Search for high-mass W and Z resonances using hadronic W/Z boson decays from 139 fb−1 of pp collisions at p s = 13 TeV with the ATLAS detector

Atlas Collaboration; Aguilar Saavedra, Juan Antonio; Rodríguez Chala, Mikael; Universidad de Granada

doi:10.1007/JHEP07(2023)125

Search for high-mass W and Z resonances using hadronic W/Z boson decays from 139 fb−1 of pp collisions at p s = 13 TeV with the ATLAS detector

Atlas Collaboration,Aguilar Saavedra, Juan Antonio,Rodríguez Chala, Mikael

Published: 2023-07-01
Publisher: Springer Nature
DOI: 10.1007/JHEP07(2023)125
Repository: digibug.ugr.es
Language: en

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JHEP07(2023)125 Published for SISSA by Springer Received:April 25, 2023 Accepted:July 3, 2023 Published:July 14, 2023 Search for high-mass W γ and Zγ resonances using hadronic W/Z boson decays from 139 fb−1of pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: A search for high-mass charged and neutral bosons decaying to Wγ and Zγ final states is presented in this paper. The analysis uses a data sample of √s= 13 TeV proton-proton collisions with an integrated luminosity of 139fb−1collected by the ATLAS detector during LHC Run 2 operation. The sensitivity of the search is determined using models of the production and decay of spin-1 charged bosons and spin-0/2 neutral bosons. The range of resonance masses explored extends from 1.0TeV to 6.8TeV. At these high resonance masses, it is beneficial to target the hadronic decays of the Wand Zbosons because of their large branching fractions. The decay products of the high-momentum W/Z bosons are strongly collimated and boosted-boson tagging techniques are employed to improve the sensitivity. No evidence of a signal above the Standard Model backgrounds is observed, and upper limits on the production cross-sections of these bosons times their branching fractions to Wγ and Zγ are derived for various boson production models. Keywords: Hadron-Hadron Scattering ArXiv ePrint: 2304.11962 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP07(2023)125 JHEP07(2023)125 Contents 1 Introduction 1 2 ATLAS detector 2 3 Data collection and Monte Carlo event simulation 3 3.1 Data samples 3 3.2 Monte Carlo simulation 3 4 Event reconstruction 5 4.1 Particle reconstruction 5 4.2 Event selection and categorization 7 5 Signal and background modelling 9 5.1 SM background modelling 10 5.2 BSM signal modelling 11 6 Systematic uncertainties 11 7 Statistical analysis 13 8 Results 15 9 Conclusion 21 The ATLAS collaboration 27 1 Introduction Speculations about physics phenomena beyond those described by the Standard Model (SM) often result in the introduction of new bosons, due to either additional gauge symmetries or postulated extensions of the Higgs sector [1–3]. The high-energy proton-proton (pp) collisions provided by the Large Hadron Collider (LHC) make it possible to produce these new bosons with masses up to approximately one hundred times the mass of the SM Wand Zbosons. A broad range of beyond-the-SM (BSM) scenarios can therefore be tested with experiments at the LHC that search for high-mass charged and neutral bosons. Some of the BSM theories predict new charged X±and neutral X0bosons [3,4]. From an experimental perspective, Wγ or Zγ final states are attractive since a high-energy photon signature efficiently selects signal events and rejects background. For bosons with masses of the order of TeV, decays of the type X±→W±γor X0→Zγ result in a highly boosted Wor Zboson, where the decay products of such a boson are very collimated. This – 1 – JHEP07(2023)125 analysis targets the hadronic decay modes of Wand Zbosons to quark-antiquark pairs reconstructed as large-radius (large-R) jets that have a two-prong structure identified using jet-substructure information [5]. The complete reconstruction of the Wγ or Zγ final state can then be used to determine the mass and other properties of the new bosons. This paper presents searches for massive X±and X0bosons using 139 fb−1of pp collisions at a centre-of-mass energy (√s) of 13 TeV recorded with the ATLAS detector. The searches assume that the decay width of the heavy bosons is small compared to the experimental resolution, but are otherwise generic, looking for any excess of events above smooth SM background Wγ and Zγ invariant mass spectra. The measurements are compared with the predictions of models of the production and decay of spin-1 charged bosons and spin-0/2 neutral bosons. These include q¯ q0annihilation production of spin-1 X±→ W±γ, gluon-gluon fusion production of spin-0 X0→Zγ, and both gluon-gluon fusion and q¯qannihilation production of spin-2 X0→Zγ. A boson mass (mX) range from 1.0 to 6.8 TeV is covered by these searches. Previous searches for bosons of mass greater than 1.0 TeV decaying to Wγ and Zγ final states have been carried out at the LHC by the ATLAS [6–8] and CMS [9–12] Collaborations. Compared to the previous ATLAS search based on 36.1fb−1of Run 2 √s= 13 TeV pp collision data [8], the search reported in this paper achieves better sensitivity in part by including the entire dataset collected by the ATLAS experiment during Run 2. In addition to the four times larger dataset, the search is further improved by a factor of two via optimising the identification of the hadronic decays of highly boosted Wand Zbosons. 2 ATLAS detector The ATLAS experiment is a multipurpose detector [13] having a forward-backward symmetric cylindrical geometry and almost 4πcoverage in solid angle. The inner tracking detectors are immersed in a 2 T magnetic field produced by a thin superconducting solenoid. The tracking detectors cover a pseudorapidity1range |η|<2.5using a combination of silicon pixel detectors closest to the beam pipe, followed by silicon microstrip trackers and an outer transition radiation tracker. The innermost layer, known as the insertable B-layer [14,15], provides high-resolution hits at small radius to improve the tracking performance. The inner tracking detectors are surrounded by calorimeters and a muon spectrometer. The electromagnetic (EM) calorimeter is a lead/liquid-argon (LAr) sampling calorimeter with high granularity. Its barrel (|η|<1.475) and endcap (1.375 <|η|<3.2) components provide EM energy measurements of electrons and photons up to a pseudorapidity |η|= 3.2. In the range used for precision measurements of electrons and photons (|η|<2.5excluding a transition region 1.37 <|η|<1.52), the EM calorimeter is segmented into three layers along the shower depth, providing excellent measurements of photon properties and allow1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upward. Cylindrical 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 θas η=−ln tan(θ/2). – 2 – JHEP07(2023)125 ing precise photon identification. A steel/scintillator-tile hadronic calorimeter covers the central pseudorapidity range |η|<1.7. The endcap and forward regions are instrumented up to |η|= 4.9with LAr calorimeters for EM and hadronic energy measurements. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by superconducting air-core toroidal magnets. The field integral of the toroids ranges between 2.0and 6.0 T m across most of the detector. A set of precision chambers covers the region |η|<2.7 with three layers of monitored drift tubes, complemented by cathode-strip chambers in the forward region, where the background is highest. The muon trigger system covers the range |η|<2.4with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. Events are selected from the LHC’s pp bunch crossings, which occur at a rate of 40 MHz, by a first-level trigger implemented in custom hardware followed by a softwarebased high-level trigger that employs algorithms similar to those used in offline event reconstruction [16]. The first-level trigger selects events at a rate of 100 kHz by using a subset of detector information, with the high-level trigger then accepting events for offline analysis at the rate of about 1 kHz. An extensive software suite [17] is used in data simulation, in the reconstruction and analysis of real and simulated data, in detector operation, and in the trigger and data acquisition systems of the experiment. 3 Data collection and Monte Carlo event simulation 3.1 Data samples The data used for this analysis were collected by the ATLAS detector from 2015 to 2018 when the LHC provided pp collisions at √s= 13 TeV. Events were selected using a singlephoton trigger with loose photon identification requirements based upon EM calorimeter cluster shower-shape variables [18]. The trigger with a photon transverse energy (Eγ T) threshold of 140 GeV is fully efficient for events used in this search. In addition to the trigger selection, events are required to have at least one offline reconstructed signal photon matched to the object that fired the photon trigger. After requiring that all detector systems were recording high-quality data, the final dataset has an integrated luminosity of 139fb−1[19,20]. 3.2 Monte Carlo simulation Monte Carlo (MC) event generators were used to simulate SM background events and BSM heavy-boson signal events. These simulated event samples are used to optimize the event selection for the new-BSM-boson search and validate the parameterization of the templates used to fit the W/Z +γmass distributions. The largest background is due to single-photon production in association with jets (γ+jets) where the jet fulfils the boson-tagging criteria used to identity the large-Rjets from W/Z boson hadronic decays. These events were simulated using the Sherpa 2.2.2 generator [21], with up to two additional parton emissions included at next-to-leading-order (NLO) precision and up to four additional partons – 3 – JHEP07(2023)125 at leading-order (LO) precision. The matrix elements of these events were calculated with the Comix [22] and OpenLoops [23,24] libraries and then matched to the Sherpa parton shower [25] using the MEPS@NLO prescription [26–29]. The NNPDF3.0nnlo [30] parton distribution function (PDF) set was used to describe the parton distributions in the incoming protons. The irreducible SM background from the hadronic decays of W and Zbosons produced with a radiated photon was simulated at LO precision with the Sherpa 2.1.1 generator, and the parton distributions were modelled with the CT10 PDF set [31]. The SM t¯ t+γprocess was simulated with a matrix element at LO with MadGraph5_aMC@NLO 2.3.3 [32], followed by Pythia 8.186 [33] for the parton showering. The NNPDF2.3lo PDF set [34] and a set of tuned parameters called the A14 tune [35] were used for this t¯ t+γevent generation. Various samples of simulated BSM boson signal events are used to optimize the event selection criteria and to estimate the acceptance and efficiency for the detection of the X±→W±γand X0→Zγ signals. The production of the X±and X0bosons was modelled in a narrow-width approximation where the natural width of the bosons is much smaller than the expected experimental resolution of the invariant mass of the W±γand Zγ resonances. The production of a spin-0 boson decaying into Zγ was simulated in gluon-gluon fusion, gg →X0→Zγ [36]. This process was modelled with the MC generator Powheg Box v2 [37] at NLO precision as used for SM H→Zγ production, with the Higgs boson mass varied. The CT10 PDF set was used to generate these events. The parton showering was modelled with Pythia 8.212 [38] with the AZNLO tune [39]. The spin-1 resonance q¯ q0→X±→W±γsignal process event generation utilized the heavy-vector-triplet framework [3] for event kinematic modelling. The simulations of the spin-2 gg →X0→Zγ and q¯q→X0→Zγ signal events are based on a resonance model benchmarked from the Higgs characterization model framework with s-channel direct couplings between the spin-2 heavy resonance and the SM Zboson and the γ[40–42]. The MadGraph5_aMC@NLO v2.3.3 MC generator was used at LO precision, followed by Pythia 8.212 for the parton showering with the NNPDF2.3lo PDF set and the A14 tune. In these models the W(Z) boson is produced longitudinally (transversely) polarized. In samples with Pythia used for parton showering, decays of cand b-hadrons were simulated with EvtGen 1.2.0 [43]. The resulting MC event samples were processed using a detailed simulation of the ATLAS detector with Geant4 [44,45], and then passed through the same reconstruction algorithms as those used for the data. Effects of multiple pp collisions (pile-up) are included during reconstruction by overlaying inelastic events simulated with Pythia 8.186 using the A3 tune [46] and the NNPDF2.3lo PDF set. These minimum-bias events are overlaid with multiplicity distributions that approximately match the pile-up observed in the data. A pile-up reweighting approach is then performed to correct for the residual difference between simulation and data in the analysis. A summary of the MC generators used for the SM and BSM processes is given in table 1. – 4 – JHEP07(2023)125 Process Matrix element generator QCD order PDF Parton shower SM backgrounds SM γ+jets Sherpa 2.2.2 NLO NNPDF3.0nnlo Sherpa MEPS@NLO SM Wγ and Zγ Sherpa 2.1.1 LO CT10 Sherpa MEPS@LO SM t¯ t+γMadGraph5_aMC@NLO 2.3.3 LO NNPDF2.3lo Pythia 8.186 + EvtGen 1.2.0 Signals Spin-0 gg →X0→Zγ Powheg Box v2 NLO CT10 Pythia 8.212 + EvtGen 1.2.0 Spin-2 gg →X0→Zγ MadGraph5_aMC@NLO 2.3.3 LO NNPDF2.3lo Pythia 8.212 + EvtGen 1.2.0 Spin-2 q¯q→X0→Zγ MadGraph5_aMC@NLO 2.3.3 LO NNPDF2.3lo Pythia 8.212 + EvtGen 1.2.0 Spin-1 q¯ q0→X±→W±γMadGraph5_aMC@NLO 2.3.3 LO NNPDF2.3lo Pythia 8.212 + EvtGen 1.2.0 Table 1. Generators used for the simulation of SM backgrounds and BSM signals. 4 Event reconstruction Events are required to pass a loose identification photon trigger with a transverse energy (Eγ T) threshold of 140 GeV. Each of these events is then processed through offline particle reconstruction to identify high-ETphotons and to search for jets that pass a W/Z boson tagging requirement. The details of the photon, jet and W/Z boson reconstruction and identification are described in this section, along with the categorization applied to define the signal regions for the X±→W±γand X0→Zγ BSM boson searches. 4.1 Particle reconstruction Photon candidates are reconstructed from clusters of energy in the EM calorimeter and classified either as converted photons (those with a reconstructed vertex consistent with a γ→e+e−conversion) or as unconverted photons [47]. The photon identification algorithm uses shower shape variables measured from both the fine segmentation of the inner layers of the EM calorimeter and the outer layers of the EM and hadronic calorimeters to suppress background from photons from neutral meson decays in jets. For this analysis, tight photons are selected, with a measured photon identification efficiency greater than 90%(95%) for unconverted (converted) photon candidates with Eγ T>200 GeV [47]. To further reduce backgrounds from jets, an isolation requirement [47] is imposed on the photons, using the transverse energy (Eiso T) deposited in the EM calorimeter within a cone of size ∆R≡p(∆η)2+ (∆φ)2= 0.4centred on the photon candidate, excluding the photon transverse energy within an area ∆η×∆φ= 0.125 ×0.175. After corrections for photon energy leakage into the isolation cone and contributions from the underlying event and pile-up interactions, the photon isolation transverse energy Eiso Tis required to be less than 0.022 ×Eγ T+ 2.45 GeV. For the signal photons passing the reconstruction and identification requirements, the isolation efficiency is approximately 98%. Events selected for analysis must have at least one isolated photon candidate with Eγ T>200 GeV and |ηγ|<1.37. The ηrequirement is motivated by the fact that the photon from a signal event tends to be more central than those from the background. Jets are reconstructed using charged-particle tracks and calorimeter energy clusters, combining their information to optimize the measurement of the jet direction and energy [48]. The clustering method is that of the anti-ktalgorithm [49,50] with radius – 5 – JHEP07(2023)125 [GeV] J m 0 20 40 60 80 100 120 140 160 180 200 Normalized Distribution 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 γ Z→ 0 X→Spin-0 gg γ ± W→ ± X→' qSpin-1 q Simulation ATLAS = 13 TeVs = 1000 GeV X m (a) [GeV] J m 0 20 40 60 80 100 120 140 160 180 200 Normalized Distribution 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 γ Z→ 0 X→Spin-0 gg γ ± W→ ± X→' qSpin-1 q Simulation ATLAS = 13 TeVs = 4000 GeV X m (b) Figure 1. The jet mass distribution of large-Rjets originating from the hadronic decay of W and Zbosons produced from the decay of BSM bosons with mass (a) mX= 1000 GeV and (b) mX= 4000 GeV. The decays simulated are for the production models q¯ q0→X±→W±γwith a spin-1 resonance X±and gg →X0→Zγ with a spin-0 resonance X0. The Zbosons from Zγ decays of spin-2 resonances have jet mass distributions very similar to those shown for spin-0 resonances. parameter R= 1.0. In order to reduce contributions to the jet from pile-up, a trimming algorithm [51] is applied, which removes contributions from sub-jets clustered using the kt algorithm [52] with R= 0.2if they carry less than 5%of the jet’s transverse momentum. The jets are calibrated to the level of stable final-state particles using MC simulations [53]. Jets are selected if they have a transverse momentum pJ T>200 GeV and are within a pseudorapidity region |ηJ|<2.0, where the inner tracker has good charged-particle tracking coverage. The jets are also required to be separated from photons by ∆R(J, γ)>1.0. AWor Zboson produced by the decay of a boson with a mass of the order of a few TeV is highly boosted, with the di-quark decay products often forming a single large-Rjet. The characteristics of these di-quark jets can be used to distinguish W/Z bosons from a large background of jets originating from single quarks or gluons. The main distinguishing features are the jet mass and the presence of two-prong substructure within the jet. The jet mass (mJ) is calculated using a combination of particle four-momenta measured from charged-particle tracks and calorimeter cell energies [54]. The jet mass resolution ranges from 8%to 15%for jets with transverse momentum between 500 and 2500 GeV, respectively. Reconstructed jet mass distributions from simulated hadronic decays of W and Zbosons are shown in figure 1. The low mass tail is caused by events where the decay products from a Wor Zboson are not fully captured in the large-Rjet. The effects are different for Zand Wbosons since the Zboson from X0decay is transversely polarized whereas the Wboson from X±decay is longitudinally polarized. The jet mass is required to be in a window around the boson mass where the window’s size is optimized as a function of pJ Tto maximize the significance of the Wor Zboson selection over multijet backgrounds [55]. The size of the mass window increases from about 20 to 50 GeV as pJ T increases from 500 to 2500 GeV. For a large-Rjet with pT<500 GeV (pT>2500 GeV), the criterion defined at pT= 500 GeV (pT= 2500 GeV) is applied. – 6 – JHEP07(2023)125 The two-prong jet substructure from hadronic W/Z boson decays is identified using the energies and pairwise angular distances between clusters of particles within the large-R jets. This is quantified with a variable D2defined as the ratio 3/[2]3of N-point energy correlation functions Ncomputed from the jet constituents [56,57]. This variable exploits the sensitivity of 2to the hadronic shower produced from a single quark or gluon versus 3, which is sensitive to the two hadronic-jet clusters produced from the di-quark decay of W/Z bosons. Studies using simulations and data were used to choose the requirements on D2that optimize the W/Z boson identification significance [55]. The chosen upper limit on D2varies from 1.0 at low jet pTto slightly above 2.0 at high jet pTfor the W/Z hadronic jets used in this analysis. For the fraction of Zbosons that decay into b¯ b, the purity of the selection can be further improved by applying b-hadron identification requirements. A tagging algorithm is used that exploits the long lifetime of b-hadrons, which leads to tracks with large impact parameters and to secondary vertices. The outputs of three b-tagging techniques are combined into a single multivariate discriminant, called MV2c10, allowing the selection of b-hadrons with various efficiencies and background rejections [58]. This b-tagging algorithm is applied to variable-radius (VR) track-jets associated with the large-Rjet, as determined by the ghost-association algorithm [59]. The VR track-jets are reconstructed from ID tracks using the anti-ktalgorithm with a variable radius parameter Rthat ranges between 0.02 and 0.4 depending on the jet pT[60]. The tagging efficiency is determined with simulated t¯ tevents and corrected to the measurement in data [61]. A working point with a b-tagging efficiency of 70%is used. Two VR track-jets are required to pass this b-tagging requirement to select Z→b¯ bevents. 4.2 Event selection and categorization The events selected are required to have a photon with Eγ T>200 GeV and |ηγ|<1.37 and a jet with pJ T>200 GeV and |ηJ|<2.0, using the identification criteria described above. These selection criteria are called the “baseline selection” for this analysis. The pp interaction vertex selected for reconstruction of these physics objects is the one with the highest sum of the p2 Tof the tracks coming from the vertex. If multiple photons or jets satisfy the photon/jet selection criteria, those with the highest transverse energy or momentum are used. The search considers resonances with masses larger than 1 TeV. Below this mass, the signal selection efficiency drops significantly because of the criteria used to select the hadronic decays of the W/Z bosons, and searches for pp →X→W/Z +γ with leptonic W/Z boson decays are more sensitive. The search range is limited to 6.8 TeV using the highest-mass γ+jet event observed in data. The selected events are further sorted into exclusive categories of different Wand Zboson identification purities to maximize the signal sensitivity. For the X±→W±γsearch, two categories are defined according to the D2and jet mass criteria shown below, with the category designation indicated in parentheses. •pass D2and Wboson mass selection (D2), •fail D2and pass Wboson mass selection (WMASS). – 7 – JHEP07(2023)125 Events passed baseline selection Wboson mass window? D2substructure? D2 WMASS yes yes no Events passed baseline selection Zboson mass window? Double btagging? BTAG D2substructure? D2 ZMASS yes yes yes no no X±→W±γ Categorization X0→Zγ Categorization Figure 2. The flow charts of event categorization of X±→W±γand X0→Zγ. For the X0→Zγ search, three categories are defined, based on the b-tagging, D2and jet mass criteria shown below. •pass two b-tagged sub-jets and pass Zboson mass selection (BTAG), •fail two b-tagged sub-jets: pass D2and Zboson mass selection (D2), •fail two b-tagged sub-jets: fail D2and pass Zboson mass selection (ZMASS). Figure 2illustrates the categorization of X±→W±γand X0→Zγ events. The rejection of the dominant γ+jet background varies strongly among the categories, being highest in those using jet substructure and mass information. A further optimization of the signal sensitivity is implemented by varying the photon Eγ Tthreshold as a function of the invariant mass mJγ of the photon and large-Rjet, where the figure of merit is the statistical-only significance of the simulated BSM signal over the expected SM backgrounds, where the SM backgrounds are estimated from the simulated background samples described in section 3.2. This photon Eγ Toptimization is done separately for each of the event categories, taking advantage of the large difference in photon and jet kinematics between signal and background. The photon Eγ Tthreshold increases with mJγ, varying from about 300 to 1200 GeV. This results in a small loss of signal efficiency, but a very large suppression of the SM backgrounds. Figure 3shows the total signal selection efficiencies after optimization of the photon Eγ Tthresholds, and also the contributions to the signal selection from each of the individual categories. The BTAG category has the lowest efficiency but the highest signal purity. The spin-2 Zγ channel with gg production mode – 8 – JHEP07(2023)125 Channel BTAG D2 VMASS Spin-0 gg →X0→Zγ 436 5659 20728 Spin-2 gg →X0→Zγ 436 10772 32281 Spin-2 q¯q→X0→Zγ 436 5618 18264 Spin-1 q¯ q0→X±→W±γ— 6373 25146 Table 3. Data yields in various categories defined for the four search channels. p-value reflects the possibility of background to produce a signal-like excess larger than that found in the fit to the data, which is reported as the significance according to the normal distribution. Beside the significance, an exclusion of the signal model is derived and presented as the 95% confidence level (CL) upper limit on the resonance production cross-section times branching fraction of X→W/Z +γfor hadronic decay of the W/Z bosons. Similar to the p-value, the upper limit is also calculated from PLR distributions but with a running POI value to indicate various signal cross-section hypotheses. The CLs approach [72,73] is used for the limit calculation. The limits are calculated in the low resonance mass regions at 20 GeV steps and are based on the asymptotic approach. In the high resonance mass region, limits are derived by using the pseudo-experiment sampling method. To obtain smooth expected limit bands, the expected limits and the corresponding bands are calculated at 500 GeV steps in the high resonance mass region while the observed ones are obtained at 100 GeV steps. Upper limits on σ(pp →X)×B(X→W/Z +γ) are derived by assuming the branching fractions of Wand Zbosons to hadrons to be 67.41% [74] and 69.91% [74] respectively. 8 Results Table 3presents the observed number of events in different categories after the final event selection. The yields quoted are for mJγ ≥800 GeV in the BTAG and D2 categories and for mJγ ≥1000 GeV in the VMASS (ZMASS or WMASS) categories. The BTAG categories are defined in the same way for the three Zsignal hypotheses, while for the D2 and VMASS categories the selection criteria for the photon and jet are chosen differently for each channel. The latter optimizes the signal significance by exploiting differences in the W/Z +γproduction angular distributions and in the decays of the longitudinally polarized Wbosons and transversely polarized Zbosons. The mJγ distributions in different categories are shown in figures 4–7for the four signal channels. The background-only fit result is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal mass hypotheses are also plotted, where the signal cross-sections correspond to the expected upper limits obtained in this analysis. For the BTAG category, the fit range is limited to below 3200 GeV due to the significant loss of sensitivity because of the decrease in b-tagging efficiency beyond that range, while for other categories the fit upper boundary is 7000 GeV. The bottom panel presents the binned local significance (filled bars) from a comparison – 15 – JHEP07(2023)125 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ ± W→ ± X→' qSpin-1 q D2 category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (a) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ ± W→ ± X→' qSpin-1 q WMASS category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (b) Figure 4. The mJγ distributions of data events selected for the spin-1 q¯ q0→X±→W±γsearch in the (a) D2 and (b) WMASS categories. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits are shown as dashed lines. The bottom panel presents the binned local significance (filled bars) from a comparison of the data with the background fit using a Poisson model [75]. of the data with the background fit using a Poisson model [75]. The background-only model fits the data well, with most of the deviations of the data from the background-only hypothesis having a local significance below two standard deviations. Having found no significant deviation of the data from the SM background predictions, upper limits on signal cross-sections are calculated at a 95% confidence level for each of the four search channels. The observed cross-section limits (solid curves) are presented in figure 8, along with the expected limits (dotted curves) obtained by assuming only SM backgrounds. The limits range between approximately 0.05 fb and 10 fb for mXbetween 1 and 6.8 TeV. The oneand two-standard-deviation bands around the expected limits cover the observed limits almost everywhere, which is consistent with the observation that the data agree well with the background-only expectations. The largest deviation of the observed cross-section limit above the expectation is 2.5σfor spin-0 gg →X0→Zγ production from gluon-gluon fusion at mX= 3640 GeV. – 16 – JHEP07(2023)125 2− 10 1− 10 1 10 2 10 3 10 4 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 2000 GeV) X Signal (m = 3000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-0 gg BTAG category 1000 1500 2000 2500 3000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (a) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-0 gg D2 category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (b) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-0 gg ZMASS category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (c) Figure 5. The mJγ distributions of data events selected for the spin-0 gg →X0→Zγ search in the (a) BTAG, (b) D2, and (c) ZMASS categories. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits are shown as dashed lines. The bottom panel presents the binned local significance (filled bars) from a comparison of the data with the background fit using a Poisson model [75]. – 17 – JHEP07(2023)125 2− 10 1− 10 1 10 2 10 3 10 4 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 2000 GeV) X Signal (m = 3000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-2 gg BTAG category 1000 1500 2000 2500 3000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (a) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-2 gg D2 category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (b) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-2 gg ZMASS category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (c) Figure 6. The mJγ distributions of data events selected for the spin-2 gg →X0→Zγ search in the (a) BTAG, (b) D2, and (c) ZMASS categories. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits are shown as dashed lines. The bottom panel presents the binned local significance (filled bars) from a comparison of the data with the background fit using a Poisson model [75]. – 18 – JHEP07(2023)125 2− 10 1− 10 1 10 2 10 3 10 4 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 2000 GeV) X Signal (m = 3000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→ qSpin-2 q BTAG category 1000 1500 2000 2500 3000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (a) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→ qSpin-2 q D2 category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (b) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 Events / 40 GeV Data σ 1±Background fit = 1500 GeV) X Signal (m = 3000 GeV) X Signal (m = 6000 GeV) X Signal (m ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→ qSpin-2 q ZMASS category 1000 2000 3000 4000 5000 6000 7000 [GeV] γJ m 3−2−1− 0 1 2 3 Significance (c) Figure 7. The mJγ distributions of data events selected for the spin-2 q¯q→X0→Zγ search in the (a) BTAG, (b) D2, and (c) ZMASS categories. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines. The bottom panel presents the binned local significance (filled bars) from a comparison of the data with the background fit using a Poisson model [75]. – 19 – JHEP07(2023)125 1000 2000 3000 4000 5000 6000 7000 [GeV] X m 2− 10 1− 10 1 10 2 10 ) [fb]γ Z→ B(X× X)→(ppσ95% CL limit on ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-0 gg Observed Expected σ 1±Expected σ 2±Expected (a) 1000 2000 3000 4000 5000 6000 7000 [GeV] X m 2− 10 1− 10 1 10 2 10 ) [fb]γ Z→ B(X× X)→(ppσ95% CL limit on ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→Spin-2 gg Observed Expected σ 1±Expected σ 2±Expected (b) 1000 2000 3000 4000 5000 6000 7000 [GeV] X m 2− 10 1− 10 1 10 2 10 ) [fb]γ Z→ B(X× X)→(ppσ95% CL limit on ATLAS -1 = 13 TeV, 139 fbs γ Z→ 0 X→ qSpin-2 q Observed Expected σ 1±Expected σ 2±Expected (c) 1000 2000 3000 4000 5000 6000 7000 [GeV] X m 2− 10 1− 10 1 10 2 10 ) [fb]γ W→ B(X× X)→(ppσ95% CL limit on ATLAS -1 = 13 TeV, 139 fbs γ ± W→ ± X→' qSpin-1 q Observed Expected σ 1±Expected σ 2±Expected (d) Figure 8. The 95% CL upper limits on σ(pp →X)×B(X→W/Zγ)as a function of mXfor (a) spin-0 gg →X0→Zγ, (b) spin-2 gg →X0→Zγ, (c) spin-2 q¯q→X0→Zγ and (d) spin-1 q¯ q0→X±→W±γ. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1σ(2σ) uncertainty band presented as the green (yellow) band. Small discontinuities in pp →X0→Zγ limits are due to dropping the BTAG category from the limit calculation for mass points with mX>3000 GeV. Limits for mX<4000 GeV are derived with the asymptotic approach, while the ones for higher masses are calculated with the pseudoexperiment sampling method. – 20 – JHEP07(2023)125 9 Conclusion Results of searches for high-mass bosons decaying to Wγ and Zγ final states are presented, using 139fb−1of √s= 13 TeV pp collision data collected with the ATLAS detector during the operation of the LHC from 2015 to 2018. The analysis maximizes the sensitivity of the search by selecting events passing a high-ETphoton trigger and identifying jets from the hadronic decays of highly boosted Wand Zbosons. Distributions of the invariant mass of the photon-jet pairs in the mass range from 1.0 to 6.8 TeV are used to search for X±→W±γ and X0→Zγ signals above a smoothly falling SM background. No evidence of a new resonance is found, and 95% confidence-level upper limits on the resonance production cross-section times decay branching fraction are set. These vary from about 10 to 0.05 fb as the heavy-boson mass increases from 1.0 to 6.8 TeV. Individual studies are carried out for resonances with spin 0, 1, and 2 produced via gluon-gluon fusion and q¯qannihilation, currently providing the most stringent exclusion limits for these processes. Due to improved analysis techniques, the search sensitivity at high mass has been improved by a factor of two relative to that expected from the increase in integrated luminosity of the analysed data. Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We 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; IN2P3CNRS 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; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. – 21 – JHEP07(2023)125 The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. 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Jones 89, J. Jovicevic 14, X. Ju 16a, J.J. Junggeburth 34, A. Juste Rozas 12,u, S. Kabana 134e, A. Kaczmarska 83, M. Kado 72a,72b, H. Kagan 116, M. Kagan 140, A. Kahn39, A. Kahn 125, C. Kahra 97, T. Kaji 164, E. Kajomovitz 147, C.W. Kalderon 27, A. Kamenshchikov 35, M. Kaneda 150, N.J. Kang 133, S. Kang 78, Y. Kano 108, D. Kar 31f, K. Karava 123, M.J. Kareem 153b, I. Karkanias 149, S.N. Karpov 36, Z.M. Karpova 36, V. Kartvelishvili 88, A.N. Karyukhin 35, E. Kasimi 149, C. Kato 60d, J. Katzy 46, K. Kawade 137, K. Kawagoe 86, T. Kawaguchi 108, T. Kawamoto 132, G. Kawamura53, E.F. Kay 161, F.I. Kaya 155, S. Kazakos 12, V.F. Kazanin 35, Y. Ke 142, J.M. Keaveney 31a, R. Keeler 161, J.S. Keller 32, A.S. Kelly93, D. Kelsey 143, J.J. Kempster 19, J. Kendrick 19, K.E. Kennedy 39, O. Kepka 128, S. Kersten 167, B.P. Kerševan 90, S. Ketabchi Haghighat 152, M. Khandoga 124, A. Khanov 118, A.G. Kharlamov 35, T. Kharlamova 35, E.E. Khoda 135, T.J. Khoo 17, G. Khoriauli 162, J. Khubua 146b, S. Kido 81, M. Kiehn 34, A. Kilgallon 120, E. Kim 151, Y.K. Kim 37, N. Kimura 93, A. Kirchhoff 53, D. Kirchmeier 48, C. Kirfel 22, J. Kirk 131, A.E. Kiryunin 107, T. Kishimoto 150, D.P. Kisliuk152, C. Kitsaki 9, O. Kivernyk 22, – 32 – JHEP07(2023)125 T. Klapdor-Kleingrothaus 52, M. Klassen 61a, C. Klein 32, L. Klein 162, M.H. Klein 103, M. Klein 89, U. Klein 89, P. Klimek 34, A. Klimentov 27, F. Klimpel 107, T. Klingl 22, T. Klioutchnikova 34, F.F. Klitzner 106, P. Kluit 111, S. Kluth 107, E. Kneringer 76, T.M. Knight 152, A. Knue 52, D. Kobayashi86, R. Kobayashi 84, M. Kobel 48, M. Kocian 140, T. Kodama150, P. Kodyš 130, D.M. Koeck 143, P.T. Koenig 22, T. Koffas 32, N.M. Köhler 34, M. Kolb 132, I. Koletsou 4, T. Komarek 119, K. Köneke 52, A.X.Y. Kong 1, T. Kono 115, V. Konstantinides93, N. Konstantinidis 93, B. Konya 95, R. Kopeliansky 65, S. Koperny 82a, K. Korcyl 83, K. Kordas 149, G. Koren 148, A. Korn 93, S. Korn 53, I. Korolkov 12, E.V. Korolkova136, N. Korotkova 35, B. Kortman 111, O. Kortner 107, S. Kortner 107, W.H. Kostecka 112, V.V. Kostyukhin 138,35, A. Kotsokechagia 64, A. Kotwal 49, A. Koulouris 34, A. Kourkoumeli-Charalampidi 70a,70b, C. Kourkoumelis 8, E. Kourlitis 5, O. Kovanda 143, R. Kowalewski 161, W. Kozanecki 132, A.S. Kozhin 35, V.A. Kramarenko 35, G. Kramberger 90, P. Kramer 97, D. Krasnopevtsev 60a, M.W. Krasny 124, A. Krasznahorkay 34, J.A. Kremer 97, J. Kretzschmar 89, K. Kreul 17, P. Krieger 152, F. Krieter 106, S. Krishnamurthy 100, A. Krishnan 61b, M. Krivos 130, K. Krizka 16a, K. Kroeninger 47, H. Kroha 107, J. Kroll 128, J. Kroll 125, K.S. Krowpman 104, U. Kruchonak 36, H. Krüger 22, N. Krumnack78, M.C. Kruse 49, J.A. Krzysiak 83, A. Kubota 151, O. Kuchinskaia 35, S. Kuday 3a, D. Kuechler 46, J.T. Kuechler 46, S. Kuehn 34, T. Kuhl 46, V. Kukhtin 36, Y. Kulchitsky 35,a, S. Kuleshov 134d, M. Kumar 31f, N. Kumari 99, M. Kuna 58, A. Kupco 128, T. Kupfer47, O. Kuprash 52, H. Kurashige 81, L.L. Kurchaninov 153a, Y.A. Kurochkin 35, A. Kurova 35, M.G. Kurth13a,13d, E.S. Kuwertz 34, M. Kuze 151, A.K. Kvam 135, J. Kvita 119, T. Kwan 101, K.W. Kwok 62a, C. Lacasta 159, F. Lacava 72a,72b, H. Lacker 17, D. Lacour 124, N.N. Lad 93, E. Ladygin 36, R. Lafaye 4, B. Laforge 124, T. Lagouri 134e, S. Lai 53, I.K. Lakomiec 82a, N. Lalloue 58, J.E. Lambert 117, S. Lammers 65, W. Lampl 6, C. Lampoudis 149, E. Lançon 27, U. Landgraf 52, M.P.J. Landon 91, V.S. Lang 52, J.C. Lange 53, R.J. Langenberg 100, A.J. Lankford 156, F. Lanni 27, K. Lantzsch 22, A. Lanza 70a, A. Lapertosa 55b,55a, J.F. Laporte 132, T. Lari 68a, F. Lasagni Manghi 21b, M. Lassnig 34, V. Latonova 128, T.S. Lau 62a, A. Laudrain 97, A. Laurier 32, M. Lavorgna 69a,69b, S.D. Lawlor 92, Z. Lawrence 98, M. Lazzaroni 68a,68b, B. Le98, B. Leban 90, A. Lebedev 78, M. LeBlanc 34, T. LeCompte 5, F. Ledroit-Guillon 58, A.C.A. Lee93, G.R. Lee 15, L. Lee 59, S.C. Lee 145, S. Lee 78, L.L. Leeuw 31c, B. Lefebvre 153a, H.P. Lefebvre 92, M. Lefebvre 161, C. Leggett 16a, K. Lehmann 139, N. Lehmann 18, G. Lehmann Miotto 34, W.A. Leight 46, A. Leisos 149,t, M.A.L. Leite 79d, C.E. Leitgeb 46, R. Leitner 130, K.J.C. Leney 42, T. Lenz 22, S. Leone 71a, C. Leonidopoulos 50, A. Leopold 141, C. Leroy 105, R. Les 104, C.G. Lester 30, M. Levchenko 35, J. Levêque 4, D. Levin 103, L.J. Levinson 165, D.J. Lewis 19, B. Li 13b, B. Li 60b, C. Li60a, C-Q. Li 60c,60d, H. Li 60a, H. Li 60b, H. Li 60b, J. Li 60c, K. Li 135, L. Li 60c, M. Li 13a,13d, Q.Y. Li 60a, S. Li 60d,60c,d, T. Li 60b, X. Li 46, Y. Li 46, Z. Li 60b, Z. Li 123, Z. Li 101, Z. Li 89, Z. Liang 13a, M. Liberatore 46, B. Liberti 73a, K. Lie 62c, J. Lieber Marin 79b, K. Lin 104, R.A. Linck 65, R.E. Lindley 6, J.H. Lindon 2, A. Linss 46, E. Lipeles 125, A. Lipniacka 15, T.M. Liss 158,ag, A. Lister 160, J.D. Little 7, B. Liu 13a, B.X. Liu 139, J.B. Liu 60a, J.K.K. Liu 37, K. Liu 60d,60c, – 33 – JHEP07(2023)125 M. Liu 60a, M.Y. Liu 60a, P. Liu 13a, X. Liu 60a, Y. Liu 46, Y. Liu 13c,13d, Y.L. Liu 103, Y.W. Liu 60a, M. Livan 70a,70b, J. Llorente Merino 139, S.L. Lloyd 91, E.M. Lobodzinska 46, P. Loch 6, S. Loffredo 73a,73b, T. Lohse 17, K. Lohwasser 136, M. Lokajicek 128,∗, J.D. Long 158, I. Longarini 72a,72b, L. Longo 34, R. Longo 158, I. Lopez Paz 12, A. Lopez Solis 46, J. Lorenz 106, N. Lorenzo Martinez 4, A.M. Lory 106, A. Lösle 52, X. Lou 45a,45b, X. Lou 13a,13d, A. Lounis 64, J. Love 5, P.A. Love 88, J.J. Lozano Bahilo 159, G. Lu 13a,13d, M. Lu 60a, S. Lu 125, Y.J. Lu 63, H.J. Lubatti 135, C. Luci 72a,72b, F.L. Lucio Alves 13c, A. Lucotte 58, F. Luehring 65, I. Luise 142, L. Luminari72a, O. Lundberg 141, B. Lund-Jensen 141, N.A. Luongo 120, M.S. Lutz 148, D. Lynn 27, H. Lyons89, R. Lysak 128, E. Lytken 95, F. Lyu 13a, V. Lyubushkin 36, T. Lyubushkina 36, H. Ma 27, L.L. Ma 60b, Y. Ma 93, D.M. Mac Donell 161, G. Maccarrone 51, C.M. Macdonald 136, J.C. MacDonald 136, R. Madar 38, W.F. Mader 48, M. Madugoda Ralalage Don 118, N. Madysa 48, J. Maeda 81, T. Maeno 27, M. Maerker 48, V. Magerl 52, J. Magro 66a,66c, D.J. Mahon 39, C. Maidantchik 79b, A. Maio 127a,127b,127d, K. Maj 82a, O. Majersky 26a, S. Majewski 120, N. Makovec 64, V. Maksimovic 14, B. Malaescu 124, Pa. Malecki 83, V.P. Maleev 35, F. Malek 58, D. Malito 41b,41a, U. Mallik 77, C. Malone 30, S. Maltezos9, S. Malyukov36, J. Mamuzic 159, G. Mancini 51, J.P. Mandalia 91, I. Mandić 90, L. Manhaes de Andrade Filho 79a, I.M. Maniatis 149, M. Manisha 132, J. Manjarres Ramos 48, K.H. Mankinen 95, A. Mann 106, A. Manousos 76, B. Mansoulie 132, I. Manthos 149, S. Manzoni 111, A. Marantis 149,t, G. Marchiori 124, M. Marcisovsky 128, L. Marcoccia 73a,73b, C. Marcon 95, M. Marjanovic 117, Z. Marshall 16a, S. Marti-Garcia 159, T.A. Martin 163, V.J. Martin 50, B. Martin dit Latour 15, L. Martinelli 72a,72b, M. Martinez 12,u, P. Martinez Agullo 159, V.I. Martinez Outschoorn 100, S. Martin-Haugh 131, V.S. Martoiu 25b, A.C. Martyniuk 93, A. Marzin 34, S.R. Maschek 107, L. Masetti 97, T. Mashimo 150, J. Masik 98, A.L. Maslennikov 35, L. Massa 21b, P. Massarotti 69a,69b, P. Mastrandrea 71a,71b, A. Mastroberardino 41b,41a, T. Masubuchi 150, D. Matakias27, T. Mathisen 157, A. Matic 106, N. Matsuzawa150, J. Maurer 25b, B. Maček 90, D.A. Maximov 35, R. Mazini 145, I. Maznas 149, S.M. Mazza 133, C. Mc Ginn 27, J.P. Mc Gowan 101, S.P. Mc Kee 103, T.G. McCarthy 107, W.P. McCormack 16a, E.F. McDonald 102, A.E. McDougall 111, J.A. Mcfayden 143, G. Mchedlidze 146b, M.A. McKay42, K.D. McLean 161, S.J. McMahon 131, P.C. McNamara 102, R.A. McPherson 161,x, J.E. Mdhluli 31f, Z.A. Meadows 100, S. Meehan 34, T. Megy 38, S. Mehlhase 106, A. Mehta 89, B. Meirose 43, D. Melini 147, B.R. Mellado Garcia 31f, A.H. Melo 53, F. Meloni 46, A. Melzer 22, E.D. Mendes Gouveia 127a, A.M. Mendes Jacques Da Costa 19, H.Y. Meng 152, L. Meng 34, S. Menke 107, M. Mentink 34, E. Meoni 41b,41a, C. Merlassino 123, P. Mermod 54,∗, L. Merola 69a,69b, C. Meroni 68a, G. Merz103, O. Meshkov 35, J.K.R. Meshreki 138, J. Metcalfe 5, A.S. Mete 5, C. Meyer 65, J-P. Meyer 132, M. Michetti 17, R.P. Middleton 131, L. Mijović 50, G. Mikenberg 165, M. Mikestikova 128, M. Mikuž 90, H. Mildner 136, A. Milic 152, C.D. Milke 42, D.W. Miller 37, L.S. Miller 32, A. Milov 165, D.A. Milstead45a,45b, T. Min13c, A.A. Minaenko 35, I.A. Minashvili 146b, L. Mince 57, A.I. Mincer 114, B. Mindur 82a, M. Mineev 36, Y. Minegishi150, Y. Mino 84, L.M. Mir 12, M. Miralles Lopez 159, M. Mironova 123, T. Mitani 164, V.A. Mitsou 159, – 34 – JHEP07(2023)125 M. Mittal60c, O. Miu 152, P.S. Miyagawa 91, Y. Miyazaki86, A. Mizukami 80, J.U. Mjörnmark 95, T. Mkrtchyan 61a, M. Mlynarikova 112, T. Moa 45a,45b, S. Mobius 53, K. Mochizuki 105, P. Moder 46, P. Mogg 106, A.F. Mohammed 13a,13d, S. Mohapatra 39, G. Mokgatitswane 31f, B. Mondal 138, S. Mondal 129, K. Mönig 46, E. Monnier 99, L. Monsonis Romero159, A. Montalbano 139, J. Montejo Berlingen 34, M. Montella 116, F. Monticelli 87, N. Morange 64, A.L. Moreira De Carvalho 127a, M. Moreno Llácer 159, C. Moreno Martinez 12, P. Morettini 55b, S. Morgenstern 163, D. Mori 139, M. Morii 59, M. Morinaga 150, V. Morisbak 122, A.K. Morley 34, A.P. Morris 93, L. Morvaj 34, P. Moschovakos 34, B. Moser 111, M. Mosidze146b, T. Moskalets 52, P. Moskvitina 110, J. Moss 29,o, E.J.W. Moyse 100, S. Muanza 99, J. Mueller 126, D. Muenstermann 88, R. Müller 18, G.A. Mullier 95, J.J. Mullin125, D.P. Mungo 68a,68b, J.L. Munoz Martinez 12, F.J. Munoz Sanchez 98, M. Murin 98, P. Murin 26b, W.J. Murray 163,131, A. Murrone 68a,68b, J.M. Muse 117, M. Muškinja 16a, C. Mwewa 27, A.G. Myagkov 35,a, A.J. Myers 7, A.A. Myers126, G. Myers 65, M. Myska 129, B.P. Nachman 16a, O. Nackenhorst 47, A. Nag 48, K. Nagai 123, K. Nagano 80, J.L. Nagle 27, E. Nagy 99, A.M. Nairz 34, Y. Nakahama 108, K. Nakamura 80, H. Nanjo 121, F. Napolitano 61a, R. Narayan 42, E.A. Narayanan 109, I. Naryshkin 35, M. Naseri 32, C. Nass 22, T. Naumann 46, G. Navarro 20a, J. Navarro-Gonzalez 159, R. Nayak 148, P.Y. Nechaeva 35, F. Nechansky 46, T.J. Neep 19, A. Negri 70a,70b, M. Negrini 21b, C. Nellist 110, C. Nelson 101, K. Nelson 103, S. Nemecek 128, M. Nessi 34,g, M.S. Neubauer 158, F. Neuhaus 97, J. Neundorf 46, R. Newhouse 160, P.R. Newman 19, C.W. Ng 126, Y.S. Ng17, Y.W.Y. Ng 156, B. Ngair 33e, H.D.N. Nguyen 105, R.B. Nickerson 123, R. Nicolaidou 132, D.S. Nielsen 40, J. Nielsen 133, M. Niemeyer 53, N. Nikiforou 10, V. Nikolaenko 35,a, I. Nikolic-Audit 124, K. Nikolopoulos 19, P. Nilsson 27, H.R. Nindhito 54, A. Nisati 72a, N. Nishu 2, R. Nisius 107, T. Nitta 164, T. Nobe 150, D.L. Noel 30, Y. Noguchi 84, I. Nomidis 124, M.A. Nomura27, M.B. Norfolk 136, R.R.B. Norisam 93, J. Novak 90, T. Novak 46, O. Novgorodova 48, L. Novotny 129, R. Novotny 109, L. Nozka 119, K. Ntekas 156, E. Nurse93, F.G. Oakham 32,ai, J. Ocariz 124, A. Ochi 81, I. Ochoa 127a, J.P. Ochoa-Ricoux 134a, S. Oda 86, S. Odaka 80, S. Oerdek 157, A. Ogrodnik 82a, A. Oh 98, C.C. Ohm 141, H. Oide 151, R. Oishi 150, M.L. Ojeda 46, Y. Okazaki 84, M.W. O’Keefe89, Y. Okumura 150, A. Olariu25b, L.F. Oleiro Seabra 127a, S.A. Olivares Pino 134e, D. Oliveira Damazio 27, D. Oliveira Goncalves 79a, J.L. Oliver 156, M.J.R. Olsson 156, A. Olszewski 83, J. Olszowska 83,∗, Ö.O. Öncel 22, D.C. O’Neil 139, A.P. O’Neill 123, A. Onofre 127a,127e, P.U.E. Onyisi 10, R.G. Oreamuno Madriz112, M.J. Oreglia 37, G.E. Orellana 87, D. Orestano 74a,74b, N. Orlando 12, R.S. Orr 152, V. O’Shea 57, R. Ospanov 60a, G. Otero y Garzon 28, H. Otono 86, P.S. Ott 61a, G.J. Ottino 16a, M. Ouchrif 33d, J. Ouellette 27, F. Ould-Saada 122, A. Ouraou 132,∗, Q. Ouyang 13a, M. Owen 57, R.E. Owen 131, K.Y. Oyulmaz 11c, V.E. Ozcan 11c, N. Ozturk 7, S. Ozturk 11c, J. Pacalt 119, H.A. Pacey 30, K. Pachal 49, A. Pacheco Pages 12, C. Padilla Aranda 12, S. Pagan Griso 16a, G. Palacino 65, S. Palazzo 50, S. Palestini 34, M. Palka 82b, P. Palni 82a, D.K. Panchal 10, C.E. Pandini 54, J.G. Panduro Vazquez 92, P. Pani 46, G. Panizzo 66a,66c, L. Paolozzi 54, C. Papadatos 105, S. Parajuli 42, A. Paramonov 5, C. Paraskevopoulos 9, D. Paredes Hernandez 62b, S.R. Paredes Saenz 123, – 35 – JHEP07(2023)125 B. Parida 165, T.H. Park 152, A.J. Parker 29, M.A. Parker 30, F. Parodi 55b,55a, E.W. Parrish 112, J.A. Parsons 39, U. Parzefall 52, L. Pascual Dominguez 148, V.R. Pascuzzi 16a, F. Pasquali 111, E. Pasqualucci 72a, S. Passaggio 55b, F. Pastore 92, P. Pasuwan 45a,45b, J.R. Pater 98, A. Pathak 166, J. Patton89, T. Pauly 34, J. Pearkes 140, M. Pedersen 122, L. Pedraza Diaz 110, R. Pedro 127a, T. Peiffer 53, S.V. Peleganchuk 35, O. Penc 128, C. Peng 62b, H. Peng 60a, M. Penzin 35, B.S. Peralva 79a, A.P. Pereira Peixoto 127a, L. Pereira Sanchez 45a,45b, D.V. Perepelitsa 27, E. Perez Codina 153a, M. Perganti 9, L. Perini 68a,68b,∗, H. Pernegger 34, S. Perrella 34, A. Perrevoort 111, K. Peters 46, R.F.Y. Peters 98, B.A. Petersen 34, T.C. Petersen 40, E. Petit 99, V. Petousis 129, C. Petridou 149, P. Petroff64, F. Petrucci 74a,74b, A. Petrukhin 138, M. Pettee 168, N.E. Pettersson 34, K. Petukhova 130, A. Peyaud 132, R. Pezoa 134f, L. Pezzotti 34, G. Pezzullo 168, T. Pham 102, P.W. Phillips 131, M.W. Phipps 158, G. Piacquadio 142, E. Pianori 16a, F. Piazza 68a,68b, A. Picazio 100, R. Piegaia 28, D. Pietreanu 25b, J.E. Pilcher 37, A.D. Pilkington 98, M. Pinamonti 66a,66c, J.L. Pinfold 2, C. Pitman Donaldson93, D.A. Pizzi 32, L. Pizzimento 73a,73b, A. Pizzini 111, M.-A. Pleier 27, V. Plesanovs52, V. Pleskot 130, E. Plotnikova36, P. Podberezko 35, R. Poettgen 95, R. Poggi 54, L. Poggioli 124, I. Pogrebnyak 104, D. Pohl 22, I. Pokharel 53, G. Polesello 70a, A. Poley 139,153a, A. Policicchio 72a,72b, R. Polifka 130, A. Polini 21b, C.S. Pollard 123, Z.B. Pollock 116, V. Polychronakos 27, D. Ponomarenko 35, L. Pontecorvo 34, S. Popa 25a, G.A. Popeneciu 25d, L. Portales 4, D.M. Portillo Quintero 153a, S. Pospisil 129, P. Postolache 25c, K. Potamianos 123, I.N. Potrap 36, C.J. Potter 30, H. Potti 1, T. Poulsen 46, J. Poveda 159, T.D. Powell 136, G. Pownall 46, M.E. Pozo Astigarraga 34, A. Prades Ibanez 159, P. Pralavorio 99, M.M. Prapa 44, S. Prell 78, D. Price 98, M. Primavera 67a, M.A. Principe Martin 96, M.L. Proffitt 135, N. Proklova 35, K. Prokofiev 62c, S. Protopopescu 27, J. Proudfoot 5, M. Przybycien 82a, D. Pudzha 35, P. Puzo64, D. Pyatiizbyantseva 35, J. Qian 103, Y. Qin 98, T. Qiu 91, A. Quadt 53, M. Queitsch-Maitland 34, G. Rabanal Bolanos 59, F. Ragusa 68a,68b, J.A. Raine 54, S. Rajagopalan 27, K. Ran 13a,13d, D.F. Rassloff 61a, D.M. Rauch 46, S. Rave 97, B. Ravina 57, I. Ravinovich 165, M. Raymond 34, A.L. Read 122, N.P. Readioff 136, D.M. Rebuzzi 70a,70b, G. Redlinger 27, K. Reeves 43, D. Reikher 148, A. Reiss97, A. Rej 138, C. Rembser 34, A. Renardi 46, M. Renda 25b, M.B. Rendel107, A.G. Rennie 57, S. Resconi 68a, M. Ressegotti 55b,55a, E.D. Resseguie 16a, S. Rettie 93, B. Reynolds116, E. Reynolds 19, M. Rezaei Estabragh 167, O.L. Rezanova 35, P. Reznicek 130, E. Ricci 75a,75b, R. Richter 107, S. Richter 46, E. Richter-Was 82b, M. Ridel 124, P. Rieck 107, P. Riedler 34, O. Rifki 46, M. Rijssenbeek 142, A. Rimoldi 70a,70b, M. Rimoldi 46, L. Rinaldi 21b,21a, T.T. Rinn 158, M.P. Rinnagel 106, G. Ripellino 141, I. Riu 12, P. Rivadeneira 46, J.C. Rivera Vergara 161, F. Rizatdinova 118, E. Rizvi 91, C. Rizzi 54, B.A. Roberts 163, B.R. Roberts 16a, S.H. Robertson 101,x, M. Robin 46, D. Robinson 30, C.M. Robles Gajardo134f, M. Robles Manzano 97, A. Robson 57, A. Rocchi 73a,73b, C. Roda 71a,71b, S. Rodriguez Bosca 61a, A. Rodriguez Rodriguez 52, A.M. Rodríguez Vera 153b, S. Roe34, A.R. Roepe-Gier 117, J. Roggel 167, O. Røhne 122, R.A. Rojas 161, B. Roland 52, C.P.A. Roland 65, J. Roloff 27, A. Romaniouk 35, M. Romano 21b, A.C. Romero Hernandez 158, N. Rompotis 89, – 36 – JHEP07(2023)125 M. Ronzani 114, L. Roos 124, S. Rosati 72a, B.J. Rosser 125, E. Rossi 152, E. Rossi 4, E. Rossi 69a,69b, L.P. Rossi 55b, L. Rossini 46, R. Rosten 116, M. Rotaru 25b, B. Rottler 52, D. Rousseau 64, D. Rousso 30, G. Rovelli 70a,70b, A. Roy 10, A. Rozanov 99, Y. Rozen 147, X. Ruan 31f, A.J. Ruby 89, T.A. Ruggeri 1, F. Rühr 52, A. Ruiz-Martinez 159, A. Rummler 34, Z. Rurikova 52, N.A. Rusakovich 36, H.L. Russell 34, L. Rustige 38, J.P. Rutherfoord 6, E.M. Rüttinger 136, M. Rybar 130, E.B. Rye 122, A. Ryzhov 35, J.A. Sabater Iglesias 46, P. Sabatini 159, L. Sabetta 72a,72b, H.F-W. Sadrozinski 133, F. Safai Tehrani 72a, B. Safarzadeh Samani 143, M. Safdari 140, S. Saha 101, M. Sahinsoy 107, A. Sahu 167, M. Saimpert 132, M. Saito 150, T. Saito 150, D. Salamani 34, G. Salamanna 74a,74b, A. Salnikov 140, J. Salt 159, A. Salvador Salas 12, D. Salvatore 41b,41a, F. Salvatore 143, A. Salzburger 34, D. Sammel 52, D. Sampsonidis 149, D. Sampsonidou 60d,60c, J. Sánchez 159, A. Sanchez Pineda 4, V. Sanchez Sebastian 159, H. Sandaker 122, C.O. Sander 46, I.G. Sanderswood 88, J.A. Sandesara 100, M. Sandhoff 167, C. Sandoval 20b, D.P.C. Sankey 131, M. Sannino 55b,55a, A. Sansoni 51, C. Santoni 38, H. Santos 127a,127b, S.N. Santpur 16a, A. Santra 165, K.A. Saoucha 136, J.G. Saraiva 127a,127d, J. Sardain 99, O. Sasaki 80, K. Sato 154, C. Sauer61b, F. Sauerburger 52, E. Sauvan 4, P. Savard 152,ai, R. Sawada 150, C. Sawyer 131, L. Sawyer 94, I. Sayago Galvan159, C. Sbarra 21b, A. Sbrizzi 21b,21a, T. Scanlon 93, J. Schaarschmidt 135, P. Schacht 107, D. Schaefer 37, U. Schäfer 97, A.C. Schaffer 64, D. Schaile 106, R.D. Schamberger 142, E. Schanet 106, C. Scharf 17, N. Scharmberg 98, V.A. Schegelsky 35, D. Scheirich 130, F. Schenck 17, M. Schernau 156, C. Schiavi 55b,55a, L.K. Schildgen 22, Z.M. Schillaci 24, E.J. Schioppa 67a,67b, M. Schioppa 41b,41a, B. Schlag 97, K.E. Schleicher 52, S. Schlenker 34, K. Schmieden 97, C. Schmitt 97, S. Schmitt 46, L. Schoeffel 132, A. Schoening 61b, P.G. Scholer 52, E. Schopf 123, M. Schott 97, J. Schovancova 34, S. Schramm 54, F. Schroeder 167, H-C. Schultz-Coulon 61a, M. Schumacher 52, B.A. Schumm 133, Ph. Schune 132, A. Schwartzman 140, T.A. Schwarz 103, Ph. Schwemling 132, R. Schwienhorst 104, A. Sciandra 133, G. Sciolla 24, F. Scuri 71a, F. Scutti102, C.D. Sebastiani 89, K. Sedlaczek 47, P. Seema 17, S.C. Seidel 109, A. Seiden 133, B.D. Seidlitz 27, T. Seiss 37, C. Seitz 46, J.M. Seixas 79b, G. Sekhniaidze 69a, S.J. Sekula 42, L. Selem 4, N. Semprini-Cesari 21b,21a, S. Sen 49, C. Serfon 27, L. Serin 64, L. Serkin 66a,66b, M. Sessa 74a,74b, H. Severini 117, S. Sevova 140, F. Sforza 55b,55a, A. Sfyrla 54, E. Shabalina 53, R. Shaheen 141, J.D. Shahinian 125, N.W. Shaikh 45a,45b, D. Shaked Renous 165, L.Y. Shan 13a, M. Shapiro 16a, A. Sharma 34, A.S. Sharma 1, S. Sharma 46, P.B. Shatalov 35, K. Shaw 143, S.M. Shaw 98, P. Sherwood 93, L. Shi 93, C.O. Shimmin 168, Y. Shimogama 164, J.D. Shinner 92, I.P.J. Shipsey 123, S. Shirabe 54, M. Shiyakova 36,am, J. Shlomi 165, M.J. Shochet 37, J. Shojaii 102, D.R. Shope 141, S. Shrestha 116, E.M. Shrif 31f, M.J. Shroff 161, E. Shulga 165, P. Sicho 128, A.M. Sickles 158, E. Sideras Haddad 31f, O. Sidiropoulou 34, A. Sidoti 21b, F. Siegert 48, Dj. Sijacki 14, J.M. Silva 19, M.V. Silva Oliveira 34, S.B. Silverstein 45a, S. Simion64, R. Simoniello 34, N.D. Simpson95, S. Simsek 11b, P. Sinervo 152, V. Sinetckii 35, S. Singh 139, S. Singh 152, S. Sinha 46, S. Sinha 31f, M. Sioli 21b,21a, I. Siral 120, S.Yu. Sivoklokov 35,∗, J. Sjölin 45a,45b, A. Skaf 53, E. Skorda 95, P. Skubic 117, M. Slawinska 83, K. Sliwa 155, V. Smakhtin165, B.H. Smart 131, – 37 – JHEP07(2023)125 J. Smiesko 130, S.Yu. Smirnov 35, Y. Smirnov 35, L.N. Smirnova 35,a, O. Smirnova 95, E.A. Smith 37, H.A. Smith 123, M. Smizanska 88, K. Smolek 129, A. Smykiewicz 83, A.A. Snesarev 35, H.L. Snoek 111, S. Snyder 27, R. Sobie 161,x, A. Soffer 148, F. Sohns 53, C.A. Solans Sanchez 34, E.Yu. Soldatov 35, U. Soldevila 159, A.A. Solodkov 35, S. Solomon 52, A. Soloshenko 36, O.V. Solovyanov 35, V. Solovyev 35, P. Sommer 136, H. Son 155, A. Sonay 12, W.Y. Song 153b, A. Sopczak 129, A.L. Sopio 93, F. Sopkova 26b, S. Sottocornola 70a,70b, R. Soualah 66a,66c, Z. Soumaimi 33e, D. South 46, S. Spagnolo 67a,67b, M. Spalla 107, M. Spangenberg 163, F. Spanò 92, D. Sperlich 52, T.M. Spieker 61a, G. Spigo 34, M. Spina 143, D.P. Spiteri 57, M. Spousta 130, A. Stabile 68a,68b, R. Stamen 61a, M. Stamenkovic 111, A. Stampekis 19, M. Standke 22, E. Stanecka 83, B. Stanislaus 34, M.M. Stanitzki 46, M. Stankaityte 123, B. Stapf 46, E.A. Starchenko 35, G.H. Stark 133, J. Stark 99,ac, D.M. Starko153b, P. Staroba 128, P. Starovoitov 61a, S. Stärz 101, R. Staszewski 83, G. Stavropoulos 44, P. Steinberg 27, A.L. Steinhebel 120, B. Stelzer 139,153a, H.J. Stelzer 126, O. Stelzer-Chilton 153a, H. Stenzel 56, T.J. Stevenson 143, G.A. Stewart 34, M.C. Stockton 34, G. Stoicea 25b, M. Stolarski 127a, S. Stonjek 107, A. Straessner 48, J. Strandberg 141, S. Strandberg 45a,45b, M. Strauss 117, T. Strebler 99, P. Strizenec 26b, R. Ströhmer 162, D.M. Strom 120, L.R. Strom 46, R. Stroynowski 42, A. Strubig 45a,45b, S.A. Stucci 27, B. Stugu 15, J. Stupak 117, N.A. Styles 46, D. Su 140, S. Su 60a, W. Su 60d,135,60c, X. Su 60a, K. Sugizaki 150, V.V. Sulin 35, M.J. Sullivan 89, D.M.S. Sultan 54, L. Sultanaliyeva 35, S. Sultansoy 3c, T. Sumida 84, S. Sun 103, S. Sun 166, X. Sun 98, O. Sunneborn Gudnadottir 157, C.J.E. Suster 144, M.R. Sutton 143, M. Svatos 128, M. Swiatlowski 153a, T. Swirski 162, I. Sykora 26a, M. Sykora 130, T. Sykora 130, D. Ta 97, K. Tackmann 46,v, A. Taffard 156, R. Tafirout 153a, R.H.M. Taibah 124, R. Takashima 85, K. Takeda 81, T. Takeshita 137, E.P. Takeva 50, Y. Takubo 80, M. Talby 99, A.A. Talyshev 35, K.C. Tam 62b, N.M. Tamir148, A. Tanaka 150, J. Tanaka 150, R. Tanaka 64, J. Tang60c, Z. Tao 160, S. Tapia Araya 78, S. Tapprogge 97, A. Tarek Abouelfadl Mohamed 104, S. Tarem 147, K. Tariq 60b, G. Tarna 25b, G.F. Tartarelli 68a, P. Tas 130, M. Tasevsky 128, E. Tassi 41b,41a, G. Tateno 150, Y. Tayalati 33e, G.N. Taylor 102, W. Taylor 153b, H. Teagle89, A.S. Tee 166, R. Teixeira De Lima 140, P. Teixeira-Dias 92, H. Ten Kate34, J.J. Teoh 111, K. Terashi 150, J. Terron 96, S. Terzo 12, M. Testa 51, R.J. Teuscher 152,x, N. Themistokleous 50, T. Theveneaux-Pelzer 17, O. Thielmann 167, D.W. Thomas92, J.P. Thomas 19, E.A. Thompson 46, P.D. Thompson 19, E. Thomson 125, E.J. Thorpe 91, Y. Tian 53, V. Tikhomirov 35,a, Yu.A. Tikhonov 35, S. Timoshenko35, P. Tipton 168, S. Tisserant 99, S.H. Tlou 31f, A. Tnourji 38, K. Todome 21b,21a, S. Todorova-Nova 130, S. Todt48, M. Togawa 80, J. Tojo 86, S. Tokár 26a, K. Tokushuku 80, E. Tolley 116, R. Tombs 30, M. Tomoto 80,108, L. Tompkins 140,q, P. Tornambe 100, E. Torrence 120, H. Torres 48, E. Torró Pastor 159, M. Toscani 28, C. Tosciri 37, J. Toth 99,w, D.R. Tovey 136, A. Traeet15, C.J. Treado 114, T. Trefzger 162, A. Tricoli 27, I.M. Trigger 153a, S. Trincaz-Duvoid 124, D.A. Trischuk 160, B. Trocmé 58, A. Trofymov 64, C. Troncon 68a, F. Trovato 143, L. Truong 31c, M. Trzebinski 83, A. Trzupek 83, F. Tsai 142, A. Tsiamis 149, P.V. Tsiareshka35,a, A. Tsirigotis 149,t, V. Tsiskaridze 142, E.G. Tskhadadze146a, – 38 – JHEP07(2023)125 M. Tsopoulou 149, Y. Tsujikawa 84, I.I. Tsukerman 35, V. Tsulaia 16a, S. Tsuno 80, O. Tsur147, D. Tsybychev 142, Y. Tu 62b, A. Tudorache 25b, V. Tudorache 25b, A.N. Tuna 34, S. Turchikhin 36, I. Turk Cakir 3a, R.J. Turner19, R. Turra 68a, P.M. Tuts 39, S. Tzamarias 149, P. Tzanis 9, E. Tzovara 97, K. Uchida150, F. Ukegawa 154, P.A. Ulloa Poblete 134d, G. Unal 34, M. Unal 10, A. Undrus 27, G. Unel 156, F.C. Ungaro 102, K. Uno 150, J. Urban 26b, P. Urquijo 102, G. Usai 7, R. Ushioda 151, M. Usman 105, Z. Uysal 11d, V. Vacek 129, B. Vachon 101, K.O.H. Vadla 122, T. Vafeiadis 34, C. Valderanis 106, E. Valdes Santurio 45a,45b, M. Valente 153a, S. Valentinetti 21b,21a, A. Valero 159, R.A. Vallance 19, A. Vallier 99,ac, J.A. Valls Ferrer 159, T.R. Van Daalen 135, P. Van Gemmeren 5, S. Van Stroud 93, I. Van Vulpen 111, M. Vanadia 73a,73b, W. Vandelli 34, M. Vandenbroucke 132, E.R. Vandewall 118, D. Vannicola 148, L. Vannoli 55b,55a, R. Vari 72a, E.W. Varnes 6, C. Varni 16a, T. Varol 145, D. Varouchas 64, K.E. Varvell 144, M.E. Vasile 25b, L. Vaslin38, G.A. Vasquez 161, F. Vazeille 38, D. Vazquez Furelos 12, T. Vazquez Schroeder 34, J. Veatch 53, V. Vecchio 98, M.J. Veen 111, I. Veliscek 123, L.M. Veloce 152, F. Veloso 127a,127c, S. Veneziano 72a, A. Ventura 67a,67b, A. Verbytskyi 107, M. Verducci 71a,71b, C. Vergis 22, M. Verissimo De Araujo 79b, W. Verkerke 111, A.T. Vermeulen 111, J.C. Vermeulen 111, C. Vernieri 140, P.J. Verschuuren 92, M. Vessella 100, M.L. Vesterbacka 114, M.C. Vetterli 139,ai, A. Vgenopoulos 149, N. Viaux Maira 134f, T. Vickey 136, O.E. Vickey Boeriu 136, G.H.A. Viehhauser 123, L. Vigani 61b, M. Villa 21b,21a, M. Villaplana Perez 159, E.M. Villhauer50, E. Vilucchi 51, M.G. Vincter 32, G.S. Virdee 19, A. Vishwakarma 50, C. Vittori 21b,21a, I. Vivarelli 143, V. Vladimirov163, E. Voevodina 107, M. Vogel 167, P. Vokac 129, J. Von Ahnen 46, E. Von Toerne 22, V. Vorobel 130, K. Vorobev 35, M. Vos 159, J.H. Vossebeld 89, M. Vozak 98, L. Vozdecky 91, N. Vranjes 14, M. Vranjes Milosavljevic 14, V. Vrba129,∗, M. Vreeswijk 111, R. Vuillermet 34, O. Vujinovic 97, I. Vukotic 37, S. Wada 154, C. Wagner100, W. Wagner 167, S. Wahdan 167, H. Wahlberg 87, R. Wakasa 154, M. Wakida 108, V.M. Walbrecht 107, J. Walder 131, R. Walker 106, S.D. Walker92, W. Walkowiak 138, A.M. Wang 59, A.Z. Wang 166, C. Wang 60a, C. Wang 60c, H. Wang 16a, J. Wang 62a, P. Wang 42, R.-J. Wang 97, R. Wang 59, R. Wang 112, S.M. Wang 145, S. Wang 60b, T. Wang 60a, W.T. Wang 77, W.X. Wang 60a, X. Wang 13c, X. Wang 158, X. Wang 60c, Y. Wang 60a, Z. Wang 103, C. Wanotayaroj 34, A. Warburton 101, C.P. Ward 30, R.J. Ward 19, N. Warrack 57, A.T. Watson 19, M.F. Watson 19, G. Watts 135, B.M. Waugh 93, A.F. Webb 10, C. Weber 27, M.S. Weber 18, S.A. Weber 32, S.M. Weber 61a, C. Wei60a, Y. Wei 123, A.R. Weidberg 123, J. Weingarten 47, M. Weirich 97, C. Weiser 52, T. Wenaus 27, B. Wendland 47, T. Wengler 34, S. Wenig 34, N. Wermes 22, M. Wessels 61a, K. Whalen 120, A.M. Wharton 88, A.S. White 59, A. White 7, M.J. White 1, D. Whiteson 156, L. Wickremasinghe 121, W. Wiedenmann 166, C. Wiel 48, M. Wielers 131, N. Wieseotte97, C. Wiglesworth 40, L.A.M. Wiik-Fuchs 52, D.J. Wilbern117, H.G. Wilkens 34, L.J. Wilkins 92, D.M. Williams 39, H.H. Williams125, S. Williams 30, S. Willocq 100, P.J. Windischhofer 123, I. Wingerter-Seez 4, F. Winklmeier 120, B.T. Winter 52, M. Wittgen140, M. Wobisch 94, A. Wolf 97, R. Wölker 123, J. Wollrath156, M.W. Wolter 83, – 39 – JHEP07(2023)125 H. Wolters 127a,127c, V.W.S. Wong 160, A.F. Wongel 46, S.D. Worm 46, B.K. Wosiek 83, K.W. Woźniak 83, K. Wraight 57, J. Wu 13a,13d, S.L. Wu 166, X. Wu 54, Y. Wu 60a, Z. Wu 132,60a, J. Wuerzinger 123, T.R. Wyatt 98, B.M. Wynne 50, S. Xella 40, L. Xia 13c, M. Xia 13b, J. Xiang 62c, X. Xiao 103, M. Xie 60a, X. Xie 60a, I. Xiotidis143, D. Xu 13a, H. Xu60a, H. Xu 60a, L. Xu 60a, R. Xu 125, T. Xu 60a, W. Xu 103, Y. Xu 13b, Z. Xu 60b, Z. Xu 140, B. Yabsley 144, S. Yacoob 31a, N. Yamaguchi 86, Y. Yamaguchi 151, M. Yamatani150, H. Yamauchi 154, T. Yamazaki 16a, Y. Yamazaki 81, J. Yan60c, S. Yan 123, Z. Yan 23, H.J. Yang 60c,60d, H.T. Yang 16a, S. Yang 60a, T. Yang 62c, X. Yang 60a, X. Yang 13a, Y. Yang 150, Z. Yang 60a,103, W-M. Yao 16a, Y.C. Yap 46, H. Ye 13c, J. Ye 42, S. Ye 27, I. Yeletskikh 36, M.R. Yexley 88, P. Yin 39, K. Yorita 164, K. Yoshihara 78, C.J.S. Young 52, C. Young 140, M. Yuan 103, R. Yuan 60b,j , X. Yue 61a, M. Zaazoua 33e, B. Zabinski 83, G. Zacharis 9, E. Zaid50, T. Zakareishvili 146b, N. Zakharchuk 32, S. Zambito 34, D. Zanzi 52, S.V. Zeißner 47, C. Zeitnitz 167, J.C. Zeng 158, D.T. Zenger Jr 24, O. Zenin 35, T. Ženiš 26a, S. Zenz 91, S. Zerradi 33a, D. Zerwas 64, B. Zhang 13c, D.F. Zhang 136, G. Zhang 13b, J. Zhang 5, K. Zhang 13a,13d, L. Zhang 13c, M. Zhang 158, R. Zhang 166, S. Zhang 103, X. Zhang 60c, X. Zhang 60b, Z. Zhang 64, P. Zhao 49, T. Zhao 60b, Y. Zhao 133, Z. Zhao 60a, A. Zhemchugov 36, Z. Zheng 140, D. Zhong 158, B. Zhou103, C. Zhou 166, H. Zhou 6, N. Zhou 60c, Y. Zhou6, C.G. Zhu 60b, C. Zhu 13a,13d, H.L. Zhu 60a, H. Zhu 13a, J. Zhu 103, Y. Zhu 60a, X. Zhuang 13a, K. Zhukov 35, V. Zhulanov 35, D. Zieminska 65, N.I. Zimine 36, S. Zimmermann 52,∗, J. Zinsser 61b, M. Ziolkowski 138, L. Živković 14, A. Zoccoli 21b,21a, K. Zoch 54, T.G. Zorbas 136, O. Zormpa 44, W. Zou 39, L. Zwalinski 34 1Department of Physics, University of Adelaide, Adelaide; Australia 2Department of Physics, University of Alberta, Edmonton AB; Canada 3 (a)Department of Physics, Ankara University, Ankara;(b)Istanbul Aydin University, Application and Research Center for Advanced Studies, Istanbul;(c)Division of Physics, TOBB University of Economics and Technology, Ankara; Türkiye 4LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 5High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 6Department of Physics, University of Arizona, Tucson AZ; United States of America 7Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 8Physics Department, National and Kapodistrian University of Athens, Athens; Greece 9Physics Department, National Technical University of Athens, Zografou; Greece 10 Department of Physics, University of Texas at Austin, Austin TX; United States of America 11 (a)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul;(b)Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul;(c)Department of Physics, Bogazici University, Istanbul;(d)Department of Physics Engineering, Gaziantep University, Gaziantep; Türkiye 12 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 13 (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)University of Chinese Academy of Science (UCAS), Beijing; China 14 Institute of Physics, University of Belgrade, Belgrade; Serbia 15 Department for Physics and Technology, University of Bergen, Bergen; Norway 16 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA;(b)University of California, Berkeley CA; United States of America 17 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany – 40 –