
, C.M. Delitzsch
49
, M. Della Pietra
72a,72b
, D. Della Volpe
56
,
A. Dell’Acqua 36 , L. Dell’Asta 71a,71b, M. Delmastro 4, P.A. Delsart 60 , S. Demers 172 ,
M. Demichev 38 , S.P. Denisov 37, L. D’Eramo 40 , D. Derendarz 87 , F. Derue 127,
P. Dervan 92 , K. Desch 24 , C. Deutsch 24, F.A. Di Bello 57b,57a, A. Di Ciaccio 76a,76b,
L. Di Ciaccio 4, A. Di Domenico 75a,75b, C. Di Donato 72a,72b, A. Di Girolamo 36,
G. Di Gregorio 36, A. Di Luca 78a,78b, B. Di Micco 77a,77b, R. Di Nardo 77a,77b,
M. Diamantopoulou 34, F.A. Dias 114 , T. Dias Do Vale 143, M.A. Diaz 137a,137b,
F.G. Diaz Capriles 24, M. Didenko 163 , E.B. Diehl 106, L. Diehl 54 , S. Díez Cornell 48,
C. Diez Pardos 142 , C. Dimitriadi 161,24, A. Dimitrievska 17a, J. Dingfelder 24, I-M. Dinu 27b,
S.J. Dittmeier 63b, F. Dittus 36, F. Djama 102 , T. Djobava 150b, C. Doglioni 101,98,
A. Dohnalova 28a, J. Dolejsi 133, Z. Dolezal 133 , K.M. Dona 39 , M. Donadelli 83c,
– 28 –

JHEP01(2025)053
B. Dong
107
, J. Donini
40
, A. D’Onofrio
72a,72b
, M. D’Onofrio
92
, J. Dopke
134
, A. Doria
72a
,
N. Dos Santos Fernandes
130a
, P. Dougan
101
, M.T. Dova
90
, A.T. Doyle
59
, M.A. Draguet
126
,
E. Dreyer 169 , I. Drivas-koulouris 10, M. Drnevich 117, M. Drozdova 56 , D. Du 62a,
T.A. du Pree 114, F. Dubinin 37 , M. Dubovsky 28a, E. Duchovni 169 , G. Duckeck 109,
O.A. Ducu 27b, D. Duda 52, A. Dudarev 36 , E.R. Duden 26, M. D’uffizi 101 , L. Duflot 66 ,
M. Dührssen 36, A.E. Dumitriu 27b, M. Dunford 63a, S. Dungs 49 , K. Dunne 47a,47b,
A. Duperrin 102 , H. Duran Yildiz 3a, M. Düren 58, A. Durglishvili 150b, B.L. Dwyer 115,
G.I. Dyckes 17a, M. Dyndal 86a, B.S. Dziedzic 87 , Z.O. Earnshaw 147 , G.H. Eberwein 126,
B. Eckerova
28a
, S. Eggebrecht
55
, E. Egidio Purcino De Souza
127
, L.F. Ehrke
56
, G. Eigen
16
,
K. Einsweiler 17a, T. Ekelof 161 , P.A. Ekman 98, S. El Farkh 35b, Y. El Ghazali 35b,
H. El Jarrari 36, A. El Moussaouy 108 , V. Ellajosyula 161 , M. Ellert 161, F. Ellinghaus 171 ,
N. Ellis 36, J. Elmsheuser 29 , M. Elsing 36, D. Emeliyanov 134, Y. Enari 154 , I. Ene 17a,
S. Epari 13, P.A. Erland 87 , M. Errenst 171, M. Escalier 66 , C. Escobar 163 , E. Etzion 152,
G. Evans
130a
, H. Evans
68
, L.S. Evans
95
, M.O. Evans
147
, A. Ezhilov
37
, S. Ezzarqtouni
35a
,
F. Fabbri 59, L. Fabbri 23b,23a, G. Facini 96 , V. Fadeyev 136, R.M. Fakhrutdinov 37 ,
D. Fakoudis 100, S. Falciano 75a, L.F. Falda Ulhoa Coelho 36, P.J. Falke 24 , J. Faltova 133 ,
C. Fan 162, Y. Fan 14a, Y. Fang 14a,14e, M. Fanti 71a,71b, M. Faraj 69a,69b, Z. Farazpay 97 ,
A. Farbin 8, A. Farilla 77a, T. Farooque 107, S.M. Farrington 52 , F. Fassi 35e,
D. Fassouliotis 9, M. Faucci Giannelli 76a,76b, W.J. Fawcett 32, L. Fayard 66, P. Federic 133,
P. Federicova 131 , O.L. Fedin 37,a, G. Fedotov 37 , M. Feickert 170 , L. Feligioni 102,
D.E. Fellers 123, C. Feng 62b, M. Feng 14b, Z. Feng 114 , M.J. Fenton 159 , A.B. Fenyuk37 ,
L. Ferencz 48, R.A.M. Ferguson 91 , S.I. Fernandez Luengo 137f, P. Fernandez Martinez 13 ,
M.J.V. Fernoux 102, J. Ferrando 91 , A. Ferrari 161, P. Ferrari 114,113, R. Ferrari 73a,
D. Ferrere 56, C. Ferretti 106 , F. Fiedler 100 , P. Fiedler 132 , A. Filipčič 93, E.K. Filmer 1,
F. Filthaut 113, M.C.N. Fiolhais 130a,130c,c , L. Fiorini 163, W.C. Fisher 107 , T. Fitschen 101,
P.M. Fitzhugh135, I. Fleck 142 , P. Fleischmann 106 , T. Flick 171, M. Flores 33d,ae ,
L.R. Flores Castillo 64a, L. Flores Sanz De Acedo 36 , F.M. Follega 78a,78b, N. Fomin 16 ,
J.H. Foo
155
, A. Formica
135
, A.C. Forti
101
, E. Fortin
36
, A.W. Fortman
17a
, M.G. Foti
17a
,
L. Fountas 9,j , D. Fournier 66, H. Fox 91 , P. Francavilla 74a,74b, S. Francescato 61,
S. Franchellucci 56, M. Franchini 23b,23a, S. Franchino 63a, D. Francis36, L. Franco 113,
V. Franco Lima 36, L. Franconi 48 , M. Franklin 61 , G. Frattari 26, A.C. Freegard 94 ,
W.S. Freund 83b, Y.Y. Frid 152, J. Friend 59 , N. Fritzsche 50 , A. Froch 54 , D. Froidevaux 36 ,
J.A. Frost 126, Y. Fu 62a, S. Fuenzalida Garrido 137f, M. Fujimoto 102, K.Y. Fung 64a,
E. Furtado De Simas Filho 83b, M. Furukawa 154, J. Fuster 163 , A. Gabrielli 23b,23a,
A. Gabrielli 155, P. Gadow 36, G. Gagliardi 57b,57 a, L.G. Gagnon 17a, E.J. Gallas 126 ,
B.J. Gallop 134, K.K. Gan 119 , S. Ganguly 154, Y. Gao 52 , F.M. Garay Walls 137a,137b,
B. Garcia29, C. García 163 , A. Garcia Alonso 114, A.G. Garcia Caffaro 172 ,
J.E. García Navarro 163 , M. Garcia-Sciveres 17a, G.L. Gardner 128 , R.W. Gardner 39,
N. Garelli
158
, D. Garg
80
, R.B. Garg
144,n
, J.M. Gargan
52
, C.A. Garner
155
, C.M. Garvey
33a
,
P. Gaspar 83b, V.K. Gassmann158 , G. Gaudio 73a, V. Gautam13 , P. Gauzzi 75a,75b,
I.L. Gavrilenko 37 , A. Gavrilyuk 37 , C. Gay 164 , G. Gaycken 48 , E.N. Gazis 10,
A.A. Geanta 27b, C.M. Gee 136 , A. Gekow119, C. Gemme 57b, M.H. Genest 60,
S. Gentile 75a,75b, A.D. Gentry 112 , S. George 95, W.F. George 20 , T. Geralis 46,
– 29 –

JHEP01(2025)053
P. Gessinger-Befurt 36 , M.E. Geyik 171 , M. Ghani 167 , M. Ghneimat 142, K. Ghorbanian 94 ,
A. Ghosal 142, A. Ghosh 159 , A. Ghosh 7, B. Giacobbe 23b, S. Giagu 75a,75b, T. Giani 114 ,
P. Giannetti 74a, A. Giannini 62a, S.M. Gibson 95 , M. Gignac 136 , D.T. Gil 86b,
A.K. Gilbert 86a, B.J. Gilbert 41 , D. Gillberg 34 , G. Gilles 114, L. Ginabat 127 ,
D.M. Gingrich 2,ah , M.P. Giordani 69a,69c, P.F. Giraud 135 , G. Giugliarelli 69a,69c,
D. Giugni
71a
, F. Giuli
36
, I. Gkialas
9,j
, L.K. Gladilin
37
, C. Glasman
99
, G.R. Gledhill
123
,
G. Glemža 48, M. Glisic123 , I. Gnesi 43b,f , Y. Go 29, M. Goblirsch-Kolb 36, B. Gocke 49 ,
D. Godin108 , B. Gokturk 21a, S. Goldfarb 105, T. Golling 56 , M.G.D. Gololo 33g,
D. Golubkov 37 , J.P. Gombas 107 , A. Gomes 130a,130b, G. Gomes Da Silva 142 ,
A.J. Gomez Delegido 163, R. Gonçalo 130a,130c, G. Gonella 123 , L. Gonella 20,
A. Gongadze 150c, F. Gonnella 20, J.L. Gonski 41 , R.Y. González Andana 52,
S. González de la Hoz 163, R. Gonzalez Lopez 92, C. Gonzalez Renteria 17a,
M.V. Gonzalez Rodrigues 48 , R. Gonzalez Suarez 161, S. Gonzalez-Sevilla 56 ,
G.R. Gonzalvo Rodriguez 163, L. Goossens 36, B. Gorini 36 , E. Gorini 70a,70b, A. Gorišek 93,
T.C. Gosart 128, A.T. Goshaw 51, M.I. Gostkin 38 , S. Goswami 121, C.A. Gottardo 36 ,
S.A. Gotz 109, M. Gouighri 35b, V. Goumarre 48 , A.G. Goussiou 139, N. Govender 33c,
I. Grabowska-Bold
86a
, K. Graham
34
, E. Gramstad
125
, S. Grancagnolo
70a,70b
, M. Grandi
147
,
C.M. Grant1,135, P.M. Gravila 27f, F.G. Gravili 70a,70b, H.M. Gray 17a, M. Greco 70a,70b,
C. Grefe 24, I.M. Gregor 48 , P. Grenier 144, S.G. Grewe110 , C. Grieco 13, A.A. Grillo 136 ,
K. Grimm
31
, S. Grinstein
13,u
, J.-F. Grivaz
66
, E. Gross
169
, J. Grosse-Knetter
55
, C. Grud
106
,
J.C. Grundy 126, L. Guan 106 , W. Guan 29, C. Gubbels 164, J.G.R. Guerrero Rojas 163,
G. Guerrieri 69a,69c, F. Guescini 110, R. Gugel 100 , J.A.M. Guhit 106, A. Guida 18 ,
E. Guilloton 167,134, S. Guindon 36 , F. Guo 14a,14e, J. Guo 62c, L. Guo 48, Y. Guo 106 ,
R. Gupta 48, R. Gupta 129 , S. Gurbuz 24, S.S. Gurdasani 54 , G. Gustavino 36, M. Guth 56 ,
P. Gutierrez
120
, L.F. Gutierrez Zagazeta
128
, M. Gutsche
50
, C. Gutschow
96
, C. Gwenlan
126
,
C.B. Gwilliam 92, E.S. Haaland 125 , A. Haas 117, M. Habedank 48, C. Haber 17a,
H.K. Hadavand 8, A. Hadef 50, S. Hadzic 110 , A.I. Hagan 91 , J.J. Hahn 142, E.H. Haines 96 ,
M. Haleem 166, J. Haley 121 , J.J. Hall 140, G.D. Hallewell 102 , L. Halser 19, K. Hamano 165 ,
M. Hamer 24, G.N. Hamity 52, E.J. Hampshire 95 , J. Han 62b, K. Han 62a, L. Han 14c,
L. Han 62a, S. Han 17a, Y.F. Han 155, K. Hanagaki 84 , M. Hance 136, D.A. Hangal 41 ,
H. Hanif 143, M.D. Hank 128 , J.B. Hansen 42, P.H. Hansen 42 , K. Hara 157, D. Harada 56 ,
T. Harenberg 171, S. Harkusha 37 , M.L. Harris 103, Y.T. Harris 126 , J. Harrison 13 ,
N.M. Harrison
119
, P.F. Harrison
167
, N.M. Hartman
110
, N.M. Hartmann
109
, Y. Hasegawa
141
,
R. Hauser 107, C.M. Hawkes 20, R.J. Hawkings 36, Y. Hayashi 154, S. Hayashida 111,
D. Hayden
107
, C. Hayes
106
, R.L. Hayes
114
, C.P. Hays
126
, J.M. Hays
94
, H.S. Hayward
92
,
F. He 62a, M. He 14a,14e, Y. He 138, Y. He 48 , N.B. Heatley 94, V. Hedberg 98,
A.L. Heggelund 125, N.D. Hehir 94,∗, C. Heidegger 54 , K.K. Heidegger 54, W.D. Heidorn 81 ,
J. Heilman 34, S. Heim 48 , T. Heim 17a, J.G. Heinlein 128, J.J. Heinrich 123 ,
L. Heinrich
110,af
, J. Hejbal
131
, L. Helary
48
, A. Held
170
, S. Hellesund
16
, C.M. Helling
164
,
S. Hellman 47a,47b, R.C.W. Henderson91, L. Henkelmann 32 , A.M. Henriques Correia36,
H. Herde 98, Y. Hernández Jiménez 146 , L.M. Herrmann 24, T. Herrmann 50 , G. Herten 54 ,
R. Hertenberger
109
, L. Hervas
36
, M.E. Hesping
100
, N.P. Hessey
156a
, H. Hibi
85
, E. Hill
155
,
S.J. Hillier 20, J.R. Hinds 107 , F. Hinterkeuser 24 , M. Hirose 124, S. Hirose 157 ,
– 30 –

JHEP01(2025)053
D. Hirschbuehl
171
, T.G. Hitchings
101
, B. Hiti
93
, J. Hobbs
146
, R. Hobincu
27e
, N. Hod
169
,
M.C. Hodgkinson 140 , B.H. Hodkinson 32, A. Hoecker 36 , D.D. Hofer 106, J. Hofer 48 ,
T. Holm 24, M. Holzbock 110 , L.B.A.H. Hommels 32 , B.P. Honan 101, J. Hong 62c,
T.M. Hong 129, B.H. Hooberman 162, W.H. Hopkins 6, Y. Horii 111 , S. Hou 149 ,
A.S. Howard
93
, J. Howarth
59
, J. Hoya
6
, M. Hrabovsky
122
, A. Hrynevich
48
, T. Hryn’ova
4
,
P.J. Hsu 65, S.-C. Hsu 139 , Q. Hu 62a, Y.F. Hu 14a,14e, S. Huang 64b, X. Huang 14c,
X. Huang
14a,14e
, Y. Huang
140
, Y. Huang
14a
, Z. Huang
101
, Z. Hubacek
132
, M. Huebner
24
,
F. Huegging 24, T.B. Huffman 126 , C.A. Hugli 48, M. Huhtinen 36 , S.K. Huiberts 16 ,
R. Hulsken
104
, N. Huseynov
12
, J. Huston
107
, J. Huth
61
, R. Hyneman
144
, G. Iacobucci
56
,
G. Iakovidis 29 , I. Ibragimov 142 , L. Iconomidou-Fayard 66 , J.P. Iddon 36 , P. Iengo 72a,72b,
R. Iguchi 154 , T. Iizawa 126 , Y. Ikegami 84 , N. Ilic 155, H. Imam 35a, M. Ince Lezki 56 ,
T. Ingebretsen Carlson 47a,47b, G. Introzzi 73a,73b, M. Iodice 77a, V. Ippolito 75a,75b,
R.K. Irwin 92, M. Ishino 154 , W. Islam 170, C. Issever 18,48 , S. Istin 21a,am, H. Ito 168 ,
J.M. Iturbe Ponce 64a, R. Iuppa 78a,78b, A. Ivina 169, J.M. Izen 45 , V. Izzo 72a,
P. Jacka 131,132, P. Jackson 1, B.P. Jaeger 143, C.S. Jagfeld 109 , G. Jain 156a, P. Jain 54 ,
K. Jakobs 54 , T. Jakoubek 169, J. Jamieson 59 , K.W. Janas 86a, M. Javurkova 103,
L. Jeanty 123 , J. Jejelava 150a,ab, P. Jenni 54,g , C.E. Jessiman 34 , S. Jézéquel 4, C. Jia 62b,
J. Jia 146, X. Jia 61 , X. Jia 14a,14e, Z. Jia 14c, S. Jiggins 48, J. Jimenez Pena 13 , S. Jin 14c,
A. Jinaru 27b, O. Jinnouchi 138 , P. Johansson 140, K.A. Johns 7, J.W. Johnson 136 ,
D.M. Jones 32, E. Jones 48 , P. Jones 32, R.W.L. Jones 91 , T.J. Jones 92 , H.L. Joos 55,36,
R. Joshi 119, J. Jovicevic 15, X. Ju 17a, J.J. Junggeburth 103 , T. Junkermann 63a,
A. Juste Rozas 13,u, M.K. Juzek 87 , S. Kabana 137e, A. Kaczmarska 87, M. Kado 110 ,
H. Kagan 119, M. Kagan 144 , A. Kahn41, A. Kahn 128 , C. Kahra 100, T. Kaji 154 ,
E. Kajomovitz
151
, N. Kakati
169
, I. Kalaitzidou
54
, C.W. Kalderon
29
, A. Kamenshchikov
155
,
N.J. Kang 136, D. Kar 33g, K. Karava 126 , M.J. Kareem 156b, E. Karentzos 54 ,
I. Karkanias 153, O. Karkout 114, S.N. Karpov 38 , Z.M. Karpova 38 , V. Kartvelishvili 91 ,
A.N. Karyukhin
37
, E. Kasimi
153
, J. Katzy
48
, S. Kaur
34
, K. Kawade
141
, M.P. Kawale
120
,
C. Kawamoto 88 , T. Kawamoto 62a, E.F. Kay 36 , F.I. Kaya 158 , S. Kazakos 107,
V.F. Kazanin
37
, Y. Ke
146
, J.M. Keaveney
33a
, R. Keeler
165
, G.V. Kehris
61
, J.S. Keller
34
,
A.S. Kelly96, J.J. Kempster 147 , K.E. Kennedy 41 , P.D. Kennedy 100, O. Kepka 131 ,
B.P. Kerridge 167, S. Kersten 171, B.P. Kerševan 93, S. Keshri 66 , L. Keszeghova 28a,
S. Ketabchi Haghighat 155, R.A. Khan 129 , A. Khanov 121 , A.G. Kharlamov 37,
T. Kharlamova 37, E.E. Khoda 139, M. Kholodenko 37 , T.J. Khoo 18 , G. Khoriauli 166,
J. Khubua 150b,∗, Y.A.R. Khwaira 66, A. Kilgallon 123 , D.W. Kim 47a,47b, Y.K. Kim 39,
N. Kimura 96 , M.K. Kingston 55, A. Kirchhoff 55 , C. Kirfel 24, F. Kirfel 24 , J. Kirk 134,
A.E. Kiryunin 110, C. Kitsaki 10 , O. Kivernyk 24 , M. Klassen 63a, C. Klein 34, L. Klein 166 ,
M.H. Klein 44, M. Klein 92,∗, S.B. Klein 56 , U. Klein 92, P. Klimek 36 , A. Klimentov 29 ,
T. Klioutchnikova 36 , P. Kluit 114, S. Kluth 110 , E. Kneringer 79 , T.M. Knight 155 ,
A. Knue
49
, R. Kobayashi
88
, D. Kobylianskii
169
, S.F. Koch
126
, M. Kocian
144
, P. Kodyš
133
,
D.M. Koeck 123 , P.T. Koenig 24 , T. Koffas 34 , O. Kolay 50 , I. Koletsou 4, T. Komarek 122 ,
K. Köneke 54 , A.X.Y. Kong 1, T. Kono 118, N. Konstantinidis 96, P. Kontaxakis 56,
B. Konya 98, R. Kopeliansky 68 , S. Koperny 86a, K. Korcyl 87 , K. Kordas 153,e, A. Korn 96,
S. Korn
55
, I. Korolkov
13
, N. Korotkova
37
, B. Kortman
114
, O. Kortner
110
, S. Kortner
110
,
– 31 –

JHEP01(2025)053
W.H. Kostecka
115
, V.V. Kostyukhin
142
, A. Kotsokechagia
135
, A. Kotwal
51
, A. Koulouris
36
,
A. Kourkoumeli-Charalampidi 73a,73b, C. Kourkoumelis 9, E. Kourlitis 110,af , O. Kovanda 147,
R. Kowalewski
165
, W. Kozanecki
135
, A.S. Kozhin
37
, V.A. Kramarenko
37
, G. Kramberger
93
,
P. Kramer 100, M.W. Krasny 127 , A. Krasznahorkay 36 , J.W. Kraus 171 , J.A. Kremer 48,
T. Kresse 50, J. Kretzschmar 92, K. Kreul 18 , P. Krieger 155, S. Krishnamurthy 103,
M. Krivos 133 , K. Krizka 20, K. Kroeninger 49, H. Kroha 110 , J. Kroll 131 , J. Kroll 128 ,
K.S. Krowpman 107 , U. Kruchonak 38, H. Krüger 24 , N. Krumnack81, M.C. Kruse 51 ,
O. Kuchinskaia 37 , S. Kuday 3a, S. Kuehn 36, R. Kuesters 54 , T. Kuhl 48, V. Kukhtin 38 ,
Y. Kulchitsky 37,a , S. Kuleshov 137d,137b, M. Kumar 33g, N. Kumari 48, P. Kumari 156b,
A. Kupco 131 , T. Kupfer49, A. Kupich 37, O. Kuprash 54 , H. Kurashige 85 ,
L.L. Kurchaninov 156a, O. Kurdysh 66 , Y.A. Kurochkin 37, A. Kurova 37 , M. Kuze 138,
A.K. Kvam 103 , J. Kvita 122, T. Kwan 104, N.G. Kyriacou 106 , L.A.O. Laatu 102,
C. Lacasta 163, F. Lacava 75a,75b, H. Lacker 18 , D. Lacour 127 , N.N. Lad 96, E. Ladygin 38 ,
B. Laforge 127, T. Lagouri 27b, F.Z. Lahbabi 35a, S. Lai 55, I.K. Lakomiec 86a, N. Lalloue 60 ,
J.E. Lambert 165, S. Lammers 68 , W. Lampl 7, C. Lampoudis 153,e , A.N. Lancaster 115,
E. Lançon 29, U. Landgraf 54 , M.P.J. Landon 94, V.S. Lang 54 , R.J. Langenberg 103 ,
O.K.B. Langrekken 125 , A.J. Lankford 159, F. Lanni 36 , K. Lantzsch 24 , A. Lanza 73a,
A. Lapertosa 57b,57a, J.F. Laporte 135 , T. Lari 71a, F. Lasagni Manghi 23b, M. Lassnig 36,
V. Latonova 131, A. Laudrain 100 , A. Laurier 151 , S.D. Lawlor 140 , Z. Lawrence 101 ,
R. Lazaridou167, M. Lazzaroni 71a,71b, B. Le101 , E.M. Le Boulicaut 51 , B. Leban 93 ,
A. Lebedev 81 , M. LeBlanc 101 , F. Ledroit-Guillon 60 , A.C.A. Lee96, S.C. Lee 149 ,
S. Lee
47a,47b
, T.F. Lee
92
, L.L. Leeuw
33c
, H.P. Lefebvre
95
, M. Lefebvre
165
, C. Leggett
17a
,
G. Lehmann Miotto 36, M. Leigh 56 , W.A. Leight 103 , W. Leinonen 113 , A. Leisos 153,t,
M.A.L. Leite 83c, C.E. Leitgeb 18, R. Leitner 133 , K.J.C. Leney 44, T. Lenz 24 , S. Leone 74a,
C. Leonidopoulos 52 , A. Leopold 145, C. Leroy 108 , R. Les 107, C.G. Lester 32 ,
M. Levchenko 37, J. Levêque 4, D. Levin 106 , L.J. Levinson 169, G. Levrini 23b,23a,
M.P. Lewicki
87
, D.J. Lewis
4
, A. Li
5
, B. Li
62b
, C. Li
62a
, C-Q. Li
110
, H. Li
62a
, H. Li
62b
,
H. Li 14c, H. Li 14b, H. Li 62 b, J. Li 62c, K. Li 139 , L. Li 62c, M. Li 14a,14e, Q.Y. Li 62a,
S. Li 14a,14e, S. Li 62d,62c,d, T. Li 5, X. Li 104 , Z. Li 126, Z. Li 104 , Z. Li 14a,14e,
S. Liang 14a,14e, Z. Liang 14a, M. Liberatore 135 , B. Liberti 76a, K. Lie 64c,
J. Lieber Marin 83b, H. Lien 68 , K. Lin 107 , R.E. Lindley 7, J.H. Lindon 2, E. Lipeles 128 ,
A. Lipniacka 16 , A. Lister 164, J.D. Little 4, B. Liu 14a, B.X. Liu 143 , D. Liu 62d,62c,
J.B. Liu 62a, J.K.K. Liu 32, K. Liu 62d,62c, M. Liu 62a, M.Y. Liu 62a, P. Liu 14a,
Q. Liu 62d,139,62c, X. Liu 62a, X. Liu 62b, Y. Liu 14d,14e, Y.L. Liu 62b, Y.W. Liu 62a,
J. Llorente Merino 143 , S.L. Lloyd 94, E.M. Lobodzinska 48, P. Loch 7, T. Lohse 18 ,
K. Lohwasser 140 , E. Loiacono 48 , M. Lokajicek 131,∗, J.D. Lomas 20 , J.D. Long 162 ,
I. Longarini 159, L. Longo 70a,70b, R. Longo 162 , I. Lopez Paz 67, A. Lopez Solis 48,
N. Lorenzo Martinez
4
, A.M. Lory
109
, G. Löschcke Centeno
147
, O. Loseva
37
, X. Lou
47a,47b
,
X. Lou 14a,14e, A. Lounis 66, J. Love 6, P.A. Love 91, G. Lu 14a,14e, M. Lu 80 , S. Lu 128 ,
Y.J. Lu 65, H.J. Lubatti 139 , C. Luci 75a,75b, F.L. Lucio Alves 14c, A. Lucotte 60 ,
F. Luehring 68, I. Luise 146 , O. Lukianchuk 66 , O. Lundberg 145 , B. Lund-Jensen 145,∗,
N.A. Luongo 6, M.S. Lutz 36, A.B. Lux 25 , D. Lynn 29, H. Lyons92 , R. Lysak 131,
E. Lytken
98
, V. Lyubushkin
38
, T. Lyubushkina
38
, M.M. Lyukova
146
, H. Ma
29
, K. Ma
62a
,
– 32 –

JHEP01(2025)053
L.L. Ma 62b, W. Ma 62a, Y. Ma 121, D.M. Mac Donell 165 , G. Maccarrone 53,
J.C. MacDonald 100, P.C. Machado De Abreu Farias 83b, R. Madar 40 , W.F. Mader 50,
T. Madula 96, J. Maeda 85 , T. Maeno 29, H. Maguire 140 , V. Maiboroda 135 ,
A. Maio 130a,130b,130d, K. Maj 86a, O. Majersky 48 , S. Majewski 123 , N. Makovec 66,
V. Maksimovic 15 , B. Malaescu 127, Pa. Malecki 87, V.P. Maleev 37 , F. Malek 60,o ,
M. Mali 93, D. Malito 95 , U. Mallik 80,∗, S. Maltezos10, S. Malyukov38, J. Mamuzic 13 ,
G. Mancini 53, M.N. Mancini 26 , G. Manco 73a,73b, J.P. Mandalia 94, I. Mandić 93 ,
L. Manhaes de Andrade Filho 83a, I.M. Maniatis 169, J. Manjarres Ramos 102,ac ,
D.C. Mankad 169, A. Mann 109 , S. Manzoni 36, L. Mao 62c, X. Mapekula 33c,
A. Marantis 153,t , G. Marchiori 5, M. Marcisovsky 131 , C. Marcon 71a, M. Marinescu 20,
S. Marium 48, M. Marjanovic 120, E.J. Marshall 91, Z. Marshall 17a, S. Marti-Garcia 163 ,
T.A. Martin 167, V.J. Martin 52 , B. Martin dit Latour 16, L. Martinelli 75a,75b,
M. Martinez
13,u
, P. Martinez Agullo
163
, V.I. Martinez Outschoorn
103
, P. Martinez Suarez
13
,
S. Martin-Haugh
134
, V.S. Martoiu
27b
, A.C. Martyniuk
96
, A. Marzin
36
, D. Mascione
78a,78b
,
L. Masetti 100, T. Mashimo 154 , J. Masik 101, A.L. Maslennikov 37, P. Massarotti 72a,72b,
P. Mastrandrea 74a,74b, A. Mastroberardino 43b,43a, T. Masubuchi 154 , T. Mathisen 161,
J. Matousek 133, N. Matsuzawa154, J. Maurer 27b, B. Maček 93 , D.A. Maximov 37 ,
R. Mazini 149, I. Maznas 153 , M. Mazza 107, S.M. Mazza 136 , E. Mazzeo 71a,71b,
C. Mc Ginn
29
, J.P. Mc Gowan
104
, S.P. Mc Kee
106
, C.C. McCracken
164
, E.F. McDonald
105
,
A.E. McDougall 114, J.A. Mcfayden 147 , R.P. McGovern 128 , G. Mchedlidze 150b,
R.P. Mckenzie 33g, T.C. Mclachlan 48 , D.J. Mclaughlin 96, S.J. McMahon 134 ,
C.M. Mcpartland 92, R.A. McPherson 165,y , S. Mehlhase 109 , A. Mehta 92 , D. Melini 163 ,
B.R. Mellado Garcia 33g, A.H. Melo 55, F. Meloni 48 , A.M. Mendes Jacques Da Costa 101 ,
H.Y. Meng 155, L. Meng 91 , S. Menke 110 , M. Mentink 36, E. Meoni 43b,43a, G. Mercado 115 ,
C. Merlassino 69a,69c, L. Merola 72a,72b, C. Meroni 71a,71b, J. Metcalfe 6, A.S. Mete 6,
C. Meyer 68 , J-P. Meyer 135, R.P. Middleton 134 , L. Mijović 52, G. Mikenberg 169 ,
M. Mikestikova
131
, M. Mikuž
93
, H. Mildner
100
, A. Milic
36
, C.D. Milke
44
, D.W. Miller
39
,
E.H. Miller
144
, L.S. Miller
34
, A. Milov
169
, D.A. Milstead
47a,47b
, T. Min
14c
, A.A. Minaenko
37
,
I.A. Minashvili
150b
, L. Mince
59
, A.I. Mincer
117
, B. Mindur
86a
, M. Mineev
38
, Y. Mino
88
,
L.M. Mir 13, M. Miralles Lopez 59, M. Mironova 17a, A. Mishima154 , M.C. Missio 113,
A. Mitra 167, V.A. Mitsou 163 , Y. Mitsumori 111, O. Miu 155 , P.S. Miyagawa 94 ,
T. Mkrtchyan 63a, M. Mlinarevic 96 , T. Mlinarevic 96, M. Mlynarikova 36, S. Mobius 19 ,
P. Mogg 109, M.H. Mohamed Farook 112, A.F. Mohammed 14a,14e, S. Mohapatra 41 ,
G. Mokgatitswane 33g, L. Moleri 169 , B. Mondal 142, S. Mondal 132 , K. Mönig 48 ,
E. Monnier 102, L. Monsonis Romero163 , J. Montejo Berlingen 13, M. Montella 119,
F. Montereali 77a,77b, F. Monticelli 90 , S. Monzani 69a,69c, N. Morange 66,
A.L. Moreira De Carvalho
130a
, M. Moreno Llácer
163
, C. Moreno Martinez
56
, P. Morettini
57b
,
S. Morgenstern 36, M. Morii 61 , M. Morinaga 154, A.K. Morley 36 , F. Morodei 75a,75b,
L. Morvaj 36 , P. Moschovakos 36, B. Moser 36 , M. Mosidze 150b, T. Moskalets 54,
P. Moskvitina 113, J. Moss 31,l , E.J.W. Moyse 103 , O. Mtintsilana 33g, S. Muanza 102,
J. Mueller 129, D. Muenstermann 91 , R. Müller 19, G.A. Mullier 161 , A.J. Mullin32 ,
J.J. Mullin128, D.P. Mungo 155 , D. Munoz Perez 163, F.J. Munoz Sanchez 101, M. Murin 101 ,
W.J. Murray 167,134 , A. Murrone 71a,71b, M. Muškinja 17a, C. Mwewa 29 , A.G. Myagkov 37,a,
– 33 –

JHEP01(2025)053
A.J. Myers 8, G. Myers 68 , M. Myska 132 , B.P. Nachman 17a, O. Nackenhorst 49 ,
K. Nagai 126, K. Nagano 84 , J.L. Nagle 29,ak, E. Nagy 102 , A.M. Nairz 36 , Y. Nakahama 84,
K. Nakamura 84 , K. Nakkalil 5, H. Nanjo 124, R. Narayan 44, E.A. Narayanan 112,
I. Naryshkin 37, M. Naseri 34 , S. Nasri 116b, C. Nass 24, G. Navarro 22a,
J. Navarro-Gonzalez 163, R. Nayak 152 , A. Nayaz 18 , P.Y. Nechaeva 37 , F. Nechansky 48 ,
L. Nedic 126, T.J. Neep 20 , A. Negri 73a,73b, M. Negrini 23b, C. Nellist 114, C. Nelson 104 ,
K. Nelson 106, S. Nemecek 131 , M. Nessi 36,h, M.S. Neubauer 162 , F. Neuhaus 100 ,
J. Neundorf 48, R. Newhouse 164 , P.R. Newman 20, C.W. Ng 129 , Y.W.Y. Ng 48,
B. Ngair 116a, H.D.N. Nguyen 108 , R.B. Nickerson 126 , R. Nicolaidou 135, J. Nielsen 136 ,
M. Niemeyer 55 , J. Niermann 55,36, N. Nikiforou 36 , V. Nikolaenko 37,a , I. Nikolic-Audit 127 ,
K. Nikolopoulos 20, P. Nilsson 29 , I. Ninca 48, H.R. Nindhito 56 , G. Ninio 152 , A. Nisati 75a,
N. Nishu 2, R. Nisius 110 , J-E. Nitschke 50 , E.K. Nkadimeng 33g, T. Nobe 154 , D.L. Noel 32 ,
T. Nommensen 148, M.B. Norfolk 140 , R.R.B. Norisam 96, B.J. Norman 34 , M. Noury 35a,
J. Novak 93, T. Novak 48 , L. Novotny 132 , R. Novotny 112, L. Nozka 122 , K. Ntekas 159 ,
N.M.J. Nunes De Moura Junior 83b, E. Nurse96, J. Ocariz 127 , A. Ochi 85, I. Ochoa 130a,
S. Oerdek 48,v, J.T. Offermann 39 , A. Ogrodnik 133 , A. Oh 101 , C.C. Ohm 145 , H. Oide 84 ,
R. Oishi
154
, M.L. Ojeda
48
, Y. Okumura
154
, L.F. Oleiro Seabra
130a
, S.A. Olivares Pino
137d
,
D. Oliveira Damazio 29 , D. Oliveira Goncalves 83a, J.L. Oliver 159 , Ö.O. Öncel 54,
A.P. O’Neill 19, A. Onofre 130a,130 e, P.U.E. Onyisi 11 , M.J. Oreglia 39 , G.E. Orellana 90,
D. Orestano 77a,77b, N. Orlando 13, R.S. Orr 155 , V. O’Shea 59, L.M. Osojnak 128,
R. Ospanov 62a, G. Otero y Garzon 30 , H. Otono 89, P.S. Ott 63a, G.J. Ottino 17 a,
M. Ouchrif 35d, F. Ould-Saada 125 , M. Owen 59, R.E. Owen 134, K.Y. Oyulmaz 21a,
V.E. Ozcan 21a, F. Ozturk 87, N. Ozturk 8, S. Ozturk 82 , H.A. Pacey 126 ,
A. Pacheco Pages 13, C. Padilla Aranda 13 , G. Padovano 75a,75b, S. Pagan Griso 17a,
G. Palacino
68
, A. Palazzo
70a,70b
, J. Pan
172
, T. Pan
64a
, D.K. Panchal
11
, C.E. Pandini
114
,
J.G. Panduro Vazquez 95, H.D. Pandya 1, H. Pang 14b, P. Pani 48, G. Panizzo 69a,69c,
L. Paolozzi 56 , C. Papadatos 108 , S. Parajuli 162 , A. Paramonov 6, C. Paraskevopoulos 53 ,
D. Paredes Hernandez 64b, K.R. Park 41 , T.H. Park 155 , M.A. Parker 32 , F. Parodi 57b,57a,
E.W. Parrish 115 , V.A. Parrish 52, J.A. Parsons 41, U. Parzefall 54 , B. Pascual Dias 108 ,
L. Pascual Dominguez 152 , E. Pasqualucci 75a, S. Passaggio 57b, F. Pastore 95,
P. Pasuwan 47a,47b, P. Patel 87, U.M. Patel 51 , J.R. Pater 101 , T. Pauly 36, J. Pearkes 144 ,
M. Pedersen 125 , R. Pedro 130a, S.V. Peleganchuk 37 , O. Penc 36 , E.A. Pender 52 ,
K.E. Penski 109 , M. Penzin 37, B.S. Peralva 83d, A.P. Pereira Peixoto 60,
L. Pereira Sanchez 47a,47b, D.V. Perepelitsa 29,ak , E. Perez Codina 156a, M. Perganti 10 ,
H. Pernegger 36 , O. Perrin 40 , K. Peters 48, R.F.Y. Peters 101 , B.A. Petersen 36 ,
T.C. Petersen 42 , E. Petit 102, V. Petousis 132, C. Petridou 153,e , A. Petrukhin 142 ,
M. Pettee 17a, N.E. Pettersson 36 , A. Petukhov 37, K. Petukhova 133 , R. Pezoa 137f,
L. Pezzotti 36 , G. Pezzullo 172 , T.M. Pham 170 , T. Pham 105, P.W. Phillips 134 ,
G. Piacquadio 146, E. Pianori 17a, F. Piazza 123 , R. Piegaia 30, D. Pietreanu 27b,
A.D. Pilkington 101, M. Pinamonti 69a,69c, J.L. Pinfold 2, B.C. Pinheiro Pereira 130a,
A.E. Pinto Pinoargote 100,135 , L. Pintucci 69a,69c, K.M. Piper 147 , A. Pirttikoski 56 ,
D.A. Pizzi 34, L. Pizzimento 64b, A. Pizzini 114 , M.-A. Pleier 29 , V. Plesanovs54,
V. Pleskot 133 , E. Plotnikova38, G. Poddar 4, R. Poettgen 98 , L. Poggioli 127, I. Pokharel 55 ,
– 34 –

JHEP01(2025)053
S. Polacek 133 , G. Polesello 73 a, A. Poley 143,156a, R. Polifka 132, A. Polini 23b,
C.S. Pollard 167 , Z.B. Pollock 119, V. Polychronakos 29 , E. Pompa Pacchi 75a,75b,
D. Ponomarenko 113 , L. Pontecorvo 36, S. Popa 27a, G.A. Popeneciu 27d, A. Poreba 36,
D.M. Portillo Quintero 156a, S. Pospisil 132, M.A. Postill 140 , P. Postolache 27c,
K. Potamianos
167
, P.A. Potepa
86a
, I.N. Potrap
38
, C.J. Potter
32
, H. Potti
1
, T. Poulsen
48
,
J. Poveda 163, M.E. Pozo Astigarraga 36 , A. Prades Ibanez 163, J. Pretel 54 , D. Price 101,
M. Primavera 70a, M.A. Principe Martin 99 , R. Privara 122 , T. Procter 59 , M.L. Proffitt 139,
N. Proklova 128, K. Prokofiev 64c, G. Proto 110 , S. Protopopescu 29 , J. Proudfoot 6,
M. Przybycien 86a, W.W. Przygoda 86b, A. Psallidas 46, J.E. Puddefoot 140, D. Pudzha 37 ,
D. Pyatiizbyantseva 37 , J. Qian 106 , D. Qichen 101, Y. Qin 101 , T. Qiu 52, A. Quadt 55 ,
M. Queitsch-Maitland 101 , G. Quetant 56, R.P. Quinn 164 , G. Rabanal Bolanos 61,
D. Rafanoharana 54, F. Ragusa 71a,71b, J.L. Rainbolt 39 , J.A. Raine 56, S. Rajagopalan 29,
E. Ramakoti 37 , I.A. Ramirez-Berend 34, K. Ran 48,14e, N.P. Rapheeha 33g, H. Rasheed 27b,
V. Raskina 127, D.F. Rassloff 63a, A. Rastogi 17a, S. Rave 100, B. Ravina 55,
I. Ravinovich 169, M. Raymond 36 , A.L. Read 125, N.P. Readioff 140 , D.M. Rebuzzi 73a,73b,
G. Redlinger
29
, A.S. Reed
110
, K. Reeves
26
, J.A. Reidelsturz
171
, D. Reikher
152
, A. Rej
49
,
C. Rembser 36 , A. Renardi 48, M. Renda 27b, M.B. Rendel110 , F. Renner 48 , A.G. Rennie 159,
A.L. Rescia 48, S. Resconi 71a, M. Ressegotti 57b,57a, S. Rettie 36 , J.G. Reyes Rivera 107 ,
E. Reynolds 17a, O.L. Rezanova 37, P. Reznicek 133 , N. Ribaric 91 , E. Ricci 78a,78b,
R. Richter 110 , S. Richter 47a,47b, E. Richter-Was 86b, M. Ridel 127 , S. Ridouani 35d,
P. Rieck 117, P. Riedler 36 , E.M. Riefel 47a,47b, J.O. Rieger 114, M. Rijssenbeek 146 ,
A. Rimoldi 73a,73b, M. Rimoldi 36, L. Rinaldi 23b,23a, T.T. Rinn 29 , M.P. Rinnagel 109,
G. Ripellino 161 , I. Riu 13 , P. Rivadeneira 48, J.C. Rivera Vergara 165 , F. Rizatdinova 121,
E. Rizvi 94, B.A. Roberts 167 , B.R. Roberts 17a, S.H. Robertson 104,y , D. Robinson 32,
C.M. Robles Gajardo137f, M. Robles Manzano 100 , A. Robson 59, A. Rocchi 76a,76b,
C. Roda 74a,74b, S. Rodriguez Bosca 63a, Y. Rodriguez Garcia 22a, A. Rodriguez Rodriguez 54 ,
A.M. Rodríguez Vera 156b, S. Roe36 , J.T. Roemer 159 , A.R. Roepe-Gier 136 , J. Roggel 171 ,
O. Røhne 125, R.A. Rojas 103 , C.P.A. Roland 127 , J. Roloff 29, A. Romaniouk 37 ,
E. Romano
73a,73b
, M. Romano
23b
, A.C. Romero Hernandez
162
, N. Rompotis
92
, L. Roos
127
,
S. Rosati 75a, B.J. Rosser 39, E. Rossi 126 , E. Rossi 72a,72b, L.P. Rossi 57b, L. Rossini 54,
R. Rosten
119
, M. Rotaru
27b
, B. Rottler
54
, C. Rougier
102,ac
, D. Rousseau
66
, D. Rousso
32
,
A. Roy 162 , S. Roy-Garand 155 , A. Rozanov 102, Z.M.A. Rozario 59 , Y. Rozen 151,
X. Ruan 33g, A. Rubio Jimenez 163 , A.J. Ruby 92, V.H. Ruelas Rivera 18, T.A. Ruggeri 1,
A. Ruggiero 126 , A. Ruiz-Martinez 163, A. Rummler 36, Z. Rurikova 54, N.A. Rusakovich 38 ,
H.L. Russell 165 , G. Russo 75a,75b, J.P. Rutherfoord 7, S. Rutherford Colmenares 32 ,
K. Rybacki91, M. Rybar 133 , E.B. Rye 125 , A. Ryzhov 44 , J.A. Sabater Iglesias 56,
P. Sabatini 163, H.F-W. Sadrozinski 136, F. Safai Tehrani 75a, B. Safarzadeh Samani 134 ,
M. Safdari 144, S. Saha 165 , M. Sahinsoy 110 , A. Saibel 163 , M. Saimpert 135 , M. Saito 154,
T. Saito 154, D. Salamani 36 , A. Salnikov 144 , J. Salt 163, A. Salvador Salas 152,
D. Salvatore 43b,43a, F. Salvatore 147 , A. Salzburger 36, D. Sammel 54 , D. Sampsonidis 153,e ,
D. Sampsonidou 123, J. Sánchez 163, A. Sanchez Pineda 4, V. Sanchez Sebastian 163 ,
H. Sandaker 125 , C.O. Sander 48, J.A. Sandesara 103 , M. Sandhoff 171 , C. Sandoval 22b,
D.P.C. Sankey 134, T. Sano 88 , A. Sansoni 53, L. Santi 75a,75b, C. Santoni 40,
– 35 –

JHEP01(2025)053
H. Santos 130a,130b, A. Santra 169 , K.A. Saoucha 160 , J.G. Saraiva 130a,130d, J. Sardain 7,
O. Sasaki 84, K. Sato 157 , C. Sauer63b, F. Sauerburger 54, E. Sauvan 4, P. Savard 155,ah ,
R. Sawada 154 , C. Sawyer 134, L. Sawyer 97, I. Sayago Galvan163, C. Sbarra 23b,
A. Sbrizzi 23b,23a, T. Scanlon 96, J. Schaarschmidt 139, U. Schäfer 100, A.C. Schaffer 66,44 ,
D. Schaile 109 , R.D. Schamberger 146, C. Scharf 18, M.M. Schefer 19 , V.A. Schegelsky 37 ,
D. Scheirich 133 , F. Schenck 18, M. Schernau 159 , C. Scheulen 55 , C. Schiavi 57b,57a,
E.J. Schioppa 70a,70b, M. Schioppa 43b,43a, B. Schlag 144, K.E. Schleicher 54, S. Schlenker 36,
J. Schmeing 171 , M.A. Schmidt 171, K. Schmieden 100, C. Schmitt 100 , N. Schmitt 100 ,
S. Schmitt 48 , L. Schoeffel 135, A. Schoening 63b, P.G. Scholer 54 , E. Schopf 126 ,
M. Schott 100 , J. Schovancova 36, S. Schramm 56 , F. Schroeder 171, T. Schroer 56 ,
H-C. Schultz-Coulon 63a, M. Schumacher 54 , B.A. Schumm 136, Ph. Schune 135 ,
A.J. Schuy
139
, H.R. Schwartz
136
, A. Schwartzman
144
, T.A. Schwarz
106
, Ph. Schwemling
135
,
R. Schwienhorst 107 , A. Sciandra 136, G. Sciolla 26 , F. Scuri 74a, C.D. Sebastiani 92,
K. Sedlaczek 115, P. Seema 18 , S.C. Seidel 112, A. Seiden 136 , B.D. Seidlitz 41 , C. Seitz 48,
J.M. Seixas 83b, G. Sekhniaidze 72a, L. Selem 60, N. Semprini-Cesari 23b,23a, D. Sengupta 56 ,
V. Senthilkumar 163 , L. Serin 66, L. Serkin 69a,69b, M. Sessa 76a,76b, H. Severini 120 ,
F. Sforza 57b,57a, A. Sfyrla 56, E. Shabalina 55 , R. Shaheen 145, J.D. Shahinian 128 ,
D. Shaked Renous 169 , L.Y. Shan 14a, M. Shapiro 17a, A. Sharma 36, A.S. Sharma 164 ,
P. Sharma 80, S. Sharma 48 , P.B. Shatalov 37 , K. Shaw 147 , S.M. Shaw 101 ,
A. Shcherbakova 37 , Q. Shen 62c,5, D.J. Sheppard 143, P. Sherwood 96, L. Shi 96 , X. Shi 14a,
C.O. Shimmin 172, J.D. Shinner 95 , I.P.J. Shipsey 126,∗, S. Shirabe 89 , M. Shiyakova 38,w ,
J. Shlomi 169, M.J. Shochet 39 , J. Shojaii 105 , D.R. Shope 125 , B. Shrestha 120,
S. Shrestha 119,al, E.M. Shrif 33g, M.J. Shroff 165 , P. Sicho 131, A.M. Sickles 162 ,
E. Sideras Haddad 33g, A. Sidoti 23b, F. Siegert 50, Dj. Sijacki 15 , F. Sili 90 , J.M. Silva 20 ,
M.V. Silva Oliveira 29 , S.B. Silverstein 47a, S. Simion66 , R. Simoniello 36, E.L. Simpson 59 ,
H. Simpson
147
, L.R. Simpson
106
, N.D. Simpson
98
, S. Simsek
82
, S. Sindhu
55
, P. Sinervo
155
,
S. Singh 155, S. Sinha 48 , S. Sinha 101, M. Sioli 23b,23a, I. Siral 36 , E. Sitnikova 48,
S.Yu. Sivoklokov 37,∗, J. Sjölin 47a,47b, A. Skaf 55 , E. Skorda 20 , P. Skubic 120 ,
M. Slawinska 87 , V. Smakhtin169, B.H. Smart 134 , S.Yu. Smirnov 37, Y. Smirnov 37 ,
L.N. Smirnova 37,a, O. Smirnova 98 , A.C. Smith 41 , E.A. Smith 39, H.A. Smith 126 ,
J.L. Smith
92
, R. Smith
144
, M. Smizanska
91
, K. Smolek
132
, A.A. Snesarev
37
, S.R. Snider
155
,
H.L. Snoek 114 , S. Snyder 29 , R. Sobie 165,y , A. Soffer 152, C.A. Solans Sanchez 36,
E.Yu. Soldatov 37, U. Soldevila 163 , A.A. Solodkov 37 , S. Solomon 26 , A. Soloshenko 38 ,
K. Solovieva 54, O.V. Solovyanov 40 , V. Solovyev 37 , P. Sommer 36 , A. Sonay 13 ,
W.Y. Song 156b, A. Sopczak 132 , A.L. Sopio 96 , F. Sopkova 28b, J.D. Sorenson 112 ,
I.R. Sotarriva Alvarez 138, V. Sothilingam63a, O.J. Soto Sandoval 137c,137b, S. Sottocornola 68,
R. Soualah 160, Z. Soumaimi 35e, D. South 48 , N. Soybelman 169, S. Spagnolo 70a,70b,
M. Spalla 110, D. Sperlich 54 , G. Spigo 36, S. Spinali 91 , D.P. Spiteri 59 , M. Spousta 133,
E.J. Staats 34, R. Stamen 63a, A. Stampekis 20, M. Standke 24, E. Stanecka 87 ,
M.V. Stange 50, B. Stanislaus 17a, M.M. Stanitzki 48 , B. Stapf 48, E.A. Starchenko 37,
G.H. Stark 136, J. Stark 102,ac , P. Staroba 131, P. Starovoitov 63a, S. Stärz 104,
R. Staszewski 87, G. Stavropoulos 46 , J. Steentoft 161 , P. Steinberg 29 , B. Stelzer 143,156a,
H.J. Stelzer 129, O. Stelzer-Chilton 156a, H. Stenzel 58 , T.J. Stevenson 147, G.A. Stewart 36,
– 36 –

JHEP01(2025)053
J.R. Stewart 121 , M.C. Stockton 36, G. Stoicea 27b, M. Stolarski 130 a, S. Stonjek 110 ,
A. Straessner 50, J. Strandberg 145, S. Strandberg 47a,47 b, M. Stratmann 171 , M. Strauss 120 ,
T. Strebler 102, P. Strizenec 28b, R. Ströhmer 166 , D.M. Strom 123, R. Stroynowski 44,
A. Strubig 47a,47b, S.A. Stucci 29, B. Stugu 16 , J. Stupak 120, N.A. Styles 48 , D. Su 144,
S. Su 62a, W. Su 62d, X. Su 62a,66, K. Sugizaki 154 , V.V. Sulin 37, M.J. Sullivan 92,
D.M.S. Sultan 78a,78b, L. Sultanaliyeva 37 , S. Sultansoy 3b, T. Sumida 88 , S. Sun 106,
S. Sun 170, O. Sunneborn Gudnadottir 161, N. Sur 102 , M.R. Sutton 147, H. Suzuki 157 ,
M. Svatos 131 , M. Swiatlowski 156a, T. Swirski 166 , I. Sykora 28a, M. Sykora 133 ,
T. Sykora 133 , D. Ta 100, K. Tackmann 48,v , A. Taffard 159 , R. Tafirout 156a,
J.S. Tafoya Vargas 66 , E.P. Takeva 52, Y. Takubo 84 , M. Talby 102, A.A. Talyshev 37 ,
K.C. Tam
64b
, N.M. Tamir
152
, A. Tanaka
154
, J. Tanaka
154
, R. Tanaka
66
, M. Tanasini
57b,57a
,
Z. Tao 164, S. Tapia Araya 137f, S. Tapprogge 100, A. Tarek Abouelfadl Mohamed 107,
S. Tarem
151
, K. Tariq
14a
, G. Tarna
102,27b
, G.F. Tartarelli
71a
, P. Tas
133
, M. Tasevsky
131
,
E. Tassi 43b,43a, A.C. Tate 162, G. Tateno 154 , Y. Tayalati 35e,x, G.N. Taylor 105,
W. Taylor 156b, A.S. Tee 170, R. Teixeira De Lima 144 , P. Teixeira-Dias 95 , J.J. Teoh 155,
K. Terashi 154, J. Terron 99 , S. Terzo 13 , M. Testa 53, R.J. Teuscher 155,y , A. Thaler 79 ,
O. Theiner 56, N. Themistokleous 52 , T. Theveneaux-Pelzer 102 , O. Thielmann 171,
D.W. Thomas95, J.P. Thomas 20 , E.A. Thompson 17a, P.D. Thompson 20, E. Thomson 128 ,
Y. Tian 55, V. Tikhomirov 37,a, Yu.A. Tikhonov 37 , S. Timoshenko37, D. Timoshyn 133,
E.X.L. Ting 1, P. Tipton 172, S.H. Tlou 33g, A. Tnourji 40 , K. Todome 138 ,
S. Todorova-Nova 133 , S. Todt50, M. Togawa 84, J. Tojo 89 , S. Tokár 28a, K. Tokushuku 84,
O. Toldaiev 68, R. Tombs 32 , M. Tomoto 84,111 , L. Tompkins 144,n, K.W. Topolnicki 86b,
E. Torrence 123, H. Torres 102,ac , E. Torró Pastor 163 , M. Toscani 30, C. Tosciri 39 ,
M. Tost 11, D.R. Tovey 140, A. Traeet16, I.S. Trandafir 27b, T. Trefzger 166 , A. Tricoli 29 ,
I.M. Trigger 156a, S. Trincaz-Duvoid 127, D.A. Trischuk 26, B. Trocmé 60, C. Troncon 71a,
L. Truong 33c, M. Trzebinski 87, A. Trzupek 87, F. Tsai 146 , M. Tsai 106 , A. Tsiamis 153,e,
P.V. Tsiareshka37, S. Tsigaridas 156a, A. Tsirigotis 153,t, V. Tsiskaridze 155 ,
E.G. Tskhadadze
150a
, M. Tsopoulou
153,e
, Y. Tsujikawa
88
, I.I. Tsukerman
37
, V. Tsulaia
17a
,
S. Tsuno 84, K. Tsuri 118 , D. Tsybychev 146, Y. Tu 64b, A. Tudorache 27b, V. Tudorache 27b,
A.N. Tuna 61, S. Turchikhin 57b,57a, I. Turk Cakir 3a, R. Turra 71a, T. Turtuvshin 38,z ,
P.M. Tuts 41, S. Tzamarias 153,e , P. Tzanis 10 , E. Tzovara 100 , F. Ukegawa 157 ,
P.A. Ulloa Poblete 137c,137b, E.N. Umaka 29, G. Unal 36 , M. Unal 11, A. Undrus 29 ,
G. Unel 159, J. Urban 28b, P. Urquijo 105 , P. Urrejola 137a, G. Usai 8, R. Ushioda 138 ,
M. Usman 108, Z. Uysal 82 , V. Vacek 132, B. Vachon 104 , K.O.H. Vadla 125, T. Vafeiadis 36 ,
A. Vaitkus 96, C. Valderanis 109 , E. Valdes Santurio 47a,47b, M. Valente 156a,
S. Valentinetti 23b,23a, A. Valero 163, E. Valiente Moreno 163 , A. Vallier 102,ac,
J.A. Valls Ferrer 163 , D.R. Van Arneman 114, T.R. Van Daalen 139 , A. Van Der Graaf 49,
P. Van Gemmeren 6, M. Van Rijnbach 125,36 , S. Van Stroud 96 , I. Van Vulpen 114 ,
M. Vanadia 76a,76b, W. Vandelli 36, E.R. Vandewall 121 , D. Vannicola 152, L. Vannoli 57b,57a,
R. Vari 75a, E.W. Varnes 7, C. Varni 17b, T. Varol 149, D. Varouchas 66 , L. Varriale 163 ,
K.E. Varvell
148
, M.E. Vasile
27b
, L. Vaslin
84
, G.A. Vasquez
165
, A. Vasyukov
38
, F. Vazeille
40
,
T. Vazquez Schroeder 36 , J. Veatch 31 , V. Vecchio 101, M.J. Veen 103 , I. Veliscek 126 ,
L.M. Veloce 155 , F. Veloso 130a,130c, S. Veneziano 75a, A. Ventura 70a,70b,
– 37 –

JHEP01(2025)053
S. Ventura Gonzalez 135, A. Verbytskyi 110, M. Verducci 74a,74b, C. Vergis 24,
M. Verissimo De Araujo 83b, W. Verkerke 114, J.C. Vermeulen 114 , C. Vernieri 144,
M. Vessella
103
, M.C. Vetterli
143,ah
, A. Vgenopoulos
153,e
, N. Viaux Maira
137f
, T. Vickey
140
,
O.E. Vickey Boeriu 140 , G.H.A. Viehhauser 126 , L. Vigani 63b, M. Villa 23b,23a,
M. Villaplana Perez 163 , E.M. Villhauer52 , E. Vilucchi 53 , M.G. Vincter 34, G.S. Virdee 20 ,
A. Vishwakarma 52 , A. Visibile114, C. Vittori 36 , I. Vivarelli 147 , E. Voevodina 110 ,
F. Vogel 109, J.C. Voigt 50 , P. Vokac 132 , Yu. Volkotrub 86a, J. Von Ahnen 48 ,
E. Von Toerne 24 , B. Vormwald 36 , V. Vorobel 133 , K. Vorobev 37 , M. Vos 163 , K. Voss 142,
J.H. Vossebeld 92 , M. Vozak 114, L. Vozdecky 94 , N. Vranjes 15, M. Vranjes Milosavljevic 15 ,
M. Vreeswijk 114, N.K. Vu 62d,62c, R. Vuillermet 36, O. Vujinovic 100, I. Vukotic 39 ,
S. Wada
157
, C. Wagner
103
, J.M. Wagner
17a
, W. Wagner
171
, S. Wahdan
171
, H. Wahlberg
90
,
M. Wakida 111, J. Walder 134 , R. Walker 109, W. Walkowiak 142 , A. Wall 128,
T. Wamorkar 6, A.Z. Wang 136, C. Wang 100 , C. Wang 11 , H. Wang 17a, J. Wang 64c,
R.-J. Wang 100, R. Wang 61 , R. Wang 6, S.M. Wang 149 , S. Wang 62b, T. Wang 62a,
W.T. Wang 80, W. Wang 14a, X. Wang 14c, X. Wang 162 , X. Wang 62c, Y. Wang 62d,
Y. Wang
14c
, Z. Wang
106
, Z. Wang
62d,51,62c
, Z. Wang
106
, A. Warburton
104
, R.J. Ward
20
,
N. Warrack 59, S. Waterhouse 95 , A.T. Watson 20, H. Watson 59 , M.F. Watson 20,
E. Watton
59,134
, G. Watts
139
, B.M. Waugh
96
, C. Weber
29
, H.A. Weber
18
, M.S. Weber
19
,
S.M. Weber
63a
, C. Wei
62a
, Y. Wei
126
, A.R. Weidberg
126
, E.J. Weik
117
, J. Weingarten
49
,
M. Weirich 100, C. Weiser 54 , C.J. Wells 48, T. Wenaus 29 , B. Wendland 49 , T. Wengler 36,
N.S. Wenke110, N. Wermes 24 , M. Wessels 63a, A.M. Wharton 91, A.S. White 61 , A. White 8,
M.J. White
1
, D. Whiteson
159
, L. Wickremasinghe
124
, W. Wiedenmann
170
, M. Wielers
134
,
C. Wiglesworth 42 , D.J. Wilbern120 , H.G. Wilkens 36, D.M. Williams 41 , H.H. Williams128,
S. Williams 32, S. Willocq 103, B.J. Wilson 101 , P.J. Windischhofer 39, F.I. Winkel 30 ,
F. Winklmeier 123, B.T. Winter 54 , J.K. Winter 101 , M. Wittgen144, M. Wobisch 97 ,
Z. Wolffs 114, J. Wollrath159, M.W. Wolter 87 , H. Wolters 130a,130c, A.F. Wongel 48,
E.L. Woodward 41 , S.D. Worm 48, B.K. Wosiek 87 , K.W. Woźniak 87 , S. Wozniewski 55,
K. Wraight 59, C. Wu 20 , J. Wu 14a,14e, M. Wu 64a, M. Wu 113 , S.L. Wu 170, X. Wu 56 ,
Y. Wu 62a, Z. Wu 135, J. Wuerzinger 110,af , T.R. Wyatt 101 , B.M. Wynne 52, S. Xella 42 ,
L. Xia 14c, M. Xia 14b, J. Xiang 64c, M. Xie 62a, X. Xie 62a, S. Xin 14a,14e, A. Xiong 123 ,
J. Xiong 17a, D. Xu 14a, H. Xu 62a, L. Xu 62a, R. Xu 128 , T. Xu 106 , Y. Xu 14b, Z. Xu 52 ,
Z. Xu14c, B. Yabsley 148, S. Yacoob 33a, Y. Yamaguchi 138 , E. Yamashita 154,
H. Yamauchi 157, T. Yamazaki 17a, Y. Yamazaki 85 , J. Yan62c, S. Yan 126 , Z. Yan 25,
H.J. Yang 62c,62d, H.T. Yang 62a, S. Yang 62a, T. Yang 64c, X. Yang 36, X. Yang 14a,
Y. Yang 44, Y. Yang62a, Z. Yang 62a, W-M. Yao 17a, Y.C. Yap 48 , H. Ye 14c, H. Ye 55,
J. Ye 14a, S. Ye 29, X. Ye 62a, Y. Yeh 96 , I. Yeletskikh 38 , B. Yeo 17b, M.R. Yexley 96,
P. Yin 41, K. Yorita 168 , S. Younas 27b, C.J.S. Young 36 , C. Young 144, C. Yu 14a,14e,aj ,
Y. Yu 62a, M. Yuan 106, R. Yuan 62b, L. Yue 96 , M. Zaazoua 62a, B. Zabinski 87 , E. Zaid52,
Z.K. Zak
87
, T. Zakareishvili
163
, N. Zakharchuk
34
, S. Zambito
56
, J.A. Zamora Saa
137d,137b
,
J. Zang 154, D. Zanzi 54 , O. Zaplatilek 132, C. Zeitnitz 171 , H. Zeng 14a, J.C. Zeng 162 ,
D.T. Zenger Jr 26, O. Zenin 37 , T. Ženiš 28a, S. Zenz 94, S. Zerradi 35a, D. Zerwas 66 ,
M. Zhai 14a,14e, B. Zhang 14c, D.F. Zhang 140, J. Zhang 62b, J. Zhang 6, K. Zhang 14a,14e,
L. Zhang 14c, P. Zhang 14a,14e, R. Zhang 170, S. Zhang 106 , S. Zhang 44 , T. Zhang 154 ,
– 38 –

JHEP01(2025)053
X. Zhang 62c, X. Zhang 62b, Y. Zhang 62c,5, Y. Zhang 96 , Y. Zhang 14c, Z. Zhang 17a,
Z. Zhang 66, H. Zhao 139 , T. Zhao 62b, Y. Zhao 136, Z. Zhao 62a, A. Zhemchugov 38 ,
J. Zheng 14c, K. Zheng 162, X. Zheng 62a, Z. Zheng 144 , D. Zhong 162, B. Zhou 106 ,
H. Zhou 7, N. Zhou 62c, Y. Zhou 14c, Y. Zhou7, C.G. Zhu 62b, J. Zhu 106, Y. Zhu 62c,
Y. Zhu 62a, X. Zhuang 14a, K. Zhukov 37, V. Zhulanov 37, N.I. Zimine 38 , J. Zinsser 63b,
M. Ziolkowski 142 , L. Živković 15, A. Zoccoli 23b,23a, K. Zoch 61, T.G. Zorbas 140 ,
O. Zormpa 46, W. Zou 41 , L. Zwalinski 36
1Department of Physics, University of Adelaide, Adelaide; Australia
2Department of Physics, University of Alberta, Edmonton AB; Canada
3 (a)Department of Physics, Ankara University, Ankara;(b)Division of Physics, TOBB University of
Economics and Technology, Ankara; Türkiye
4LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France
5APC, Université Paris Cité, CNRS/IN2P3, Paris; France
6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America
7Department of Physics, University of Arizona, Tucson AZ; United States of America
8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America
9Physics Department, National and Kapodistrian University of Athens, Athens; Greece
10 Physics Department, National Technical University of Athens, Zografou; Greece
11 Department of Physics, University of Texas at Austin, Austin TX; United States of America
12 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan
13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona;
Spain
14 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b)Physics Department,
Tsinghua University, Beijing;
(c)
Department of Physics, Nanjing University, Nanjing;
(d)
School of Science,
Shenzhen Campus of Sun Yat-sen University;(e)University of Chinese Academy of Science (UCAS),
Beijing; China
15 Institute of Physics, University of Belgrade, Belgrade; Serbia
16 Department for Physics and Technology, University of Bergen, Bergen; Norway
17 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA;(b)University of California,
Berkeley CA; United States of America
18 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany
19 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of
Bern, Bern; Switzerland
20 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom
21 (a)
Department of Physics, Bogazici University, Istanbul;
(b)
Department of Physics Engineering, Gaziantep
University, Gaziantep;(c)Department of Physics, Istanbul University, Istanbul; Türkiye
22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño,
Bogotá;(b)Departamento de Física, Universidad Nacional de Colombia, Bogotá; Colombia
23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna;(b)INFN Sezione di
Bologna; Italy
24 Physikalisches Institut, Universität Bonn, Bonn; Germany
25 Department of Physics, Boston University, Boston MA; United States of America
26 Department of Physics, Brandeis University, Waltham MA; United States of America
27 (a)Transilvania University of Brasov, Brasov;(b)Horia Hulubei National Institute of Physics and Nuclear
Engineering, Bucharest;
(c)
Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi;
(d)
National
Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department,
Cluj-Napoca;
(e)
National University of Science and Technology Politechnica, Bucharest;
(f)
West University
in Timisoara, Timisoara;(g)Faculty of Physics, University of Bucharest, Bucharest; Romania
28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava;(b)Department of
Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak
Republic
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JHEP01(2025)053
29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America
30 Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, y
CONICET, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires; Argentina
31 California State University, CA; United States of America
32 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom
33 (a)Department of Physics, University of Cape Town, Cape Town;(b)iThemba Labs, Western
Cape;(c)Department of Mechanical Engineering Science, University of Johannesburg,
Johannesburg;(d)National Institute of Physics, University of the Philippines Diliman
(Philippines);(e)University of South Africa, Department of Physics, Pretoria;(f)University of Zululand,
KwaDlangezwa;(g)School of Physics, University of the Witwatersrand, Johannesburg; South Africa
34 Department of Physics, Carleton University, Ottawa ON; Canada
35 (a)
Faculté des Sciences Ain Chock, Université Hassan II de Casablanca;
(b)
Faculté des Sciences, Université
Ibn-Tofail, Kénitra;
(c)
Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech;
(d)
LPMR,
Faculté des Sciences, Université Mohamed Premier, Oujda;
(e)
Faculté des sciences, Université Mohammed
V, Rabat;(f)Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco
36 CERN, Geneva; Switzerland
37 Affiliated with an institute covered by a cooperation agreement with CERN
38 Affiliated with an international laboratory covered by a cooperation agreement with CERN
39 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America
40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France
41 Nevis Laboratory, Columbia University, Irvington NY; United States of America
42 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark
43 (a)Dipartimento di Fisica, Università della Calabria, Rende;(b)INFN Gruppo Collegato di Cosenza,
Laboratori Nazionali di Frascati; Italy
44 Physics Department, Southern Methodist University, Dallas TX; United States of America
45 Physics Department, University of Texas at Dallas, Richardson TX; United States of America
46 National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece
47 (a)Department of Physics, Stockholm University;(b)Oskar Klein Centre, Stockholm; Sweden
48 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany
49 Fakultät Physik, Technische Universität Dortmund, Dortmund; Germany
50 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany
51 Department of Physics, Duke University, Durham NC; United States of America
52 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom
53 INFN e Laboratori Nazionali di Frascati, Frascati; Italy
54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany
55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany
56 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland
57 (a)Dipartimento di Fisica, Università di Genova, Genova;(b)INFN Sezione di Genova; Italy
58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany
59 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom
60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France
61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of
America
62 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics,
University of Science and Technology of China, Hefei;
(b)
Institute of Frontier and Interdisciplinary Science
and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University,
Qingdao;
(c)
School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for Particle
Astrophysics and Cosmology (MOE), SKLPPC, Shanghai;
(d)
Tsung-Dao Lee Institute, Shanghai;
(e)
School
of Physics, Zhengzhou University; China
63 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg;(b)Physikalisches
Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany
64 (a)
Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;
(b)
Department of
Physics, University of Hong Kong, Hong Kong;
(c)
Department of Physics and Institute for Advanced Study,
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JHEP01(2025)053
Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China
65 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan
66 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France
67 Centro Nacional de Microelectrónica (IMB-CNM-CSIC), Barcelona; Spain
68 Department of Physics, Indiana University, Bloomington IN; United States of America
69 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b)ICTP, Trieste;(c)Dipartimento
Politecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy
70 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy
71 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Università di Milano, Milano; Italy
72 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy
73 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy
74 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy
75 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy
76 (a)
INFN Sezione di Roma Tor Vergata;
(b)
Dipartimento di Fisica, Università di Roma Tor Vergata, Roma;
Italy
77 (a)
INFN Sezione di Roma Tre;
(b)
Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy
78 (a)INFN-TIFPA;(b)Università degli Studi di Trento, Trento; Italy
79 Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria
80 University of Iowa, Iowa City IA; United States of America
81 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America
82 Istinye University, Sariyer, Istanbul; Türkiye
83 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de
Fora;(b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro;(c)Instituto de Física,
Universidade de São Paulo, São Paulo;(d)Rio de Janeiro State University, Rio de Janeiro; Brazil
84 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan
85 Graduate School of Science, Kobe University, Kobe; Japan
86 (a)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow;(b)Marian
Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland
87 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland
88 Faculty of Science, Kyoto University, Kyoto; Japan
89
Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka;
Japan
90 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina
91 Physics Department, Lancaster University, Lancaster; United Kingdom
92 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom
93 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics,
University of Ljubljana, Ljubljana; Slovenia
94 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom
95 Department of Physics, Royal Holloway University of London, Egham; United Kingdom
96 Department of Physics and Astronomy, University College London, London; United Kingdom
97 Louisiana Tech University, Ruston LA; United States of America
98 Fysiska institutionen, Lunds universitet, Lund; Sweden
99 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain
100 Institut für Physik, Universität Mainz, Mainz; Germany
101 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom
102 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseil le; France
103 Department of Physics, University of Massachusetts, Amherst MA; United States of America
104 Department of Physics, McGill University, Montreal QC; Canada
105 School of Physics, University of Melbourne, Victoria; Australia
106 Department of Physics, University of Michigan, Ann Arbor MI; United States of America
107 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of
America
108 Group of Particle Physics, University of Montreal, Montreal QC; Canada
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JHEP01(2025)053
109 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany
110 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany
111 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan
112 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of
America
113 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen;
Netherlands
114 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands
115 Department of Physics, Northern Illinois University, DeKalb IL; United States of America
116 (a)
New York University Abu Dhabi, Abu Dhabi;
(b)
United Arab Emirates University, Al Ain; United Arab
Emirates
117 Department of Physics, New York University, New York NY; United States of America
118 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan
119 Ohio State University, Columbus OH; United States of America
120 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United
States of America
121 Department of Physics, Oklahoma State University, Stillwater OK; United States of America
122 Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic
123 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America
124 Graduate School of Science, Osaka University, Osaka; Japan
125 Department of Physics, University of Oslo, Oslo; Norway
126 Department of Physics, Oxford University, Oxford; United Kingdom
127 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France
128 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America
129
Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America
130 (a)Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa;(b)Departamento de
Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa;
(c)
Departamento de Física, Universidade de
Coimbra, Coimbra;(d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa;(e)Departamento de
Física, Universidade do Minho, Braga;(f)Departamento de Física Teórica y del Cosmos, Universidad de
Granada, Granada (Spain);
(g)
Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa,
Lisboa; Portugal
131 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic
132 Czech Technical University in Prague, Prague; Czech Republic
133 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic
134 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom
135 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France
136 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United
States of America
137 (a)
Departamento de Física, Pontificia Universidad Católica de Chile, Santiago;
(b)
Millennium Institute for
Subatomic physics at high energy frontier (SAPHIR), Santiago;(c)Instituto de Investigación
Multidisciplinario en Ciencia y Tecnología, y Departamento de Física, Universidad de La
Serena;(d)Universidad Andres Bello, Department of Physics, Santiago;(e)Instituto de Alta Investigación,
Universidad de Tarapacá, Arica;(f)Departamento de Física, Universidad Técnica Federico Santa María,
Valparaíso; Chile
138 Department of Physics, Institute of Science, Tokyo; Japan
139 Department of Physics, University of Washington, Seattle WA; United States of America
140 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom
141 Department of Physics, Shinshu University, Nagano; Japan
142 Department Physik, Universität Siegen, Siegen; Germany
143 Department of Physics, Simon Fraser University, Burnaby BC; Canada
144 SLAC National Accelerator Laboratory, Stanford CA; United States of America
145 Department of Physics, Royal Institute of Technology, Stockholm; Sweden
146 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of
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JHEP01(2025)053
America
147 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom
148 School of Physics, University of Sydney, Sydney; Australia
149 Institute of Physics, Academia Sinica, Taipei; Taiwan
150 (a)
E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;
(b)
High Energy
Physics Institute, Tbilisi State University, Tbilisi;(c)University of Georgia, Tbilisi; Georgia
151 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel
152 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel
153 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece
154 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo,
Tokyo; Japan
155 Department of Physics, University of Toronto, Toronto ON; Canada
156 (a)TRIUMF, Vancouver BC;(b)Department of Physics and Astronomy, York University, Toronto ON;
Canada
157 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied
Sciences, University of Tsukuba, Tsukuba; Japan
158 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America
159 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of
America
160 University of Sharjah, Sharjah; United Arab Emirates
161 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden
162 Department of Physics, University of Illinois, Urbana IL; United States of America
163 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain
164 Department of Physics, University of British Columbia, Vancouver BC; Canada
165 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada
166 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany
167 Department of Physics, University of Warwick, Coventry; United Kingdom
168 Waseda University, Tokyo; Japan
169 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel
170 Department of Physics, University of Wisconsin, Madison WI; United States of America
171 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal,
Wuppertal; Germany
172 Department of Physics, Yale University, New Haven CT; United States of America
aAlso Affiliated with an institute covered by a cooperation agreement with CERN
bAlso at An-Najah National University, Nablus; Palestine
c
Also at Borough of Manhattan Community College, City University of New York, New York NY; United
States of America
dAlso at Center for High Energy Physics, Peking University; China
eAlso at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki; Greece
fAlso at Centro Studi e Ricerche Enrico Fermi; Italy
gAlso at CERN, Geneva; Switzerland
h
Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland
iAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain
jAlso at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece
kAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel
lAlso at Department of Physics, California State University, Sacramento; United States of America
mAlso at Department of Physics, King’s College London, London; United Kingdom
nAlso at Department of Physics, Stanford University, Stanford CA; United States of America
oAlso at Department of Physics, Stellenbosch University; South Africa
pAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland
qAlso at Department of Physics, University of Thessaly; Greece
rAlso at Department of Physics, Westmont College, Santa Barbara; United States of America
sAlso at Faculty of Physics, Sofia University, ‘St. Kliment Ohridski’, Sofia; Bulgaria
– 43 –

JHEP01(2025)053
tAlso at Hellenic Open University, Patras; Greece
uAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain
vAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany
wAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of
Sciences, Sofia; Bulgaria
xAlso at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco
yAlso at Institute of Particle Physics (IPP); Canada
zAlso at Institute of Physics and Technology, Mongolian Academy of Sciences, Ulaanbaatar; Mongolia
aa Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan
ab Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia
ac Also at L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse; France
ad Also at Lawrence Livermore National Laboratory, Livermore; United States of America
ae Also at National Institute of Physics, University of the Philippines Diliman (Philippines); Philippines
af Also at Technical University of Munich, Munich; Germany
ag Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China
ah Also at TRIUMF, Vancouver BC; Canada
ai Also at Università di Napoli Parthenope, Napoli; Italy
aj Also at University of Chinese Academy of Sciences (UCAS), Beijing; China
ak Also at University of Colorado Boulder, Department of Physics, Colorado; United States of America
al Also at Washington College, Chestertown, MD; United States of America
am Also at Yeditepe University, Physics Department, Istanbul; Türkiye
∗Deceased
– 44 –