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Journal of High Energy Physics. 2024 Nov 07;2024(11):47
The Author(s)
ATLAS Collaboration
A search for the resonant production of a heavy scalar X decaying into a Higgs boson and a new lighter scalar S, through the process X → S(→ b b ¯ )H(→γγ), where the two photons are consistent with the Higgs boson decay, is performed. The search is conducted using an integrated luminosity of 140 fb−1 of proton-proton collision data at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider. The search is performed over the mass range 170 ≤ mX ≤ 1000 GeV and 15 ≤ mS ≤ 500 GeV. Parameterised neural networks are used to enhance the signal purity and to achieve continuous sensitivity in a domain of the (mX, mS) plane. No significant excess above the expected background is found and 95% CL upper limits are set on the cross section times branching ratio, ranging from 39 fb to 0.09 fb. The largest deviation from the background-only expectation occurs for (mX, mS) = (575, 200) GeV with a local (global) significance of 3.5 (2.0) standard deviations.
JHEP11(2024)047PuAlished for SISS Ay SpringerReceived: April 22, 2024Accepted: September 30, 2024Published: November 7, 2024Search for a resonance decaying into a scalar particleand a Higgs boson in the final state with two bottomquarks and two photons in proton–proton collisions at√s = 13TeV with the ATLAS detectorThe ATLAS collaborationE-mail: atlas.publications@cern.chAbstract: A search for the resonant production of a heavy scalar X decaying into a Higgsboson and a new lighter scalar S, through the process X → S(→bb¯)H(→γγ), where the twophotons are consistent with the Higgs boson decay, is performed. The search is conducted usingan integrated luminosity of 140 fb−1 of proton-proton collision data at a centre-of-mass energyof 13TeV recorded with the ATLAS detector at the Large Hadron Collider. The search isperformed over the mass range 170 ≤ mX ≤ 1000GeV and 15 ≤ mS ≤ 500GeV. Parameterisedneural networks are used to enhance the signal purity and to achieve continuous sensitivityin a domain of the (mX , mS) plane. No significant excess above the expected background isfound and 95% CL upper limits are set on the cross section times branching ratio, rangingfrom 39 fb to 0.09 fb. The largest deviation from the background-only expectation occurs for(mX , mS) = (575, 200) GeV with a local (global) significance of 3.5 (2.0) standard deviations.Keywords: Hadron-Hadron Scattering, Beyond Standard Model, Higgs Physics,Hadron-Hadron scatteringArXiv ePrint: 2404.12915Open Access, Copyright CERN,for the benefit of the ATLAS Collaboration.Article funded by SCOAP3.https://doi.org/10.1007/JHEP11(2024)047JHEP11(2024)047Contents1 Introduction 12 ATLAS detector 33 Data and simulated event samples 44 Object definitions 65 Analysis strategy 85.1 Event preselection 85.2 Signal region definitions 95.3 Final signal-to-background discriminant 95.4 Signal interpolation 106 Background estimation 116.1 Non-resonant diphoton background 126.2 Higgs boson processes 137 Systematic uncertainties 137.1 Experimental systematic uncertainties 137.2 Theoretical systematic uncertainties 147.3 Systematic uncertainties in the non-resonant diphoton background 158 Results 168.1 Statistical model 168.2 Control region validation 178.3 Result and interpretation 179 Conclusion 19The ATLAS collaboration 281 IntroductionThe properties of the Higgs boson (H) discovered in 2012 [1, 2] by the ATLAS and CMSexperiments at the Large Hadron Collider (LHC) are consistent with the Standard Model(SM) predictions [3, 4]. However the current experimental precision does not exclude thatH may have a small mixing with additional scalar bosons, and may be part of an extendedHiggs sector. Many Beyond the Standard Model (BSM) theories predict such an extendedHiggs sector, where one of the physical Higgs boson states could correspond to the spin-0boson observed with a mass of 125 GeV, while additional scalars remain to be discovered [5].– 1 –JHEP11(2024)047ggbb¯γγXSHFigure 1. Example of a Feynman diagram showing gluon–gluon fusion production of a scalar Xdecaying into a scalar S and a Standard Model Higgs boson, which in turn decay into a pair ofb-quarks and a pair of photons.In this paper, the complete proton-proton dataset collected by the ATLAS experimentduring Run 2 of the LHC is used to search for two additional scalar bosons X and S. Under thecondition mX > mS+mH , the decay X → SH is kinematically allowed. This phenomenologymay arise in models where the SM Higgs sector is extended with either a complex singlet [6] ortwo real singlets [7], and in models such as the complex two-Higgs-doublet model (2HDM) [8],the 2HDM extended by a real scalar singlet [9, 10] or the Next-to-Minimal SupersymmetricStandard Model [11, 12].The sensitivity of the LHC to the decay X → SH has been explored in several benchmarkscenarios [7, 13, 14]. The decay of the scalar S is model- and mass-dependent. The CMSCollaboration has performed searches for X → S(→bb¯)H(→bb¯), X → S(→bb¯)H(→τ τ¯)and X → S(→bb¯)H(→γγ) using Run 2 data [15–17]. In the X → S(→bb¯)H(→γγ) finalstate, CMS observed a deviation from the background-only hypothesis with a local (global)significance of 3.8 (2.8) standard deviations at mX = 650 GeV and mS = 90 GeV. ATLASpublished results on the search for X → S(→V V )H(→τ τ¯), where V denotes a W or Zboson [18].This paper is focused on the search for X → S(→bb¯)H(→γγ) and uses the same Run2 dataset already exploited by ATLAS to search for Higgs boson pair production [19]. Adi-photon mass peak arises from H → γγ, while the two b-tagged jets arise from the S → bb¯decay, thus leading to a characteristic signal with three resonant mass peaks from H → γγ,S → bb¯ and X → bb¯γγ. The natural widths of the new bosons are assumed to be much smallerthan the experimental resolutions. In the particular scenario where the scalar S has couplingssimilar to those of the SM Higgs boson, S → bb¯ is the predominant decay for mS < 130 GeV.The Feynman diagram for the main production mode of this process is illustrated in figure 1.The rate of production for the scalar X, and the decay branching ratios BR(X → SH)and BR(S → bb¯), are strongly dependent on which model is realised, and on the specificvalues of the parameters of the extended Higgs sector. Therefore, the results are expressedas 95% confidence level (CL) upper limits on σ(pp→ X)×BR(X → SH)×BR(S → bb¯)×BR(H → γγ), denoted σ(X → SH → bb¯γγ), rather than on specific models, probing themX range between 170 and 1000 GeV and the mS range between 15 and 500 GeV.This article is structured as follows. Section 2 briefly introduces the ATLAS detector.Data and simulated event samples are given in section 3. Object definitions are introduced in– 2 –JHEP11(2024)047section 4 while the event selection is described in section 5. The strategy for backgroundestimation is explained in section 6. Section 7 is devoted to the description of the systematicuncertainties. Statistical modelling, validation of the background calculations and results arepresented in section 8. Finally the conclusions are given in section 9.2 ATLAS detectorThe ATLAS detector [20] at the LHC covers nearly the entire solid angle around the collisionpoint.1 It consists of an inner tracking detector surrounded by a thin superconductingsolenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporatingthree large superconducting air-core toroidal magnets.The inner-detector system (ID) is immersed in a 2T axial magnetic field and providescharged-particle tracking in the range |η| < 2.5. The high-granularity silicon pixel detectorcovers the vertex region and typically provides four measurements per track, the first hitgenerally being in the insertable B-layer (IBL) installed before Run 2 [21, 22]. It is followedby the SemiConductor Tracker (SCT), which usually provides eight measurements per track.These silicon detectors are complemented by the transition radiation tracker (TRT), whichenables radially extended track reconstruction up to |η| = 2.0. The TRT also provideselectron identification information based on the fraction of hits (typically 30 in total) above ahigher energy-deposit threshold corresponding to transition radiation.The calorimeter system covers the pseudorapidity range |η| < 4.9. Within the region|η| < 3.2, electromagnetic calorimetry is provided by a lead/liquid-argon (LAr) samplingcalorimeter with accordion geometry. It is divided into a barrel section covering |η| < 1.475and two endcap sections covering 1.375 < |η| < 3.2. For |η| < 2.5, it is divided into three layersin depth, which are finely segmented in η and ϕ. An additional thin LAr presampler layercovering |η| < 1.8 is used to correct for energy loss in material upstream of the calorimeters.Hadronic calorimetry is provided by the steel/scintillator-tile calorimeter, segmented intothree barrel structures within |η| < 1.7, and two copper/LAr hadronic endcap calorimeters.The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimetermodules optimised for electromagnetic and hadronic energy measurements respectively.The muon spectrometer (MS) comprises separate trigger and high-precision trackingchambers measuring the deflection of muons in a magnetic field generated by the superconduc-ting air-core toroidal magnets. The field integral of the toroids ranges between 2.0 and6.0Tm across most of the detector. Three layers of precision chambers, each consisting oflayers of monitored drift tubes, cover the region |η| < 2.7, complemented by cathode-stripchambers in the forward region, where the background is highest. The muon trigger systemcovers the range |η| < 2.4 with resistive-plate chambers in the barrel, and thin-gap chambersin the endcap regions.1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in thecentre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre ofthe LHC ring, and the y-axis points upwards. Polar coordinates (r, ϕ) are used in the transverse plane, ϕbeing the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θ asη = − ln tan(θ/2) and is equal to the rapidity y = 12 ln(E+pzcE−pzc)in the relativistic limit. Angular distance ismeasured in units of ∆R ≡√(∆y)2 + (∆ϕ)2.– 3 –JHEP11(2024)047The luminosity is measured mainly by the LUCID–2 [23] detector that records Cherenkovlight produced in the quartz windows of photomultipliers located close to the beampipe.Events are selected by the first-level trigger system implemented in custom hardware,followed by selections made by algorithms implemented in software in the high-level trigger [24].The first-level trigger accepts events from the 40MHz bunch crossings at a rate below 100 kHz,which the high-level trigger further reduces in order to record complete events to disk atabout 1 kHz.A software suite [25] is used in data simulation, in the reconstruction and analysis ofreal and simulated data, in detector operations, and in the trigger and data acquisitionsystems of the experiment.3 Data and simulated event samplesThe data used in this search were collected by the ATLAS experiment between 2015 and 2018,from proton-proton collisions at√s = 13 TeV at the LHC. After data quality requirements [26],this corresponds to an integrated luminosity of 140 fb−1. The uncertainty in the combined2015–2018 integrated luminosity is 0.83% [27], obtained using the LUCID-2 detector [23] forprimary luminosity measurements, complemented by measurements using the inner detectorand calorimeters.Events are recorded using diphoton triggers that require two reconstructed photoncandidates with minimum transverse energies of 35 GeV and 25 GeV [28]. The triggersused in 2015 and 2016 require both the photons to satisfy the Loose photon identificationcriterion defined in ref. [29], while the Medium criterion [29] is used for 2017–2018 to copewith the increased pp interaction rate.The Monte Carlo (MC) simulated event samples used in the analysis are listed in table 1,along with the generator used in the simulation, the parton distribution function (PDF) set,the showering model and the set of tuned parameters (tune). The X → SH signal process issimulated at leading-order (LO) in QCD with Pythia 8.2 [30]. The Higgs boson is forced todecay into two photons, while the scalar S is forced to decay into two b-quarks. The X andS scalar decays are generated in the narrow-width approximation. A total of 161 signal masspoints are generated in the range 170 ≤ mX ≤ 1000 GeV and 15 ≤ mS ≤ 500 GeV.The backgrounds can be divided into three categories. The largest background categoryconsists of events with two photon candidates featuring a smoothly falling, non-resonantdiphoton mass spectrum. This population arises from processes with two prompt photonsor from jet processes where one or both the photon candidates are misidentified jets viainstrumental effects. The processes Z(→qq¯)γγ and tt¯γγ are also included in this category.All these backgrounds together are referred to as ‘non-resonant diphoton background’, thecategory is denoted γγ+jets for short but does include γ+jets and dijet processes. Dueto the significant contribution from instrumental background in the γγ+jets category, itsnormalisation is constrained with data using dedicated control regions detailed in section 6.1.The processes γγ+jets and Z(→qq¯)γγ, with two real photons, are simulated withSherpa 2.2.4 and Sherpa 2.2.11 [33] respectively. Matrix elements at next-to-leading-order(NLO) in QCD for up to one parton and at LO for up to three partons are calculatedwith the Comix [52] and OpenLoops [53–55] libraries. An alternative γγ+jets MC sample– 4 –JHEP11(2024)047Process Generator PDF set Showering TuneX → SH Pythia 8.2 [30] NNPDF2.3lo [31] Pythia 8.2 [30] A14 [32]γγ+jets Sherpa 2.2.4 [33] NNPDF3.0nnlo [34] – –tt¯γγ MadGraph5_aMC@NLO [35] NNPDF2.3lo Pythia 8.2 A14Z(→qq¯)γγ Sherpa 2.2.11 [33] NNPDF3.0nnlo – –ggF H Nnlops [36–38] [39, 40] PDF4LHC15 [41] Pythia 8.2 AZNLO [42]VBF H PowhegBox v2 [43–46] PDF4LHC15 Pythia 8.2 AZNLOWH PowhegBox v2 [47, 48] PDF4LHC15 Pythia 8.2 AZNLOqq → ZH PowhegBox v2 [47, 48] PDF4LHC15 Pythia 8.2 AZNLOgg → ZH PowhegBox v2 [47, 48] PDF4LHC15 Pythia 8.2 AZNLOtt¯H PowhegBox v2 [49] NNPDF3.0nlo Pythia 8.2 A14bb¯H PowhegBox v2 [37] NNPDF3.0nlo Pythia 8.2 A14tHq MadGraph5_aMC@NLO NNPDF3.0nlo Pythia 8.2 A14tHW MadGraph5_aMC@NLO NNPDF3.0nlo Pythia 8.2 A14ggF HH PowhegBox v2 +FT [46, 50, 51] PDFLHC Pythia 8.2 A14VBF HH MadGraph5_aMC@NLO NNPDF3.0nlo Pythia 8.2 A14Table 1. Summary of the main signal and background samples, split by production mode: signalsamples, continuum background samples, single Higgs boson processes and Higgs-boson pair productionsamples. The generator used in the simulation, the PDF set, the showering model and the set of tunedparameters are also provided.generated with MadGraph5_aMC@NLO [35] including production of up to two jets atNLO is considered for evaluation of systematic uncertainties. The tt¯γγ process is generatedwith MadGraph5_aMC@NLO.The second largest background category consists of processes with photons from a singleHiggs boson decay, this includes processes where the Higgs boson may be produced inassociation with other particles. This includes single Higgs boson production via gluon–gluonfusion (ggF), vector-boson fusion (VBF), WH, ZH (qq → ZH and gg → ZH), tt¯H, bb¯H,tHq and tHW . The cross sections of the single Higgs boson processes are set to the mostprecise available theoretical values [56].The last background category consists of Standard Model Higgs boson pair production(HH) processes via ggF and VBF. The ggF Higgs boson pair production cross section iscalculated at next-to-next-to-leading-order (NNLO) accuracy including finite top-quark masseffects [57–60]. The cross section for Higgs boson pair production via VBF is calculated atnext-to-next-to-next-to-leading-order (N3LO) [57]. The analysis assumes a branching ratioof 0.227% for the Higgs boson decay into two photons and a branching ratio of 58.2% forthe Higgs boson decay into two b-quarks [56, 61].The samples use EvtGen [62] for the modelling of b- and c-hadron decays. A fullsimulation of the ATLAS detector [63] based on Geant4 [64] is used to reproduce thedetector response for single Higgs boson processes. The samples for signal, non-resonantphoton production and Standard Model Higgs boson pair production are processed withthe fast simulation AtlFastII [65] which employs Geant4 except for a parameterisationof the calorimeter response.– 5 –JHEP11(2024)047Varying numbers of minimum-bias interactions produced with Pythia 8.186 [66] usingthe NNPDF2.3LO PDF set with the A3 tune [67] are overlaid on the hard-scattering eventof all samples to simulate the effect of multiple pp interactions (pile-up) in the same or nearbybunch crossings. The events are reweighted as a function of the number of interactions perbunch crossing to match the distribution in data.4 Object definitionsEvents are required to have at least one reconstructed collision vertex, defined as a vertexassociated with at least two tracks with transverse momentum (pT) larger than 0.5 GeV. Theprimary vertex is selected from the reconstructed collision vertices using a neural networkalgorithm [68] based on extrapolated photon trajectories and the tracks associated witheach candidate vertex.Photons are reconstructed from topologically connected clusters [29] of energy depositsin the electromagnetic calorimeter in the region |η| < 2.37, excluding the transition regionbetween the barrel and endcap calorimeters 1.37 < |η| < 1.52. Photon candidates are classifiedas converted or unconverted based on whether or not they can be associated to conversionvertices or tracks consistent with photon conversions.The calibration of the photon energy is based on a multivariate regression algorithmtrained with MC samples, where the simulated distributions of the input variables arecorrected with data-driven techniques. The calibrated energy is brought to the absolutescale by applying scale factors derived from Z → e+e− events [29]. The photon direction isreconstructed using the longitudinal development of the shower in the calorimeters constrainedto the luminous region of the proton-beam collisions. In the case of converted photons theinformation about the position of the conversion vertex and the tracks associated withconversion is also used.Photon identification is based on the lateral shower profile of the energy deposits in thefirst and second electromagnetic calorimeter layers and on the energy leakage fraction into thehadronic calorimeter [29]. It reduces the misidentification of hadronic jets containing largeneutral components, primarily neutral pions, which decay into a pair of highly collimatedphotons. Identification criteria are tuned for converted and unconverted photons separately,and the Tight criteria defined in ref. [29] are applied.To improve rejection of misidentified photons, two isolation variables are defined toquantify the amount of activity around a photon. Calorimeter-based isolation EisoT is definedas the sum of the transverse energy of topological clusters within a cone of size ∆R =√∆η2 +∆ϕ2 = 0.2 around the photon, after first correcting for the energy of the photoncandidate itself and for an average expected pile-up contribution. Track-based isolationpisoT is defined as the scalar sum of the transverse momenta of all tracks with pT > 1 GeVoriginating from the primary vertex and within a cone of size ∆R = 0.2 around the photon.To be considered isolated a photon must have EisoT /ET < 0.065 and pisoT /ET < 0.05. Forisolated photons with transverse energies between 30 GeV and 250 GeV, the identificationefficiency ranges from 84% to 98% [29].Electrons are reconstructed from energy deposits measured in the electromagneticcalorimeter that are matched to ID tracks [29]. They are required to be in the region |η| <– 6 –JHEP11(2024)0472.37, excluding the transition region between the barrel and endcap calorimeters 1.37 < |η| <1.52, and to have pT > 10 GeV. Electrons are required to satisfy a Medium identificationcriterion based on the shower shape, track-cluster matching and TRT information in alikelihood-based algorithm [29]. Muons are reconstructed from high-quality tracks found inthe MS [69]. A matching of the MS tracks to ID tracks is required in the region |η| < 2.5.Muons are required to have |η| < 2.7 and pT > 10 GeV and to satisfy a Medium identificationcriterion [70]. Electrons and muons are both matched to the primary vertex via requirementson the longitudinal and transverse impact parameters on the tracks, |z0| and |d0|, respectively.These requirements are |z0| sin θ < 0.5 mm, and |d0|/σd0 < 5 (3) for electrons (muons).Reconstructed jets are based on particle-flow objects built from noise-suppressed positive-energy topological clusters in the calorimeter and reconstructed tracks [71]. The anti-ktalgorithm [72, 73] with radius parameter R = 0.4 is used. The jet energy is calibratedby applying several simulation-based corrections and techniques correcting for differencesbetween simulation and data [74]. Jets are required to have rapidity |y| < 4.4 and pT >25 GeV. To suppress jets produced in pile-up interactions, each jet within the trackingacceptance of |η| < 2.4 and with pT < 60 GeV is required to satisfy the Tight jet-vertextagger [75] criteria used to identify jets from the selected primary vertex.The flavour of jets is determined using a deep-learning neural network, DL1r [76]. TheDL1r b-tagging is based on distinctive features of b-hadron decays in terms of the impactparameters of the tracks and the displaced vertices reconstructed in the ID. The inputs ofthe DL1r network also include discriminating variables constructed by a recurrent neuralnetwork (RNNIP) [77], which exploits the spatial and kinematic correlations between tracksoriginating from the same b-hadron. For each jet, DL1r gives three different probabilitiespb-, pc- and plight for the jet to originate from a b, c or light quark respectively. The threeprobabilities are combined to define the final discriminant. The DL1r algorithm is optimisedto maximise performance on particle-flow jets and extends the algorithm performance to veryhigh jet pT. Only central jets with |η| < 2.5 are considered for flavour tagging. Workingpoints are defined by single requirement values on the DL1r discriminant output distribution,and can be chosen to provide a specific b-jet efficiency for an inclusive tt¯ MC sample. Theanalysis makes use of the DL1r working point with a 77% efficiency to select jets containingb-hadrons in simulated tt¯ events. For this working point the misidentification rate is 1/130 forlight-flavour jets and 1/4.9 for charm jets. Scale factors are applied to correct for differencesin b-tagging efficiency between data and simulation. The scale factors are measured as afunction of the jet pT using a likelihood-based method in a sample enriched in tt¯ events [78].Scale factors are applied to correct for differences in b-tagging efficiency between data andsimulation. The scale factors for jets originating from a b quark are measured as a function ofthe jet pT using a likelihood-based method in a sample enriched in tt¯ events [78]. The scalefactors for jets originating from a c quark are measured in tt¯ events containing a W → csdecay [79]. For light flavour jets, the scale factors are derived using Z+jets events [80].The energy of b-tagged jets is corrected for the possible contribution of muons fromsemileptonic b-hadron decays. Additionally, the undetected energy of neutrinos and out-of-cone effects are corrected for with scale factors derived as a function of the b-jet pT from a tt¯MC sample. The two corrections together improve the resolution of the invariant mass of– 7 –JHEP11(2024)047the two jets with the highest b-tagging discriminant by about 20%. This procedure closelyfollows that in ref. [81].Overlap removal procedures are applied to avoid using the same detector signals toreconstruct multiple objects. In this analysis priority is given to photons, by removing jets,electrons and muons within ∆R < 0.4 of a selected photon. Next, jets within ∆R < 0.2of electrons are removed. Finally, electrons and muons within ∆R < 0.4 of any remainingjet are removed.5 Analysis strategyA wide range of masses is considered for the scalars X and S, leading to significantly varyingevent kinematics at different hypothetical values of mX and mS . When mX ≫ mS + mH , thescalar S can become so boosted that its decay products, two b-quarks, become very collimatedand are reconstructed within the same R = 0.4 jet. For smaller values of mX − (mS +mH),two separate b-tagged jets are reconstructed. Therefore two mutually exclusive regions aredefined with either one or two b-tagged jets, referred to as the 1 b-tagged and 2 b-taggedregions, and respectively dedicated to the boosted and non-boosted scenarios. A preselection,common to both regions, is introduced below. A final discriminating variable is then definedfor each signal hypothesis using parameterised neural networks. The final experimentalconstraint on the signal is obtained from a signal-plus-background fit to the distribution ofthe discriminating variable in data. In the special case where mS =125 GeV, this analysisstrategy can be compared with an alternative strategy similar to that applied in the ATLASsearch for HH → bb¯γγ [19], and it was found that for any given signal point the differencebetween estimated upper limits on σ(X → SH → bb¯γγ) is small.5.1 Event preselectionEvents are selected using diphoton triggers described in section 3. Beyond the triggerrequirements, events are selected if:• At least two photons satisfy the requirements in section 4.• The invariant mass of the two leading photons satisfies 105 < mγγ < 160 GeV.• The leading photon has pT > 0.35mγγ and the subleading photon has pT > 0.25mγγ .• No electrons or muons, as defined in section 4, are present.• The number of central (|η| < 2.5) jets is at least two and no more than five. Thisreduces the tt¯H background where top quarks decay hadronically.• There is exactly one or two b-tagged jet at the 77% working point. Events with morethan two b-tagged jets are removed to ensure orthogonality with the bb¯bb¯ final statefrom the same signal.– 8 –JHEP11(2024)0475.2 Signal region definitionsThe number of b-tagged jets is used to categorise events in two regions, requiring 1 or 2 b-taggedjets. The signal events contain the characteristic H → γγ decay with the mγγ distributionpeaking around the Higgs boson mass at ∼125 GeV. Therefore, two mutually exclusive signalregions (SR) dedicated respectively to the boosted and non-boosted scenarios are definedrequiring 120 < mγγ < 130 GeV. The shape of the final signal-to-background discriminant,defined in section 5.3 for each signal hypothesis, and obtained in the signal region, is used forthe final statistical test of the signal-plus-background and background only hypothesis.Events with mγγ outside the [120, 130] GeV interval are instead used to construct thesideband control regions for background estimation as described in section 6. The 2 b-taggedand 1 b-tagged sideband regions (SB) are found to contain about 85% of γγ+jets with tworeal photons and are expected to contain less than 0.1% of Higgs boson processes.The fraction of signal events with two resolved b-tagged jets is below 50% ifmS/mX < 0.09,as derived from simulations. The 1 b-tagged signal region is thus used to analyse signalpoints for which mS/mX ≲ 0.09 and, conversely, the 2 b-tagged selection is used to analysesignal points for which mS/mX 0.09.5.3 Final signal-to-background discriminantMultivariate discriminants are used to separate signal from background events in the signalregions. Two distinct parameterised neural networks (PNNs) [82] are trained with events inthe 2 b-tagged and the 1 b-tagged signal and sideband regions. PNNs take as input a vectorof event characteristics x¯ and a vector of phase space parameters θ¯ and yield a responsefunction that is parameterised in θ¯. The parameterisation provides a unique discriminantfor each signal hypothesis, separating the targeted signal events from background events.Therefore, for each value of θ¯ = (mS ,mX), the PNN(θ¯) is effectively a different observable.The PNNs provide sensitivity over the considered mass range and allow interpolation tovalues of θ¯ not explicitly included in the training. In the 2 b-tagged signal region the PNNis parameterised in the plane of the two particle masses θ¯ = (mS ,mX), and as a functionof θ¯ = (mX) in the 1 b-tagged region.The decay chain X → S(→bb¯)H(→γγ) and the masses mS and mX are encoded in theinvariant mass of the final state particles, thus the most effective features to train the PNNsare the invariant masses of the final state photons and b-tagged jets.For the 2 b-tagged signal region the input features are x¯ = (mbb,m∗bbγγ) where m∗bbγγ =mbbγγ − (mγγ − 125 GeV). The replacement of mγγ by the Higgs boson mass of 125 GeVallows to remove correlations between the PNN score and mγγ , allowing to create sidebandregions for background normalisation as described in section 6.1. For the 1 b-tagged signalregion the input variables are x¯ = (pbT,m∗bγγ) where pbT is the pT of the b-tagged jet, andm∗bγγ is derived from the invariant mass mbγγ of the only available b-tagged jet and the twophotons as m∗bγγ = mbγγ − (mγγ − 125 GeV). Additional variables were considered in thetraining but did not bring significant improvements and were therefore disregarded. Theusage of the variables mbb and m∗bbγγ alone allows for the signal interpolation applied in the2 b-tagged signal region and described in section 5.4.– 9 –JHEP11(2024)047The PNNs are trained using Keras [83] with the Tensorflow [84] backend. The trainingis performed using 69 simulated signal samples chosen from the entire investigated massgrid, as well as the largest background processes: non-resonant diphoton+jets, tt¯H, ZHand ggF H. In the 1 b-tagged signal region the VBF H, and Higgs boson pair productionprocesses are also considered for training. As the vector of parameters θ¯ is not meaningfulfor the background samples, each background event has θ¯ values assigned at random fromthe distribution of values in the signal samples during training.After the signal region selections, most events arise from the γγ + jets backgroundcategory, leading to very unbalanced training classes, which makes it difficult for the PNNsto differentiate between signal and background. The imbalance is reduced by giving a unitweight to all MC events used in the training. The effect of using a unit event weight on theshape of the input features x¯ is found to be negligible.The PNN internal architectures are optimised using KerasTuner [83], which chooses thehyper-parameters maximizing the Area-Under-Curve calculated on an evaluation set andusing Bayesian Optimisation [85]. The class weight defined as wc = 0.5ntot/nc is used, wherec is either signal or background, nc is the number of events in the given class and ntot isthe total number of events. Furthermore, given the number ns (nb) of signal (background)events, an initial bias of log(ns/nb) is applied to the last layer of the PNN.The PNNs use binary cross entropy as the loss function and stochastic gradient-basedoptimisation using the Adam algorithm [86]. All hidden layers have a standard dense traininglayer using a rectified linear unit activation function and each has a dropout layer with adropout rate between 2% and 20%. The output layers have a single node and use a sigmoidactivation function. The PNN used in the 2 b-tagged signal region is trained with signalsamples with mX ≥ 170 GeV and mS ≥ 30 GeV. For the 1 b-tagged signal region, onlysignal points where S has enough boost are used, this includes eleven points with 15 ≤mS≤70 GeV. The PNN of the 2 b-tagged signal region has four hidden layers with 85, 49, 45and 81 nodes. The PNN of the 1 b-tagged signal region has three hidden layers with 101,29 and 101 nodes. After training, the PNN output shape is compared between data andMC in dedicated sideband regions to validate the modelling of the PNN distribution. Theresults of these comparisons are covered in section 8.2.5.4 Signal interpolationTo set continuous limits in the (mX , mS) plane, it is desirable to set limits on intermediatesignal models where no sample is simulated. In order to constrain an intermediate signalpoint defined by (mintX , mintS ), the shape of the PNN output is interpolated from a nearbyreference signal sample simulated with low statistical uncertainty and referred to as (mrefX ,mrefS ). The distributions of the input features for (mintX , mintS ) are derived from the referencesample in two steps: first a rescaling step that takes into account the different masses at thereference and intermediate points, and second a reweighting step that takes different massresolutions at different mass points into account. The interpolated input features are thenused as input to the PNN to obtain the PNN(θ) distribution for the interpolated signal.For each selected event in the reference sample, the four-vectors of H, S and X aremeasured using the selected b-jets and photons. The four-vectors of H and S are recomputed– 10 –JHEP11(2024)047in the rest frame of X using a Lorentz transformation defined by the four-vector of X. Inabsence of any experimental effects, the four-vectors of H and S in the X rest frame wouldbe set to ideal values defined by the kinematics of the X → SH two-body decay and thespecific values of mX and mS . In practice, the rest frame quantities are distributions aroundtheir ideal values, while the shape of the distributions are determined by experimental effects.The kinematics at the intermediate point (mintX , mintS ) are emulated from (mrefX , mrefS ) byrescaling the rest frame four-vectors of H and S so that they are distributed around theirnew ideal theoretical values at (mintX , mintS ), and events of the reference sample are weightedto reproduce the expected experimental resolution at the intermediate mass.The resolution effects are much larger for jets than for photons; therefore, the resolutionweighting only considers the mbb resolution. The mbb resolution is measured for the simulatedpoints and modelled with a Bukin probability [87]. Each parameter of the Bukin probabilitybecomes a 2D map in the (mX , mS) plane. The values of the Bukin parameters can thenbe interpolated to any mass point (mintX , mintS ) using Delaunay triangulation [88]. The finaldistribution of PNN(mintX , mintS ) is obtained by feeding the values of mbb and m∗bbγγ to thePNN, after four-vector rescaling and resolution weighting. This technique works well at highmomenta where the change in resolution effects between nearby simulated points is small.For this reason the interpolation is only applied in parts of the 2 b-tagged signal region,defined by mX >300 GeV and mS >70 GeV.The quality of the PNN shape interpolation is evaluated by studying its impact onthe expected upper limits on σ(X → SH → bb¯γγ). The upper limits obtained with thePNN output shape from simulated signal events are compared with those obtained withthe interpolated PNN output shape. In the domain where the interpolation is applied theexpected limits on σ(X → SH → bb¯γγ) change by less than 5% for mS ≥100 GeV whenreplacing the actual PNN output shape with the interpolated one.For lower masses (below mS =70 GeV) fast changes in resolution mean that the limitsobtained from interpolated PNN shapes can differ by more than 10% from those obtainedwith the actual PNN shape. For this reason the interpolation procedure is not applied at lowmasses or in the 1 b-tagged region. Instead a much finer grid of simulated signal samplesin the (mX , mS) plane is used. The required granularity is studied by performing injectiontests and checking the sensitivity of one PNN at a given θ¯ to neighbouring signal samplesgenerated at a different (mX , mS) point. The grid spacing is chosen small enough so that asignal excess at one simulated grid point would also appear in the PNN output of at leastone nearby simulated signal sample.6 Background estimationThe final interpretation in terms of a search for a possible X → S(→bb¯)H(→γγ) signalrequires the ability to predict for each background process the number of events in the signalregion, and the shape of the PNN output. In the following sections several methods areemployed: a data-driven technique to study the composition of the non-resonant diphotonbackground category in terms of events with zero, one or two misidentified photons, a data-driven method to normalise the non-resonant diphoton background category, and finallysimulations to evaluate contributions from irreducible backgrounds.– 11 –JHEP11(2024)0476.1 Non-resonant diphoton backgroundThe largest background category is the non-resonant γγ+jets background, which includesinstrumental components such as γ+jets and dijets where jets are misidentified as photons.Other contributions to this category are the tt¯γγ process which represents less than 2%(0.5%) of the background in the 2 b-tagged (1 b-tagged) signal region, and the Z(→qq¯)γγprocess, which represents less than 1% (0.3%) in the 2 b-tagged (1 b-tagged) region. Theselast two processes are estimated directly from simulation.The fractions of γγ+jets events with zero, one or two misidentified photons and theirassociated systematic uncertainties are determined by the double two-dimensional sidebandmethod, a data-driven technique which relies on several photon identification criteria, alreadyemployed in the search for Higgs boson pair production in the bb¯γγ final state [19], themethod is described in ref. [89] and references therein. The fractions of misidentified photonbackgrounds are derived individually in the 1 and 2 b-tagged regions. The fraction of eventswith two real photons is found to be 87% in the 1 b-tagged region and 84% in the 2 b-tagged region. The data-driven method is also used later to derive the mbbγγ , mγγ , mbb andPNN distributions for the different components with true or misidentified photons. Thesedistributions are compared in the sideband regions with the data and the Sherpa γγ+jetsMC sample. No large difference is found between the shapes of the components, indicatingthat it is possible to directly build high-statistics templates from Sherpa samples withoutadding contributions from misidentified photons.Based on these studies, the zero, one or two misidentified photon components of theγγ+jets background category are modelled directly with the Sherpa γγ+jets MC sample,with appropriate systematic uncertainties described in section 7. The normalisation of theSherpa γγ+jets MC sample is fitted to the data in the sideband and signal regions accordingto the statistical model described in section 8.1, the fit effectively rescales the Sherpa γγ+jetsMC sample to the sum of the zero, one and two misidentified photon components of thenon-resonant diphoton background in data. The rescaling of the Sherpa γγ+jets MC sampletherefore reflects both misidentified photon contributions and higher order processes presentin data but not in the simulation. Several systematic uncertainties can affect the ratio ofthe γγ+jets background in the signal region over the corresponding sideband as detailed insection 7. In a background-only fit where only the data in the sidebands are considered, thisprocedure scales the Sherpa γγ+jets MC sample by a factor 1.03 ± 0.01 in the 1 b-taggedregion and a factor 1.26 ± 0.03 in the 2 b-tagged region.The shape of the PNN output for the non-resonant diphoton category is studied in thesideband regions in both data and simulation, and in the signal regions with simulation.It is observed that the shapes of the PNN output for the zero, one or two misidentifiedphoton components are well reproduced by the Sherpa γγ+jets MC sample. In the Sherpaγγ+jets simulation the input variables to the PNN are observed to be consistent withinstatistical uncertainties between the signal regions and two narrow sidebands defined by mγγ ∈[115,120] ∪ [130,135] GeV. Finally the modelling of the PNN output by simulation is validatedin the full sideband control regions, by comparing the predicted PNN distributions fromSherpa γγ+jets simulations, with the observed PNN distributions in data. Theoretical andexperimental systematic uncertainties in the PNN output shapes for the γγ+jets backgroundcategory are considered in section 7.– 12 –JHEP11(2024)0476.2 Higgs boson processesThis category of background consists of all processes that contain single Higgs boson productionvia ggF or VBF,WH , ZH (qq → ZH and gg → ZH), tt¯H , bb¯H , tHq and tHW . Higgs bosonpair production via ggF and VBF is also included in this category. Following the strategy inref. [19], the normalisation and shape of these processes are obtained from simulated MCsamples generated at NLO, and normalised using state-of-the-art theoretical cross sections [56].Several systematic uncertainties are assigned to these processes as discussed in section 7.7 Systematic uncertaintiesThree categories of systematic uncertainties are considered. Experimental systematicuncertainties account for possible differences between the performance of the detector insimulation and in data, they are applied to all quantities derived from simulation and aredescribed in section 7.1. Theoretical systematic uncertainties arise where theoretical inputsare used, such as cross sections, or where effects on shapes from higher-order correctionsshould be considered, this is described in section 7.2. The γγ+jets background is derivedfrom a combination of MC simulation and a data-driven technique, and the related sytematicuncertainties are detailed in section 7.3.7.1 Experimental systematic uncertaintiesExperimental systematic uncertainties affect selection efficiencies and PNN shapes for allHiggs boson processes and the X → S(→bb¯)H(→γγ) signal. The effect of experimentalsystematic uncertainties is also propagated to the PNN shape of the γγ+jets background. Theexperimental systematic uncertainties are categorised into four groups, in order of decreasingimpact on the expected upper limits on σ(X → SH → bb¯γγ): flavour tagging, photons, jetsand pile-up. The limits on σ(X → SH → bb¯γγ) are compared with and without experimentalsystematic uncertainties. For mX above ∼400 GeV the effect of experimental systematicuncertainties on the limits is less than 1% but grows to 2%–20% at lower mX values.Flavour tagging uncertainties [78–80] are the leading source of experimental uncertaintiesin this search. They include the uncertainties in the efficiency to b-tag a jet containinga b-hadron, and the probability to b-tag a jet containing a c-hadron or a light-flavour jetby mistake. The combined effect of all flavour tagging uncertainties is the largest in the 2b-tagged signal region with a 5% uncertainty in the signal efficiency for models with low mSand decreasing to 2% at high mS . In the 1 b-tagged signal region that uncertainty remainsin the range 0.5%–1.5%. The corresponding uncertainty in the predicted number of singleand double Higgs boson processes is about 3%. Once incorporated into the fit and takinginto account the effect on both the yields and the PNN shapes of signal and backgrounds,flavour tagging uncertainties impact the upper limits by about 6%.The category of photon-related uncertainties includes uncertainties from efficiencies of thephoton triggers, photon identification and isolation, and uncertainties in the photon energyresolution and scale. These are computed in data using data-driven techniques [28, 29] andare propagated to the simulation-based estimates of background yields and signal efficiencies.The combined effect of all photon uncertainties on the predicted signal efficiency and predicted– 13 –JHEP11(2024)047number of events from single and double Higgs boson processes is about 2.5% in both thesignal regions. Once incorporated into the fit and taking into account the effect on both theyields and the PNN shapes on signal and backgrounds, these translate into an uncertaintyof 2%–4.5% in the signal sensitivity.The category of jet-related uncertainties include uncertainties in the jet energy scale andjet energy resolution. These are derived using data-driven techniques [74] and compared withtheir counterpart in simulation. These uncertainties are propagated to the signal efficiencyand background estimates. The combined effect of all jet-related uncertainties is largest inthe 2 b-tagged signal region with up to 14% uncertainty in the signal efficiency for modelswith low mS and decreasing to about 2% at high mS . In the 1 b-tagged signal region thatuncertainty remains smaller than 3%. The corresponding uncertainty in the predicted numberof single and double Higgs boson processes is 5% (7%) in the 1 b-tagged (2 b-tagged) signalregion. However the effect of these systematic uncertainties on the PNN shape remains small,of the order of 1% in the most signal-like PNN bin. Once incorporated into the fit and takinginto account the effect on both the yields and the PNN shapes of signal and backgrounds,these translate into an uncertainty of about 1.5% in the signal sensitivity for mS > 110 GeV.The impact grows at lower masses where it can reach up to 15%.The category of pile-up related uncertainties comes from the reweighting of the MCsimulation to the pile-up profile in data. This results in an uncertainty of at most 1% in thepredicted number of events from Higgs boson processes. The effect on the signal efficiency issmaller than 1% in both the signal regions and for most of the parameter space, except in the2 b-tagged signal region for low mX where it can reach 1.8%. Finally the 0.83% uncertainty inthe measured ATLAS Run 2 integrated luminosity is propagated to all processes normalisedwith their theoretical cross sections.7.2 Theoretical systematic uncertaintiesTheoretical systematic uncertainties affect the backgrounds normalised by their theoreticalcross sections, but also the signal efficiency, and the non-resonant diphoton background.The latter is discussed in section 7.3.For processes with significant contributions to the 1 b-tagged and 2 b-tagged signalregions and where the dominant heavy-flavour production is already taken into accountat LO (ttH and ZH), several theoretical systematic uncertainties are considered. Scaleuncertainties due to missing higher-order corrections in the production rates are estimated byvarying the factorisation and renormalisation scales up and down from their nominal valuesby a factor two, taking the envelope of these variations. Parton shower uncertainties areevaluated by comparing against alternative samples where the parton shower is performedwith Herwig 7.1.5 [90, 91]. The resulting bin-to-bin variations of the PNN output rangefrom a few percent up to 10% in some bins, while the resulting impact on the exclusion limitsis below 1%. Effects of the choice of PDF and αS are estimated by varying them followingthe prescription in ref. [41]. These systematic uncertainties can lead to up to 5% bin-to-binvariations in the shape of the PNN discriminant output, but have a small impact on the limits.For smaller Higgs boson backgrounds where the dominant heavy flavour production occursat LO, the inclusive cross section uncertainties from ref. [56] are used along with scale, parton– 14 –JHEP11(2024)047shower, PDF and αS uncertainties derived as described above. The same procedure is followedfor the Z(→ qq¯)γγ process. The final impact of these uncertainties on the results is small.In single Higgs boson processes where b-quarks are not produced at LO (VBF H, WH,ggF H), a single 100% normalisation uncertainty is used. This is motivated by studiesof heavy-flavour production in association with top-quark pairs [92, 93] and of W -bosonproduction in association with b-jets [94]. These have a very small impact on the finalsensitivity, except when searching for models with low mS , where the 100% uncertainty inggF H can lead to a 10% decrease in sensitivity to the signal.The uncertainties in the Higgs boson decay branching ratios BR(H → γγ) and BR(H →bb¯) propagate to the background yields, impacting the σ(X → SH → bb¯γγ) limits by ∼3%and ∼1.5% respectively.To better understand the overall impact of theoretical background systematic uncertainties,the limits on σ(X → SH → bb¯γγ) are computed with and without the theoretical backgroundsystematic uncertainties described above. It is observed that the limits worsen by about4% when including theoretical background systematic uncertainties. It can be noted thatthis is smaller than the effect of systematic uncertainties in the non-resonant diphotonbackground described in the next section and smaller than the effect of theoretical signalsystematic uncertainties.Theoretical systematic uncertainties are also considered for the X → S(→bb¯)H(→γγ)signal, both in terms of the shape of the PNN output and the predicted event yields. Theyinclude scale, parton shower, PDF and αS uncertainties calculated as described earlier. Theimpact of these systematic uncertainties on the signal efficiency and the shape of the PNNoutput are taken into account. These uncertainties lead to 2%–10% bin-to-bin variationsof the PNN output shape, it is most pronounced for the parton shower uncertainty. Thesystematic uncertainties obtained by changing to an alternative PDF and αS lead to up to11% change in the predicted signal yield, particularly at high mX ∼1000 GeV. The impactof theoretical signal uncertainties on the sensitivity to σ(X → SH → bb¯γγ) can reach 10%.A systematic uncertainty associated with the interpolation of the PNN score in the regionmX >300 GeV and mS >70 GeV is estimated by considering a shape systematic uncertaintyin the PNN score, derived by varying the parameters of the Bukin probabilities within theirerrors. The impact on the exclusion limits at interpolated (mX ,mS) points is at most 10%.7.3 Systematic uncertainties in the non-resonant diphoton backgroundThe normalisation of the non-resonant diphoton background is derived in a fit which includesthe sideband control regions. The following theoretical uncertainties, introduced earlier, affectthe PNN output shape and the overall estimated number of events in the signal and sidebandregions: scale uncertainties, choice of PDF, αs and parton shower. The uncertainties associatedwith the scales, PDFs and αs are calculated using the same methods as described in section 7.2.The parton shower uncertainties are evaluated by using alternative samples generated withMadGraph [35] for the parton shower. An additional MC modelling uncertainty is consideredby using an alternative γγ+jets sample generated with MadGraph5_aMC@NLO. Thissample models diphoton production with up to two jets at NLO. The corresponding uncertaintyis found to be the most significant in the analysis; its impact in the 2 b-tagged signal region– 15 –JHEP11(2024)047degrades the expected upper limits on the signal by up to 20%. This impact is larger forlow values of mX , while for mX above 600 GeV the impact is always below 5%. For the 1b-tagged signal region the modelling uncertainty has an impact of a few percent on the upperlimits, except for two signal points at (500, 30) GeV and (230, 15) GeV where the effect isaround 40% due to statistical fluctuations in the alternative MadGraph samples.8 Results8.1 Statistical modelThe results of the analysis are obtained from a maximum-likelihood fit of the binned PNNoutput distribution, performed simultaneously over a signal region and its correspondingsideband region. The PNN binning is constructed starting from the rightmost signal-likebin. The size of this bin is chosen to maximise the signal-to-background ratio and wideneduntil there is at least one background event. The same procedure is repeated with the nextbin, and requiring that the number of background events is above a certain threshold, inorder to avoid bins with large statistical errors. When the signal-to-background ratio in thebin drops below that of the full un-binned distribution, the iterative process is stopped anda single background-like bin is constructed for the remaining distribution at PNN outputsclose to zero. The binning is optimised independently for each signal hypothesis. For the1 b-jet selection a similar method is used.The likelihood function is defined as:L =Pois(nSB∣∣∣∣∣µγγNγγSB(θ) +∑pNpSB(θ))·∏iPois(nSR,i∣∣∣∣∣µγγNγγSR(θ)fγγi (θ) +∑pNpSR(θ)fpi (θ))·G(θ)(8.1)where the index p runs over physics processes other than γγ+jets, the index i runs overthe bins of the PNN output, nSR,i and nSB are the observed number of events in the signalregion PNN bin i and in the corresponding sideband region, NpSR(θ) is the expected numberof events from process p in the signal region, and NSB(θ) is the total expected number ofevents in the corresponding sideband region. The superscript γγ is used for parameters thatspecifically apply to the γγ+jets background. The factor µγγ is the free parameter thatfits the γγ+jets normalisation to the data. The function fpi gives the shape or probabilitydensity function (pdf) of the PNN output discriminant for each background or signal process;therefore NpSRfpi is the expected number of events of process p in the PNN bin i. Finally θ isa vector of nuisance parameters, and G(θ) are constrained pdfs for the nuisance parameters.Correlation of the nuisance parameters across different signal and background components,as well as categories, is taken into account.The nominal yields of the single and double Higgs boson background processes areinitially set to values from simulation. The likelihood function includes all the nuisanceparameters that describe the systematic uncertainties. The signal cross section is a freeparameter in the fit. The measurement of the parameter of interest is carried out usinga statistical test based on the profile likelihood ratio [95]. In the absence of signal, upper– 16 –JHEP11(2024)047limits on σ(X → SH → bb¯γγ) at the 95% CL are set. The limits are calculated using theasymptotic formula with a profile-likelihood-ratio-based test statistic [95], and are based onthe CLS method [96]. The binning in PNN score was chosen to ensure that the asymptoticapproximation would be valid.8.2 Control region validationThe ability of the background model to reproduce the data is studied in the sideband regionswhere a background-only fit is performed to the data. Distributions are then comparedbetween the post-fit model and the data as shown in figure 2. A good agreement between thedata and the model shows the ability of the model to reproduce the PNN discriminant outputin both the 2 b-tagged and 1 b-tagged regions. For each value of θ¯ = (mS ,mX) the PNN(θ¯) iseffectively a different observable, for this reason the post-fit data-to-prediction comparison inthe sideband is performed for all analysed signal mass points, and good agreement is observed.8.3 Result and interpretationThe results of the background-only fit in the signal regions and sidebands to the data isshown in table 2. The signal region and sideband yields are independent of the parameterisedneural network, while the column “Signal-like bin” illustrates the signal and backgroundyields in the most signal-like bin of the PNN for two choices of θ¯, namely (250, 100) GeV and(1000, 70) GeV. Figure 3 illustrates the post-fit distribution of the PNN in the 2 b-taggedand 1 b-tagged regions at two different θ¯. PNN(θ¯) is sensitive not only to a signal at the samemasses defined by θ¯, but also to signals with nearby masses θ¯′. To check for discovery of awide range of signal masses, the background-only hypothesis is tested against the data for ahighly granular grid of PNN parameters θ¯. The step size between two consecutive parametersθ¯ is selected by studying the sensitivity of PNN(θ¯) to a signal with different masses θ¯′. If therewas an excess in data due to a signal with masses θ¯′ observed with the discriminant PNN(θ¯),the significance of this excess would degrade with increasing distance between θ¯′ and θ¯. A stepsize in the θ¯ grid is chosen such that the degradation of the significance does not exceed 10%between two consecutive grid points. In practice the step size goes from 5 GeV in the densestregions to 25 GeV for mX ≥ 250 GeV, and to 50 GeV for mX ≥ 600 GeV and mS ≥ 200 GeV.For most mass points good agreement is observed between data and the SM background-only expectation; however some deviation is observed for a few points. The largest excessof the observation over the SM background-only hypothesis occurs for (mX ,mS) = (575,200) GeV with a local significance of 3.5σ. The ‘look-elsewhere effect’ is taken into accountusing the asymptotic method described in ref. [97]. An asymptotic formula for the Eulercharacteristic as a function of the maximum of the test statistic across each signal point isderived using toy MC experiments. The resulting global significance is calculated to be 2.0σ.At this mass point the signal PNN output shape was initially derived from interpolation. Theanalysis was repeated using a simulated sample; the observed significance remained howeverunchanged, confirming the validity of the interpolation method.A parameter point of particular interest is (mX , mS) = (650, 90) GeV where the CMSCollaboration reports a deviation between observation and background-only expectation,corresponding to a local (global) significance of 3.8 (2.8) standard deviations [17]. Injecting– 17 –JHEP11(2024)0471−1010310510710810Events / binATLAS-1 = 13 TeV, 140 fbsγγbb→SH→X-tagged SBb2 Pre-fit (B-only)Data 1.26×+jets γγSingle HiggsγγZγγttHH+jets unc.γγ200 300 400 500 600 700 800 900 1000 1100 [GeV]γγbbm*00.511.52Data / Bkg.(a)1−1010310510710910Events / bin ATLAS-1 = 13 TeV, 140 fbsγγbb→SH→X-tagged SBb2 Pre-fit (B-only)Data 1.26×+jets γγSingle HiggsγγZγγttHH+jets unc.γγ0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 = 100 GeV)S = 250 GeV, mXPNN(m00.511.52Data / Bkg.(b)1−1010310510710910Events / binATLAS-1 = 13 TeV, 140 fbsγγbb→SH→X-tagged SBb1 Pre-fit (B-only)Data 1.03×+jets γγSingle HiggsγγZγγttHH+jets unc.γγ200 400 600 800 1000 [GeV]γγbm*00.511.52Data / Bkg.(c)13106109101210Events / bin ATLAS-1 = 13 TeV, 140 fbsγγbb→SH→X-tagged SBb1 Pre-fit (B-only)Data 1.03×+jets γγSingle HiggsγγZγγttHH+jets unc.γγ0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 = 70 GeV)S = 1 TeV, mXPNN(m00.511.52Data / Bkg.(d)Figure 2. Distributions of (a) m∗bbγγ , (c) m∗bγγ and (b,d) PNN for two choices of θ¯ = (mS ,mX)in data and in the predicted model, in the sidebands of the 2 b-tagged region (top) and 1 b-taggedregion (bottom). The γγ + jets background is rescaled to its post-fit normalisation in a background-only fit. The variables m∗bbγγ and m∗bγγ are defined as m∗bbγγ = mbbγγ − (mγγ − 125 GeV) andm∗bγγ = mbγγ − (mγγ − 125 GeV). The γγ + jets category represents the sum of γγ + jets, γ + jetsand dijet backgrounds. The error band corresponds to the dominant uncertainty, which arises fromthe non-resonant γγ + jets background.a MC signal with a production cross section of 0.35 fb (the best fit reported by the CMSexperiment) in the analysis performed in this paper yields a local excess in observation toSM expectation of 2.7 standard deviations, demonstrating the sensitivity of this analysis to asignal consistent with the excess observed by CMS. The results of the analysis of ATLAS datafor this specific parameter point instead shows good agreement between observation and SMbackground expectation (the p-value of the background-only hypothesis is larger than 0.5).The 95% CL upper limit on σ(X → SH → bb¯γγ) for this specific mass point is 0.2 fb.The statistical analysis sets 95% CL upper limits on σ(X → SH → bb¯γγ) for all valuesof θ¯ = (mX ,mS) by performing a signal-plus-background fit to the PNN output distributionin data. For each mass point the signal region which gives the best expected upper limit isselected. The resulting expected and observed upper limits are presented in figure 4, andrange from observed (expected) limits of 39 (25) fb at mX = 170 GeV and mS = 30 GeV,– 18 –JHEP11(2024)0472 b-tagged region 1 b-tagged regionBackground Sideband Signal region Signal-like bin Sideband Signal region Signal-like binNon-res. γγ 1480± 37 372± 16 1.64± 0.37 13450± 110 3392± 53 2.45± 0.43Single Higgs 0.46± 0.11 19.9± 5.3 0.04± 0.01 2.3± 1.1 92± 44 0.21± 0.10ggF+bb¯H 0.14± 0.11 6.5± 5.2 0.01± 0.01 1.5± 1.1 56± 43 0.11± 0.09tt¯H 0.21± 0.01 7.91± 0.77 0.01± 0.01 0.31± 0.01 11.4± 1.1 0.03± 0.01ZH 0.08± 0.01 3.56± 0.30 0.02± 0.01 0.17± 0.01 7.35± 0.60 0.02± 0.01Other 0.03± 0.01 1.94± 0.70 < 0.005 0.40± 0.23 17± 10 0.05± 0.03Double Higgs 0.03± 0.01 1.65± 0.25 < 0.005 0.03± 0.01 1.79± 0.27 0.01± 0.01Total 1480± 37 394± 16 1.67± 0.37 13450± 110 3486± 48 2.67± 0.45Signal (mX , mS)(250, 100) GeV 0.38± 0.04 8.3± 1.2 1.43± 0.21(1000, 70) GeV 0.97± 0.10 33.3± 5.8 23.9± 4.2Data 1479 395 0 13450 3491 4Table 2. Number of events for the different process categories obtained from a background-onlyfit to data in the signal regions and sidebands. Observed and fitted event yields in the two signalregions and sidebands, independent of phase space parameters θ¯, are given in the columns “Signalregion” and “Sideband”. The yields in the most signal-like bin of the PNN distribution depend on theselected phase space parameters θ¯, here shown under the column “Signal-like bin” for θ¯ = (mX , mS)= (250, 100) GeV and (1000, 70) GeV. The expected number of events from the two correspondingbenchmark signals with a 1 fb cross section is also given. The uncertainties are symmetrised aroundthe central value. The uncertainty in the total background is calculated taking correlations betweenthe individual contributions into account. For the single Higgs boson processes, “Other” includes thefollowing production modes: VBF, WH, tHq, and tHW .to 0.09 (0.14) fb at mX = 1000 GeV and mS between 250 and 300 GeV. The upper limitsimprove at higher masses, consistent with the fact that signals with higher mX become easierto differentiate from SM processes. In contrast, they worsen in the boosted signal regimeat lower mS , where the 1 b-tagged region is employed, and consistent with the lower signal-to-background ratio shown in table 2 for the 1 b-tagged region. At low mX the sensitivitysuffers from an increasing fraction of b-jets falling below the jet pT reconstruction threshold.9 ConclusionA search for a signal from a hypothetical scalar X is performed, considering the case where itdecays into another hypothetical scalar S and a Higgs boson, which subsequently decay intopairs of b-quarks and photons, respectively. Two signal regions targeting resolved or boostedS → bb¯ decays are analysed using parameterised neural networks, which provide continuoussensitivity in the probed (mX , mS) plane. In the region mX > 300 GeV and mS > 70 GeV thevalidity of the limits for intermediate mass points is ensured by interpolating the signal shapesto a much finer signal grid, and the finer grid spacing is guided by the sensitivity range of thePNN to values of (mX , mS) where it was not trained. At lower masses the validity of the limitsfor intermediate mass points is ensured by using a very fine grid of simulated signal samples.– 19 –JHEP11(2024)0471−1010310510710810Events / bin ATLAS-1 = 13 TeV, 140 fbsγγbb→SH→X-tagged SRb2 Post-fit (B-only)Data+jetsγγSingle HiggsγγZγγttHHUncertainty(250,100)X,Sm0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 = 100 GeV)S = 250 GeV, mXPNN(m00.511.52Data / Bkg.(a)1−10103105107109101110Events / bin ATLAS-1 = 13 TeV, 140 fbsγγbb→SH→X-tagged SRb1 Post-fit (B-only)Data+jetsγγSingle HiggsγγZγγttHHUncertainty(1000,70)X,Sm0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 = 70 GeV)S = 1 TeV, mXPNN(m00.511.52Data / Bkg.(b)Figure 3. Post-fit distributions of the PNN discriminant output in the (a) 2 b-tagged signal regionfor mX = 250 GeV and mS = 100 GeV and (b) 1 b-tagged signal region for mX = 1000 GeV andmS = 70 GeV, after a background-only fit to data. The signals corresponding to the two PNNparameterisations, normalised to a 1 fb cross section, are illustrated for comparison. The γγ+ jetscategory represents the sum of γγ+ jets, γ + jets and dijet backgrounds. The error band correspondsto the total systematic uncertainty after fit.(a) (b)Figure 4. (a) Expected and (b) observed 95% CL upper limits on the signal cross section timesbranching fraction for the X→ SH signal, in the (mX , mS) plane. The points show where the limitswere evaluated. The band at mS = 125 GeV is not shown as those points are equivalent to thosealready probed in ref. [19].No significant excess with respect to the Standard Model background is found. Therefore,95% CL upper limits are set on σ(X → SH → bb¯γγ) in the ranges 170 ≤ mX ≤ 1000 GeVand 15 ≤ mS ≤ 500 GeV, expanding earlier LHC results to lower masses and providinghigher sensitivity. The largest deviation from the background-only expectation occurs for(mX ,mS) = (575, 200) GeV with a local (global) significance of 3.5 (2.0) standard deviations.For the mass point (mX ,mS) = (650, 90) GeV, where CMS reported an excess with a local(global) significance of 3.8 (2.8) standard deviations, this analysis shows good agreement– 20 –JHEP11(2024)047with the background-only hypothesis and sets a 95% CL upper limit on the signal crosssection of 0.2 fb.AcknowledgmentsWe thank CERN for the very successful operation of the LHC and its injectors, as wellas the support staff at CERN and at our institutions worldwide without whom ATLAScould not be operated efficiently.The crucial computing support from all WLCG partners is acknowledged gratefully, inparticular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF(Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF(Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilitiesworldwide and large non-WLCG resource providers. Major contributors of computingresources are listed in ref. [98].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;MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia;ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and WallenbergFoundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC,Taipei; TENMAK, Türkiye; STFC/UKRI, United Kingdom; DOE and NSF, United Statesof America.Individual groups and members have received support from BCKDF, CANARIE, CRCand DRAC, Canada; CERN-CZ, PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic;COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Skłodowska-CurieActions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idexand ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeiaprogrammes 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; LaCaixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEOand 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: EuropeanOrganization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigación yDesarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT1230987); China: National Natural Science Foundation of China (NSFC - 12175119, NSFC12275265, 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, ERC 101089007), Horizon 2020 FrameworkProgramme (MUCCA - CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelli-– 21 –JHEP11(2024)047gence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High PerformanceComputing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: AgenceNationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022,ANR-22-EDIR-0002), Investissements d’Avenir Labex (ANR-11-LABX-0012); Germany:Baden-Württemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), DeutscheForschungsgemeinschaft (DFG - 469666862, DFG - CR 312/5-2); Italy: Istituto Nazionale diFisica Nucleare (ICSC, NextGenerationEU), Ministero dell’Università e della Ricerca (PRIN -20223N7F8K - PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPSKAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPSKAKENHI JP22KK0227); Netherlands: Netherlands Organisation for Scientific Research(NWO Veni 2020 - VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472);Poland: Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001),Polish National Science Centre (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, UMO-2022/47/O/ST2/00148); Slovenia: Slovenian Research Agency (ARISgrant J1-3010); Spain: BBVA Foundation (LEO22-1-603), Generalitat Valenciana (Artemisa,FEDER, IDIFEDER/2018/048), La Caixa Banking 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 Programmes Generalitat Valenciana(CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (VR2018-00482, VR 2022-03845, VR 2022-04683, VR grant 2021-03651), Knut and Alice WallenbergFoundation (KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW2022.0358); Switzerland: Swiss National Science Foundation (SNSF - PCEFP2_194658);United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004); United Statesof America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer FamilyFoundation.Open Access. 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Abramowicz 152, H. Abreu 151, Y. Abulaiti 118,B.S. Acharya 69a,69b,l, A. Ackermann 63a, C. Adam Bourdarios 4, L. Adamczyk 86a,S.V. Addepalli 26, M.J. Addison 102, J. Adelman 116, A. Adiguzel 21c, T. Adye 135,A.A. Affolder 137, Y. Afik 39, M.N. Agaras 13, J. Agarwala 73a,73b, A. Aggarwal 101,C. Agheorghiesei 27c, A. Ahmad 36, F. Ahmadov 38,z, W.S. Ahmed 105, S. Ahuja 96,X. Ai 62e, G. Aielli 76a,76b, A. Aikot 164, M. Ait Tamlihat 35e, B. Aitbenchikh 35a,I. Aizenberg 170, M. Akbiyik 101, T.P.A. Åkesson 99, A.V. Akimov 37, D. Akiyama 169,N.N. Akolkar 24, S. Aktas 21a, K. Al Khoury 41, G.L. Alberghi 23b, J. Albert 166,P. Albicocco 53, G.L. Albouy 60, S. Alderweireldt 52, Z.L. Alegria 122, M. Aleksa 36,I.N. Aleksandrov 38, C. Alexa 27b, T. Alexopoulos 10, F. Alfonsi 23b, M. Algren 56,M. Alhroob 142, B. Ali 133, H.M.J. Ali 92, S. Ali 149, S.W. Alibocus 93, 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 138f , P.P. Allport 20, A. Aloisio 72a,72b, F. Alonso 91, C. Alpigiani 139,M. Alvarez Estevez 100, A. Alvarez Fernandez 101, M. Alves Cardoso 56, M.G. Alviggi 72a,72b,M. Aly 102, Y. Amaral Coutinho 83b, A. Ambler 105, C. Amelung36, M. Amerl 102,C.G. Ames 110, D. Amidei 107, K.J. Amirie 156, S.P. Amor Dos Santos 131a, K.R. Amos 164,V. Ananiev 126, 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,ab, 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 164, M.A. Aparo 147, L. Aperio Bella 48, C. Appelt 18,A. Apyan 26, S.J. Arbiol Val 87, C. Arcangeletti 53, A.T.H. Arce 51, E. Arena 93,J-F. Arguin 109, S. Argyropoulos 54, J.-H. Arling 48, O. Arnaez 4, H. Arnold 115,G. Artoni 75a,75b, H. Asada 112, K. Asai 119, S. Asai 154, N.A. Asbah 36, K. Assamagan 29,R. Astalos 28a, S. Atashi 160, R.J. Atkin 33a, M. Atkinson163, H. Atmani35f ,P.A. Atmasiddha 129, K. Augsten 133, S. Auricchio 72a,72b, A.D. Auriol 20, V.A. Austrup 102,G. Avolio 36, K. Axiotis 56, G. Azuelos 109,af , D. Babal 28b, H. Bachacou 136,K. Bachas 153,p, A. Bachiu 34, F. Backman 47a,47b, A. Badea 39, T.M. Baer 107,P. Bagnaia 75a,75b, M. Bahmani 18, D. Bahner 54, K. Bai 124, A.J. Bailey 164, J.T. Baines 135,L. Baines 95, O.K. Baker 173, E. Bakos 15, D. Bakshi Gupta 8, V. Balakrishnan 121,R. Balasubramanian 115, E.M. Baldin 37, P. Balek 86a, E. Ballabene 23b,23a, F. Balli 136,L.M. Baltes 63a, W.K. Balunas 32, J. Balz 101, E. Banas 87, M. Bandieramonte 130,A. Bandyopadhyay 24, S. Bansal 24, L. Barak 152, M. Barakat 48, E.L. Barberio 106,D. Barberis 57b,57a, M. Barbero 103, M.Z. Barel 115, K.N. Barends 33a, T. Barillari 111,M-S. Barisits 36, T. Barklow 144, P. Baron 123, D.A. Baron Moreno 102, A. Baroncelli 62a,G. Barone 29, A.J. Barr 127, J.D. Barr 97, F. Barreiro 100, J. Barreiro Guimarães da Costa 14a,U. Barron 152, M.G. Barros Teixeira 131a, S. Barsov 37, F. Bartels 63a, R. Bartoldus 144,A.E. Barton 92, P. Bartos 28a, A. Basan 101, M. Baselga 49, A. Bassalat 66,b, M.J. Basso 157a,R.L. Bates 59, S. Batlamous35e, B. Batool 142, M. Battaglia 137, D. Battulga 18,M. Bauce 75a,75b, M. Bauer 36, P. Bauer 24, L.T. Bazzano Hurrell 30, J.B. Beacham 51,T. Beau 128, J.Y. Beaucamp 91, P.H. Beauchemin 159, P. Bechtle 24, H.P. Beck 19,o,K. Becker 168, A.J. Beddall 82, V.A. Bednyakov 38, C.P. Bee 146, L.J. Beemster 15,– 28 –JHEP11(2024)047T.A. Beermann 36, M. Begalli 83d, M. Begel 29, A. Behera 146, J.K. Behr 48, J.F. Beirer 36,F. Beisiegel 24, M. Belfkir 117b, G. Bella 152, L. Bellagamba 23b, A. Bellerive 34, P. Bellos 20,K. Beloborodov 37, D. Benchekroun 35a, F. Bendebba 35a, Y. Benhammou 152,K.C. Benkendorfer 61, L. Beresford 48, M. Beretta 53, E. Bergeaas Kuutmann 162, N. Berger 4,B. Bergmann 133, J. Beringer 17a, G. Bernardi 5, C. Bernius 144, F.U. Bernlochner 24,F. Bernon 36,103, A. Berrocal Guardia 13, T. Berry 96, P. Berta 134, A. Berthold 50,S. Bethke 111, A. Betti 75a,75b, A.J. Bevan 95, N.K. Bhalla 54, M. Bhamjee 33c, S. Bhatta 146,D.S. Bhattacharya 167, P. Bhattarai 144, K.D. Bhide 54, V.S. Bhopatkar 122, R.M. Bianchi 130,G. Bianco 23b,23a, O. Biebel 110, R. Bielski 124, M. Biglietti 77a, C.S. Billingsley44, M. Bindi 55,A. Bingul 21b, C. Bini 75a,75b, A. Biondini 93, C.J. Birch-sykes 102, G.A. Bird 32,M. Birman 170, M. Biros 134, S. Biryukov 147, T. Bisanz 49, E. Bisceglie 43b,43a,J.P. Biswal 135, D. Biswas 142, K. Bjørke 126, I. Bloch 48, A. Blue 59, U. Blumenschein 95,J. Blumenthal 101, V.S. Bobrovnikov 37, M. Boehler 54, B. Boehm 167, D. Bogavac 36,A.G. Bogdanchikov 37, C. Bohm 47a, V. Boisvert 96, P. Bokan 36, T. Bold 86a, M. Bomben 5,M. Bona 95, M. Boonekamp 136, C.D. Booth 96, A.G. Borbély 59, I.S. Bordulev 37,H.M. Borecka-Bielska 109, G. Borissov 92, D. Bortoletto 127, D. Boscherini 23b, M. Bosman 13,J.D. Bossio Sola 36, K. Bouaouda 35a, N. Bouchhar 164, J. Boudreau 130,E.V. Bouhova-Thacker 92, D. Boumediene 40, R. Bouquet 57b,57a, A. Boveia 120, J. Boyd 36,D. Boye 29, I.R. Boyko 38, J. Bracinik 20, N. Brahimi 4, G. Brandt 172, O. Brandt 32,F. Braren 48, B. Brau 104, J.E. Brau 124, R. Brener 170, L. Brenner 115, R. Brenner 162,S. Bressler 170, D. Britton 59, D. Britzger 111, I. Brock 24, R. Brock 108, G. Brooijmans 41,E. Brost 29, L.M. Brown 166, L.E. Bruce 61, T.L. Bruckler 127, P.A. Bruckman de Renstrom 87,B. Brüers 48, A. Bruni 23b, G. Bruni 23b, D. Brunner 47b, M. Bruschi 23b, N. Bruscino 75a,75b,T. Buanes 16, Q. Buat 139, D. Buchin 111, A.G. Buckley 59, O. Bulekov 37, B.A. Bullard 144,S. Burdin 93, C.D. Burgard 49, A.M. Burger 36, 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 101,P.J. Bussey 59, J.M. Butler 25, C.M. Buttar 59, J.M. Butterworth 97, W. Buttinger 135,C.J. Buxo Vazquez 108, A.R. Buzykaev 37, S. Cabrera Urbán 164, L. Cadamuro 66,D. Caforio 58, H. Cai 130, Y. Cai 14a,14e, Y. Cai 14c, V.M.M. Cairo 36, O. Cakir 3a,N. Calace 36, P. Calafiura 17a, G. Calderini 128, P. Calfayan 68, G. Callea 59, L.P. Caloba83b,D. Calvet 40, S. Calvet 40, M. Calvetti 74a,74b, R. Camacho Toro 128, S. Camarda 36,D. Camarero Munoz 26, P. Camarri 76a,76b, M.T. Camerlingo 72a,72b, D. Cameron 36,C. Camincher 166, M. Campanelli 97, A. Camplani 42, V. Canale 72a,72b, A.C. Canbay 3a,J. Cantero 164, Y. Cao 163, F. Capocasa 26, M. Capua 43b,43a, A. Carbone 71a,71b,R. Cardarelli 76a, J.C.J. Cardenas 8, F. Cardillo 164, G. Carducci 43b,43a, T. Carli 36,G. Carlino 72a, J.I. Carlotto 13, B.T. Carlson 130,q, E.M. Carlson 166,157a, L. Carminati 71a,71b,A. Carnelli 136, M. Carnesale 75a,75b, S. Caron 114, E. Carquin 138f , S. Carrá 71a,G. Carratta 23b,23a, A.M. Carroll 124, T.M. Carter 52, M.P. Casado 13,i, M. Caspar 48,F.L. Castillo 4, L. Castillo Garcia 13, V. Castillo Gimenez 164, N.F. Castro 131a,131e,A. Catinaccio 36, J.R. Catmore 126, T. Cavaliere 4, V. Cavaliere 29, N. Cavalli 23b,23a,Y.C. Cekmecelioglu 48, E. Celebi 21a, S. Cella 36, F. Celli 127, M.S. Centonze 70a,70b,V. Cepaitis 56, K. Cerny 123, A.S. Cerqueira 83a, A. Cerri 147, L. Cerrito 76a,76b,F. Cerutti 17a, B. Cervato 142, A. Cervelli 23b, G. Cesarini 53, S.A. Cetin 82,– 29 –JHEP11(2024)047D. Chakraborty 116, J. Chan 17a, W.Y. Chan 154, J.D. Chapman 32, E. Chapon 136,B. Chargeishvili 150b, D.G. Charlton 20, M. Chatterjee 19, C. Chauhan 134, Y. Che 14c,S. Chekanov 6, S.V. Chekulaev 157a, G.A. Chelkov 38,a, A. Chen 107, B. Chen 152,B. Chen 166, H. Chen 14c, H. Chen 29, J. Chen 62c, J. Chen 143, M. Chen 127, S. Chen 154,S.J. Chen 14c, X. Chen 62c,136, X. Chen 14b,ae, Y. Chen 62a, C.L. Cheng 171, H.C. Cheng 64a,S. Cheong 144, A. Cheplakov 38, E. Cheremushkina 48, E. Cherepanova 115,R. Cherkaoui El Moursli 35e, E. Cheu 7, K. Cheung 65, L. Chevalier 136, V. Chiarella 53,G. Chiarelli 74a, N. Chiedde 103, G. Chiodini 70a, A.S. Chisholm 20, A. Chitan 27b,M. Chitishvili 164, M.V. Chizhov 38,r, K. Choi 11, Y. Chou 139, E.Y.S. Chow 114,K.L. Chu 170, M.C. Chu 64a, X. Chu 14a,14e, J. Chudoba 132, J.J. Chwastowski 87,D. Cieri 111, K.M. Ciesla 86a, V. Cindro 94, A. Ciocio 17a, F. Cirotto 72a,72b, Z.H. Citron 170,M. Citterio 71a, D.A. Ciubotaru27b, A. Clark 56, P.J. Clark 52, C. Clarry 156,J.M. Clavijo Columbie 48, S.E. Clawson 48, C. Clement 47a,47b, J. Clercx 48, Y. Coadou 103,M. Cobal 69a,69c, A. Coccaro 57b, R.F. Coelho Barrue 131a, R. Coelho Lopes De Sa 104,S. Coelli 71a, B. Cole 41, J. Collot 60, P. Conde Muiño 131a,131g, M.P. Connell 33c,S.H. Connell 33c, E.I. Conroy 127, F. Conventi 72a,ag, H.G. Cooke 20, A.M. Cooper-Sarkar 127,A. Cordeiro Oudot Choi 128, L.D. Corpe 40, M. Corradi 75a,75b, F. Corriveau 105,x,A. Cortes-Gonzalez 18, M.J. Costa 164, F. Costanza 4, D. Costanzo 140, B.M. Cote 120,G. Cowan 96, K. Cranmer 171, D. Cremonini 23b,23a, S. Crépé-Renaudin 60, F. Crescioli 128,M. Cristinziani 142, M. Cristoforetti 78a,78b, V. Croft 115, J.E. Crosby 122, G. Crosetti 43b,43a,A. Cueto 100, T. Cuhadar Donszelmann 160, H. Cui 14a,14e, Z. Cui 7, W.R. Cunningham 59,F. Curcio 164, J.R. Curran 52, P. Czodrowski 36, M.M. Czurylo 36,M.J. Da Cunha Sargedas De Sousa 57b,57a, J.V. Da Fonseca Pinto 83b, C. Da Via 102,W. Dabrowski 86a, T. Dado 49, S. Dahbi 149, T. Dai 107, D. Dal Santo 19, C. Dallapiccola 104,M. Dam 42, G. D’amen 29, V. D’Amico 110, J. Damp 101, J.R. Dandoy 34, M. Danninger 143,V. Dao 36, G. Darbo 57b, S. Darmora 6, S.J. Das 29,ah, S. D’Auria 71a,71b, A. D’Avanzo 131a,C. David 33a, T. Davidek 134, B. Davis-Purcell 34, I. Dawson 95, H.A. Day-hall 133, K. De 8,R. De Asmundis 72a, N. De Biase 48, S. De Castro 23b,23a, N. De Groot 114, P. de Jong 115,H. De la Torre 116, 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 115, A.M. Deiana 44, F. Del Corso 23b,23a, J. Del Peso 100, F. Del Rio 63a,L. Delagrange 128, F. Deliot 136, 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 173,M. Demichev 38, S.P. Denisov 37, L. D’Eramo 40, D. Derendarz 87, F. Derue 128,P. Dervan 93, 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 115, T. Dias Do Vale 143, M.A. Diaz 138a,138b,F.G. Diaz Capriles 24, M. Didenko 164, E.B. Diehl 107, S. Díez Cornell 48, C. Diez Pardos 142,C. Dimitriadi 162,24, A. Dimitrievska 17a, J. Dingfelder 24, I-M. Dinu 27b, S.J. Dittmeier 63b,F. Dittus 36, M. Divisek 134, F. Djama 103, T. Djobava 150b, C. Doglioni 102,99,A. Dohnalova 28a, J. Dolejsi 134, Z. Dolezal 134, K.M. Dona 39, M. Donadelli 83c,B. Dong 108, J. Donini 40, A. D’Onofrio 72a,72b, M. D’Onofrio 93, J. Dopke 135, A. Doria 72a,– 30 –JHEP11(2024)047N. Dos Santos Fernandes 131a, P. Dougan 102, M.T. Dova 91, A.T. Doyle 59, M.A. Draguet 127,E. Dreyer 170, I. Drivas-koulouris 10, M. Drnevich 118, M. Drozdova 56, D. Du 62a,T.A. du Pree 115, F. Dubinin 37, M. Dubovsky 28a, E. Duchovni 170, G. Duckeck 110,O.A. Ducu 27b, D. Duda 52, A. Dudarev 36, E.R. Duden 26, M. D’uffizi 102, L. Duflot 66,M. Dührssen 36, A.E. Dumitriu 27b, M. Dunford 63a, S. Dungs 49, K. Dunne 47a,47b,A. Duperrin 103, H. Duran Yildiz 3a, M. Düren 58, A. Durglishvili 150b, B.L. Dwyer 116,G.I. Dyckes 17a, M. Dyndal 86a, B.S. Dziedzic 87, Z.O. Earnshaw 147, G.H. Eberwein 127,B. Eckerova 28a, S. Eggebrecht 55, E. Egidio Purcino De Souza 128, L.F. Ehrke 56, G. Eigen 16,K. Einsweiler 17a, T. Ekelof 162, P.A. Ekman 99, S. El Farkh 35b, Y. El Ghazali 35b,H. El Jarrari 36, A. El Moussaouy 109, V. Ellajosyula 162, M. Ellert 162, F. Ellinghaus 172,N. Ellis 36, J. Elmsheuser 29, M. Elsing 36, D. Emeliyanov 135, Y. Enari 154, I. Ene 17a,S. Epari 13, P.A. Erland 87, M. Errenst 172, M. Escalier 66, C. Escobar 164, E. Etzion 152,G. Evans 131a, H. Evans 68, L.S. Evans 96, A. Ezhilov 37, S. Ezzarqtouni 35a,F. Fabbri 23b,23a, L. Fabbri 23b,23a, G. Facini 97, V. Fadeyev 137, R.M. Fakhrutdinov 37,D. Fakoudis 101, S. Falciano 75a, L.F. Falda Ulhoa Coelho 36, P.J. Falke 24, J. Faltova 134,C. Fan 163, Y. Fan 14a, Y. Fang 14a,14e, M. Fanti 71a,71b, M. Faraj 69a,69b, Z. Farazpay 98,A. Farbin 8, A. Farilla 77a, T. Farooque 108, S.M. Farrington 52, F. Fassi 35e,D. Fassouliotis 9, M. Faucci Giannelli 76a,76b, W.J. Fawcett 32, L. Fayard 66, P. Federic 134,P. Federicova 132, O.L. Fedin 37,a, M. Feickert 171, L. Feligioni 103, D.E. Fellers 124,C. Feng 62b, M. Feng 14b, Z. Feng 115, M.J. Fenton 160, L. Ferencz 48, R.A.M. Ferguson 92,S.I. Fernandez Luengo 138f , P. Fernandez Martinez 13, M.J.V. Fernoux 103, J. Ferrando 92,A. Ferrari 162, P. Ferrari 115,114, R. Ferrari 73a, D. Ferrere 56, C. Ferretti 107, F. Fiedler 101,P. Fiedler 133, A. Filipčič 94, E.K. Filmer 1, F. Filthaut 114, M.C.N. Fiolhais 131a,131c,c,L. Fiorini 164, W.C. Fisher 108, T. Fitschen 102, P.M. Fitzhugh136, I. Fleck 142,P. Fleischmann 107, T. Flick 172, M. Flores 33d,ac, L.R. Flores Castillo 64a,L. Flores Sanz De Acedo 36, F.M. Follega 78a,78b, N. Fomin 16, J.H. Foo 156, A. Formica 136,A.C. Forti 102, E. Fortin 36, A.W. Fortman 17a, M.G. Foti 17a, L. Fountas 9,j , D. Fournier 66,H. Fox 92, P. Francavilla 74a,74b, S. Francescato 61, S. Franchellucci 56, M. Franchini 23b,23a,S. Franchino 63a, D. Francis36, L. Franco 114, V. Franco Lima 36, L. Franconi 48,M. Franklin 61, G. Frattari 26, W.S. Freund 83b, Y.Y. Frid 152, J. Friend 59, N. Fritzsche 50,A. Froch 54, D. Froidevaux 36, J.A. Frost 127, Y. Fu 62a, S. Fuenzalida Garrido 138f ,M. Fujimoto 103, K.Y. Fung 64a, E. Furtado De Simas Filho 83e, M. Furukawa 154,J. Fuster 164, A. Gabrielli 23b,23a, A. Gabrielli 156, P. Gadow 36, G. Gagliardi 57b,57a,L.G. Gagnon 17a, S. Galantzan 152, E.J. Gallas 127, B.J. Gallop 135, K.K. Gan 120,S. Ganguly 154, Y. Gao 52, F.M. Garay Walls 138a,138b, B. Garcia29, C. García 164,A. Garcia Alonso 115, A.G. Garcia Caffaro 173, J.E. García Navarro 164, M. Garcia-Sciveres 17a,G.L. Gardner 129, R.W. Gardner 39, N. Garelli 159, D. Garg 80, R.B. Garg 144,m,J.M. Gargan 52, C.A. Garner156, C.M. Garvey 33a, P. Gaspar 83b, V.K. Gassmann159,G. Gaudio 73a, V. Gautam13, P. Gauzzi 75a,75b, I.L. Gavrilenko 37, A. Gavrilyuk 37,C. Gay 165, G. Gaycken 48, E.N. Gazis 10, A.A. Geanta 27b, C.M. Gee 137, A. Gekow120,C. Gemme 57b, M.H. Genest 60, A.D. Gentry 113, S. George 96, W.F. George 20,T. Geralis 46, P. Gessinger-Befurt 36, M.E. Geyik 172, M. Ghani 168, M. Ghneimat 142,K. Ghorbanian 95, A. Ghosal 142, A. Ghosh 160, A. Ghosh 7, B. Giacobbe 23b,– 31 –JHEP11(2024)047S. Giagu 75a,75b, T. Giani 115, P. Giannetti 74a, A. Giannini 62a, S.M. Gibson 96,M. Gignac 137, D.T. Gil 86b, A.K. Gilbert 86a, B.J. Gilbert 41, D. Gillberg 34, G. Gilles 115,L. Ginabat 128, D.M. Gingrich 2,af , M.P. Giordani 69a,69c, P.F. Giraud 136,G. Giugliarelli 69a,69c, D. Giugni 71a, F. Giuli 36, I. Gkialas 9,j , L.K. Gladilin 37,C. Glasman 100, G.R. Gledhill 124, G. Glemža 48, M. Glisic124, I. Gnesi 43b,f , Y. Go 29,M. Goblirsch-Kolb 36, B. Gocke 49, D. Godin109, B. Gokturk 21a, S. Goldfarb 106,T. Golling 56, M.G.D. Gololo 33g, D. Golubkov 37, J.P. Gombas 108, A. Gomes 131a,131b,G. Gomes Da Silva 142, A.J. Gomez Delegido 164, R. Gonçalo 131a,131c, L. Gonella 20,A. Gongadze 150c, F. Gonnella 20, J.L. Gonski 144, R.Y. González Andana 52,S. González de la Hoz 164, R. Gonzalez Lopez 93, C. Gonzalez Renteria 17a,M.V. Gonzalez Rodrigues 48, R. Gonzalez Suarez 162, S. Gonzalez-Sevilla 56, L. Goossens 36,B. Gorini 36, E. Gorini 70a,70b, A. Gorišek 94, T.C. Gosart 129, A.T. Goshaw 51,M.I. Gostkin 38, S. Goswami 122, C.A. Gottardo 36, S.A. Gotz 110, M. Gouighri 35b,V. Goumarre 48, A.G. Goussiou 139, N. Govender 33c, I. Grabowska-Bold 86a, K. Graham 34,E. Gramstad 126, S. Grancagnolo 70a,70b, C.M. Grant1,136, 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. Grewe111, A.A. Grillo 137, K. Grimm 31, S. Grinstein 13,t, J.-F. Grivaz 66,E. Gross 170, J. Grosse-Knetter 55, J.C. Grundy 127, L. Guan 107, C. Gubbels 165,J.G.R. Guerrero Rojas 164, G. Guerrieri 69a,69c, F. Guescini 111, R. Gugel 101,J.A.M. Guhit 107, A. Guida 18, E. Guilloton 168, S. Guindon 36, F. Guo 14a,14e, J. Guo 62c,L. Guo 48, Y. Guo 107, R. Gupta 48, R. Gupta 130, S. Gurbuz 24, S.S. Gurdasani 54,G. Gustavino 36, M. Guth 56, P. Gutierrez 121, L.F. Gutierrez Zagazeta 129, M. Gutsche 50,C. Gutschow 97, C. Gwenlan 127, C.B. Gwilliam 93, E.S. Haaland 126, A. Haas 118,M. Habedank 48, C. Haber 17a, H.K. Hadavand 8, A. Hadef 50, S. Hadzic 111, A.I. Hagan 92,J.J. Hahn 142, E.H. Haines 97, M. Haleem 167, J. Haley 122, J.J. Hall 140, G.D. Hallewell 103,L. Halser 19, K. Hamano 166, M. Hamer 24, G.N. Hamity 52, E.J. Hampshire 96, J. Han 62b,K. Han 62a, L. Han 14c, L. Han 62a, S. Han 17a, Y.F. Han 156, K. Hanagaki 84,M. Hance 137, D.A. Hangal 41, H. Hanif 143, M.D. Hank 129, J.B. Hansen 42, P.H. Hansen 42,K. Hara 158, D. Harada 56, T. Harenberg 172, S. Harkusha 37, M.L. Harris 104,Y.T. Harris 127, J. Harrison 13, N.M. Harrison 120, P.F. Harrison168, N.M. Hartman 111,N.M. Hartmann 110, Y. Hasegawa 141, R. Hauser 108, C.M. Hawkes 20, R.J. Hawkings 36,Y. Hayashi 154, S. Hayashida 112, D. Hayden 108, C. Hayes 107, R.L. Hayes 115, C.P. Hays 127,J.M. Hays 95, H.S. Hayward 93, F. He 62a, M. He 14a,14e, Y. He 155, Y. He 48, Y. He 97,N.B. Heatley 95, V. Hedberg 99, A.L. Heggelund 126, N.D. Hehir 95,∗, C. Heidegger 54,K.K. Heidegger 54, W.D. Heidorn 81, J. Heilman 34, S. Heim 48, T. Heim 17a,J.G. Heinlein 129, J.J. Heinrich 124, L. Heinrich 111,ad, J. Hejbal 132, A. Held 171,S. Hellesund 16, C.M. Helling 165, S. Hellman 47a,47b, R.C.W. Henderson92, L. Henkelmann 32,A.M. Henriques Correia36, H. Herde 99, Y. Hernández Jiménez 146, L.M. Herrmann 24,T. Herrmann 50, G. Herten 54, R. Hertenberger 110, L. Hervas 36, M.E. Hesping 101,N.P. Hessey 157a, E. Hill 156, S.J. Hillier 20, J.R. Hinds 108, F. Hinterkeuser 24, M. Hirose 125,S. Hirose 158, D. Hirschbuehl 172, T.G. Hitchings 102, B. Hiti 94, J. Hobbs 146,R. Hobincu 27e, N. Hod 170, M.C. Hodgkinson 140, B.H. Hodkinson 127, A. Hoecker 36,D.D. Hofer 107, J. Hofer 48, T. Holm 24, M. Holzbock 111, L.B.A.H. Hommels 32,– 32 –JHEP11(2024)047B.P. Honan 102, J. Hong 62c, T.M. Hong 130, B.H. Hooberman 163, W.H. Hopkins 6,Y. Horii 112, S. Hou 149, A.S. Howard 94, J. Howarth 59, J. Hoya 6, M. Hrabovsky 123,A. Hrynevich 48, T. Hryn’ova 4, P.J. Hsu 65, S.-C. Hsu 139, Q. Hu 62a, S. Huang 64b,X. Huang 14a,14e, Y. Huang 140, Y. Huang 14a, Z. Huang 102, Z. Hubacek 133, M. Huebner 24,F. Huegging 24, T.B. Huffman 127, C.A. Hugli 48, M. Huhtinen 36, S.K. Huiberts 16,R. Hulsken 105, N. Huseynov 12, J. Huston 108, 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 127, Y. Ikegami 84, N. Ilic 156, 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 93, M. Ishino 154, W. Islam 171, C. Issever 18,48, S. Istin 21a,aj , H. Ito 169,R. Iuppa 78a,78b, A. Ivina 170, J.M. Izen 45, V. Izzo 72a, P. Jacka 132,133, P. Jackson 1,B.P. Jaeger 143, C.S. Jagfeld 110, G. Jain 157a, P. Jain 54, K. Jakobs 54, T. Jakoubek 170,J. Jamieson 59, K.W. Janas 86a, M. Javurkova 104, L. Jeanty 124, J. Jejelava 150a,aa,P. Jenni 54,g, C.E. Jessiman 34, 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 155, P. Johansson 140,K.A. Johns 7, J.W. Johnson 137, D.M. Jones 32, E. Jones 48, P. Jones 32, R.W.L. Jones 92,T.J. Jones 93, H.L. Joos 55,36, R. Joshi 120, J. Jovicevic 15, X. Ju 17a, J.J. Junggeburth 104,T. Junkermann 63a, A. Juste Rozas 13,t, M.K. Juzek 87, S. Kabana 138e, A. Kaczmarska 87,M. Kado 111, H. Kagan 120, M. Kagan 144, A. Kahn41, A. Kahn 129, C. Kahra 101,T. Kaji 154, E. Kajomovitz 151, N. Kakati 170, I. Kalaitzidou 54, C.W. Kalderon 29,N.J. Kang 137, D. Kar 33g, K. Karava 127, M.J. Kareem 157b, E. Karentzos 54,I. Karkanias 153, O. Karkout 115, S.N. Karpov 38, Z.M. Karpova 38, V. Kartvelishvili 92,A.N. Karyukhin 37, E. Kasimi 153, J. Katzy 48, S. Kaur 34, K. Kawade 141, M.P. Kawale 121,C. Kawamoto 88, T. Kawamoto 62a, E.F. Kay 36, F.I. Kaya 159, S. Kazakos 108,V.F. Kazanin 37, Y. Ke 146, J.M. Keaveney 33a, R. Keeler 166, G.V. Kehris 61, J.S. Keller 34,A.S. Kelly97, J.J. Kempster 147, P.D. Kennedy 101, O. Kepka 132, B.P. Kerridge 135,S. Kersten 172, B.P. Kerševan 94, L. Keszeghova 28a, S. Ketabchi Haghighat 156,R.A. Khan 130, A. Khanov 122, A.G. Kharlamov 37, T. Kharlamova 37, E.E. Khoda 139,M. Kholodenko 37, T.J. Khoo 18, G. Khoriauli 167, J. Khubua 150b,∗, Y.A.R. Khwaira 66,B. Kibirige33g, A. Kilgallon 124, D.W. Kim 47a,47b, Y.K. Kim 39, N. Kimura 97,M.K. Kingston 55, A. Kirchhoff 55, C. Kirfel 24, F. Kirfel 24, J. Kirk 135, A.E. Kiryunin 111,C. Kitsaki 10, O. Kivernyk 24, M. Klassen 63a, C. Klein 34, L. Klein 167, M.H. Klein 44,S.B. Klein 56, U. Klein 93, P. Klimek 36, A. Klimentov 29, T. Klioutchnikova 36, P. Kluit 115,S. Kluth 111, E. Kneringer 79, T.M. Knight 156, A. Knue 49, R. Kobayashi 88,D. Kobylianskii 170, S.F. Koch 127, M. Kocian 144, P. Kodyš 134, D.M. Koeck 124,P.T. Koenig 24, T. Koffas 34, O. Kolay 50, I. Koletsou 4, T. Komarek 123, K. Köneke 54,A.X.Y. Kong 1, T. Kono 119, N. Konstantinidis 97, P. Kontaxakis 56, B. Konya 99,R. Kopeliansky 41, S. Koperny 86a, K. Korcyl 87, K. Kordas 153,e, A. Korn 97, S. Korn 55,I. Korolkov 13, N. Korotkova 37, B. Kortman 115, O. Kortner 111, S. Kortner 111,W.H. Kostecka 116, V.V. Kostyukhin 142, A. Kotsokechagia 136, A. Kotwal 51, A. Koulouris 36,A. Kourkoumeli-Charalampidi 73a,73b, C. Kourkoumelis 9, E. Kourlitis 111,ad, O. Kovanda 124,R. Kowalewski 166, W. Kozanecki 136, A.S. Kozhin 37, V.A. Kramarenko 37, G. Kramberger 94,P. Kramer 101, M.W. Krasny 128, A. Krasznahorkay 36, J.W. Kraus 172, J.A. Kremer 48,– 33 –JHEP11(2024)047T. Kresse 50, J. Kretzschmar 93, K. Kreul 18, P. Krieger 156, S. Krishnamurthy 104,M. Krivos 134, K. Krizka 20, K. Kroeninger 49, H. Kroha 111, J. Kroll 132, J. Kroll 129,K.S. Krowpman 108, 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 138d,138b, M. Kumar 33g, N. Kumari 48, P. Kumari 157b,A. Kupco 132, T. Kupfer49, A. Kupich 37, O. Kuprash 54, H. Kurashige 85,L.L. Kurchaninov 157a, O. Kurdysh 66, Y.A. Kurochkin 37, A. Kurova 37, M. Kuze 155,A.K. Kvam 104, J. Kvita 123, T. Kwan 105, N.G. Kyriacou 107, L.A.O. Laatu 103,C. Lacasta 164, F. Lacava 75a,75b, H. Lacker 18, D. Lacour 128, N.N. Lad 97, E. Ladygin 38,A. Lafarge 40, B. Laforge 128, T. Lagouri 173, F.Z. Lahbabi 35a, S. Lai 55, I.K. Lakomiec 86a,N. Lalloue 60, J.E. Lambert 166, S. Lammers 68, W. Lampl 7, C. Lampoudis 153,e,G. Lamprinoudis101, A.N. Lancaster 116, E. Lançon 29, U. Landgraf 54, M.P.J. Landon 95,V.S. Lang 54, O.K.B. Langrekken 126, A.J. Lankford 160, F. Lanni 36, K. Lantzsch 24,A. Lanza 73a, A. Lapertosa 57b,57a, J.F. Laporte 136, T. Lari 71a, F. Lasagni Manghi 23b,M. Lassnig 36, V. Latonova 132, A. Laudrain 101, A. Laurier 151, S.D. Lawlor 140,Z. Lawrence 102, R. Lazaridou168, M. Lazzaroni 71a,71b, B. Le102, E.M. Le Boulicaut 51,B. Leban 23b,23a, A. Lebedev 81, M. LeBlanc 102, F. Ledroit-Guillon 60, A.C.A. Lee97,S.C. Lee 149, S. Lee 47a,47b, T.F. Lee 93, L.L. Leeuw 33c, H.P. Lefebvre 96, M. Lefebvre 166,C. Leggett 17a, G. Lehmann Miotto 36, M. Leigh 56, W.A. Leight 104, W. Leinonen 114,A. Leisos 153,s, M.A.L. Leite 83c, C.E. Leitgeb 18, R. Leitner 134, K.J.C. Leney 44, T. Lenz 24,S. Leone 74a, C. Leonidopoulos 52, A. Leopold 145, C. Leroy 109, R. Les 108, C.G. Lester 32,M. Levchenko 37, J. Levêque 4, L.J. Levinson 170, G. Levrini 23b,23a, M.P. Lewicki 87,D.J. Lewis 4, A. Li 5, B. Li 62b, C. Li62a, C-Q. Li 111, H. Li 62a, H. Li 62b, H. Li 14c,H. Li 14b, H. Li 62b, 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 105, Z. Li 127, Z. Li 154, Z. Li 14a,14e, S. Liang 14a,14e,Z. Liang 14a, M. Liberatore 136, B. Liberti 76a, K. Lie 64c, J. Lieber Marin 83b, H. Lien 68,K. Lin 108, R.E. Lindley 7, J.H. Lindon 2, E. Lipeles 129, A. Lipniacka 16, A. Lister 165,J.D. Little 4, B. Liu 14a, B.X. Liu 143, D. Liu 62d,62c, E.H.L. Liu 20, J.B. Liu 62a,J.K.K. Liu 32, K. Liu 62d, 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 95, E.M. Lobodzinska 48, P. Loch 7, T. Lohse 18,K. Lohwasser 140, E. Loiacono 48, M. Lokajicek 132,∗, J.D. Lomas 20, J.D. Long 163,I. Longarini 160, L. Longo 70a,70b, R. Longo 163, I. Lopez Paz 67, A. Lopez Solis 48,N. Lorenzo Martinez 4, A.M. Lory 110, G. Löschcke Centeno 147, O. Loseva 37, X. Lou 47a,47b,X. Lou 14a,14e, A. Lounis 66, P.A. Love 92, G. Lu 14a,14e, M. Lu 80, S. Lu 129, Y.J. Lu 65,H.J. Lubatti 139, C. Luci 75a,75b, F.L. Lucio Alves 14c, 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, R. Lysak 132, E. Lytken 99, V. Lyubushkin 38, T. Lyubushkina 38,M.M. Lyukova 146, H. Ma 29, K. Ma 62a, L.L. Ma 62b, W. Ma 62a, Y. Ma 122,D.M. Mac Donell 166, G. Maccarrone 53, J.C. MacDonald 101,P.C. Machado De Abreu Farias 83b, R. Madar 40, T. Madula 97, J. Maeda 85, T. Maeno 29,H. Maguire 140, V. Maiboroda 136, A. Maio 131a,131b,131d, K. Maj 86a, O. Majersky 48,S. Majewski 124, N. Makovec 66, V. Maksimovic 15, B. Malaescu 128, Pa. Malecki 87,– 34 –JHEP11(2024)047V.P. Maleev 37, F. Malek 60,n, M. Mali 94, D. Malito 96, U. Mallik 80,∗, S. Maltezos10,S. Malyukov38, J. Mamuzic 13, G. Mancini 53, M.N. Mancini 26, G. Manco 73a,73b,J.P. Mandalia 95, I. Mandić 94, L. Manhaes de Andrade Filho 83a, I.M. Maniatis 170,J. Manjarres Ramos 90, D.C. Mankad 170, A. Mann 110, S. Manzoni 36, L. Mao 62c,X. Mapekula 33c, A. Marantis 153,s, G. Marchiori 5, M. Marcisovsky 132, C. Marcon 71a,M. Marinescu 20, S. Marium 48, M. Marjanovic 121, M. Markovitch 66, E.J. Marshall 92,Z. Marshall 17a, S. Marti-Garcia 164, T.A. Martin 168, V.J. Martin 52, B. Martin dit Latour 16,L. Martinelli 75a,75b, M. Martinez 13,t, P. Martinez Agullo 164, V.I. Martinez Outschoorn 104,P. Martinez Suarez 13, S. Martin-Haugh 135, G. Martinovicova 134, V.S. Martoiu 27b,A.C. Martyniuk 97, A. Marzin 36, D. Mascione 78a,78b, L. Masetti 101, T. Mashimo 154,J. Masik 102, A.L. Maslennikov 37, P. Massarotti 72a,72b, P. Mastrandrea 74a,74b,A. Mastroberardino 43b,43a, T. Masubuchi 154, T. Mathisen 162, J. Matousek 134,N. Matsuzawa154, J. Maurer 27b, A.J. Maury 66, B. Maček 94, D.A. Maximov 37,R. Mazini 149, I. Maznas 116, M. Mazza 108, S.M. Mazza 137, E. Mazzeo 71a,71b,C. Mc Ginn 29, J.P. Mc Gowan 166, S.P. Mc Kee 107, C.C. McCracken 165, E.F. McDonald 106,A.E. McDougall 115, J.A. Mcfayden 147, R.P. McGovern 129, G. Mchedlidze 150b,R.P. Mckenzie 33g, T.C. Mclachlan 48, D.J. Mclaughlin 97, S.J. McMahon 135,C.M. Mcpartland 93, R.A. McPherson 166,x, S. Mehlhase 110, A. Mehta 93, D. Melini 164,B.R. Mellado Garcia 33g, A.H. Melo 55, F. Meloni 48, A.M. Mendes Jacques Da Costa 102,H.Y. Meng 156, L. Meng 92, S. Menke 111, M. Mentink 36, E. Meoni 43b,43a, G. Mercado 116,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 136, R.P. Middleton 135, L. Mijović 52, G. Mikenberg 170,M. Mikestikova 132, M. Mikuž 94, H. Mildner 101, A. Milic 36, D.W. Miller 39, E.H. Miller 144,L.S. Miller 34, A. Milov 170, D.A. Milstead47a,47b, T. Min14c, A.A. Minaenko 37,I.A. Minashvili 150b, L. Mince 59, A.I. Mincer 118, 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 114,A. Mitra 168, V.A. Mitsou 164, Y. Mitsumori 112, O. Miu 156, P.S. Miyagawa 95,T. Mkrtchyan 63a, M. Mlinarevic 97, T. Mlinarevic 97, M. Mlynarikova 36, S. Mobius 19,P. Mogg 110, M.H. Mohamed Farook 113, A.F. Mohammed 14a,14e, S. Mohapatra 41,G. Mokgatitswane 33g, L. Moleri 170, B. Mondal 142, S. Mondal 133, K. Mönig 48,E. Monnier 103, L. Monsonis Romero164, J. Montejo Berlingen 13, M. Montella 120,F. Montereali 77a,77b, F. Monticelli 91, S. Monzani 69a,69c, N. Morange 66,A.L. Moreira De Carvalho 131a, M. Moreno Llácer 164, C. Moreno Martinez 56, P. Morettini 57b,S. Morgenstern 36, M. Morii 61, M. Morinaga 154, F. Morodei 75a,75b, L. Morvaj 36,P. Moschovakos 36, B. Moser 36, M. Mosidze 150b, T. Moskalets 54, P. Moskvitina 114,J. Moss 31,k, A. Moussa 35d, E.J.W. Moyse 104, O. Mtintsilana 33g, S. Muanza 103,J. Mueller 130, D. Muenstermann 92, R. Müller 19, G.A. Mullier 162, A.J. Mullin32,J.J. Mullin129, D.P. Mungo 156, D. Munoz Perez 164, F.J. Munoz Sanchez 102, M. Murin 102,W.J. Murray 168,135, M. Muškinja 94, C. Mwewa 29, A.G. Myagkov 37,a, A.J. Myers 8,G. Myers 107, M. Myska 133, B.P. Nachman 17a, O. Nackenhorst 49, K. Nagai 127,K. Nagano 84, J.L. Nagle 29,ah, E. Nagy 103, A.M. Nairz 36, Y. Nakahama 84,K. Nakamura 84, K. Nakkalil 5, H. Nanjo 125, R. Narayan 44, E.A. Narayanan 113,I. Naryshkin 37, M. Naseri 34, S. Nasri 117b, C. Nass 24, G. Navarro 22a,– 35 –JHEP11(2024)047J. Navarro-Gonzalez 164, R. Nayak 152, A. Nayaz 18, P.Y. Nechaeva 37, F. Nechansky 48,L. Nedic 127, T.J. Neep 20, A. Negri 73a,73b, M. Negrini 23b, C. Nellist 115, C. Nelson 105,K. Nelson 107, S. Nemecek 132, M. Nessi 36,h, M.S. Neubauer 163, F. Neuhaus 101,J. Neundorf 48, R. Newhouse 165, P.R. Newman 20, C.W. Ng 130, Y.W.Y. Ng 48,B. Ngair 117a, H.D.N. Nguyen 109, R.B. Nickerson 127, R. Nicolaidou 136, J. Nielsen 137,M. Niemeyer 55, J. Niermann 55, N. Nikiforou 36, V. Nikolaenko 37,a, I. Nikolic-Audit 128,K. Nikolopoulos 20, P. Nilsson 29, I. Ninca 48, H.R. Nindhito 56, G. Ninio 152, A. Nisati 75a,N. Nishu 2, R. Nisius 111, 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 97, B.J. Norman 34, M. Noury 35a,J. Novak 94, T. Novak 48, L. Novotny 133, R. Novotny 113, L. Nozka 123, K. Ntekas 160,N.M.J. Nunes De Moura Junior 83b, J. Ocariz 128, A. Ochi 85, I. Ochoa 131a, S. Oerdek 48,u,J.T. Offermann 39, A. Ogrodnik 134, A. Oh 102, C.C. Ohm 145, H. Oide 84, R. Oishi 154,M.L. Ojeda 48, Y. Okumura 154, L.F. Oleiro Seabra 131a, S.A. Olivares Pino 138d,D. Oliveira Damazio 29, D. Oliveira Goncalves 83a, J.L. Oliver 160, Ö.O. Öncel 54,A.P. O’Neill 19, A. Onofre 131a,131e, P.U.E. Onyisi 11, M.J. Oreglia 39, G.E. Orellana 91,D. Orestano 77a,77b, N. Orlando 13, R.S. Orr 156, V. O’Shea 59, L.M. Osojnak 129,R. Ospanov 62a, G. Otero y Garzon 30, H. Otono 89, P.S. Ott 63a, G.J. Ottino 17a,M. Ouchrif 35d, F. Ould-Saada 126, T. Ovsiannikova 139, M. Owen 59, R.E. Owen 135,K.Y. Oyulmaz 21a, V.E. Ozcan 21a, F. Ozturk 87, N. Ozturk 8, S. Ozturk 82, H.A. Pacey 127,A. Pacheco Pages 13, C. Padilla Aranda 13, G. Padovano 75a,75b, S. Pagan Griso 17a,G. Palacino 68, A. Palazzo 70a,70b, J. Pampel 24, J. Pan 173, T. Pan 64a, D.K. Panchal 11,C.E. Pandini 115, J.G. Panduro Vazquez 96, H.D. Pandya 1, H. Pang 14b, P. Pani 48,G. Panizzo 69a,69c, L. Panwar 128, L. Paolozzi 56, S. Parajuli 163, A. Paramonov 6,C. Paraskevopoulos 53, D. Paredes Hernandez 64b, A. Pareti 73a,73b, K.R. Park 41,T.H. Park 156, M.A. Parker 32, F. Parodi 57b,57a, E.W. Parrish 116, V.A. Parrish 52,J.A. Parsons 41, U. Parzefall 54, B. Pascual Dias 109, L. Pascual Dominguez 152,E. Pasqualucci 75a, S. Passaggio 57b, F. Pastore 96, P. Patel 87, U.M. Patel 51, J.R. Pater 102,T. Pauly 36, C.I. Pazos 159, J. Pearkes 144, M. Pedersen 126, R. Pedro 131a,S.V. Peleganchuk 37, O. Penc 36, E.A. Pender 52, G.D. Penn 173, K.E. Penski 110,M. Penzin 37, B.S. Peralva 83d, A.P. Pereira Peixoto 139, L. Pereira Sanchez 144,D.V. Perepelitsa 29,ah, E. Perez Codina 157a, M. Perganti 10, H. Pernegger 36, O. Perrin 40,K. Peters 48, R.F.Y. Peters 102, B.A. Petersen 36, T.C. Petersen 42, E. Petit 103,V. Petousis 133, C. Petridou 153,e, T. Petru 134, A. Petrukhin 142, M. Pettee 17a,N.E. Pettersson 36, A. Petukhov 37, K. Petukhova 134, R. Pezoa 138f , L. Pezzotti 36,G. Pezzullo 173, T.M. Pham 171, T. Pham 106, P.W. Phillips 135, G. Piacquadio 146,E. Pianori 17a, F. Piazza 124, R. Piegaia 30, D. Pietreanu 27b, A.D. Pilkington 102,M. Pinamonti 69a,69c, J.L. Pinfold 2, B.C. Pinheiro Pereira 131a, A.E. Pinto Pinoargote 101,136,L. Pintucci 69a,69c, K.M. Piper 147, A. Pirttikoski 56, D.A. Pizzi 34, L. Pizzimento 64b,A. Pizzini 115, M.-A. Pleier 29, V. Plesanovs54, V. Pleskot 134, E. Plotnikova38, G. Poddar 95,R. Poettgen 99, L. Poggioli 128, I. Pokharel 55, S. Polacek 134, G. Polesello 73a,A. Poley 143,157a, A. Polini 23b, C.S. Pollard 168, Z.B. Pollock 120, E. Pompa Pacchi 75a,75b,D. Ponomarenko 114, L. Pontecorvo 36, S. Popa 27a, G.A. Popeneciu 27d, A. Poreba 36,D.M. Portillo Quintero 157a, S. Pospisil 133, M.A. Postill 140, P. Postolache 27c,– 36 –JHEP11(2024)047K. Potamianos 168, P.A. Potepa 86a, I.N. Potrap 38, C.J. Potter 32, H. Potti 1, J. Poveda 164,M.E. Pozo Astigarraga 36, A. Prades Ibanez 164, J. Pretel 54, D. Price 102, M. Primavera 70a,M.A. Principe Martin 100, R. Privara 123, T. Procter 59, M.L. Proffitt 139, N. Proklova 129,K. Prokofiev 64c, G. Proto 111, 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 107,D. Qichen 102, Y. Qin 13, T. Qiu 52, A. Quadt 55, M. Queitsch-Maitland 102, G. Quetant 56,R.P. Quinn 165, 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 128, D.F. Rassloff 63a,A. Rastogi 17a, S. Rave 101, B. Ravina 55, I. Ravinovich 170, M. Raymond 36, A.L. Read 126,N.P. Readioff 140, D.M. Rebuzzi 73a,73b, G. Redlinger 29, A.S. Reed 111, K. Reeves 26,J.A. Reidelsturz 172, D. Reikher 152, A. Rej 49, C. Rembser 36, M. Renda 27b, M.B. Rendel111,F. Renner 48, A.G. Rennie 160, A.L. Rescia 48, S. Resconi 71a, M. Ressegotti 57b,57a,S. Rettie 36, J.G. Reyes Rivera 108, E. Reynolds 17a, O.L. Rezanova 37, P. Reznicek 134,H. Riani 35d, N. Ribaric 92, E. Ricci 78a,78b, R. Richter 111, S. Richter 47a,47b,E. Richter-Was 86b, M. Ridel 128, S. Ridouani 35d, P. Rieck 118, P. Riedler 36,E.M. Riefel 47a,47b, J.O. Rieger 115, M. Rijssenbeek 146, M. Rimoldi 36, L. Rinaldi 23b,23a,T.T. Rinn 29, M.P. Rinnagel 110, G. Ripellino 162, I. Riu 13, J.C. Rivera Vergara 166,F. Rizatdinova 122, E. Rizvi 95, B.R. Roberts 17a, S.H. Robertson 105,x, D. Robinson 32,C.M. Robles Gajardo138f , M. Robles Manzano 101, A. Robson 59, A. Rocchi 76a,76b,C. Roda 74a,74b, S. Rodriguez Bosca 36, Y. Rodriguez Garcia 22a, A. Rodriguez Rodriguez 54,A.M. Rodríguez Vera 116, S. Roe36, J.T. Roemer 160, A.R. Roepe-Gier 137, J. Roggel 172,O. Røhne 126, R.A. Rojas 104, C.P.A. Roland 128, J. Roloff 29, A. Romaniouk 37,E. Romano 73a,73b, M. Romano 23b, A.C. Romero Hernandez 163, N. Rompotis 93, L. Roos 128,S. Rosati 75a, B.J. Rosser 39, E. Rossi 127, E. Rossi 72a,72b, L.P. Rossi 61, L. Rossini 54,R. Rosten 120, M. Rotaru 27b, B. Rottler 54, C. Rougier 90, D. Rousseau 66, D. Rousso 32,A. Roy 163, S. Roy-Garand 156, A. Rozanov 103, Z.M.A. Rozario 59, Y. Rozen 151,A. Rubio Jimenez 164, A.J. Ruby 93, V.H. Ruelas Rivera 18, T.A. Ruggeri 1, A. Ruggiero 127,A. Ruiz-Martinez 164, A. Rummler 36, Z. Rurikova 54, N.A. Rusakovich 38, H.L. Russell 166,G. Russo 75a,75b, J.P. Rutherfoord 7, S. Rutherford Colmenares 32, K. Rybacki92, M. Rybar 134,E.B. Rye 126, A. Ryzhov 44, J.A. Sabater Iglesias 56, P. Sabatini 164, H.F-W. Sadrozinski 137,F. Safai Tehrani 75a, B. Safarzadeh Samani 135, M. Safdari 144, S. Saha 1, M. Sahinsoy 111,A. Saibel 164, M. Saimpert 136, M. Saito 154, T. Saito 154, D. Salamani 36, A. Salnikov 144,J. Salt 164, A. Salvador Salas 152, D. Salvatore 43b,43a, F. Salvatore 147, A. Salzburger 36,D. Sammel 54, E. Sampson 92, D. Sampsonidis 153,e, D. Sampsonidou 124, J. Sánchez 164,V. Sanchez Sebastian 164, H. Sandaker 126, C.O. Sander 48, J.A. Sandesara 104,M. Sandhoff 172, C. Sandoval 22b, D.P.C. Sankey 135, T. Sano 88, A. Sansoni 53,L. Santi 75a,75b, C. Santoni 40, H. Santos 131a,131b, A. Santra 170, K.A. Saoucha 161,J.G. Saraiva 131a,131d, J. Sardain 7, O. Sasaki 84, K. Sato 158, C. Sauer63b, F. Sauerburger 54,E. Sauvan 4, P. Savard 156,af , R. Sawada 154, C. Sawyer 135, L. Sawyer 98,I. Sayago Galvan164, C. Sbarra 23b, A. Sbrizzi 23b,23a, T. Scanlon 97, J. Schaarschmidt 139,U. Schäfer 101, A.C. Schaffer 66,44, D. Schaile 110, R.D. Schamberger 146, C. Scharf 18,M.M. Schefer 19, V.A. Schegelsky 37, D. Scheirich 134, F. Schenck 18, M. Schernau 160,– 37 –JHEP11(2024)047C. Scheulen 55, C. Schiavi 57b,57a, M. Schioppa 43b,43a, B. Schlag 144,m, K.E. Schleicher 54,S. Schlenker 36, J. Schmeing 172, M.A. Schmidt 172, K. Schmieden 101, C. Schmitt 101,N. Schmitt 101, S. Schmitt 48, L. Schoeffel 136, A. Schoening 63b, P.G. Scholer 34,E. Schopf 127, M. Schott 101, J. Schovancova 36, S. Schramm 56, T. Schroer 56,H-C. Schultz-Coulon 63a, M. Schumacher 54, B.A. Schumm 137, Ph. Schune 136,A.J. Schuy 139, H.R. Schwartz 137, A. Schwartzman 144, T.A. Schwarz 107, Ph. Schwemling 136,R. Schwienhorst 108, A. Sciandra 137, G. Sciolla 26, F. Scuri 74a, C.D. Sebastiani 93,K. Sedlaczek 116, P. Seema 18, S.C. Seidel 113, A. Seiden 137, 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 164, L. Serin 66, L. Serkin 69a,69b, M. Sessa 76a,76b, H. Severini 121,F. Sforza 57b,57a, A. Sfyrla 56, Q. Sha 14a, E. Shabalina 55, R. Shaheen 145,J.D. Shahinian 129, D. Shaked Renous 170, L.Y. Shan 14a, M. Shapiro 17a, A. Sharma 36,A.S. Sharma 165, P. Sharma 80, P.B. Shatalov 37, K. Shaw 147, S.M. Shaw 102,A. Shcherbakova 37, Q. Shen 62c,5, D.J. Sheppard 143, P. Sherwood 97, L. Shi 97, X. Shi 14a,C.O. Shimmin 173, J.D. Shinner 96, I.P.J. Shipsey 127, S. Shirabe 89, M. Shiyakova 38,v,J. Shlomi 170, M.J. Shochet 39, J. Shojaii 106, D.R. Shope 126, B. Shrestha 121,S. Shrestha 120,ai, E.M. Shrif 33g, M.J. Shroff 166, P. Sicho 132, A.M. Sickles 163,E. Sideras Haddad 33g, A. Sidoti 23b, F. Siegert 50, Dj. Sijacki 15, F. Sili 91, J.M. Silva 52,M.V. Silva Oliveira 29, S.B. Silverstein 47a, S. Simion66, R. Simoniello 36, E.L. Simpson 59,H. Simpson 147, L.R. Simpson 107, N.D. Simpson99, S. Simsek 82, S. Sindhu 55, P. Sinervo 156,S. Singh 156, S. Sinha 48, S. Sinha 102, M. Sioli 23b,23a, I. Siral 36, E. Sitnikova 48,J. Sjölin 47a,47b, A. Skaf 55, E. Skorda 20, P. Skubic 121, M. Slawinska 87, V. Smakhtin170,B.H. Smart 135, S.Yu. Smirnov 37, Y. Smirnov 37, L.N. Smirnova 37,a, O. Smirnova 99,A.C. Smith 41, E.A. Smith 39, H.A. Smith 127, J.L. Smith 102, R. Smith144, M. Smizanska 92,K. Smolek 133, A.A. Snesarev 37, S.R. Snider 156, H.L. Snoek 115, S. Snyder 29,R. Sobie 166,x, A. Soffer 152, C.A. Solans Sanchez 36, E.Yu. Soldatov 37, U. Soldevila 164,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 157b, A. Sopczak 133, A.L. Sopio 97,F. Sopkova 28b, J.D. Sorenson 113, I.R. Sotarriva Alvarez 155, V. Sothilingam63a,O.J. Soto Sandoval 138c,138b, S. Sottocornola 68, R. Soualah 161, Z. Soumaimi 35e, D. South 48,N. Soybelman 170, S. Spagnolo 70a,70b, M. Spalla 111, D. Sperlich 54, G. Spigo 36,S. Spinali 92, D.P. Spiteri 59, M. Spousta 134, 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 137, J. Stark 90, P. Staroba 132,P. Starovoitov 63a, S. Stärz 105, R. Staszewski 87, G. Stavropoulos 46, J. Steentoft 162,P. Steinberg 29, B. Stelzer 143,157a, H.J. Stelzer 130, O. Stelzer-Chilton 157a, H. Stenzel 58,T.J. Stevenson 147, G.A. Stewart 36, J.R. Stewart 122, M.C. Stockton 36, G. Stoicea 27b,M. Stolarski 131a, S. Stonjek 111, A. Straessner 50, J. Strandberg 145, S. Strandberg 47a,47b,M. Stratmann 172, M. Strauss 121, T. Strebler 103, P. Strizenec 28b, R. Ströhmer 167,D.M. Strom 124, R. Stroynowski 44, A. Strubig 47a,47b, S.A. Stucci 29, B. Stugu 16,J. Stupak 121, N.A. Styles 48, D. Su 144, S. Su 62a, W. Su 62d, X. Su 62a, D. Suchy 28a,K. Sugizaki 154, V.V. Sulin 37, M.J. Sullivan 93, D.M.S. Sultan 127, L. Sultanaliyeva 37,S. Sultansoy 3b, T. Sumida 88, S. Sun 107, S. Sun 171, O. Sunneborn Gudnadottir 162,– 38 –JHEP11(2024)047N. Sur 103, M.R. Sutton 147, H. Suzuki 158, M. Svatos 132, M. Swiatlowski 157a,T. Swirski 167, I. Sykora 28a, M. Sykora 134, T. Sykora 134, D. Ta 101, K. Tackmann 48,u,A. Taffard 160, R. Tafirout 157a, J.S. Tafoya Vargas 66, Y. Takubo 84, M. Talby 103,A.A. Talyshev 37, K.C. Tam 64b, N.M. Tamir152, A. Tanaka 154, J. Tanaka 154, R. Tanaka 66,M. Tanasini 57b,57a, Z. Tao 165, S. Tapia Araya 138f , S. Tapprogge 101,A. Tarek Abouelfadl Mohamed 108, S. Tarem 151, K. Tariq 14a, G. Tarna 103,27b,G.F. Tartarelli 71a, P. Tas 134, M. Tasevsky 132, E. Tassi 43b,43a, A.C. Tate 163,G. Tateno 154, Y. Tayalati 35e,w, G.N. Taylor 106, W. Taylor 157b, A.S. Tee 171,R. Teixeira De Lima 144, P. Teixeira-Dias 96, J.J. Teoh 156, K. Terashi 154, J. Terron 100,S. Terzo 13, M. Testa 53, R.J. Teuscher 156,x, A. Thaler 79, O. Theiner 56,N. Themistokleous 52, T. Theveneaux-Pelzer 103, O. Thielmann 172, D.W. Thomas96,J.P. Thomas 20, E.A. Thompson 17a, P.D. Thompson 20, E. Thomson 129, R.E. Thornberry 44,Y. Tian 55, V. Tikhomirov 37,a, Yu.A. Tikhonov 37, S. Timoshenko37, D. Timoshyn 134,E.X.L. Ting 1, P. Tipton 173, S.H. Tlou 33g, K. Todome 155, S. Todorova-Nova 134, S. Todt50,M. Togawa 84, J. Tojo 89, S. Tokár 28a, K. Tokushuku 84, O. Toldaiev 68, R. Tombs 32,M. Tomoto 84,112, L. Tompkins 144,m, K.W. Topolnicki 86b, E. Torrence 124, H. Torres 90,E. Torró Pastor 164, M. Toscani 30, C. Tosciri 39, M. Tost 11, D.R. Tovey 140, A. Traeet16,I.S. Trandafir 27b, T. Trefzger 167, A. Tricoli 29, I.M. Trigger 157a, S. Trincaz-Duvoid 128,D.A. Trischuk 26, B. Trocmé 60, L. Truong 33c, M. Trzebinski 87, A. Trzupek 87, F. Tsai 146,M. Tsai 107, A. Tsiamis 153,e, P.V. Tsiareshka37, S. Tsigaridas 157a, A. Tsirigotis 153,s,V. Tsiskaridze 156, E.G. Tskhadadze 150a, M. Tsopoulou 153, Y. Tsujikawa 88,I.I. Tsukerman 37, V. Tsulaia 17a, S. Tsuno 84, K. Tsuri 119, 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,y, P.M. Tuts 41, S. Tzamarias 153,e, E. Tzovara 101,F. Ukegawa 158, P.A. Ulloa Poblete 138c,138b, E.N. Umaka 29, G. Unal 36, A. Undrus 29,G. Unel 160, J. Urban 28b, P. Urquijo 106, P. Urrejola 138a, G. Usai 8, R. Ushioda 155,M. Usman 109, Z. Uysal 82, V. Vacek 133, B. Vachon 105, K.O.H. Vadla 126, T. Vafeiadis 36,A. Vaitkus 97, C. Valderanis 110, E. Valdes Santurio 47a,47b, M. Valente 157a,S. Valentinetti 23b,23a, A. Valero 164, E. Valiente Moreno 164, A. Vallier 90,J.A. Valls Ferrer 164, D.R. Van Arneman 115, T.R. Van Daalen 139, A. Van Der Graaf 49,P. Van Gemmeren 6, M. Van Rijnbach 126, S. Van Stroud 97, I. Van Vulpen 115, P. Vana 134,M. Vanadia 76a,76b, W. Vandelli 36, E.R. Vandewall 122, D. Vannicola 152, L. Vannoli 53,R. Vari 75a, E.W. Varnes 7, C. Varni 17b, T. Varol 149, D. Varouchas 66, L. Varriale 164,K.E. Varvell 148, M.E. Vasile 27b, L. Vaslin84, G.A. Vasquez 166, A. Vasyukov 38, R. Vavricka101,F. Vazeille 40, T. Vazquez Schroeder 36, J. Veatch 31, V. Vecchio 102, M.J. Veen 104,I. Veliscek 29, L.M. Veloce 156, F. Veloso 131a,131c, S. Veneziano 75a, A. Ventura 70a,70b,S. Ventura Gonzalez 136, A. Verbytskyi 111, M. Verducci 74a,74b, C. Vergis 24,M. Verissimo De Araujo 83b, W. Verkerke 115, J.C. Vermeulen 115, C. Vernieri 144,M. Vessella 104, M.C. Vetterli 143,af , A. Vgenopoulos 153,e, N. Viaux Maira 138f , T. Vickey 140,O.E. Vickey Boeriu 140, G.H.A. Viehhauser 127, L. Vigani 63b, M. Villa 23b,23a,M. Villaplana Perez 164, E.M. Villhauer52, E. Vilucchi 53, M.G. Vincter 34, G.S. Virdee 20,A. Vishwakarma 52, A. Visibile115, C. Vittori 36, I. Vivarelli 23b,23a, E. Voevodina 111,F. Vogel 110, J.C. Voigt 50, P. Vokac 133, Yu. Volkotrub 86b, J. Von Ahnen 48,– 39 –JHEP11(2024)047E. Von Toerne 24, B. Vormwald 36, V. Vorobel 134, K. Vorobev 37, M. Vos 164, K. Voss 142,M. Vozak 115, L. Vozdecky 121, N. Vranjes 15, M. Vranjes Milosavljevic 15, M. Vreeswijk 115,N.K. Vu 62d,62c, R. Vuillermet 36, O. Vujinovic 101, I. Vukotic 39, S. Wada 158, C. Wagner104,J.M. Wagner 17a, W. Wagner 172, S. Wahdan 172, H. Wahlberg 91, M. Wakida 112,J. Walder 135, R. Walker 110, W. Walkowiak 142, A. Wall 129, E.J. Wallin 99, T. Wamorkar 6,A.Z. Wang 137, C. Wang 101, C. Wang 11, H. Wang 17a, J. Wang 64c, R.-J. Wang 101,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 163, X. Wang 62c, Y. Wang 62d, Y. Wang 14c,Z. Wang 107, Z. Wang 62d,51,62c, Z. Wang 107, A. Warburton 105, R.J. Ward 20,N. Warrack 59, S. Waterhouse 96, A.T. Watson 20, H. Watson 59, M.F. Watson 20,E. Watton 59,135, G. Watts 139, B.M. Waugh 97, C. Weber 29, H.A. Weber 18, M.S. Weber 19,S.M. Weber 63a, C. Wei 62a, Y. Wei 127, A.R. Weidberg 127, E.J. Weik 118, J. Weingarten 49,M. Weirich 101, C. Weiser 54, C.J. Wells 48, T. Wenaus 29, B. Wendland 49, T. Wengler 36,N.S. Wenke111, N. Wermes 24, M. Wessels 63a, A.M. Wharton 92, A.S. White 61, A. White 8,M.J. White 1, D. Whiteson 160, L. Wickremasinghe 125, W. Wiedenmann 171, M. Wielers 135,C. Wiglesworth 42, D.J. Wilbern121, H.G. Wilkens 36, D.M. Williams 41, H.H. Williams129,S. Williams 32, S. Willocq 104, B.J. Wilson 102, P.J. Windischhofer 39, F.I. Winkel 30,F. Winklmeier 124, B.T. Winter 54, J.K. Winter 102, M. Wittgen144, M. Wobisch 98,Z. Wolffs 115, J. Wollrath160, M.W. Wolter 87, H. Wolters 131a,131c, M.C. Wong137,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, M. Wu 14d, M. Wu 114, S.L. Wu 171, X. Wu 56, Y. Wu 62a,Z. Wu 4, J. Wuerzinger 111,ad, T.R. Wyatt 102, 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 124, J. Xiong 17a,D. Xu 14a, H. Xu 62a, L. Xu 62a, R. Xu 129, T. Xu 107, Y. Xu 14b, Z. Xu 52, Z. Xu14c,B. Yabsley 148, S. Yacoob 33a, Y. Yamaguchi 155, E. Yamashita 154, H. Yamauchi 158,T. Yamazaki 17a, Y. Yamazaki 85, J. Yan62c, S. Yan 59, Z. Yan 104, 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, H. Ye 14c, H. Ye 55, J. Ye 14a, S. Ye 29, X. Ye 62a, Y. Yeh 97,I. Yeletskikh 38, B. Yeo 17b, M.R. Yexley 97, P. Yin 41, K. Yorita 169, S. Younas 27b,C.J.S. Young 36, C. Young 144, C. Yu 14a,14e, Y. Yu 62a, M. Yuan 107, R. Yuan 62b,L. Yue 97, M. Zaazoua 62a, B. Zabinski 87, E. Zaid52, Z.K. Zak 87, T. Zakareishvili 164,N. Zakharchuk 34, S. Zambito 56, J.A. Zamora Saa 138d,138b, J. Zang 154, D. Zanzi 54,O. Zaplatilek 133, C. Zeitnitz 172, H. Zeng 14a, J.C. Zeng 163, D.T. Zenger Jr 26, O. Zenin 37,T. Ženiš 28a, S. Zenz 95, S. Zerradi 35a, D. Zerwas 66, M. Zhai 14a,14e, D.F. Zhang 140,J. Zhang 62b, J. Zhang 6, K. Zhang 14a,14e, L. Zhang 14c, P. Zhang 14a,14e, R. Zhang 171,S. Zhang 107, S. Zhang 44, T. Zhang 154, X. Zhang 62c, X. Zhang 62b, Y. Zhang 62c,5,Y. Zhang 97, Y. Zhang 14c, Z. Zhang 17a, Z. Zhang 66, H. Zhao 139, T. Zhao 62b,Y. Zhao 137, Z. Zhao 62a, A. Zhemchugov 38, J. Zheng 14c, K. Zheng 163, X. Zheng 62a,Z. Zheng 144, D. Zhong 163, B. Zhou 107, H. Zhou 7, N. Zhou 62c, Y. Zhou 14c, Y. Zhou7,C.G. Zhu 62b, J. Zhu 107, Y. Zhu 62c, Y. Zhu 62a, X. Zhuang 14a, K. Zhukov 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 361Department of Physics, University of Adelaide, Adelaide; Australia– 40 –JHEP11(2024)0472Department of Physics, University of Alberta, Edmonton AB; Canada3 (a)Department of Physics, Ankara University, Ankara;(b)Division of Physics, TOBB University ofEconomics and Technology, Ankara; Türkiye4 LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France5APC, Université Paris Cité, CNRS/IN2P3, Paris; France6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America7Department of Physics, University of Arizona, Tucson AZ; United States of America8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America9 Physics Department, National and Kapodistrian University of Athens, Athens; Greece10 Physics Department, National Technical University of Athens, Zografou; Greece11Department of Physics, University of Texas at Austin, Austin TX; United States of America12 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona;Spain14 (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; China15 Institute of Physics, University of Belgrade, Belgrade; Serbia16Department for Physics and Technology, University of Bergen, Bergen; Norway17 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA;(b)University of California,Berkeley CA; United States of America18 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany19Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University ofBern, Bern; Switzerland20 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom21 (a)Department of Physics, Bogazici University, Istanbul;(b)Department of Physics Engineering, GaziantepUniversity, Gaziantep;(c)Department of Physics, Istanbul University, Istanbul; Türkiye22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño,Bogotá;(b)Departamento de Física, Universidad Nacional de Colombia, Bogotá; Colombia23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna;(b)INFN Sezione diBologna; Italy24 Physikalisches Institut, Universität Bonn, Bonn; Germany25Department of Physics, Boston University, Boston MA; United States of America26Department of Physics, Brandeis University, Waltham MA; United States of America27 (a)Transilvania University of Brasov, Brasov;(b)Horia Hulubei National Institute of Physics and NuclearEngineering, Bucharest;(c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi;(d)NationalInstitute for Research and Development of Isotopic and Molecular Technologies, Physics Department,Cluj-Napoca;(e)National University of Science and Technology Politechnica, Bucharest;(f)West Universityin Timisoara, Timisoara;(g)Faculty of Physics, University of Bucharest, Bucharest; Romania28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava;(b)Department ofSubnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; SlovakRepublic29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America30Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, yCONICET, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires; Argentina31California State University, CA; United States of America32Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom33 (a)Department of Physics, University of Cape Town, Cape Town;(b)iThemba Labs, WesternCape;(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– 41 –JHEP11(2024)04734Department of Physics, Carleton University, Ottawa ON; Canada35 (a)Faculté des Sciences Ain Chock, Université Hassan II de Casablanca;(b)Faculté des Sciences, UniversitéIbn-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 MohammedV, Rabat;(f)Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco36CERN, Geneva; Switzerland37Affiliated with an institute covered by a cooperation agreement with CERN38Affiliated with an international laboratory covered by a cooperation agreement with CERN39 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France41Nevis Laboratory, Columbia University, Irvington NY; United States of America42Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark43 (a)Dipartimento di Fisica, Università della Calabria, Rende;(b)INFN Gruppo Collegato di Cosenza,Laboratori Nazionali di Frascati; Italy44 Physics Department, Southern Methodist University, Dallas TX; United States of America45 Physics Department, University of Texas at Dallas, Richardson TX; United States of America46National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece47 (a)Department of Physics, Stockholm University;(b)Oskar Klein Centre, Stockholm; Sweden48Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany49 Fakultät Physik , Technische Universität Dortmund, Dortmund; Germany50 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany51Department of Physics, Duke University, Durham NC; United States of America52 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom53 INFN e Laboratori Nazionali di Frascati, Frascati; Italy54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany56Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland57 (a)Dipartimento di Fisica, Università di Genova, Genova;(b)INFN Sezione di Genova; Italy58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany59 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States ofAmerica62 (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 Scienceand 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 ParticleAstrophysics and Cosmology (MOE), SKLPPC, Shanghai;(d)Tsung-Dao Lee Institute, Shanghai;(e)Schoolof Physics and Microelectronics, Zhengzhou University; China63 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg;(b)PhysikalischesInstitut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany64 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;(b)Department ofPhysics, University of Hong Kong, Hong Kong;(c)Department of Physics and Institute for Advanced Study,Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China65Department of Physics, National Tsing Hua University, Hsinchu; Taiwan66 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France67Centro Nacional de Microelectrónica (IMB-CNM-CSIC), Barcelona; Spain68Department of Physics, Indiana University, Bloomington IN; United States of America69 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b)ICTP, Trieste;(c)DipartimentoPolitecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy70 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy71 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Università di Milano, Milano; Italy72 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy– 42 –JHEP11(2024)04773 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy74 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy75 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy76 (a)INFN Sezione di Roma Tor Vergata;(b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma;Italy77 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy78 (a)INFN-TIFPA;(b)Università degli Studi di Trento, Trento; Italy79Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria80University of Iowa, Iowa City IA; United States of America81Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America82 Istinye University, Sariyer, Istanbul; Türkiye83 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz deFora;(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;(e)FederalUniversity of Bahia, Bahia; Brazil84KEK, High Energy Accelerator Research Organization, Tsukuba; Japan85Graduate School of Science, Kobe University, Kobe; Japan86 (a)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow;(b)MarianSmoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland87 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland88 Faculty of Science, Kyoto University, Kyoto; Japan89Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka;Japan90 L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse; France91 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina92 Physics Department, Lancaster University, Lancaster; United Kingdom93Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom94Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics,University of Ljubljana, Ljubljana; Slovenia95 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom96Department of Physics, Royal Holloway University of London, Egham; United Kingdom97Department of Physics and Astronomy, University College London, London; United Kingdom98 Louisiana Tech University, Ruston LA; United States of America99 Fysiska institutionen, Lunds universitet, Lund; Sweden100Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain101 Institut für Physik, Universität Mainz, Mainz; Germany102 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom103CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France104Department of Physics, University of Massachusetts, Amherst MA; United States of America105Department of Physics, McGill University, Montreal QC; Canada106 School of Physics, University of Melbourne, Victoria; Australia107Department of Physics, University of Michigan, Ann Arbor MI; United States of America108Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States ofAmerica109Group of Particle Physics, University of Montreal, Montreal QC; Canada110 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany111Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany112Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan113Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States ofAmerica114 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen;Netherlands115Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands– 43 –JHEP11(2024)047116Department of Physics, Northern Illinois University, DeKalb IL; United States of America117 (a)New York University Abu Dhabi, Abu Dhabi;(b)United Arab Emirates University, Al Ain; United ArabEmirates118Department of Physics, New York University, New York NY; United States of America119Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan120Ohio State University, Columbus OH; United States of America121Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; UnitedStates of America122Department of Physics, Oklahoma State University, Stillwater OK; United States of America123 Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic124 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America125Graduate School of Science, Osaka University, Osaka; Japan126Department of Physics, University of Oslo, Oslo; Norway127Department of Physics, Oxford University, Oxford; United Kingdom128 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France129Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America130Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America131 (a)Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa;(b)Departamento deFísica, Faculdade de Ciências, Universidade de Lisboa, Lisboa;(c)Departamento de Física, Universidade deCoimbra, Coimbra;(d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa;(e)Departamento deFísica, Universidade do Minho, Braga;(f)Departamento de Física Teórica y del Cosmos, Universidad deGranada, Granada (Spain);(g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa,Lisboa; Portugal132 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic133Czech Technical University in Prague, Prague; Czech Republic134Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic135 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom136 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France137 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; UnitedStates of America138 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago;(b)Millennium Institute forSubatomic physics at high energy frontier (SAPHIR), Santiago;(c)Instituto de InvestigaciónMultidisciplinario en Ciencia y Tecnología, y Departamento de Física, Universidad de LaSerena;(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; Chile139Department of Physics, University of Washington, Seattle WA; United States of America140Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom141Department of Physics, Shinshu University, Nagano; Japan142Department Physik, Universität Siegen, Siegen; Germany143Department of Physics, Simon Fraser University, Burnaby BC; Canada144 SLAC National Accelerator Laboratory, Stanford CA; United States of America145Department of Physics, Royal Institute of Technology, Stockholm; Sweden146Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States ofAmerica147Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom148 School of Physics, University of Sydney, Sydney; Australia149 Institute of Physics, Academia Sinica, Taipei; Taiwan150 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;(b)High EnergyPhysics Institute, Tbilisi State University, Tbilisi;(c)University of Georgia, Tbilisi; Georgia151Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel152Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel153Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece– 44 –JHEP11(2024)047154 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo,Tokyo; Japan155Department of Physics, Tokyo Institute of Technology, Tokyo; Japan156Department of Physics, University of Toronto, Toronto ON; Canada157 (a)TRIUMF, Vancouver BC;(b)Department of Physics and Astronomy, York University, Toronto ON;Canada158Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and AppliedSciences, University of Tsukuba, Tsukuba; Japan159Department of Physics and Astronomy, Tufts University, Medford MA; United States of America160Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States ofAmerica161University of Sharjah, Sharjah; United Arab Emirates162Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden163Department of Physics, University of Illinois, Urbana IL; United States of America164 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain165Department of Physics, University of British Columbia, Vancouver BC; Canada166Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada167 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany168Department of Physics, University of Warwick, Coventry; United Kingdom169Waseda University, Tokyo; Japan170Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel171Department of Physics, University of Wisconsin, Madison WI; United States of America172 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal,Wuppertal; Germany173Department of Physics, Yale University, New Haven CT; United States of AmericaaAlso Affiliated with an institute covered by a cooperation agreement with CERNbAlso at An-Najah National University, Nablus; PalestinecAlso at Borough of Manhattan Community College, City University of New York, New York NY; UnitedStates of AmericadAlso at Center for High Energy Physics, Peking University; ChinaeAlso at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki; Greecef Also at Centro Studi e Ricerche Enrico Fermi; ItalygAlso at CERN, Geneva; SwitzerlandhAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; SwitzerlandiAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spainj Also at Department of Financial and Management Engineering, University of the Aegean, Chios; GreecekAlso at Department of Physics, California State University, Sacramento; United States of AmericalAlso at Department of Physics, King’s College London, London; United KingdommAlso at Department of Physics, Stanford University, Stanford CA; United States of AmericanAlso at Department of Physics, Stellenbosch University; South AfricaoAlso at Department of Physics, University of Fribourg, Fribourg; SwitzerlandpAlso at Department of Physics, University of Thessaly; GreeceqAlso at Department of Physics, Westmont College, Santa Barbara; United States of AmericarAlso at Faculty of Physics, Sofia University, ‘St. Kliment Ohridski’, Sofia; BulgariasAlso at Hellenic Open University, Patras; GreecetAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; SpainuAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; GermanyvAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy ofSciences, Sofia; BulgariawAlso at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; MoroccoxAlso at Institute of Particle Physics (IPP); CanadayAlso at Institute of Physics and Technology, Mongolian Academy of Sciences, Ulaanbaatar; MongoliazAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan– 45 –JHEP11(2024)047aaAlso at Institute of Theoretical Physics, Ilia State University, Tbilisi; GeorgiaabAlso at Lawrence Livermore National Laboratory, Livermore; United States of AmericaacAlso at National Institute of Physics, University of the Philippines Diliman (Philippines); PhilippinesadAlso at Technical University of Munich, Munich; GermanyaeAlso at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; Chinaaf Also at TRIUMF, Vancouver BC; CanadaagAlso at Università di Napoli Parthenope, Napoli; ItalyahAlso at University of Colorado Boulder, Department of Physics, Colorado; United States of AmericaaiAlso at Washington College, Chestertown, MD; United States of Americaaj Also at Yeditepe University, Physics Department, Istanbul; Türkiye∗Deceased– 46 –
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Hadron-Hadron Scattering
Beyond Standard Model
Higgs Physics
Hadron-Hadron scattering
Search for a resonance decaying into a scalar particle and a Higgs boson in the fnal state with two bottom quarks and two photons in proton–proton collisions at √s = 13 TeV with the ATLAS detector
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