Search for resonant and non-resonant Higgs boson pair production in the b¯b + − decay channel using 13TeV pp collision data from the ATLAS detector

JHEP07(2023)040 Published for SISSA by Springer Received:September 23, 2022 Revised:December 20, 2022 Accepted:February 5, 2023 Published:July 5, 2023 Search for resonant and non-resonant Higgs boson pair production in the b¯ bτ+τ−decay channel using 13 TeV pp collision data from the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: A search for Higgs boson pair production in events with two b-jets and two τ-leptons is presented, using a proton–proton collision dataset with an integrated luminosity of 139 fb−1collected at √s= 13 TeV by the ATLAS experiment at the LHC. Higgs boson pairs produced non-resonantly or in the decay of a narrow scalar resonance in the mass range from 251 to 1600GeV are targeted. Events in which at least one τ-lepton decays hadronically are considered, and multivariate discriminants are used to reject the backgrounds. No significant excess of events above the expected background is observed in the non-resonant search. The largest excess in the resonant search is observed at a resonance mass of 1TeV, with a local (global) significance of 3.1σ(2.0σ). Observed (expected) 95% confidence-level upper limits are set on the non-resonant Higgs boson pair-production cross-section at 4.7 (3.9) times the Standard Model prediction, assuming Standard Model kinematics, and on the resonant Higgs boson pair-production cross-section at between 21 and 900fb (12 and 840fb), depending on the mass of the narrow scalar resonance. Keywords: Higgs Physics, Hadron-Hadron Scattering, Proton-Proton Scattering ArXiv ePrint: 2209.10910 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP07(2023)040 JHEP07(2023)040 Contents 1 Introduction 1 2 ATLAS detector 4 3 Data and simulation samples 5 3.1 Data samples 5 3.2 Simulated event samples 5 4 Object reconstruction 8 5 Event selections 11 5.1 Signal regions 11 5.2 Multivariate signal extraction 13 5.3 Z+HF control region 17 6 Background modelling 18 6.1 Fake-τhad-vis background in the τlepτhad channel 18 6.2 Fake-τhad-vis background in the τhadτhad channel 20 6.2.1 Fake-τhad-vis background from multi-jet production 20 6.2.2 Fake-τhad-vis background from t¯ tproduction 22 7 Systematic uncertainties 23 8 Statistical interpretation 26 9 Results 28 10 Conclusion 31 The ATLAS collaboration 43 1 Introduction The discovery of a Higgs boson (H) with a mass of about 125 GeV [1,2] has led to a comprehensive programme of measurements and searches by the ATLAS [3] and CMS [4] collaborations at the Large Hadron Collider (LHC) [5] using proton–proton (pp) collision data. To date, all of the measured properties of the Higgs boson are found to be consistent with their Standard Model (SM) predictions [6–13], and no unexpected particles or Higgs boson decay modes have been observed. The SM predicts non-resonant HH production, with approximately 90% of the total cross-section being due to the gluon–gluon fusion – 1 – JHEP07(2023)040 g gH H H λHHH (a) g g H H (b) Figure 1. Leading-order Feynman diagrams for ggF non-resonant production of Higgs boson pairs: (a) the ‘triangle diagram’ and (b) the ‘box diagram’. The Higgs boson trilinear self-coupling is denoted by λHHH . (ggF) process. The leading-order (LO) contributions to ggF HH production are the ‘triangle diagram’, which includes a Higgs boson self-coupling vertex, and the heavy-quark ‘box diagram’, which has two fermion–fermion–Higgs vertices, as shown in figure 1(a) and figure 1(b), respectively. These diagrams interfere destructively, leading to a small SM ggF non-resonant HH cross-section, which is predicted to be 31.1+2.1 −7.2fb at next-to-next-toleading (NNLO) order in αs, including an approximation of finite top-quark-mass effects, for mH= 125 GeV and √s= 13 TeV [14–21]. The vector-boson fusion (VBF) process provides a sub-leading source of HH production in the SM, and has a cross-section of 1.73 ±0.04 fb at N3LO accuracy in QCD, for mH= 125 GeV and √s= 13 TeV [22–26]. Due to these small cross-sections, an observation of SM non-resonant HH production is not expected with the currently available LHC dataset, although significant non-resonant and resonant enhancements to the HH cross-section are predicted in many beyond-the-SM (BSM) theories. Due to the diagram shown in figure 1(a) and its interference with the diagram shown in figure 1(b), non-resonant HH production is a sensitive probe of the Higgs boson trilinear self-coupling and the shape of the Higgs field potential, which have important implications for the stability of the electroweak vacuum [27,28] and for baryogenesis [29] and inflation [30,31]. Modifications to the non-resonant HH cross-section occur in BSM scenarios with new, light, coloured scalars [32], composite Higgs models [33], theoretical scenarios with couplings between pairs of top quarks and pairs of Higgs bosons [34], as well as models with a modified coupling of the Higgs boson to the top quark. Previous searches for non-resonant HH production were performed by ATLAS and CMS in the b¯ bτ+τ−[35,36], b¯ bγγ [37,38], b¯ bb¯ b[39,40] decay channels, by ATLAS in the b¯ bqq`ν [41], WW∗γγ [42] and W W∗WW∗[43] decay channels, and by CMS in the b¯ b`+ν`−ν[44] decay channel. In the b¯ bτ+τ−decay channel, ATLAS and CMS set observed (expected) upper limits on the non-resonant HH production cross-section at 12.7 (14.8) [35] and 30 (25) [36] times the SM expectation using 36.1 fb−1and 35.9 fb−1of 13 TeV pp collision data, respectively. Using these datasets, ATLAS and CMS each combined their results from several search channels, improving these observed (expected) limits to 6.9 (10) [45] and 22.2 (12.8) [46] times the SM expectation, respectively. ATLAS and CMS recently updated many of their searches for HH production using their full – 2 – JHEP07(2023)040 Run 2 pp collision datasets. Both experiments have published their results in the b¯ bb¯ b[47– 49] and b¯ bγγ [50,51] decay modes. CMS also performed the search in the b¯ bτ+τ−[52], b¯ b`+`−`+`−[53] and WW ∗WW∗/WW∗τ+τ−/τ+τ−τ+τ−[54] decay channels. ATLAS also searched for the HH production in the b¯ b`+ν`−ν[55] final state. For the search in the b¯ bτ+τ−decay channel, CMS set an observed (expected) upper limit on the SM non-resonant HH cross-section of 3.3 (5.2) times the SM expectation by using 138 fb−1of 13 TeV pp collision data. ATLAS and CMS have performed combinations of HH searches using their full Run 2 datasets, which set observed (expected) upper limits on the SM non-resonant HH cross-section at 2.4 (2.9) and 3.4 (2.5) times the SM expectation, respectively [13,56]. Various BSM scenarios predict heavy resonances that can decay into pairs of Higgs bosons. These BSM resonances include heavy Higgs bosons from extended Higgs sectors such as those in two-Higgs-doublet models (2HDMs) [57], the minimal supersymmetric extension of the SM [58,59], twin Higgs models [60], and composite Higgs models [33, 61]. Heavy resonances that decay into pairs of Higgs bosons also include spin-0 radions and spin-2 gravitons from the Randall–Sundrum model [62–64], and stoponium states in supersymmetric models [65]. Searches for resonant HH production have been performed in many final states by ATLAS and CMS, and no significant excesses have been observed [35– 39,41–47,50,54,66–70]. This paper describes a search for non-resonant and resonant HH production in the final state with two τ-leptons and two jets containing b-hadrons (b-jets). The sizeable fraction of all possible SM decays that result in this final state, B(HH →b¯ bτ+τ−)=7.3% [71,72], and relatively low backgrounds make this one of the most sensitive HH search signatures. In the search for non-resonant HH production, the signal kinematics are assumed to follow the SM prediction, and the search is optimised for maximum sensitivity to the cross-section rather than the Higgs boson self-coupling. Additionally, only the ggF and VBF non-resonant HH production modes are considered, because other production modes are not expected to contribute significant additional sensitivity in this search. A narrow CP-even scalar particle (X) with a mass between 251 and 1600 GeV is used as the benchmark model for the resonant signal. Decay modes in which both τ-leptons decay hadronically (τhad), or in which one decays hadronically and the other leptonically (τlep), are considered; these are referred to as τhadτhad and τlepτhad, respectively. The presence of τhad are determined by detector signatures compatible with the expected visible decay products (τhad-vis). Events are categorised according to the type of trigger that accepted the event, and are required to contain two oppositely charged τhad-vis and two b-tagged jets in the τhadτhad final state, or an electron or muon and an oppositely charged τhad-vis and two b-tagged jets in the τlepτhad final state. Signal events also contain neutrinos from the decay of τ-leptons and b-hadrons, which manifest themselves as missing momentum transverse to the beamline (pmiss T). Backgrounds in this search include the production of top-quark pairs (t¯ t), single top quarks, Wand Zbosons in association with jets, dibosons (W W, WZ, ZZ), single Higgs bosons, and multi-jet events. In some background events, quarkor gluon-initiated jets are misidentified as τhad-vis. The selected events are tested for the presence of the signal by performing profile-likelihood fits to multivariate discriminant distributions. The search is performed using a dataset obtained from pp collisions delivered by the LHC – 3 – JHEP07(2023)040 during Run 2, between 2015 and 2018, at a centre-of-mass energy of √s= 13 TeV. The data were collected by the ATLAS detector [3] and correspond to an integrated luminosity of 139 fb−1. Compared to the previous ATLAS search [35], in addition to the greater integrated luminosity, this search profits from improved τhad-vis and b-jet reconstruction and identification algorithms, more sophisticated multivariate techniques used to target the resonant signal hypotheses, and new background estimation techniques. In particular, the combined reconstruction and identification efficiencies for τhad-vis and b-jets increased by around 25%–38% and 10%, respectively. The rest of this paper is organised as follows. A brief description of the ATLAS detector is given in section 2, and the data and simulation samples used are outlined in section 3. Overviews of the reconstruction of physics objects and the event selection are presented in sections 4and 5, respectively. The background modelling strategy is described in section 6. The systematic uncertainties relevant to this search and the statistical interpretation are described in sections 7and 8, respectively. The results of the search are given in section 9. Finally, section 10 presents the conclusions. 2 ATLAS detector ATLAS [3] is a general-purpose particle detector covering nearly the entire solid angle1 around the collision point. It is composed of an inner tracking detector system surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets. The inner detector, located within a 2 T axial magnetic field generated by the superconducting solenoid, is used to measure the trajectories and momenta of charged particles. The inner layers, consisting of high-granularity silicon pixel detectors, instrument a pseudorapidity range |η|<2.5. An innermost silicon pixel layer, the insertable B-layer [73,74], was added to the detector between Run 1 and Run 2. The insertable B-layer improves the ability to identify displaced vertices, which significantly improves the b-jet tagging performance [75]. Silicon strip detectors, which cover the range |η|<2.5, surround the pixel detectors. Outside the strip detectors and covering |η|<2.0, there are straw-tube tracking detectors, which also provide measurements of transition radiation that are used in electron identification. The calorimeter system covers the range |η|<4.9. Within the region |η|<3.2, electromagnetic calorimetry is provided by barrel (|η|<1.475) and endcap (1.375 <|η|<3.2) highly segmented lead/liquid-argon (LAr) electromagnetic calorimeters, with an additional thin LAr presampler covering |η|<1.8to correct for energy loss in material upstream of the calorimeters. Hadronic calorimetry is provided by a steel/scintillator-tile calorimeter, 1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. 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). The distance in (η, φ) coordinates, ∆R=p(∆η)2+ (∆φ)2, is also used to define cone sizes. Transverse momentum and energy are defined as pT=psin θand ET=Esin θ, respectively. – 4 – JHEP07(2023)040 segmented into three barrel structures within |η|<1.7, and two copper/LAr hadronic endcap calorimeters extend the coverage to |η|= 3.2. The region of 3.2<|η|<4.9 is instrumented with copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements, respectively. The outermost part of the detector is the muon spectrometer, which measures the curved trajectories of muons in the field of three large air-core toroidal magnets. Highprecision tracking is performed within the range |η|<2.7, and there are chambers for fast triggering within the range |η|<2.4. The ATLAS detector has a two-level trigger system [76] to select events of interest. The first-level (L1) trigger is implemented in custom electronics and, using a subset of the information from the detector, it accepts events from the 40 MHz LHC proton bunch crossings at a rate of about 100 kHz. This is followed by a software-based high-level trigger (HLT) that reduces the accepted event rate to approximately 1 kHz. An extensive software suite [77] is used in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 3 Data and simulation samples 3.1 Data samples The data used in this search were collected at a centre-of-mass energy of 13 TeV between 2015 and 2018, using triggers to select events with at least one lepton (where a lepton is defined as an electron or a muon), at least one τhad-vis, at least one lepton and one τhad-vis, or at least two τhad-vis. Details about these triggers are discussed in section 5.1. Events are selected for analysis only if they are of good quality and if all the relevant detector components are known to be in good operating condition [78]. The total integrated luminosity of the data, after meeting the good-quality criteria, is 139.0±2.4fb−1[79,80]. The recorded events contain an average of 34 simultaneous inelastic pp collisions per bunchcrossing (pile-up). 3.2 Simulated event samples Monte Carlo (MC) simulated events are used to model SM background production, SM non-resonant HH signal production, and BSM resonant HH signal production. The events were passed through the full ATLAS detector simulation [81] based on Geant4 [82], with the exception of the BSM resonant HH signal events, which were passed through a fast simulation in which the response of the calorimeters is parameterised [83,84] rather than fully simulated. The effects of pile-up in the same and neighbouring bunch crossings were modelled by overlaying each hard-scatter event with minimum-bias events, simulated using the soft quantum chromodynamics (QCD) processes of Pythia 8.186 [85] with a set of tuned parameters called the A3 tune [86] and the NNPDF2.3lo [87] parton distribution functions (PDFs). The EvtGen program [88] was used to model the decays of bottom and charm hadrons in all samples of simulated events, except those generated using Sherpa [89]. – 5 – JHEP07(2023)040 Process ME generator ME QCD ME PDF PS and UE model Cross-section order hadronisation tune order Signal non-resonant gg →HH (ggF) Powheg Box v2 NLO PDF4LHC15nlo Pythia 8.244 A14 NNLO FTApprox non-resonant qq →qqHH (VBF) MadGraph5_aMC@NLO 2.7.3 LO NNPDF3.0nlo Pythia 8.244 A14 N3LO(QCD) resonant gg →X→HH MadGraph5_aMC@NLO 2.6.1 LO NNPDF2.3lo Herwig 7.1.3 H7.1-Default – Top-quark t¯ tPowheg Box v2 NLO NNPDF3.0nlo Pythia 8.230 A14 NNLO+NNLL t-channel Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.230 A14 NLO s-channel Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.230 A14 NLO Wt Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.230 A14 NLO t¯ tZ Sherpa 2.2.1 NLO NNPDF3.0nnlo Sherpa 2.2.1 Default NLO t¯ tW Sherpa 2.2.8 NLO NNPDF3.0nnlo Sherpa 2.2.8 Default NLO Vector boson + jets W/Z+jets Sherpa 2.2.1 NLO (≤2jets) NNPDF3.0nnlo Sherpa 2.2.1 Default NNLO LO (3,4 jets) Diboson W W, W Z, ZZ Sherpa 2.2.1 NLO (≤1jet) NNPDF3.0nnlo Sherpa 2.2.1 Default NLO LO (2,3 jets) Single Higgs boson ggF Powheg Box v2 NNLO NNPDF3.0nlo Pythia 8.212 AZNLO N3LO(QCD)+NLO(EW) VBF Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.212 AZNLO NNLO(QCD)+NLO(EW) qq →WH Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.212 AZNLO NNLO(QCD)+NLO(EW) qq →ZH Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.212 AZNLO NNLO(QCD)+NLO(EW)(†) gg →ZH Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.212 AZNLO NLO+NLL t¯ tH Powheg Box v2 NLO NNPDF3.0nlo Pythia 8.230 A14 NLO Table 1. The generators used to simulate the signal and background processes. If not otherwise specified, the order of the cross-section calculation refers to the expansion in the strong coupling constant (αs). The acronyms ME, PS and UE are used for matrix element, parton shower and underlying event, respectively. Details of the simulation of the signal and background samples are described in the text. (†)The NNLO(QCD)+NLO(EW) cross-section calculation for the pp →ZH process already includes the gg →ZH contribution. The qq →ZH process is normalised to the NNLO(QCD)+NLO(EW) cross-section for the pp →ZH process, after subtracting the gg →ZH contribution. The samples generated with Sherpa used the bottomand charm-hadron decay model implemented within the generator. The simulated events were processed through the same reconstruction algorithms as the data. For all samples containing a SM Higgs boson, its mass was fixed to 125 GeV. The same mass value is used in the calculation of the Higgs boson decay branching fractions and in the calculation of the single-Higgs-boson and SM non-resonant HH production cross-sections. Unless otherwise specified, the order of the cross-section calculation refers to the expansion in the strong coupling constant (αs). A summary of the event samples used for the simulation of the signal and background processes is shown in table 1. Simulated SM non-resonant HH signal production includes the contributions from the ggF and VBF processes. The simulated ggF events were generated with the Powheg Box v2 generator [90] at next-to-leading order (NLO) with finite top-quark mass, and using the PDF4LHC15_nlo_30_pdfas (code 90400 in the LHAPDF database [91]) PDF set [92]. Parton showers and hadronisation were simulated using Pythia 8.244 [85] with the A14 tune [93,94] and the NNPDF2.3lo PDF set. The cross-section for ggF non-resonant HH production is calculated at next-to-next-to-leading order (NNLO) using FTApprox [20]. The VBF non-resonant HH signal events were generated at LO using the MadGraph5_aMC@NLO 2.7.3 [95] generator with the NNPDF3.0nlo [96] PDF set. Parton showering and hadronisation were performed using Pythia 8.244 with the A14 tune and the NNPDF2.3lo PDF set. The cross-section for VBF non-resonant HH production is calculated at next-to-next-to-next-to-leading order (N3LO) in QCD in the limit – 6 – JHEP07(2023)040 of no partonic exchange between the two protons [26]. The calculated cross-section values for ggF and VBF non-resonant HH production at √s= 13 TeV and mH= 125 GeV are given in section 1. Other non-resonant HH production modes are not considered because their contributions to the analysis sensitivity are expected to be negligible. The BSM resonant HH signal from the ggF production of a heavy spin-0 resonance and its decay into a pair of SM Higgs bosons, X→HH, was simulated with the MadGraph5_aMC@NLO 2.6.1 generator using the NNPDF2.3lo PDF set at LO accuracy in QCD. The simulated events were interfaced to Herwig 7.1.3 [97,98] to model the parton shower, hadronisation and underlying event, using the H7.1-Default tune [99] and the NNPDF2.3lo PDF set. The resonant HH signal was simulated for 19 values of the resonance mass, mX, between 251 GeV and 1.6 TeV. The width of the heavy scalar Xwas fixed to 10 MeV. The production of t¯ tevents, and of single-top-quark events in the Wt-, sand t-channels, was simulated using the Powheg Box v2 generator together with the NNPDF3.0nlo PDF set. The simulated events were interfaced to Pythia 8.230 for parton showering and hadronisation using the A14 tune together with the NNPDF2.3lo PDF set. The top-quark spin correlations were preserved for all these simulated top-quark processes. The top-quark mass was set to 172.5 GeV. The t¯ tproduction cross-section is calculated at next-to-next-to-leading-order and next-to-next-to-leading-logarithm (NNLO+NNLL) accuracy [100]. The cross-sections for the three single-top-quark production channels are calculated at NLO [101–103]. The t¯ t–Wt interference was handled using the diagram removal scheme. Events containing a Wor Zboson produced in association with jets, diboson (WW , WZ and ZZ) production processes, and the t¯ tZ production process were simulated with the Sherpa 2.2.1 generator [89], whereas the t¯ tW production process was simulated with the Sherpa 2.2.8 generator. These samples used the NNPDF3.0nnlo [96] PDF set with dedicated parton shower tuning developed by the Sherpa authors. For the simulation of W/Z+jets events, the matrix elements were calculated for up to two partons at NLO and up to four partons at LO using the OpenLoops [104] and Comix [105] matrix-element generators. The expected number of W/Z+jets events is normalised to the NNLO crosssections [106]. Diboson production was simulated for up to one additional parton at NLO and up to three additional partons at LO using the OpenLoops and Comix programs. The NLO cross-sections from Sherpa are used to normalise the diboson and the t¯ tW/Z events. SM single Higgs boson production is considered as part of the background in this search, and its production modes were simulated using the Powheg Box v2 generator and the NNPDF3.0nlo PDF set. Single Higgs boson production via ggF was simulated at NNLO accuracy in QCD using the Powheg NNLOPS program [107,108], whereas VBF single Higgs boson production was simulated at NLO accuracy in QCD [109]. Events from both of these production modes were interfaced to Pythia 8.212 for parton showering and hadronisation using the AZNLO tune [110] together with the CTEQ6L1 PDF set [111]. The cross-section for ggF production of single Higgs bosons is based on a computation with N3LO accuracy in QCD, and NLO accuracy in the electroweak (EW) expansion [71,112– 115], whereas the cross-section for VBF production of single Higgs bosons is taken from – 7 – JHEP07(2023)040 the NNLO(QCD)+NLO(EW) calculation [71,116–118]. The qq →WH,qq →ZH and gg →ZH simulated events were interfaced to Pythia 8.212 for parton showering and hadronisation using the AZNLO tune together with the CTEQ6L1 PDF set. The crosssections are taken from the NNLO(QCD)+NLO(EW) calculations for qq →WH and qq →ZH [119–125], and from calculations at next-to-leading-order and next-to-leadinglogarithm (NLO+NLL) accuracy in QCD for gg →ZH [126–130]. For Higgs boson production in association with a pair of top quarks (t¯ tH), the simulated events were interfaced to Pythia 8.230 for parton showering and hadronisation using the A14 tune and the NNPDF2.3lo PDF set. The cross-section for t¯ tH production is taken from NLO calculations [71]. The contributions from other single Higgs production modes are expected to be negligible and thus not considered. SM single Higgs boson production plays a more important role as a background in the non-resonant HH search than in the resonant HH search, due to more similar production kinematics. 4 Object reconstruction Electrons, muons, τhad-vis, jets from the hadronisation of quarks and gluons, including btagged jets, and pmiss Tare used in this search. An anti-τhad-vis object, defined as a τhad-vis with modified identification requirements, is also used to estimate the backgrounds from hadronic jets misidentified as τhad-vis. Tracks are used in the reconstruction, identification, isolation and vertex compatibility requirements and calibration of many of the physics objects described below, and in vertex reconstruction; they are reconstructed from hits in the inner tracking detectors, and are required to have pT>500 MeV [131,132]. Events are required to have at least one collision vertex reconstructed from two or more associated tracks. If multiple vertices are found, the one with the largest Pp2 Tof the associated tracks is selected as the primary vertex. Finally, an overlap removal procedure is applied to ensure that no detector signature is identified as multiple reconstructed objects. Electron candidates are reconstructed by matching tracks reconstructed in the inner detector to topological energy clusters in the electromagnetic calorimeter, with a reconstruction efficiency of around 98% [133]. Electron candidates are required to have pT>7GeV and |η|<2.47, and to be outside the transition region between the calorimeter’s barrel and endcaps, 1.37 <|η|<1.52. They must pass track-quality requirements, followed by a loose likelihood-based selection that requires the shower profile to be compatible with that of an electromagnetic shower. These requirements have an efficiency of around 93%. Isolation requirements are applied; these are based on the presence of tracks in a cone of pe T-dependent size ∆Raround the electron and of calorimetric energy deposits in a fixedsize cone. Lastly, the electron energy scale is calibrated in data, and the energy resolution is calibrated in simulation, using Z→ee events [133]. Muon candidates are reconstructed from tracks in the muon spectrometer, matched to tracks in the inner detector where available [134]. In the absence of full tracks in the muon spectrometer, muons with |η|<0.1can be reconstructed from track segments in the muon spectrometer, or energy deposits compatible with that of a minimum-ionising particle in the calorimeters. If an inner-detector track is present, it must match the direction and – 8 – JHEP07(2023)040 Variable τhadτhad τlepτhad SLT τlepτhad LTT mHH 3 3 3 mMMC ττ 3 3 3 mbb 3 3 3 ∆R(τ, τ)3 3 3 ∆R(b, b)3 3 ∆pT(`, τ)3 3 Sub-leading b-tagged jet pT3 mW T3 Emiss T3 pmiss Tφcentrality 3 ∆φ(`τ, bb)3 ∆φ(`, pmiss T)3 ∆φ(ττ, pmiss T)3 ST3 Table 3. Variables used as inputs to the MVAs in the three analysis categories. The same choice of input variables is used for the resonant and non-resonant production modes. The variables are defined in the main text. •∆φ(`τ, bb)is the azimuthal angle between the `+τhad-vis system and the b-tagged jet pair; •∆φ(`, pmiss T)is the azimuthal angle between the lepton and the pmiss T; •∆φ(ττ, pmiss T)is the azimuthal angle between the τ-lepton pair system (estimated using the MMC) and the pmiss T; •STis the total transverse energy in the event, summed over all jets, τhad-vis and leptons in the event and Emiss T. The mHH ,mMMC ττ and mbb distributions are shown in figure 3. For all categories of the non-resonant search, mMMC ττ and mbb are among the three most important MVA input variables. For the resonant search, five values of mXwere tested in all categories, and mHH was found to be the most important MVA input variable in all cases except at lower values of mXin the τhadτhad category. The mMMC ττ and mbb variables separate resonant H→ττ and H→bb signals (respectively) from backgrounds that do not contain these processes, and the mHH variable targets resonant X→HH decays in the resonant search. The (P)NNs use rectified linear unit and sigmoidal activation functions for the hidden and output layers, respectively, binary cross entropy as the loss function, and stochastic (mini-batch) gradient descent as the optimiser [160]. The PNN used in the τhadτhad category has 3 hidden layers of 128 nodes, followed by 1 hidden layer of 16 nodes. The (P)NN used in the τlepτhad SLT category has 2 hidden layers of 512 nodes, and the (P)NN used in the τlepτhad LTT category has 3 hidden layers of 512 (256) nodes. The (P)NN input variables are standardised by subtracting the median value and dividing by the interquartile range. Nesterov momentum and learning-rate decay were used in the training of all (P)NNs, and in the τlepτhad categories they used an L2 regularisation (‘ridge regression’) term in the – 15 – JHEP07(2023)040 1 10 2 10 3 10 4 10 5 10 6 10 Events / 75 GeV Data 100×SM HH = 500 GeV) X X (m = 1000 GeV) X X (m HH) = 1 pb→(X σ Top-quark fakes (MJ) had τ →Jet + (bb,bc,cc)ττ →Z )t fakes (t had τ →Jet Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs had τ had τ Signal Region 0 500 1000 1500 2000 2500 [GeV] HH m 0.8 1 1.2 Data/Pred. (a) 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Events / 50 GeV Data 100×SM HH = 500 GeV) X X (m = 1000 GeV) X X (m HH) = 1 pb→(X σ Top-quark fakes had τ →Jet + (bb,bc,cc)ττ →Z Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs SLT had τ lep τ Signal Region 0 500 1000 1500 2000 2500 [GeV] HH m 0.8 1 1.2 Data/Pred. (b) 1 10 2 10 3 10 4 10 5 10 6 10 Events / 50 GeV Data 100×SM HH = 500 GeV) X X (m = 1000 GeV) X X (m HH) = 1 pb→(X σ Top-quark fakes had τ →Jet + (bb,bc,cc)ττ →Z Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs LTT had τ lep τ Signal Region 0 500 1000 1500 2000 2500 [GeV] HH m 0.8 1 1.2 Data/Pred. (c) 1 10 2 10 3 10 4 10 5 10 Events / 20 GeV Data 100×SM HH Top-quark fakes (MJ) had τ →Jet + (bb,bc,cc)ττ →Z )t fakes (t had τ →Jet Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs had τ had τ Signal Region 0 100 200 300 400 500 600 700 800 [GeV] MMC ττ m 0.8 1 1.2 Data/Pred. (d) 1 10 2 10 3 10 4 10 5 10 6 10 7 10 Events / 20 GeV Data 100×SM HH Top-quark fakes had τ →Jet + (bb,bc,cc)ττ →Z Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs SLT had τ lep τ Signal Region 0 100 200 300 400 500 600 700 800 [GeV] MMC ττ m 0.8 1 1.2 Data/Pred. (e) 1 10 2 10 3 10 4 10 5 10 Events / 20 GeV Data 100×SM HH Top-quark fakes had τ →Jet + (bb,bc,cc)ττ →Z Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs LTT had τ lep τ Signal Region 0 100 200 300 400 500 600 700 800 [GeV] MMC ττ m 0.8 1 1.2 Data/Pred. (f) 1 10 2 10 3 10 4 10 5 10 Events / 20 GeV Data 100×SM HH Top-quark fakes (MJ) had τ →Jet + (bb,bc,cc)ττ →Z )t fakes (t had τ →Jet Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs had τ had τ Signal Region 0 100 200 300 400 500 600 700 800 [GeV] bb m 0.8 1 1.2 Data/Pred. (g) 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 Events / 5 GeV Data 100×SM HH Top-quark fakes had τ →Jet + (bb,bc,cc)ττ →Z Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs SLT had τ lep τ Signal Region 0 20 40 60 80 100 120 140 [GeV] bb m 0.8 1 1.2 Data/Pred. (h) 1 10 2 10 3 10 4 10 5 10 6 10 Events / 5 GeV Data 100×SM HH Top-quark fakes had τ →Jet + (bb,bc,cc)ττ →Z Other SM Higgs Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs LTT had τ lep τ Signal Region 0 20 40 60 80 100 120 140 [GeV] bb m 0.8 1 1.2 Data/Pred. (i) Figure 3. Signal (solid lines), background (filled histograms) and data (dots with error bars) distributions of mHH (top), mMMC ττ (middle row) and mbb (bottom) for events in the τhadτhad (left), τlepτhad SLT (middle column) and τlepτhad LTT (right) categories. The normalisation and shape of the backgrounds and the uncertainty in the total background shown are determined from the likelihood fit (described in section 8) to data in the non-resonant HH search. The expected nonresonant signal is overlaid with its normalisation scaled by a factor of 100, and the mX= 500 GeV and mX= 1000 GeV resonant signals are overlaid in the mHH distributions with their cross-section set to 1 pb. The dashed histogram shows the total pre-fit background. The size of the combined statistical and systematic uncertainty of the background is indicated by the hatched band. The ratio of the data to the sum of the backgrounds is shown in the lower panels. – 16 – JHEP07(2023)040 2000 4000 6000 8000 10000 12000 Events / 1 GeV Data ll + (bb,bc,cc)→Z ll + (bl,cl,ll)→Z Top-quark Other Uncertainty Pre-fit background ATLAS -1 = 13 TeV, 139 fbs Z+HF CR 75 80 85 90 95 100 105 110 [GeV] ll m 0.9 1 1.1 Data/Pred. Figure 4. Post-fit background and data m`` distributions in the Z+ HF control region. The normalisation and shape of the backgrounds and the uncertainty in the total background are shown as determined from the likelihood fit (described in section 8) to data in the non-resonant HH search. The uncertainty band includes statistical and systematic uncertainties of the total background. The dashed histogram shows the total pre-fit background. loss function [160]. The BDT uses 1500 trees with a maximum depth of 2 and a minimum node size of 1% of the training events. Gradient boosting is used with a shrinkage of 0.2. 5.3 Z+ HF control region The normalisation of the Z+ HF background is determined from data by fitting the m`` distribution in the Z+ HF CR in the likelihood fit (described in section 8). This is to account for a known discrepancy between the Z+ HF production cross-section provided at NLO by Sherpa and the cross-section observed in data. The Z+HF CR targets events containing Zboson decays into electron or muon pairs by using triggers that require either a lepton or a pair of same-flavour leptons. Exactly two oppositely charged sameflavour leptons and exactly two b-tagged jets must be reconstructed offline. The leptons are required to have pT>9GeV, pass offline pTthresholds based on the trigger thresholds, be compatible with originating from the primary vertex, and pass medium identification and loose isolation requirements. Lastly, the invariant mass of the lepton pair is required to be between 75 GeV and 110 GeV to select events which include a Zboson decay, and mbb is required to be less than 40 GeV or greater than 210 GeV to ensure orthogonality to the event selection of another ATLAS search. This region also provides constraints on the normalisation of the t¯ tbackground. Figure 4shows the post-fit background and data m`` distributions in the Z+ HF control region. – 17 – JHEP07(2023)040 6 Background modelling Backgrounds in this search are estimated using a combination of simulation-based and data-driven techniques. The main sources of background are top-quark, Z+jets, W+jets, diboson, single Higgs boson and multi-jet production. A reconstructed τhad-vis, in these background events, can originate either from a τhad decay (true-τhad-vis), or from a misidentified quarkor gluon-initiated jet (fake-τhad-vis). Events in which an electron or a muon is misidentified as a τhad-vis represent a small additional background. Most of the background events with fake-τhad-vis are from t¯ tor multi-jet production. In t¯ tevents, fake-τhad-vis typically originate from quark-initiated jets from the top-quark decay. In multi-jet events, both quarkand gluon-initiated jets may be misidentified as τhad-vis. The simulated event samples, summarised in section 3.2, are used to model background events containing true-τhad-vis and events with an electron or a muon misidentified as a τhad-vis. Events with fake-τhad-vis in t¯ tor multi-jet production are estimated using techniques relying on both simulated events and data, as detailed in the following subsections. Smaller backgrounds with fake-τhad-vis from other production processes are estimated from simulation. The normalisations of simulated t¯ t, for both the trueand fake-τhad-vis components, and Z+HF backgrounds are determined from data in the likelihood fits of signal and control regions, as outlined in section 8. 6.1 Fake-τhad-vis background in the τlepτhad channel In the τlepτhad channel, a combined fake-factor method similar to that described in ref. [35] is used to estimate multi-jet and t¯ tbackgrounds with fake-τhad-vis. A schematic depiction of this method is shown in figure 5. This method employs events in two groups of regions. The events in the identification (ID) regions require one identified τhad-vis, whereas events in the anti-identification (anti-ID) regions contain one anti-τhad-vis candidate. Prior to the last step of the overlap removal procedure outlined in section 4, if an event does not contain an identified τhad-vis, it is checked for reconstructed τhad-vis candidates satisfying the anti-τhad-vis requirements, as defined in section 4. If an event contains multiple anti-τhad-vis candidates, one is chosen randomly. In the LTT category, however, only the anti-τhad-vis candidate that is within ∆R= 0.2of the HLT τhad-vis object is considered. In order to define a fake-τhad-vis background template, an anti-ID region is defined using event selection criteria equivalent to the SR selection, with one anti-τhad-vis candidate instead of one identified τhad-vis. This region, which is enriched with fake-τhad-vis, is defined as the SR Template region. The template for estimating fake-τhad-vis in the SR is obtained by subtracting from the data distribution in the SR Template region the distribution of simulated background events in which the τhad-vis candidate is not a fake-τhad-vis originating from jets. This subtraction is referred to as the true-τhad-vis subtraction given that the number of events in which an electron or muon is misidentified as a τhad-vis, which are also subtracted, is very small. The data and simulated events that are used to build the – 18 – JHEP07(2023)040 mbb <150 GeV mbb >150 GeVMJ CR: FFt ¯ t FFcomb =rMJ ×FFMJ + (1 −rMJ)×FFt ¯ t SR Template ID Anti-ID Fraction of multi-jet FFMJ events in the template True-τhad-vis subtracted SR Anti-Iso t ¯ tCR: ID Anti-ID rMJ τlepτhad channel Figure 5. Schematic depiction of the combined fake-factor method used to estimate multi-jet and t¯ tbackgrounds with fake-τhad-vis in the τlepτhad channel. Backgrounds which are not from events with fake-τhad-vis originating from jets are estimated from simulation and are subtracted from data in all control regions. Events in which an electron or a muon is misidentified as a τhad-vis are also subtracted, but their contribution is very small. Both sources are indicated by ‘True-τhad-vis subtracted’ in the legend. template are scaled with event weights, referred to as fake-factors (FFs), to estimate the fake-τhad-vis background in the SR. The FFs are derived separately for multi-jet (FFMJ) and t¯ t(FFt¯ t) events in dedicated control regions. The multi-jet control regions (MJ CRs) and t¯ tcontrol regions (t¯ tCRs) are defined separately for the ID and the anti-ID regions, depending on whether they contain one identified τhad-vis or one anti-τhad-vis candidate, respectively. Besides the τhad-vis selection, the MJ CRs are defined using the SR selection with an inverted electron or muon isolation requirement (anti-Iso) and without the mbb <150 GeV requirement. The MJ CR’s purity in multi-jet production events varies between 65% and 90% depending on the trigger category type (SLT or LTT) and whether the MJ CR is in the ID or antiID region. Similarly, the t¯ tCRs are defined using the SR selection with an inverted mbb requirement (mbb >150 GeV). The t¯ tCR is about 95% pure in the events from t¯ tproduction. Contamination from either non-resonant or resonant HH signal in these CRs is estimated to be negligible. Backgrounds which are not from events with fakeτhad-vis originating from jets, are estimated from simulation and are subtracted from the distribution of the data in all the control regions used for the FF measurement. After the subtraction the FFs are derived as the ratio of the number of events in the ID region to the number of events in the anti-ID region. They are parameterised in terms of the τhad-vis pT, independently for 1and 3-prong τhad-vis (‘1and 3-prong’ refers to the number of tracks associated with a reconstructed τhad-vis), and separately for the SLT and LTT categories. The FFs corresponding to the individual background processes are combined as FFcomb =rMJ ×FFMJ + (1 −rMJ)×FFt¯ t, where rMJ is the expected fraction of multi-jet events in the SR Template. The number of multi-jet events in the SR Template is estimated by taking the number of data events in the SR Template and subtracting the expected number of non-multi-jet background events with both trueand fake-τhad-vis as estimated from simulation. The non-multi-jet – 19 – JHEP07(2023)040 background events are dominated by t¯ tproduction. The rMJ is parameterised as a function of the τhad-vis pT, and it is measured separately for the τeτhad and τµτhad events, for 1and 3-prong τhad-vis categories, and for the SLT and LTT categories. The FFcomb is used to scale the events used for the SR Template in order to obtain the fake-τhad-vis background prediction in the SR. The determination of the fake-τhad-vis background using the combined FF method is sensitive to the modelling of simulated t¯ tevents with true-τhad-vis given that this is the dominant background that is subtracted from data in the derivation of the FF and rMJ, and when obtaining the SR Template. Additionally, the derivation of rMJ is sensitive to the modelling of simulated t¯ tevents with fake-τhad-vis. To improve predictions, the simulated events from t¯ tproduction are differentially reweighted to data distributions depending on the jet multiplicity and the scalar sum of the transverse momentum of all visible final-state objects in the event. These reweighting factors are determined from another t¯ tcontrol region (t¯ tCR2), which is about 93% pure in events from t¯ tproduction, and is defined using a selection identical to the SR selection, but with an inverted mbb requirement (mbb > 150 GeV) and with an additional mW T>40 GeV requirement. Furthermore, events in this control region are required to have a reconstructed τhad-vis candidate, but this candidate is not required to meet the recurrent neural-network identification criteria defined in section 4. The mW Trequirement is introduced to reduce any potential contamination from multijet events. Statistical uncertainties in FFt¯ t, FFMJ and rMJ are evaluated and propagated to the final result. The difference between the fake-τhad-vis background estimates obtained with and without the aforementioned t¯ tmodelling correction is taken as an uncertainty in the background estimate. A conservative 30% modelling uncertainty is assigned to simulated non-t¯ tbackgrounds which are subtracted from data. Due to its large dependence on the modelling of simulated t¯ tevents with fake-τhad-vis, the obtained values of rMJ are varied by ±0.5, with the constraint 0≤rMJ ≤1. The impact of such a conservative uncertainty is small since the FFs in multi-jet and t¯ tevents are found to be similar. The total uncertainty in the FFcomb value for the SLT category is at most 10%, and at most 25% for the LTT category. The combined FF method is checked for closure in the t¯ tCR and it is validated in the 0-b-tagged and 1-b-tagged regions, which are the same as the τlepτhad SR except for the requirement on the number of b-tagged jets. The signal contamination in the 0-b-tagged and 1-b-tagged regions is negligible. The estimated background distributions agree well with the observed distributions in all validation regions. 6.2 Fake-τhad-vis background in the τhadτhad channel In the τhadτhad channel, two separate methods are used to estimate the backgrounds with fake-τhad-vis from t¯ tand multi-jet production. Multi-jet events can only enter the signal selection when both τhad-vis are fake, whereas for t¯ tproduction, usually no more than one reconstructed τhad-vis is fake. 6.2.1 Fake-τhad-vis background from multi-jet production In the τhadτhad channel, the fake-τhad-vis background from multi-jet production is estimated using a fake-factor method. A schematic depiction of this method is shown in figure 6. – 20 – JHEP07(2023)040 The ID region selection refers to the selection of events with two identified τhad-vis. In order to define an anti-ID region selection, prior to the last step of the overlap removal procedure outlined in section 4, events that have only one identified τhad-vis are checked for a reconstructed τhad-vis candidate satisfying the anti-τhad-vis requirements. The selected anti-τhad-vis candidate is required to be within ∆R= 0.2of an HLT τhad-vis object, except in the STT category for events in which the identified τhad-vis is already trigger-matched. If multiple anti-τhad-vis candidates fulfil the defined criteria, one is selected randomly. In order to define a multi-jet background template, an anti-ID region is defined by using a selection equivalent to the SR selection, with one identified τhad-vis and one anti-τhad-vis candidate, instead of two identified τhad-vis. The template for estimating the multi-jet background in the SR (SR Template) is obtained by subtracting simulated non-multi-jet events from data in the template region. A large fraction of the subtracted non-multi-jet events are from t¯ tproduction, and these simulated t¯ tevents with fake-τhad-vis are corrected with scale factors of the fake-τhad-vis misidentification efficiencies in the anti-ID region, which are described in section 6.2.2. Similarly to the procedure used in the τlepτhad channel, the events that are used to build the template are further scaled with FFs to estimate the multi-jet background in the SR. A multi-jet-enriched control region is defined by using the τhadτhad SR selection, but requiring the two τhad-vis to have same-sign (SS) charges, as opposed to the SR selection that requires two τhad-vis with opposite-sign (OS) charges. Additionally, events in the control region are required to have exactly one b-tagged jet per event (SS CR with 1 btagged jet). This control region, with its corresponding anti-ID counterpart, is used for FF measurements. This control region’s purity in multi-jet production events varies from 80% to 90% depending on the trigger type (STT or DTT). The FFs are measured as the ratio of the number of events in the ID region to the number of events in the anti-ID region after subtracting all simulated non-multi-jet backgrounds from data. The FFs are determined separately for the STT and DTT categories, and for the different years of data-taking to account for the changes to the τhad-vis identification algorithms and event selection topologies used in the trigger. The FFs are derived in the SS CR with 1 b-tagged jet, due to the limited number of selected events and large t¯ tbackground contamination in the SS region with 2 b-tagged jets. For that reason, transfer factors (TFs) are defined to account for the extrapolation from 1-b-tagged-jet events to 2-b-tagged-jet events. In the DTT category, the FFs are binned in pTand ηof the anti-τhad-vis. In the STT category, due to the smaller number of available events, the FFs are measured inclusively in pTand η, but separately according to whether the selected anti-τhad-vis is the leading or sub-leading τhad-vis candidate in pT. In both categories, the FFs are measured separately for events with 1or 3-prong anti-τhad-vis candidates. The TFs are defined as ratios of the FFs measured in the SS CR with 2 b-tagged jets to the FFs measured in the SS CR with 1 b-tagged jet, inclusively for the STT and DTT categories. The large contamination from t¯ tbackground in the SS CR with 2 b-tagged jets is removed in the subtraction of all simulated non-multi-jet backgrounds from data. The TFs are also measured inclusively in pTand ηof the τhad-vis, but separately for events with 1and 3-prong anti-τhad-vis candidates, separately according to whether the selected – 21 – JHEP07(2023)040 OS, 2 b-tagged jets SS, 1 b-tagged jet SS, 2 b-tagged jets SR Template FF = FF1b-tag ×TF1!2b-tags ID Anti-ID FF1b-tag TF1!2b-tags Non-multi-jet subtracted SR ID Anti-ID τhadτhad channel Figure 6. Schematic depiction of the fake-factor method to estimate the multi-jet background with fake-τhad-vis in the τhadτhad channel. Backgrounds that are not from multi-jet events are simulated and subtracted from data in all the control regions. This is indicated by ‘Non-multi-jet subtracted’ in the legend. anti-τhad-vis is the leading or sub-leading τhad-vis candidate in pT, and separately for the different years of data-taking. Their values are compatible with unity within the statistical uncertainty. The sources of uncertainty in the estimate of the fake-τhad-vis background from multijet production include the statistical uncertainties in the measured FFs and TFs, and uncertainties in the normalisation and shape of the non-multi-jet backgrounds that are subtracted from data when deriving the SR Template. An uncertainty is also introduced to account for the extrapolation from the SS events to the OS events. The associated systematic uncertainty is estimated by comparing the FFs derived from SS 1-b-tagged-jet events with those derived from OS 1-b-tagged-jet events. To ensure that the sample is dominated by multi-jet events, the OS events are additionally required to satisfy mMMC ττ > 110 GeV and Emiss T/σ(Emiss T)<3, where σ(Emiss T)is the event-based approximation to the resolution of the Emiss T[161]. The modelling of the multi-jet background is checked for closure in the SS CRs with 1 and 2 b-tagged jets and good agreement between data and prediction is observed. The FFs are measured in the SS CR with 1 b-tagged jet, and the TFs between the SS CR with 1 b-tagged jet and SS CR with 2 b-tagged jets only correct for the normalisation of the fake-τhad-vis prediction. Thus the validation performed on the SS CR with 2 b-tagged jets provides a check of the shape extrapolation from 1 b-tagged jet to 2 b-tagged jets. 6.2.2 Fake-τhad-vis background from t¯ tproduction Background events with fake-τhad-vis from t¯ tproduction in the τhadτhad channel are estimated using simulation. However, the fake-τhad-vis misidentification efficiencies are corrected by scale factors (SFs) derived from data. A schematic depiction of this method is shown in figure 7. The SFs are determined in the t¯ tCR defined within the τlepτhad SLT category, as described in section 6.1. However, in order to harmonise it with the τhadτhad SR selection, the t¯ tCR is redefined to select events with τhad-vis |η|<2.5. The SFs are extracted as a function of the fake-τhad-vis pT, separately for 1and 3-prong fake-τhad-vis objects, by fitting the mW Tdistribution of simulated events to data using a profile-likelihood fit. The fit of – 22 – JHEP07(2023)040 t ¯ twith fake-τhad-vis (corrected simulation) t ¯ twith fake-τhad-vis (simulation) SF(fake-τhad-vis) (from template fits to the m W Tdistribution) τhadτhad SR τlepτhad,t ¯ tCR τhadτhad SR τhadτhad channel Figure 7. Schematic depiction of the fake-τhad-vis scale-factor method to estimate the t¯ tbackground with fake-τhad-vis in the τhadτhad channel. the mW Tdistribution allows the contributions of the t¯ tevents with trueand fake-τhad-vis to be determined while correcting for the modelling of the t¯ tsimulation that is common to both contributions. Separate fits are performed for different trigger categories. For 1prong fake-τhad-vis the SFs are close to unity at fake-τhad-vis pTbelow 40 GeV and decrease to SF ∼0.6for fake-τhad-vis pTabove 70 GeV. The SFs for the 3-prong fake-τhad-vis are generally about ∼20% larger than for the 1-prong fake-τhad-vis objects. The t¯ tbackground with fake-τhad-vis in the τhadτhad SR is estimated from the simulated events that pass the SR selection, weighted by the corresponding SFs for each fake-τhad-vis in the event. Uncertainties in the detector response and in the modelling of the t¯ tevents, as well as of the other minor contributing processes, are taken into account in the likelihood fit when extracting the SFs. The covariance matrix of the measured SFs, which contains all statistical and systematic uncertainties of the measurement, is diagonalised and the resulting eigenvectors are used to define independent nuisance parameters which are propagated to the final signal extraction fit. Theoretical modelling uncertainties in simulated t¯ tevents to which the SFs are applied are estimated as described in section 7and they are also propagated to the final signal extraction fit. When estimating the fake-τhad-vis background from multi-jet production using the fakefactor method (cf. section 6.2.1), a large fraction of t¯ tevents containing at least one fakeτhad-vis needs to be subtracted from data in the OS 2-b-tagged-jet anti-ID region (SR Template) to estimate the multi-jet contribution in the τhadτhad SR. The modelling of the simulated t¯ tevents with fake-τhad-vis in the anti-ID region is corrected with SFs that are measured using the same method as described above. These SFs are measured in a control region similar to the t¯ tCR except that the τhad-vis candidate has to satisfy the anti-τhad-vis requirements. The measured SFs for 1-prong fake-τhad-vis in the anti-ID region are close to unity at fake-τhad-vis pTbelow 40 GeV, and follow the same trend of decreasing in value with increasing fake-τhad-vis pT, as observed in the ID region. The SFs for the 3-prong fake-τhad-vis are generally about 10%–20% larger than for the 1-prong fake-τhad-vis objects. 7 Systematic uncertainties The most significant uncertainty in this search is the statistical uncertainty of the data in the signal region. Nonetheless, experimental, theoretical, and modelling uncertainties in – 23 – JHEP07(2023)040 the normalisation and shape of the signal and background estimates give a non-negligible contribution to the total uncertainty and are thus evaluated for use in the statistical model (described in section 8); these are described below. Statistical uncertainties in the predicted background processes are modelled using a simplified version of the Beeston–Barlow technique [162], in which only the uncertainty in the total background content in each bin is considered. Systematic uncertainties in the detector response are considered in this search. Where relevant, uncertainties in the trigger, reconstruction, identification, and isolation efficiencies, and in the momentum of electrons [133], muons [134] and τhad-vis [150] are estimated. Additional uncertainties are estimated for the efficiencies of the electron veto for τhad-vis and the track-to-vertex-matching requirements for muons. Jet energy scale and resolution uncertainties [140] and the uncertainty in the efficiency of matching jets to the primary vertex [141] are estimated. These energy scale and resolution uncertainties, in addition to an uncertainty in the tracks matched to the primary vertex but not associated with other reconstructed objects in the event, are propagated to the Emiss Tcalculation [152,163]. Uncertainties in the b-jet tagging efficiency are considered, and these are evaluated as a function of pTfor b-tagged jets and c-tagged jets by using t¯ tevents [145,164], and as a function of pTfor light-flavour jets by using Z+jets events [165]. An uncertainty of 1.7% in the total integrated luminosity [79], obtained using the LUCID-2 detector [80], is assigned to physics processes whose normalisations are taken from simulation. An uncertainty arising from the correction of the pile-up distribution in simulation to that in data is also estimated. The effects of uncertainties in the parton shower on the acceptances of the resonant (non-resonant) HH signals are assessed by comparing the nominal MC samples with alternative samples which use Pythia 8 (Herwig 7) to model the parton shower. Alternative samples were not generated for some of the resonant HH signals, and uncertainties for these signals are derived by interpolating or extrapolating the uncertainties from signals nearby in mXfor which alternative samples were produced. Independent parton-shower acceptance uncertainties are accounted for in the normalisation of the non-resonant ggF, non-resonant VBF and resonant HH signals, and these range between 0.1% and 19% of the nominal acceptance values. The effects of the renormalisation and factorisation scale, PDF and αsuncertainties on the ggF and VBF HH signal acceptances are included when they are found to be non-negligible. Separate normalisation uncertainties are applied in the τhadτhad and τlepτhad LTT categories to account for observed differences in the τhad-vis trigger acceptances between fast-simulation and full-simulation resonant HH signal MC samples. The effects of renormalisation and factorisation scale, PDF and αsuncertainties on the SM ggF and VBF HH cross-sections are considered, and an uncertainty arising from the scheme and value used for the top-quark mass in calculations of the virtual top-quark loop contributions in the SM ggF HH cross-section [21] is included. Lastly, uncertainties in the H→b¯ band H→τ+τ−branching fractions [71] are considered in the HH signal and single-Higgs-boson background samples. The t¯ tnormalisation is determined in the likelihood fits, so the analysis is not sensitive to uncertainties in its expected cross-section. However, the relative acceptances of the anal- – 24 – JHEP07(2023)040 The most significant combined excess is at a signal mass hypothesis of 1 TeV with a local significance of 3.1σand a global significance of 2.0σ. The p-value defining the global significance is given by the probability of finding a maximum local excess larger than the observed value under the background-only hypothesis, regardless of the resonance mass value mXwhere the largest excess is found. It is estimated using fits of ‘toy’ experiments drawn from a model providing a joint description of all observables in the statistical interpretation. The set of observables comprises the number of events observed in a bin entering either a signal or control region, separately for all bins, as well as the central values of auxiliary measurements used to propagate systematic uncertainties to the results. This model differs from the probabilistic model used to define the likelihood functions since these describe only one PNN discriminant at a time, targeting a single signal mass hypothesis. However, non-trivial correlations between observables are expected since events are subject to the same selection criteria regardless of the resonance mass value mXto be probed, but different multivariate discriminants are used for each of these signal hypotheses. Observables describing the number of events in a given bin are generated from a multivariate Poisson distribution with the dependence structure between observables given by the copula [172] of a centred multivariate normal distribution with covariance matrix given by the expected linear correlations estimated from a simulationand data-driven background model. Observables describing the statistical precision of the background estimate are generated using a resampling approach [173]. Observables associated with all other experimental and theoretical uncertainties are varied coherently for all hypothesis tests. 10 Conclusion A search for non-resonant and resonant Higgs boson pair production in b¯ bτ+τ−events is conducted, where the non-resonant signal is assumed to be produced with SM kinematics, and the resonant signal corresponds to a narrow scalar resonance with a mass mXin the range 251 to 1600 GeV. The 13 TeV pp collision dataset used was collected at the LHC by the ATLAS experiment between 2015 and 2018, and corresponds to an integrated luminosity of 139 fb−1. The sensitivity of this search to the non-resonant signal hypothesis improves on the previous ATLAS search in this channel by around a factor of four. Roughly half of this improvement is due to the larger dataset, while most of the remaining sensitivity gain is due to significant improvements in the τhad-vis and b-jet reconstruction and identification. Analysis-level improvements include the use of more sophisticated multivariate techniques to target the resonant signal hypotheses, and new fake-τhad-vis estimation methods. The data are found to be compatible with the background-only hypothesis, with the largest deviation being found in the search for resonant HH production at a mass of 1 TeV and corresponding to a local (global) significance of 3.1σ(2.0σ). The observed (expected) upper limit on the non-resonant Higgs boson pair-production cross-section, set at the 95% confidence level, is 4.7 (3.9) times the SM expectation. Observed (expected) upper limits are placed at the 95% confidence level on resonant Higgs boson production and exclude cross-sections above 21–900fb (12–840fb), depending on the mass of the resonance. This – 31 – JHEP07(2023)040 400 600 800 1000 1200 1400 1600 [GeV] X m 10 2 10 3 10 HH) [fb]→ X → (pp σ95% CL limits on ATLAS -1 = 13 TeV, 139 fbs Comb. Exp. Exp. had τ lep τ Comb. Obs. Obs. had τ lep τ σ1±Comb. Exp. Exp. had τ had τ σ2±Comb. Exp. Obs. had τ had τ Figure 10. Observed and expected limits at 95% CL on the cross-section for resonant HH production as a function of the scalar resonance mass mX. The dashed lines show the expected limits while the solid lines show the observed limits. The blue and red lines are the limits for the τhadτhad channel and τlepτhad channel, respectively. The black lines are the combined limits of the two channels. The ±1σand ±2σvariations around the expected combined limit are indicated by the turquoise and yellow bands, respectively. The limits are obtained using the profile-likelihood test statistic and the modified frequentist CLstechnique. search provides the highest expected sensitivity to non-resonant HH production of any individual search to date, and provides limits on resonant HH production that are more stringent than, or competitive with, the most recently published ATLAS and CMS HH resonant search combinations over much of the mXrange explored. 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 – 32 – JHEP07(2023)040 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. 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 (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [174]. Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. SCOAP3supports the goals of the International Year of Basic Sciences for Sustainable Development. References [1] ATLAS collaboration, Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC,Phys. Lett. B 716 (2012) 1 [arXiv:1207.7214] [INSPIRE]. [2] CMS collaboration, Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC,Phys. Lett. B 716 (2012) 30 [arXiv:1207.7235] [INSPIRE]. [3] ATLAS collaboration, The ATLAS Experiment at the CERN Large Hadron Collider,2008 JINST 3S08003 [INSPIRE]. [4] CMS collaboration, The CMS experiment at the CERN LHC,2008 JINST 3S08004 [INSPIRE]. [5] L. Evans and P. Bryant, LHC Machine,2008 JINST 3S08001 [INSPIRE]. [6] ATLAS collaboration, Study of the spin and parity of the Higgs boson in diboson decays with the ATLAS detector,Eur. Phys. J. C 75 (2015) 476 [Erratum ibid. 76 (2016) 152] [arXiv:1506.05669] [INSPIRE]. [7] ATLAS collaboration, CP Properties of Higgs Boson Interactions with Top Quarks in the t¯ tH and tH Processes Using H→γγ with the ATLAS Detector,Phys. Rev. Lett. 125 (2020) 061802 [arXiv:2004.04545] [INSPIRE]. [8] CMS collaboration, Study of the Mass and Spin-Parity of the Higgs Boson Candidate Via Its Decays to Z Boson Pairs,Phys. Rev. Lett. 110 (2013) 081803 [Erratum ibid. 110 (2013) 189901] [arXiv:1212.6639] [INSPIRE]. – 33 – JHEP07(2023)040 [9] CMS collaboration, Constraints on anomalous Higgs boson couplings to vector bosons and fermions in its production and decay using the four-lepton final state,Phys. Rev. D 104 (2021) 052004 [arXiv:2104.12152] [INSPIRE]. [10] ATLAS collaboration, Constraints on off-shell Higgs boson production and the Higgs boson total width in ZZ →4`and ZZ →2`2νfinal states with the ATLAS detector,Phys. Lett. B 786 (2018) 223 [arXiv:1808.01191] [INSPIRE]. [11] CMS collaboration, Measurements of the Higgs boson width and anomalous HV V couplings from on-shell and off-shell production in the four-lepton final state,Phys. Rev. D 99 (2019) 112003 [arXiv:1901.00174] [INSPIRE]. [12] ATLAS collaboration, A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery,Nature 607 (2022) 52 [Erratum ibid. 612 (2022) E24] [arXiv:2207.00092] [INSPIRE]. [13] CMS collaboration, A portrait of the Higgs boson by the CMS experiment ten years after the discovery,Nature 607 (2022) 60 [arXiv:2207.00043] [INSPIRE]. [14] S. Dawson, S. Dittmaier and M. Spira, Neutral Higgs boson pair production at hadron colliders: QCD corrections,Phys. Rev. D 58 (1998) 115012 [hep-ph/9805244] [INSPIRE]. [15] S. Borowka et al., Higgs Boson Pair Production in Gluon Fusion at Next-to-Leading Order with Full Top-Quark Mass Dependence,Phys. Rev. Lett. 117 (2016) 012001 [Erratum ibid. 117 (2016) 079901] [arXiv:1604.06447] [INSPIRE]. [16] J. Baglio et al., Gluon fusion into Higgs pairs at NLO QCD and the top mass scheme,Eur. Phys. J. C 79 (2019) 459 [arXiv:1811.05692] [INSPIRE]. [17] D. de Florian and J. Mazzitelli, Higgs Boson Pair Production at Next-to-Next-to-Leading Order in QCD,Phys. Rev. Lett. 111 (2013) 201801 [arXiv:1309.6594] [INSPIRE]. [18] D.Y. Shao, C.S. Li, H.T. Li and J. Wang, Threshold resummation effects in Higgs boson pair production at the LHC,JHEP 07 (2013) 169 [arXiv:1301.1245] [INSPIRE]. [19] D. de Florian and J. Mazzitelli, Higgs pair production at next-to-next-to-leading logarithmic accuracy at the LHC,JHEP 09 (2015) 053 [arXiv:1505.07122] [INSPIRE]. [20] M. Grazzini et al., Higgs boson pair production at NNLO with top quark mass effects,JHEP 05 (2018) 059 [arXiv:1803.02463] [INSPIRE]. [21] J. Baglio et al., gg →HH : Combined uncertainties,Phys. Rev. D 103 (2021) 056002 [arXiv:2008.11626] [INSPIRE]. [22] J. Baglio et al., The measurement of the Higgs self-coupling at the LHC: theoretical status, JHEP 04 (2013) 151 [arXiv:1212.5581] [INSPIRE]. [23] R. Frederix et al., Higgs pair production at the LHC with NLO and parton-shower effects, Phys. Lett. B 732 (2014) 142 [arXiv:1401.7340] [INSPIRE]. [24] L.-S. Ling et al., NNLO QCD corrections to Higgs pair production via vector boson fusion at hadron colliders,Phys. Rev. D 89 (2014) 073001 [arXiv:1401.7754] [INSPIRE]. [25] F.A. Dreyer and A. Karlberg, Fully differential vector-boson fusion higgs pair production at next-to-next-to-leading order,Phys. Rev. D 99 (2019) 074028 [arXiv:1811.07918] [INSPIRE]. [26] F.A. Dreyer and A. Karlberg, Vector-boson fusion Higgs pair production at N3LO,Phys. Rev. D 98 (2018) 114016 [arXiv:1811.07906] [INSPIRE]. [27] V. Branchina, H. Faivre and V. Pangon, Effective potential and vacuum stability,J. Phys. G36 (2009) 015006 [arXiv:0802.4423] [INSPIRE]. – 34 – JHEP07(2023)040 [28] D. Buttazzo et al., Investigating the near-criticality of the Higgs boson,JHEP 12 (2013) 089 [arXiv:1307.3536] [INSPIRE]. [29] M. Reichert et al., Probing baryogenesis through the Higgs boson self-coupling,Phys. Rev. D 97 (2018) 075008 [arXiv:1711.00019] [INSPIRE]. [30] F.L. Bezrukov and M. Shaposhnikov, The Standard Model Higgs boson as the inflaton, Phys. Lett. B 659 (2008) 703 [arXiv:0710.3755] [INSPIRE]. [31] I. Masina and A. Notari, The Higgs mass range from Standard Model false vacuum Inflation in scalar-tensor gravity,Phys. Rev. D 85 (2012) 123506 [arXiv:1112.2659] [INSPIRE]. [32] G.D. Kribs and A. Martin, Enhanced di-Higgs production through light colored scalars, Phys. Rev. D 86 (2012) 095023 [arXiv:1207.4496] [INSPIRE]. [33] R. Gröber and M. Mühlleitner, Composite Higgs boson pair production at the LHC,JHEP 06 (2011) 020 [arXiv:1012.1562] [INSPIRE]. [34] R. Contino et al., Anomalous couplings in double Higgs production,JHEP 08 (2012) 154 [arXiv:1205.5444] [INSPIRE]. [35] ATLAS collaboration, Search for resonant and non-resonant Higgs boson pair production in the b¯ bτ+τ−decay channel in pp collisions at √s= 13 TeV with the ATLAS detector, Phys. Rev. Lett. 121 (2018) 191801 [Erratum ibid. 122 (2019) 089901] [arXiv:1808.00336] [INSPIRE]. [36] CMS collaboration, Search for Higgs boson pair production in events with two bottom quarks and two tau leptons in proton–proton collisions at √s= 13 TeV,Phys. Lett. B 778 (2018) 101 [arXiv:1707.02909] [INSPIRE]. [37] ATLAS collaboration, Search for Higgs boson pair production in the γγb¯ bfinal state with 13 TeV pp collision data collected by the ATLAS experiment,JHEP 11 (2018) 040 [arXiv:1807.04873] [INSPIRE]. [38] CMS collaboration, Search for Higgs boson pair production in the γγbb final state in pp collisions at √s= 13 TeV,Phys. Lett. B 788 (2019) 7 [arXiv:1806.00408] [INSPIRE]. [39] ATLAS collaboration, Search for pair production of Higgs bosons in the b¯ bb¯ bfinal state using proton-proton collisions at √s= 13 TeV with the ATLAS detector,JHEP 01 (2019) 030 [arXiv:1804.06174] [INSPIRE]. [40] CMS collaboration, Search for nonresonant Higgs boson pair production in the bbbb final state at √s= 13 TeV,JHEP 04 (2019) 112 [arXiv:1810.11854] [INSPIRE]. [41] ATLAS collaboration, Search for Higgs boson pair production in the b¯ bWW ∗decay mode at √s= 13 TeV with the ATLAS detector,JHEP 04 (2019) 092 [arXiv:1811.04671] [INSPIRE]. [42] ATLAS collaboration, Search for Higgs boson pair production in the γγWW∗channel using pp collision data recorded at √s= 13 TeV with the ATLAS detector,Eur. Phys. J. C 78 (2018) 1007 [arXiv:1807.08567] [INSPIRE]. [43] ATLAS collaboration, Search for Higgs boson pair production in the W W(∗)WW(∗)decay channel using ATLAS data recorded at √s= 13 TeV,JHEP 05 (2019) 124 [arXiv:1811.11028] [INSPIRE]. [44] CMS collaboration, Search for resonant and nonresonant Higgs boson pair production in the b¯ b`ν`ν final state in proton-proton collisions at √s= 13 TeV,JHEP 01 (2018) 054 [arXiv:1708.04188] [INSPIRE]. – 35 – JHEP07(2023)040 [45] ATLAS collaboration, Combination of searches for Higgs boson pairs in pp collisions at √s= 13 TeV with the ATLAS detector,Phys. Lett. B 800 (2020) 135103 [arXiv:1906.02025] [INSPIRE]. [46] CMS collaboration, Combination of searches for Higgs boson pair production in proton-proton collisions at √s= 13 TeV,Phys. Rev. Lett. 122 (2019) 121803 [arXiv:1811.09689] [INSPIRE]. [47] ATLAS collaboration, Search for the HH →b¯ bb¯ bprocess via vector-boson fusion production using proton-proton collisions at √s= 13 TeV with the ATLAS detector,JHEP 07 (2020) 108 [Erratum ibid. 01 (2021) 145] [arXiv:2001.05178] [INSPIRE]. [48] CMS collaboration, Search for Higgs boson pair production in the four b quark final state in proton-proton collisions at √s= 13 TeV,Phys. Rev. Lett. 129 (2022) 081802 [arXiv:2202.09617] [INSPIRE]. [49] CMS collaboration, Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks,arXiv:2205.06667 [INSPIRE]. [50] ATLAS collaboration, Search for Higgs boson pair production in the two bottom quarks plus two photons final state in pp collisions at √s= 13 TeV with the ATLAS detector,Phys. Rev. D 106 (2022) 052001 [arXiv:2112.11876] [INSPIRE]. [51] CMS collaboration, Search for nonresonant Higgs boson pair production in final states with two bottom quarks and two photons in proton-proton collisions at √s= 13 TeV,JHEP 03 (2021) 257 [arXiv:2011.12373] [INSPIRE]. [52] CMS collaboration, Search for nonresonant Higgs boson pair production in final state with two bottom quarks and two tau leptons in proton-proton collisions at √s= 13 TeV,Phys. Lett. B 842 (2023) 137531 [arXiv:2206.09401] [INSPIRE]. [53] CMS collaboration, Search for nonresonant Higgs boson pair production in the four leptons plus two b jets final state in proton-proton collisions at √s= 13 TeV,arXiv:2206.10657 [INSPIRE]. [54] CMS collaboration, Search for Higgs boson pairs decaying to WWWW, WWττ, and ττττ in proton-proton collisions at √s= 13 TeV,arXiv:2206.10268 [INSPIRE]. [55] ATLAS collaboration, Search for non-resonant Higgs boson pair production in the bb`ν`ν final state with the ATLAS detector in pp collisions at √s= 13 TeV,Phys. Lett. B 801 (2020) 135145 [arXiv:1908.06765] [INSPIRE]. [56] ATLAS collaboration, Constraints on the Higgs boson self-coupling from singleand double-Higgs production with the ATLAS detector using pp collisions at √s= 13 TeV,Phys. Lett. B 843 (2023) 137745 [arXiv:2211.01216] [INSPIRE]. [57] G.C. Branco et al., Theory and phenomenology of two-Higgs-doublet models,Phys. Rept. 516 (2012) 1 [arXiv:1106.0034] [INSPIRE]. [58] H.E. Haber and G.L. Kane, The Search for Supersymmetry: Probing Physics Beyond the Standard Model,Phys. Rept. 117 (1985) 75 [INSPIRE]. [59] S. Dimopoulos and H. Georgi, Softly broken supersymmetry and SU(5),Nucl. Phys. B 193 (1981) 150 [INSPIRE]. [60] Z. Chacko, Y. Nomura, M. Papucci and G. Perez, Natural little hierarchy from a partially goldstone twin Higgs,JHEP 01 (2006) 126 [hep-ph/0510273] [INSPIRE]. [61] J. Mrazek et al., The other natural two Higgs doublet model,Nucl. Phys. B 853 (2011) 1 [arXiv:1105.5403] [INSPIRE]. – 36 – JHEP07(2023)040 [62] L. Randall and R. Sundrum, A large mass hierarchy from a small extra dimension,Phys. Rev. Lett. 83 (1999) 3370 [hep-ph/9905221] [INSPIRE]. [63] Y. Tang, Implications of LHC searches for massive graviton,JHEP 08 (2012) 078 [arXiv:1206.6949] [INSPIRE]. [64] K.-M. Cheung, Phenomenology of radion in Randall-Sundrum scenario,Phys. Rev. D 63 (2001) 056007 [hep-ph/0009232] [INSPIRE]. [65] N. Kumar and S.P. Martin, LHC search for di-Higgs decays of stoponium and other scalars in events with two photons and two bottom jets,Phys. Rev. D 90 (2014) 055007 [arXiv:1404.0996] [INSPIRE]. [66] ATLAS collaboration, Search for resonant pair production of Higgs bosons in the b¯ bb¯ bfinal state using pp collisions at √s= 13 TeV with the ATLAS detector,Phys. Rev. D 105 (2022) 092002 [arXiv:2202.07288] [INSPIRE]. [67] CMS collaboration, Search for a heavy Higgs boson decaying into two lighter Higgs bosons in the ττbb final state at 13 TeV,JHEP 11 (2021) 057 [arXiv:2106.10361] [INSPIRE]. [68] CMS collaboration, Search for resonant pair production of Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at 13 TeV,JHEP 08 (2018) 152 [arXiv:1806.03548] [INSPIRE]. [69] CMS collaboration, Search for resonant pair production of Higgs bosons in the bbZZ channel in proton-proton collisions at √s= 13 TeV,Phys. Rev. D 102 (2020) 032003 [arXiv:2006.06391] [INSPIRE]. [70] ATLAS collaboration, Reconstruction and identification of boosted di-τsystems in a search for Higgs boson pairs using 13 TeV proton-proton collision data in ATLAS,JHEP 11 (2020) 163 [arXiv:2007.14811] [INSPIRE]. [71] LHC Higgs Cross Section Working Group collaboration, Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector,arXiv:1610.07922 [DOI:10.23731/CYRM-2017-002] [INSPIRE]. [72] A. Djouadi, J. Kalinowski and M. Spira, HDECAY: A Program for Higgs boson decays in the standard model and its supersymmetric extension,Comput. Phys. Commun. 108 (1998) 56 [hep-ph/9704448] [INSPIRE]. [73] ATLAS collaboration, ATLAS Insertable B-Layer Technical Design Report, ATLAS-TDR-19 (2010) [Addendum: ATLAS-TDR-19-ADD-1]. [74] ATLAS IBL collaboration, Production and Integration of the ATLAS Insertable B-Layer, 2018 JINST 13 T05008 [arXiv:1803.00844] [INSPIRE]. [75] ATLAS collaboration, Expected performance of the ATLAS b-tagging algorithms in Run-2, ATL-PHYS-PUB-2015-022, CERN, Geneva (2015). [76] ATLAS collaboration, Performance of the ATLAS trigger system in 2015,Eur. Phys. J. C 77 (2017) 317 [arXiv:1611.09661] [INSPIRE]. [77] ATLAS collaboration, The ATLAS collaboration software and firmware, ATL-SOFT-PUB-2021-001, CERN, Geneva (2021). [78] ATLAS collaboration, ATLAS data quality operations and performance for 2015–2018 data-taking,2020 JINST 15 P04003 [arXiv:1911.04632] [INSPIRE]. [79] ATLAS collaboration, Luminosity determination in pp collisions at √s= 13 TeV using the ATLAS detector at the LHC,ATLAS-CONF-2019-021, CERN, Geneva (2019). – 37 – JHEP07(2023)040 [80] G. Avoni et al., The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS,2018 JINST 13 P07017 [INSPIRE]. [81] ATLAS collaboration, The ATLAS Simulation Infrastructure,Eur. Phys. J. C 70 (2010) 823 [arXiv:1005.4568] [INSPIRE]. [82] GEANT4 collaboration, GEANT4 — a simulation toolkit,Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE]. [83] ATLAS collaboration, The simulation principle and performance of the ATLAS fast calorimeter simulation FastCaloSim,ATL-PHYS-PUB-2010-013, CERN, Geneva (2010). [84] ATLAS collaboration, The new Fast Calorimeter Simulation in ATLAS, ATL-SOFT-PUB-2018-002, CERN, Geneva (2018). [85] T. Sjöstrand, S. Mrenna and P.Z. Skands, A brief introduction to PYTHIA 8.1,Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE]. [86] ATLAS collaboration, The Pythia 8 A3 tune description of ATLAS minimum bias and inelastic measurements incorporating the Donnachie-Landshoff diffractive model, ATL-PHYS-PUB-2016-017, CERN, Geneva (2016). [87] R.D. Ball et al., Parton distributions with LHC data,Nucl. Phys. B 867 (2013) 244 [arXiv:1207.1303] [INSPIRE]. [88] D.J. Lange, The EvtGen particle decay simulation package,Nucl. Instrum. Meth. A 462 (2001) 152 [INSPIRE]. [89] E. Bothmann, Event generation with Sherpa 2.2,SciPost Phys. 7(2019) 034 [arXiv:1905.09127] [INSPIRE]. [90] S. Alioli, P. Nason, C. Oleari and E. Re, A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX,JHEP 06 (2010) 043 [arXiv:1002.2581] [INSPIRE]. [91] A. Buckley et al., LHAPDF6: parton density access in the LHC precision era,Eur. Phys. J. C75 (2015) 132 [arXiv:1412.7420] [INSPIRE]. [92] J. Butterworth et al., PDF4LHC recommendations for LHC Run II,J. Phys. G 43 (2016) 023001 [arXiv:1510.03865] [INSPIRE]. [93] ATLAS collaboration, ATLAS Pythia 8 tunes to 7 TeV data,ATL-PHYS-PUB-2014-021, CERN, Geneva (2014). [94] ATLAS collaboration, Summary of ATLAS Pythia 8 tunes, ATL-PHYS-PUB-2012-003 (2012) [INSPIRE]. [95] J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE]. [96] NNPDF collaboration, Parton distributions for the LHC Run II,JHEP 04 (2015) 040 [arXiv:1410.8849] [INSPIRE]. [97] M. Bähr et al., Herwig++ physics and manual,Eur. Phys. J. C 58 (2008) 639 [arXiv:0803.0883] [INSPIRE]. [98] J. Bellm et al., Herwig 7.0/Herwig++ 3.0 release note,Eur. Phys. J. C 76 (2016) 196 [arXiv:1512.01178] [INSPIRE]. [99] S. Gieseke, C. Röhr and A. Siodmok, Colour reconnections in Herwig++,Eur. Phys. J. C 72 (2012) 2225 [arXiv:1206.0041] [INSPIRE]. – 38 – JHEP07(2023)040 [100] M. Czakon and A. Mitov, Top++: a program for the calculation of the top-pair cross-section at hadron colliders,Comput. Phys. Commun. 185 (2014) 2930 [arXiv:1112.5675] [INSPIRE]. [101] N. Kidonakis, NNLL resummation for s-channel single top quark production,Phys. Rev. D 81 (2010) 054028 [arXiv:1001.5034] [INSPIRE]. [102] N. Kidonakis, Next-to-next-to-leading-order collinear and soft gluon corrections for t-channel single top quark production,Phys. Rev. D 83 (2011) 091503 [arXiv:1103.2792] [INSPIRE]. [103] N. Kidonakis, Two-loop soft anomalous dimensions for single top quark associated production with a W−or H−,Phys. Rev. D 82 (2010) 054018 [arXiv:1005.4451] [INSPIRE]. [104] F. Cascioli, P. Maierhöfer and S. Pozzorini, Scattering Amplitudes with Open Loops,Phys. Rev. Lett. 108 (2012) 111601 [arXiv:1111.5206] [INSPIRE]. [105] T. Gleisberg and S. Höche, Comix, a new matrix element generator,JHEP 12 (2008) 039 [arXiv:0808.3674] [INSPIRE]. [106] S. Catani et al., Vector boson production at hadron colliders: a fully exclusive QCD calculation at NNLO,Phys. Rev. Lett. 103 (2009) 082001 [arXiv:0903.2120] [INSPIRE]. [107] K. Hamilton, P. Nason, E. Re and G. Zanderighi, NNLOPS simulation of Higgs boson production,JHEP 10 (2013) 222 [arXiv:1309.0017] [INSPIRE]. [108] K. Hamilton, P. Nason and G. Zanderighi, Finite quark-mass effects in the NNLOPS POWHEG+MiNLO Higgs generator,JHEP 05 (2015) 140 [arXiv:1501.04637] [INSPIRE]. [109] P. Nason and C. Oleari, NLO Higgs boson production via vector-boson fusion matched with shower in POWHEG,JHEP 02 (2010) 037 [arXiv:0911.5299] [INSPIRE]. [110] ATLAS collaboration, Measurement of the Z/γ∗boson transverse momentum distribution in pp collisions at √s= 7 TeV with the ATLAS detector,JHEP 09 (2014) 145 [arXiv:1406.3660] [INSPIRE]. [111] J. Pumplin et al., New generation of parton distributions with uncertainties from global QCD analysis,JHEP 07 (2002) 012 [hep-ph/0201195] [INSPIRE]. [112] C. Anastasiou et al., High precision determination of the gluon fusion Higgs boson cross-section at the LHC,JHEP 05 (2016) 058 [arXiv:1602.00695] [INSPIRE]. [113] C. Anastasiou et al., Higgs boson gluon-fusion production in QCD at three loops,Phys. Rev. Lett. 114 (2015) 212001 [arXiv:1503.06056] [INSPIRE]. [114] F. Dulat, A. Lazopoulos and B. Mistlberger, iHixs 2 — Inclusive Higgs cross sections, Comput. Phys. Commun. 233 (2018) 243 [arXiv:1802.00827] [INSPIRE]. [115] S. Actis, G. Passarino, C. Sturm and S. Uccirati, NLO electroweak corrections to higgs boson production at hadron colliders,Phys. Lett. B 670 (2008) 12 [arXiv:0809.1301] [INSPIRE]. [116] M. Ciccolini, A. Denner and S. Dittmaier, Strong and electroweak corrections to the production of Higgs + 2jets via weak interactions at the LHC,Phys. Rev. Lett. 99 (2007) 161803 [arXiv:0707.0381] [INSPIRE]. [117] M. Ciccolini, A. Denner and S. Dittmaier, Electroweak and QCD corrections to Higgs production via vector-boson fusion at the LHC,Phys. Rev. D 77 (2008) 013002 [arXiv:0710.4749] [INSPIRE]. [118] P. Bolzoni, F. Maltoni, S.-O. Moch and M. Zaro, Higgs production via vector-boson fusion at NNLO in QCD,Phys. Rev. Lett. 105 (2010) 011801 [arXiv:1003.4451] [INSPIRE]. – 39 – JHEP07(2023)040 [119] M.L. Ciccolini, S. Dittmaier and M. Krämer, Electroweak radiative corrections to associated WH and ZH production at hadron colliders,Phys. Rev. D 68 (2003) 073003 [hep-ph/0306234] [INSPIRE]. [120] O. Brein, A. Djouadi and R. Harlander, NNLO QCD corrections to the Higgs-strahlung processes at hadron colliders,Phys. Lett. B 579 (2004) 149 [hep-ph/0307206] [INSPIRE]. [121] G. Ferrera, M. Grazzini and F. Tramontano, Associated WH production at hadron colliders: a fully exclusive QCD calculation at NNLO,Phys. Rev. Lett. 107 (2011) 152003 [arXiv:1107.1164] [INSPIRE]. [122] O. Brein, R. Harlander, M. Wiesemann and T. Zirke, Top-quark mediated effects in hadronic Higgs-strahlung,Eur. Phys. J. C 72 (2012) 1868 [arXiv:1111.0761] [INSPIRE]. [123] G. Ferrera, M. Grazzini and F. Tramontano, Higher-order QCD effects for associated WH production and decay at the LHC,JHEP 04 (2014) 039 [arXiv:1312.1669] [INSPIRE]. [124] G. Ferrera, M. Grazzini and F. Tramontano, Associated ZH production at hadron colliders: the fully differential NNLO QCD calculation,Phys. Lett. B 740 (2015) 51 [arXiv:1407.4747] [INSPIRE]. [125] J.M. Campbell, R.K. Ellis and C. Williams, Associated production of a Higgs boson at NNLO,JHEP 06 (2016) 179 [arXiv:1601.00658] [INSPIRE]. [126] L. Altenkamp et al., Gluon-induced Higgs-strahlung at next-to-leading order QCD,JHEP 02 (2013) 078 [arXiv:1211.5015] [INSPIRE]. [127] B. Hespel, F. Maltoni and E. Vryonidou, Higgs and Z boson associated production via gluon fusion in the SM and the 2HDM,JHEP 06 (2015) 065 [arXiv:1503.01656] [INSPIRE]. [128] R.V. Harlander, A. Kulesza, V. Theeuwes and T. Zirke, Soft gluon resummation for gluon-induced Higgs Strahlung,JHEP 11 (2014) 082 [arXiv:1410.0217] [INSPIRE]. [129] R.V. Harlander, S. Liebler and T. Zirke, Higgs Strahlung at the Large Hadron Collider in the 2-Higgs-Doublet Model,JHEP 02 (2014) 023 [arXiv:1307.8122] [INSPIRE]. [130] O. Brein, R.V. Harlander and T.J.E. Zirke, vh@nnlo — Higgs Strahlung at hadron colliders, Comput. Phys. Commun. 184 (2013) 998 [arXiv:1210.5347] [INSPIRE]. [131] ATLAS collaboration, Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2,Eur. Phys. J. C 77 (2017) 673 [arXiv:1704.07983] [INSPIRE]. [132] ATLAS collaboration, Early Inner Detector Tracking Performance in the 2015 Data at √s= 13 TeV,ATL-PHYS-PUB-2015-051 (2015). [133] ATLAS collaboration, Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data,2019 JINST 14 P12006 [arXiv:1908.00005] [INSPIRE]. [134] ATLAS collaboration, Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at √s= 13 TeV,Eur. Phys. J. C 81 (2021) 578 [arXiv:2012.00578] [INSPIRE]. [135] ATLAS collaboration, Jet reconstruction and performance using particle flow with the ATLAS Detector,Eur. Phys. J. C 77 (2017) 466 [arXiv:1703.10485] [INSPIRE]. [136] M. Cacciari, G.P. Salam and G. Soyez, The anti-ktjet clustering algorithm,JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE]. [137] M. Cacciari, G.P. Salam and G. Soyez, FastJet user manual,Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE]. – 40 – JHEP07(2023)040 F.M. Garay Walls 134a, C. García 160, J.E. García Navarro 160, J.A. García Pascual 13a, M. Garcia-Sciveres 16a, R.W. Gardner 37, D. Garg 77, R.B. Garg 141,ak, S. Gargiulo 52, C.A. Garner153, V. Garonne 122, S.J. Gasiorowski 136, P. Gaspar 79b, G. Gaudio 70a, P. Gauzzi 72a,72b, I.L. Gavrilenko 35, A. Gavrilyuk 35, C. Gay 161, G. Gaycken 46, E.N. Gazis 9, A.A. Geanta 25b, C.M. Gee 133, C.N.P. Gee 131, J. Geisen 95, M. Geisen 97, C. Gemme 55b, M.H. Genest 58, S. Gentile 72a,72b, S. George 92, W.F. George 19, T. Geralis 44, L.O. Gerlach53, P. Gessinger-Befurt 34, M. Ghasemi Bostanabad 162, M. Ghneimat 139, A. Ghosh 157, A. Ghosh 77, B. Giacobbe 21b, S. Giagu 72a,72b, N. Giangiacomi 153, P. Giannetti 71a, A. Giannini 69a,69b, S.M. Gibson 92, M. Gignac 133, D.T. Gil 82b, B.J. Gilbert 39, D. Gillberg 32, G. Gilles 111, N.E.K. Gillwald 46, D.M. Gingrich 2,af , M.P. Giordani 66a,66c, P.F. Giraud 132, G. Giugliarelli 66a,66c, D. Giugni 68a, F. Giuli 73a,73b, I. Gkialas 8,i, P. Gkountoumis 9, L.K. Gladilin 35, C. Glasman 96, G.R. Gledhill 120, M. Glisic120, I. Gnesi 41b,e, M. Goblirsch-Kolb 24, D. Godin105, S. Goldfarb 102, T. Golling 54, D. Golubkov 35, J.P. Gombas 104, A. Gomes 127a,127b, R. Goncalves Gama 53, R. Gonçalo 127a,127c, G. Gonella 120, L. Gonella 19, A. Gongadze 36, F. Gonnella 19, J.L. Gonski 39, R.Y. González Andana 134a, S. González de la Hoz 160, S. Gonzalez Fernandez 12, R. Gonzalez Lopez 89, C. Gonzalez Renteria 16a, R. Gonzalez Suarez 158, S. Gonzalez-Sevilla 54, G.R. Gonzalvo Rodriguez 160, L. Goossens 34, N.A. Gorasia 19, P.A. Gorbounov 35, B. Gorini 34, E. Gorini 67a,67b, A. Gorišek 90, A.T. Goshaw 49, M.I. Gostkin 36, C.A. Gottardo 110, M. Gouighri 33b, V. Goumarre 46, A.G. Goussiou 136, N. Govender 31c, C. Goy 4, I. Grabowska-Bold 82a, K. Graham 32, E. Gramstad 122, S. Grancagnolo 17, M. Grandi 144, V. Gratchev35,∗, P.M. Gravila 25f, F.G. Gravili 67a,67b, H.M. Gray 16a, C. Grefe 22, I.M. Gregor 46, P. Grenier 141, K. Grevtsov 46, C. Grieco 12, N.A. Grieser117, A.A. Grillo 133, K. Grimm 29,m, S. Grinstein 12,t, J.-F. Grivaz 64, S. Groh 97, E. Gross 166, J. Grosse-Knetter 53, C. Grud103, A. Grummer 109, J.C. Grundy 123, L. Guan 103, W. Guan 167, C. Gubbels 161, J. Guenther 34, J.G.R. Guerrero Rojas 160, F. Guescini 107, R. Gugel 97, A. Guida 46, T. Guillemin 4, S. Guindon 34, J. Guo 60c, L. Guo 64, Y. Guo 103, R. Gupta 46, S. Gurbuz 22, G. Gustavino 117, M. Guth 54, P. Gutierrez 117, L.F. Gutierrez Zagazeta 125, C. Gutschow 93, C. Guyot 132, C. Gwenlan 123, C.B. Gwilliam 89, E.S. Haaland 122, A. Haas 114, M. Habedank 46, C. Haber 16a, H.K. Hadavand 7, A. Hadef 97, S. Hadzic 107, M. Haleem 163, J. Haley 118, J.J. Hall 137, G. Halladjian 104, G.D. Hallewell 99, L. Halser 18, K. Hamano 162, H. Hamdaoui 33e, M. Hamer 22, G.N. Hamity 50, K. Han 60a, L. Han 13c, L. Han 60a, S. Han 16a, Y.F. Han 153, K. Hanagaki 80, M. Hance 133, M.D. Hank 37, R. Hankache 98, E. Hansen 95, J.B. Hansen 40, J.D. Hansen 40, M.C. Hansen 22, P.H. Hansen 40, K. Hara 155, T. Harenberg 168, S. Harkusha 35, Y.T. Harris 123, P.F. Harrison164, N.M. Hartman 141, N.M. Hartmann 106, Y. Hasegawa 138, A. Hasib 50, S. Hassani 132, S. Haug 18, R. Hauser 104, M. Havranek 129, C.M. Hawkes 19, R.J. Hawkings 34, S. Hayashida 108, D. Hayden 104, C. Hayes 103, R.L. Hayes 161, C.P. Hays 123, J.M. Hays 91, H.S. Hayward 89, S.J. Haywood 131, F. He 60a, Y. He 152, Y. He 124, M.P. Heath 50, V. Hedberg 95, A.L. Heggelund 122, N.D. Hehir 91, C. Heidegger 52, K.K. Heidegger 52, W.D. Heidorn 78, J. Heilman 32, S. Heim 46, T. Heim 16a, – 47 – JHEP07(2023)040 B. Heinemann 46,ac, J.G. Heinlein 125, J.J. Heinrich 120, L. Heinrich 34, J. Hejbal 128, L. Helary 46, A. Held 114, S. Hellesund 122, C.M. Helling 133, S. Hellman 45a,45b, C. Helsens 34, R.C.W. Henderson88, L. Henkelmann 30, A.M. Henriques Correia34, H. Herde 141, Y. Hernández Jiménez 143, H. Herr97, M.G. Herrmann 106, T. Herrmann 48, G. Herten 52, R. Hertenberger 106, L. Hervas 34, N.P. Hessey 154a, H. Hibi 81, S. Higashino 80, E. Higón-Rodriguez 160, K.H. Hiller46, S.J. Hillier 19, M. Hils 48, I. Hinchliffe 16a, F. Hinterkeuser 22, M. Hirose 121, S. Hirose 155, D. Hirschbuehl 168, B. Hiti 90, O. Hladik128, J. Hobbs 143, R. Hobincu 25e, N. Hod 166, M.C. Hodgkinson 137, B.H. Hodkinson 30, A. Hoecker 34, J. Hofer 46, D. Hohn 52, T. Holm 22, T.R. Holmes 37, M. Holzbock 107, L.B.A.H. Hommels 30, B.P. Honan 98, J. Hong 60c, T.M. Hong 126, Y. Hong 53, J.C. Honig 52, A. Hönle 107, B.H. Hooberman 159, W.H. Hopkins 5, Y. Horii 108, L.A. Horyn 37, S. Hou 146, J. Howarth 57, J. Hoya 87, M. Hrabovsky 119, A. Hrynevich 35, T. Hryn’ova 4, P.J. Hsu 63, S.-C. Hsu 136, Q. Hu 39, S. Hu 60c, Y.F. Hu 13a,13d,ah, D.P. Huang 93, X. Huang 13c, Y. Huang 60a, Y. Huang 13a, Z. Hubacek 129, F. Hubaut 99, M. Huebner 22, F. Huegging 22, T.B. Huffman 123, M. Huhtinen 34, S.K. Huiberts 15, R. Hulsken 58, N. Huseynov 36,x, J. Huston 104, J. Huth 59, R. Hyneman 141, S. Hyrych 26a, G. Iacobucci 54, G. Iakovidis 27, I. Ibragimov 139, L. Iconomidou-Fayard 64, P. Iengo 34, R. Iguchi 151, T. Iizawa 54, Y. Ikegami 80, A. Ilg 18, N. Ilic 153, H. Imam 33a, T. Ingebretsen Carlson 45a,45b, G. Introzzi 70a,70b, M. Iodice 74a, V. Ippolito 72a,72b, M. Ishino 151, W. Islam 167, C. Issever 17,46, S. Istin 11c,ai, J.M. Iturbe Ponce 62a, R. Iuppa 75a,75b, A. Ivina 166, J.M. Izen 43, V. Izzo 69a, P. Jacka 128, P. Jackson 1, R.M. Jacobs 46, B.P. Jaeger 140, C.S. Jagfeld 106, G. Jäkel 168, K. Jakobs 52, T. Jakoubek 166, J. Jamieson 57, K.W. Janas 82a, G. Jarlskog 95, A.E. Jaspan 89, N. Javadov36,x, T. Javůrek 34, M. Javurkova 100, F. Jeanneau 132, L. Jeanty 120, J. Jejelava 147a,y, P. Jenni 52,f , S. Jézéquel 4, J. Jia 143, Z. Jia 13c, Y. Jiang60a, S. Jiggins 50, J. Jimenez Pena 107, S. Jin 13c, A. Jinaru 25b, O. Jinnouchi 152, H. Jivan 31f, P. Johansson 137, K.A. Johns 6, C.A. Johnson 65, D.M. Jones 30, E. Jones 164, R.W.L. Jones 88, T.J. Jones 89, J. Jovicevic 14, X. Ju 16a, J.J. Junggeburth 34, A. Juste Rozas 12,t, S. Kabana 134e, A. Kaczmarska 83, M. Kado 72a,72b, H. Kagan 116, M. Kagan 141, A. Kahn39, A. Kahn 125, C. Kahra 97, T. Kaji 165, E. Kajomovitz 148, C.W. Kalderon 27, A. Kamenshchikov 35, M. Kaneda 151, N.J. Kang 133, S. Kang 78, Y. Kano 108, D. Kar 31f, K. Karava 123, M.J. Kareem 154b, I. Karkanias 150, S.N. Karpov 36, Z.M. Karpova 36, V. Kartvelishvili 88, A.N. Karyukhin 35, E. Kasimi 150, C. Kato 60d, J. Katzy 46, K. Kawade 138, K. Kawagoe 86, T. Kawaguchi 108, T. Kawamoto 132, G. Kawamura53, E.F. Kay 162, F.I. Kaya 156, S. Kazakos 12, V.F. Kazanin 35, Y. Ke 143, J.M. Keaveney 31a, R. Keeler 162, J.S. Keller 32, D. Kelsey 144, J.J. Kempster 19, J. Kendrick 19, K.E. Kennedy 39, O. Kepka 128, S. Kersten 168, B.P. Kerševan 90, S. Ketabchi Haghighat 153, M. Khandoga 124, A. Khanov 118, A.G. Kharlamov 35, T. Kharlamova 35, E.E. Khoda 136, T.J. Khoo 17, G. Khoriauli 163, J. Khubua 147b, S. Kido 81, M. Kiehn 34, A. Kilgallon 120, E. Kim 152, 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 151, D.P. Kisliuk153, C. Kitsaki 9, O. Kivernyk 22, – 48 – JHEP07(2023)040 T. Klapdor-Kleingrothaus 52, M. Klassen 61a, C. Klein 32, L. Klein 163, 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 153, A. Knue 52, D. Kobayashi86, R. Kobayashi 84, M. Kobel 48, M. Kocian 141, T. Kodama151, P. Kodyš 130, D.M. Koeck 144, 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 150, G. Koren 149, A. Korn 93, S. Korn 53, I. Korolkov 12, E.V. Korolkova137, N. Korotkova 35, B. Kortman 111, O. Kortner 107, S. Kortner 107, W.H. Kostecka 112, V.V. Kostyukhin 139,35, A. Kotsokechagia 64, A. Kotwal 49, A. Koulouris 34, A. Kourkoumeli-Charalampidi 70a,70b, C. Kourkoumelis 8, E. Kourlitis 5, O. Kovanda 144, R. Kowalewski 162, 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 153, 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 152, 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 154a, Y.A. Kurochkin 35, A. Kurova 35, M.G. Kurth13a,13d, E.S. Kuwertz 34, M. Kuze 152, A.K. Kvam 136, J. Kvita 119, T. Kwan 101, K.W. Kwok 62a, C. Lacasta 160, 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 150, 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 157, 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 146, S. Lee 78, L.L. Leeuw 31c, B. Lefebvre 154a, H.P. Lefebvre 92, M. Lefebvre 162, C. Leggett 16a, K. Lehmann 140, N. Lehmann 18, G. Lehmann Miotto 34, W.A. Leight 46, A. Leisos 150,s, M.A.L. Leite 79c, C.E. Leitgeb 46, R. Leitner 130, K.J.C. Leney 42, T. Lenz 22, S. Leone 71a, C. Leonidopoulos 50, A. Leopold 142, C. Leroy 105, R. Les 104, C.G. Lester 30, M. Levchenko 35, J. Levêque 4, D. Levin 103, L.J. Levinson 166, 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 136, 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 159,ad, A. Lister 161, J.D. Little 7, B. Liu 13a, B.X. Liu 140, J.B. Liu 60a, J.K.K. Liu 37, K. Liu 60d,60c, – 49 – JHEP07(2023)040 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 140, S.L. Lloyd 91, E.M. Lobodzinska 46, P. Loch 6, S. Loffredo 73a,73b, T. Lohse 17, K. Lohwasser 137, M. Lokajicek 128, J.D. Long 159, I. Longarini 72a,72b, L. Longo 34, R. Longo 159, 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 160, G. Lu 13a,13d, M. Lu 60a, S. Lu 125, Y.J. Lu 63, H.J. Lubatti 136, C. Luci 72a,72b, F.L. Lucio Alves 13c, A. Lucotte 58, F. Luehring 65, I. Luise 143, L. Luminari72a, O. Lundberg 142, B. Lund-Jensen 142, N.A. Luongo 120, M.S. Lutz 149, 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 162, G. Maccarrone 51, C.M. Macdonald 137, J.C. MacDonald 137, 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 160, G. Mancini 51, J.P. Mandalia 91, I. Mandić 90, L. Manhaes de Andrade Filho 79a, I.M. Maniatis 150, M. Manisha 132, J. Manjarres Ramos 48, K.H. Mankinen 95, A. Mann 106, A. Manousos 76, B. Mansoulie 132, I. Manthos 150, S. Manzoni 111, A. Marantis 150,s, G. Marchiori 124, M. Marcisovsky 128, L. Marcoccia 73a,73b, C. Marcon 95, M. Marjanovic 117, Z. Marshall 16a, S. Marti-Garcia 160, T.A. Martin 164, V.J. Martin 50, B. Martin dit Latour 15, L. Martinelli 72a,72b, M. Martinez 12,t, P. Martinez Agullo 160, 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 151, J. Masik 98, A.L. Maslennikov 35, L. Massa 21b, P. Massarotti 69a,69b, P. Mastrandrea 71a,71b, A. Mastroberardino 41b,41a, T. Masubuchi 151, D. Matakias27, T. Mathisen 158, A. Matic 106, N. Matsuzawa151, J. Maurer 25b, B. Maček 90, D.A. Maximov 35, R. Mazini 146, I. Maznas 150, 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 144, G. Mchedlidze 147b, M.A. McKay42, K.D. McLean 162, S.J. McMahon 131, P.C. McNamara 102, R.A. McPherson 162,w, 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 148, 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 153, 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 139, 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 166, M. Mikestikova 128, M. Mikuž 90, H. Mildner 137, A. Milic 153, C.D. Milke 42, D.W. Miller 37, L.S. Miller 32, A. Milov 166, D.A. Milstead45a,45b, T. Min13c, A.A. Minaenko 35, I.A. Minashvili 147b, L. Mince 57, A.I. Mincer 114, B. Mindur 82a, M. Mineev 36, Y. Minegishi151, Y. Mino 84, L.M. Mir 12, M. Miralles Lopez 160, M. Mironova 123, T. Mitani 165, V.A. Mitsou 160, – 50 – JHEP07(2023)040 M. Mittal60c, O. Miu 153, 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 139, S. Mondal 129, K. Mönig 46, E. Monnier 99, L. Monsonis Romero160, A. Montalbano 140, J. Montejo Berlingen 34, M. Montella 116, F. Monticelli 87, N. Morange 64, A.L. Moreira De Carvalho 127a, M. Moreno Llácer 160, C. Moreno Martinez 12, P. Morettini 55b, S. Morgenstern 164, D. Mori 140, M. Morii 59, M. Morinaga 151, V. Morisbak 122, A.K. Morley 34, A.P. Morris 93, L. Morvaj 34, P. Moschovakos 34, B. Moser 111, M. Mosidze147b, 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 164,131, 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 160, R. Nayak 149, 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 159, F. Neuhaus 97, J. Neundorf 46, R. Newhouse 161, P.R. Newman 19, C.W. Ng 126, Y.S. Ng17, Y.W.Y. Ng 157, 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 165, T. Nobe 151, D.L. Noel 30, Y. Noguchi 84, I. Nomidis 124, M.A. Nomura27, M.B. Norfolk 137, 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 157, E. Nurse93, F.G. Oakham 32,af , J. Ocariz 124, A. Ochi 81, I. Ochoa 127a, J.P. Ochoa-Ricoux 134a, S. Oda 86, S. Odaka 80, S. Oerdek 158, A. Ogrodnik 82a, A. Oh 98, C.C. Ohm 142, H. Oide 152, R. Oishi 151, M.L. Ojeda 46, Y. Okazaki 84, M.W. O’Keefe89, Y. Okumura 151, A. Olariu25b, L.F. Oleiro Seabra 127a, S.A. Olivares Pino 134e, D. Oliveira Damazio 27, D. Oliveira Goncalves 79a, J.L. Oliver 157, M.J.R. Olsson 157, A. Olszewski 83, J. Olszowska 83,∗, Ö.O. Öncel 22, D.C. O’Neil 140, 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 153, 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, – 51 – JHEP07(2023)040 B. Parida 166, T.H. Park 153, 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 149, 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 167, J. Patton89, T. Pauly 34, J. Pearkes 141, 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 154a, 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 150, P. Petroff64, F. Petrucci 74a,74b, A. Petrukhin 139, M. Pettee 169, N.E. Pettersson 34, K. Petukhova 130, A. Peyaud 132, R. Pezoa 134f, L. Pezzotti 34, G. Pezzullo 169, T. Pham 102, P.W. Phillips 131, M.W. Phipps 159, G. Piacquadio 143, 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 140,154a, 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 154a, 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 160, T.D. Powell 137, G. Pownall 46, M.E. Pozo Astigarraga 34, A. Prades Ibanez 160, P. Pralavorio 99, M.M. Prapa 44, S. Prell 78, D. Price 98, M. Primavera 67a, M.A. Principe Martin 96, M.L. Proffitt 136, 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 166, M. Raymond 34, A.L. Read 122, N.P. Readioff 137, D.M. Rebuzzi 70a,70b, G. Redlinger 27, K. Reeves 43, D. Reikher 149, A. Reiss97, A. Rej 139, 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 168, 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 143, A. Rimoldi 70a,70b, M. Rimoldi 46, L. Rinaldi 21b,21a, T.T. Rinn 159, M.P. Rinnagel 106, G. Ripellino 142, I. Riu 12, P. Rivadeneira 46, J.C. Rivera Vergara 162, F. Rizatdinova 118, E. Rizvi 91, C. Rizzi 54, B.A. Roberts 164, B.R. Roberts 16a, S.H. Robertson 101,w, 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 154b, S. Roe34, A.R. Roepe-Gier 117, J. Roggel 168, O. Røhne 122, R.A. Rojas 162, B. Roland 52, C.P.A. Roland 65, J. Roloff 27, A. Romaniouk 35, M. Romano 21b, A.C. Romero Hernandez 159, N. Rompotis 89, – 52 – JHEP07(2023)040 M. Ronzani 114, L. Roos 124, S. Rosati 72a, B.J. Rosser 125, E. Rossi 153, 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 148, X. Ruan 31f, A.J. Ruby 89, T.A. Ruggeri 1, F. Rühr 52, A. Ruiz-Martinez 160, A. Rummler 34, Z. Rurikova 52, N.A. Rusakovich 36, H.L. Russell 34, L. Rustige 38, J.P. Rutherfoord 6, E.M. Rüttinger 137, M. Rybar 130, E.B. Rye 122, A. Ryzhov 35, J.A. Sabater Iglesias 46, P. Sabatini 160, L. Sabetta 72a,72b, H.F-W. Sadrozinski 133, F. Safai Tehrani 72a, B. Safarzadeh Samani 144, M. Safdari 141, S. Saha 101, M. Sahinsoy 107, A. Sahu 168, M. Saimpert 132, M. Saito 151, T. Saito 151, D. Salamani 34, G. Salamanna 74a,74b, A. Salnikov 141, J. Salt 160, A. Salvador Salas 12, D. Salvatore 41b,41a, F. Salvatore 144, A. Salzburger 34, D. Sammel 52, D. Sampsonidis 150, D. Sampsonidou 60d,60c, J. Sánchez 160, A. Sanchez Pineda 4, V. Sanchez Sebastian 160, H. Sandaker 122, C.O. Sander 46, I.G. Sanderswood 88, J.A. Sandesara 100, M. Sandhoff 168, 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 166, K.A. Saoucha 137, J.G. Saraiva 127a,127d, J. Sardain 99, O. Sasaki 80, K. Sato 155, C. Sauer61b, F. Sauerburger 52, E. Sauvan 4, P. Savard 153,af , R. Sawada 151, C. Sawyer 131, L. Sawyer 94, I. Sayago Galvan160, C. Sbarra 21b, A. Sbrizzi 21b,21a, T. Scanlon 93, J. Schaarschmidt 136, P. Schacht 107, D. Schaefer 37, U. Schäfer 97, A.C. Schaffer 64, D. Schaile 106, R.D. Schamberger 143, E. Schanet 106, C. Scharf 17, N. Scharmberg 98, V.A. Schegelsky 35, D. Scheirich 130, F. Schenck 17, M. Schernau 157, 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 168, H-C. Schultz-Coulon 61a, M. Schumacher 52, B.A. Schumm 133, Ph. Schune 132, A. Schwartzman 141, 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 141, F. Sforza 55b,55a, A. Sfyrla 54, E. Shabalina 53, R. Shaheen 142, J.D. Shahinian 125, N.W. Shaikh 45a,45b, D. Shaked Renous 166, L.Y. Shan 13a, M. Shapiro 16a, A. Sharma 34, A.S. Sharma 1, S. Sharma 46, P.B. Shatalov 35, K. Shaw 144, S.M. Shaw 98, P. Sherwood 93, L. Shi 93, C.O. Shimmin 169, Y. Shimogama 165, J.D. Shinner 92, I.P.J. Shipsey 123, S. Shirabe 54, M. Shiyakova 36, J. Shlomi 166, M.J. Shochet 37, J. Shojaii 102, D.R. Shope 142, S. Shrestha 116, E.M. Shrif 31f, M.J. Shroff 162, E. Shulga 166, P. Sicho 128, A.M. Sickles 159, 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 153, V. Sinetckii 35, S. Singh 140, S. Singh 153, 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 156, V. Smakhtin166, B.H. Smart 131, – 53 – JHEP07(2023)040 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 162,w, A. Soffer 149, F. Sohns 53, C.A. Solans Sanchez 34, E.Yu. Soldatov 35, U. Soldevila 160, A.A. Solodkov 35, S. Solomon 52, A. Soloshenko 36, O.V. Solovyanov 35, V. Solovyev 35, P. Sommer 137, H. Son 156, A. Sonay 12, W.Y. Song 154b, 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 164, F. Spanò 92, D. Sperlich 52, T.M. Spieker 61a, G. Spigo 34, M. Spina 144, 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,al, D.M. Starko154b, P. Staroba 128, P. Starovoitov 61a, S. Stärz 101, R. Staszewski 83, G. Stavropoulos 44, P. Steinberg 27, A.L. Steinhebel 120, B. Stelzer 140,154a, H.J. Stelzer 126, O. Stelzer-Chilton 154a, H. Stenzel 56, T.J. Stevenson 144, G.A. Stewart 34, M.C. Stockton 34, G. Stoicea 25b, M. Stolarski 127a, S. Stonjek 107, A. Straessner 48, J. Strandberg 142, S. Strandberg 45a,45b, M. Strauss 117, T. Strebler 99, P. Strizenec 26b, R. Ströhmer 163, 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 141, S. Su 60a, W. Su 60d,136,60c, X. Su 60a, K. Sugizaki 151, 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 167, X. Sun 98, O. Sunneborn Gudnadottir 158, C.J.E. Suster 145, M.R. Sutton 144, M. Svatos 128, M. Swiatlowski 154a, T. Swirski 163, I. Sykora 26a, M. Sykora 130, T. Sykora 130, D. Ta 97, K. Tackmann 46,u, A. Taffard 157, R. Tafirout 154a, R.H.M. Taibah 124, R. Takashima 85, K. Takeda 81, T. Takeshita 138, E.P. Takeva 50, Y. Takubo 80, M. Talby 99, A.A. Talyshev 35, K.C. Tam 62b, N.M. Tamir149, A. Tanaka 151, J. Tanaka 151, R. Tanaka 64, J. Tang60c, Z. Tao 161, S. Tapia Araya 78, S. Tapprogge 97, A. Tarek Abouelfadl Mohamed 104, S. Tarem 148, K. Tariq 60b, G. Tarna 25b, G.F. Tartarelli 68a, P. Tas 130, M. Tasevsky 128, E. Tassi 41b,41a, G. Tateno 151, Y. Tayalati 33e, G.N. Taylor 102, W. Taylor 154b, H. Teagle89, A.S. Tee 167, R. Teixeira De Lima 141, P. Teixeira-Dias 92, H. Ten Kate34, J.J. Teoh 111, K. Terashi 151, J. Terron 96, S. Terzo 12, M. Testa 51, R.J. Teuscher 153,w, N. Themistokleous 50, T. Theveneaux-Pelzer 17, O. Thielmann 168, 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 169, 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 141,ak, P. Tornambe 100, E. Torrence 120, H. Torres 48, E. Torró Pastor 160, M. Toscani 28, C. Tosciri 37, J. Toth 99,v, D.R. Tovey 137, A. Traeet15, C.J. Treado 114, T. Trefzger 163, A. Tricoli 27, I.M. Trigger 154a, S. Trincaz-Duvoid 124, D.A. Trischuk 161, B. Trocmé 58, A. Trofymov 64, C. Troncon 68a, F. Trovato 144, L. Truong 31c, M. Trzebinski 83, A. Trzupek 83, F. Tsai 143, A. Tsiamis 150, P.V. Tsiareshka35,a, A. Tsirigotis 150,s, V. Tsiskaridze 143, E.G. Tskhadadze147a, – 54 – JHEP07(2023)040 M. Tsopoulou 150, Y. Tsujikawa 84, I.I. Tsukerman 35, V. Tsulaia 16a, S. Tsuno 80, O. Tsur148, D. Tsybychev 143, 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 150, P. Tzanis 9, E. Tzovara 97, K. Uchida151, F. Ukegawa 155, P.A. Ulloa Poblete 134d, G. Unal 34, M. Unal 10, A. Undrus 27, G. Unel 157, F.C. Ungaro 102, K. Uno 151, J. Urban 26b, P. Urquijo 102, G. Usai 7, R. Ushioda 152, 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 154a, S. Valentinetti 21b,21a, A. Valero 160, R.A. Vallance 19, A. Vallier 99,al, J.A. Valls Ferrer 160, T.R. Van Daalen 136, 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 149, L. Vannoli 55b,55a, R. Vari 72a, E.W. Varnes 6, C. Varni 16a, T. Varol 146, D. Varouchas 64, K.E. Varvell 145, M.E. Vasile 25b, L. Vaslin38, G.A. Vasquez 162, 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 153, 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 141, P.J. Verschuuren 92, M. Vessella 100, M.L. Vesterbacka 114, M.C. Vetterli 140,af , A. Vgenopoulos 150, N. Viaux Maira 134f, T. Vickey 137, O.E. Vickey Boeriu 137, G.H.A. Viehhauser 123, L. Vigani 61b, M. Villa 21b,21a, M. Villaplana Perez 160, E.M. Villhauer50, E. Vilucchi 51, M.G. Vincter 32, G.S. Virdee 19, A. Vishwakarma 50, C. Vittori 21b,21a, I. Vivarelli 144, V. Vladimirov164, E. Voevodina 107, M. Vogel 168, P. Vokac 129, J. Von Ahnen 46, E. Von Toerne 22, V. Vorobel 130, K. Vorobev 35, M. Vos 160, 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 155, C. Wagner100, W. Wagner 168, S. Wahdan 168, H. Wahlberg 87, R. Wakasa 155, M. Wakida 108, V.M. Walbrecht 107, J. Walder 131, R. Walker 106, S.D. Walker92, W. Walkowiak 139, A.M. Wang 59, A.Z. Wang 167, 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 146, S. Wang 60b, T. Wang 60a, W.T. Wang 77, W.X. Wang 60a, X. Wang 13c, X. Wang 159, 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 136, 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 157, L. Wickremasinghe 121, W. Wiedenmann 167, 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. Wittgen141, M. Wobisch 94, A. Wolf 97, R. Wölker 123, J. Wollrath157, M.W. Wolter 83, – 55 – JHEP07(2023)040 H. Wolters 127a,127c, V.W.S. Wong 161, 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 167, 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. Xia13b, J. Xiang 62c, X. Xiao 103, M. Xie 60a, X. Xie 60a, I. Xiotidis144, 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 141, B. Yabsley 145, S. Yacoob 31a, N. Yamaguchi 86, Y. Yamaguchi 152, M. Yamatani151, H. Yamauchi 155, 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 151, 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 165, K. Yoshihara 78, C.J.S. Young 52, C. Young 141, M. Yuan 103, R. Yuan 60b,j , X. Yue 61a, M. Zaazoua 33e, B. Zabinski 83, G. Zacharis 9, E. Zaid50, T. Zakareishvili 147b, N. Zakharchuk 32, S. Zambito 34, D. Zanzi 52, S.V. Zeißner 47, C. Zeitnitz 168, J.C. Zeng 159, 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 137, G. Zhang 13b, J. Zhang 5, K. Zhang 13a,13d, L. Zhang 13c, M. Zhang 159, R. Zhang 167, 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 141, D. Zhong 159, B. Zhou103, C. Zhou 167, 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 139, L. Živković 14, A. Zoccoli 21b,21a, K. Zoch 54, T.G. Zorbas 137, 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 – 56 –