Science, Faculty of
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Journal of High Energy Physics. 2023 Dec 27;2023(12):167
The Author(s)
ATLAS Collaboration
Searches for electroweak production of wino-like chargino pairs, χ˜ + 1 χ˜ − 1 , and of wino-like chargino and next-to-lightest neutralino, χ˜ ± 1 χ˜ 0 2 , are presented. The models explored assume that the charginos decay into a W boson and the lightest neutralino, χ˜ ± 1 → W±χ˜ 0 1 . The next-to-lightest neutralinos are degenerate in mass with the chargino and decay to χ˜ 0 1 and either a Z or a Higgs boson, χ˜ 0 2 → Zχ˜ 0 1 or hχ˜ 0 1 . The searches exploit the presence of a single isolated lepton and missing transverse momentum from the W boson decay products and the lightest neutralinos, and the presence of jets from hadronically decaying Z or W bosons or from the Higgs boson decaying into a pair of b-quarks. The searches use 139 fb−1 of √ s = 13 TeV proton-proton collisions data collected by the ATLAS detector at the Large Hadron Collider between 2015 and 2018. No deviations from the Standard Model expectations are found, and 95% confdence level exclusion limits are set. Chargino masses ranging from 260 to 520 GeV are excluded for a massless χ˜ 0 1 in chargino pair production models. Degenerate chargino and next-to-lightest neutralino masses ranging from 260 to 420 GeV are excluded for a massless χ˜ 0 1 for χ˜ 0 2 → Zχ˜ 0 1 . For decays through an on-shell Higgs boson and for mass-splitting between χ˜ ± 1 /χ˜ 0 2 and χ˜ 0 1 as small as the Higgs boson mass, mass limits are improved by up to 40 GeV in the range of 200–260 GeV and 280–470 GeV compared to previous ATLAS constraints.
JHEP12(2023)167PuAlished for SISS Ay SpringerReceived: October 13, 2023Accepted: December 14, 2023Published: December 27, 2023Search for direct production of electroweakinos in finalstates with one lepton, jets and missing transversemomentum in pp collisions at √s = 13TeV with theATLAS detectorThe ATLAS collaborationE-mail: atlas.publications@cern.chAbstract: Searches for electroweak production of wino-like chargino pairs, χ˜+1 χ˜−1 , and ofwino-like chargino and next-to-lightest neutralino, χ˜±1 χ˜02, are presented. The models exploredassume that the charginos decay into a W boson and the lightest neutralino, χ˜±1 →W±χ˜01.The next-to-lightest neutralinos are degenerate in mass with the chargino and decay to χ˜01 andeither a Z or a Higgs boson, χ˜02 → Zχ˜01 or hχ˜01. The searches exploit the presence of a singleisolated lepton and missing transverse momentum from the W boson decay products and thelightest neutralinos, and the presence of jets from hadronically decaying Z orW bosons or fromthe Higgs boson decaying into a pair of b-quarks. The searches use 139 fb−1 of√s = 13TeVproton-proton collisions data collected by the ATLAS detector at the Large Hadron Colliderbetween 2015 and 2018. No deviations from the Standard Model expectations are found, and95% confidence level exclusion limits are set. Chargino masses ranging from 260 to 520GeVare excluded for a massless χ˜01 in chargino pair production models. Degenerate chargino andnext-to-lightest neutralino masses ranging from 260 to 420GeV are excluded for a masslessχ˜01 for χ˜02 → Zχ˜01. For decays through an on-shell Higgs boson and for mass-splitting betweenχ˜±1 /χ˜02 and χ˜01 as small as the Higgs boson mass, mass limits are improved by up to 40GeVin the range of 200–260GeV and 280–470GeV compared to previous ATLAS constraints.Keywords: Hadron-Hadron Scattering, SupersymmetryArXiv ePrint: 2310.08171Open Access, Copyright CERN,for the benefit of the ATLAS Collaboration.Article funded by SCOAP3.https://doi.org/10.1007/JHEP12(2023)167JHEP12(2023)167Contents1 Introduction 12 ATLAS detector 33 Data and simulated events 44 Event reconstruction 65 Analysis strategy and event selection 85.1 C1C1-WW and C1N2-WZ SRs definition 105.2 C1N2-Wh SR definition 116 Background estimation 126.1 C1C1-WW and C1N2-WZ control and validation regions 136.2 C1N2-Wh control and validation regions 157 Systematic uncertainties 168 Results 219 Conclusion 35The ATLAS collaboration 441 IntroductionThe Standard Model (SM) is a strongly predictive effective theory, however it is not able toexplain some observed phenomena, such as the abundance of dark matter and its nature,the matter-antimatter asymmetry, and the hierarchy problem [1–4]. The ATLAS and CMSdiscovery of the SM Higgs boson [5–8] confirmed the mechanism of electroweak symmetrybreaking and heightened attention on the hierarchy problem. Supersymmetric (SUSY) [9–14]extensions to the SM can solve the hierarchy problem by introducing a new symmetry thatpredicts bosonic (fermionic) partners for the fermions (bosons) of the SM. In an R-parity [15]conserving model, the SUSY particles are produced in pairs and the lightest SUSY particle(LSP) is a viable dark-matter candidate [16, 17], as it is stable and weakly interacting.The SUSY partners of the Higgs bosons and the SM electroweak gauge bosons, collectivelycalled electroweakinos, are the higgsinos, winos (partners of the SU(2)L gauge fields), andbino (partner of the U(1) gauge field). The electroweakino mass eigenstates are referred toas charginos χ˜±i (i = 1, 2), linear combinations of higgsino and wino fields, and neutralinosχ˜0j (j = 1, 2, 3, 4), linear combinations of higgsino, wino and bino fields. These are orderedin increasing value of their masses.– 1 –JHEP12(2023)167Natural SUSY scenarios [18, 19] predict that the lightest electroweakino mass be closeto the electroweak scale. If squarks and sleptons (partners of the quarks and leptons) areheavier than a few TeV and hence decoupled, they cannot be produced at the Large HadronCollider (LHC). The dominant SUSY production mechanism at the LHC may be the directproduction of electroweakinos. SUSY models with light electroweakinos can also explain theobserved discrepancy in the g − 2 measurement compared to the SM predictions [20, 20, 21].In the models considered in this paper, the compositions of the lightest chargino (χ˜±1 ) andnext-to-lightest neutralino (χ˜02) are wino-like and the two particles are nearly mass degenerate,while the lightest neutralino (χ˜01) is assumed to be a bino-like particle and the LSP. Twodifferent SUSY processes are targeted in this paper: χ˜+1 χ˜−1 and χ˜±1 χ˜02 pair production.Three searches performed by the ATLAS Collaboration for the direct production ofelectroweakinos in proton-proton (pp) collisions produced at the LHC at√s = 13TeV arepresented. The first analysis is designed to be sensitive to the direct pair-production of twocharginos, referred to as the C1C1-WW model, where the charginos decay via χ˜±1 →W±χ˜01;the other two analyses are designed to be sensitive to the associated production of nearlymass-degenerate charginos and next-to-lightest neutralinos, latter decaying into the χ˜01 andeither a Z boson (χ˜02 → Z χ˜01) or a SM-like Higgs boson (χ˜02 → h χ˜01) [22–24], referred toas the C1N2-WZ and C1N2-Wh models, respectively.The target signature, in all scenarios, is a single isolated light lepton (electron or muon)produced by one of the W decays, or by τ -leptons produced in W decays, and missingtransverse momentum (pmissT ) from LSPs and neutrinos. In the C1C1-WW and C1N2-WZscenarios, due to the large momentum carried by the intermediate bosons, the jets areexpected to be semi-boosted, or fully boosted. Thus up to three jets are required for thesetwo models, which are produced by the hadronic decay of either a W (in the χ˜+1 χ˜−1 case)or a Z (in the χ˜±1 χ˜02 case), and the hadronic radiation. In the C1N2-Wh model, the Higgsboson candidates are identified through their decay into a pair of b-quarks (h → bb¯) andtwo jets originating from the fragmentation of b-quarks, called b-jets, are required. A set ofsimplified SUSY models [25, 26] is used to optimise the search and interpret the results. Inthese models the branching ratios of χ˜±1 → Wχ˜01 and χ˜02 → Zχ˜01 or χ˜02 → hχ˜01 are assumedto be 100%. The branching ratios of W , Z and Higgs bosons follow the SM predictions.Feynman diagrams of the processes under consideration are shown in figure 1.Previous searches for electroweakino production at the LHC targeting WW , WZ andintermediate states, and different lepton multiplicity in the final states, have been reported bythe ATLAS [27–31] and CMS [32–34] collaborations. This analysis is the first ATLAS searchtargeting final states with exactly one lepton, and profiting from the use of jet-substructureinformation for W and Z boson identification in large-R jets to target boosted regimes.The kinematic configurations where the decay products are boosted provide a handle toreduce the background. In the case of decays via Wh, stringent constraints have been setby the ATLAS [35] and CMS [36] collaborations exploiting the h → bb¯ decay mode andmultiple decay modes of the Higgs boson, respectively. This analysis targets final stateswith mass-splitting between the chargino and the LSP, ∆m(χ˜±1 , χ˜01), between mh and around250GeV, exploiting the h → bb¯ decay mode and the usage of dedicated boosted decisiontree (BDT) discriminants. The BDT-based approach improves the sensitivity in the complex– 2 –JHEP12(2023)167χ˜±1χ˜∓1W±W∓ppχ˜01`±νχ˜01qq(a)χ˜±1χ˜02W±Zppχ˜01ν`±χ˜01qq(b)χ˜±1χ˜02W±hppχ˜01ν`±χ˜01bb(c)Figure 1. Feynman Diagrams for (a) electroweakino χ˜+1 χ˜−1 and (b, c) χ˜+1 χ˜02 pair-production. Oneχ˜±1 decays into a χ˜01 and a W boson that further decays leptonically. The other χ˜±1 decays into aχ˜01 and a W boson that further decays hadronically (a). The χ˜02 decays into a χ˜01 and either a Zboson that further decays hadronically (b), or into a Higgs boson decaying into a pair of b-quarks (c).compressed phase-space where cut-and-count analyses suffer due to the similar kinematicsof signal and SM backgrounds, especially from tt¯ and Wt events.2 ATLAS detectorThe ATLAS detector [37] at the LHC covers nearly the entire solid angle around the collisionpoint.1 It consists of an inner tracking detector surrounded by a thin superconductingsolenoid, electromagnetic and hadron calorimeters, and a muon spectrometer incorporatingthree large superconducting air-core toroidal magnets.The inner-detector system (ID) is immersed in a 2T axial magnetic field and providescharged-particle tracking in the range |η| < 2.5. The high-granularity silicon pixel detectorcovers the vertex region and typically provides four measurements per track, the first hitnormally being in the insertable B-layer (IBL) installed before Run 2 [38, 39]. It is followedby the silicon microstrip tracker (SCT), which usually provides eight measurements pertrack. These silicon detectors are complemented by the transition radiation tracker (TRT),which enables radially extended track reconstruction up to |η| = 2.0. The TRT also provideselectron identification information based on the fraction of hits (typically 30 in total) above ahigher energy-deposit threshold corresponding to transition radiation.The calorimeter system covers the pseudorapidity range |η| < 4.9. Within the region|η| < 3.2, electromagnetic calorimetry is provided by barrel and endcap high-granularitylead/liquid-argon (LAr) calorimeters, with an additional thin LAr presampler covering|η| < 1.8 to correct for energy loss in material upstream of the calorimeters. Hadroncalorimetry is provided by the steel/scintillator-tile calorimeter, segmented into three barrel1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in thecentre of the detector. The positive x-axis is defined by the direction from the interaction point to the centreof the LHC ring, with the positive y-axis pointing upwards, while the beam direction defines the z-axis.Cylindrical coordinates (r, ϕ) are used in the transverse plane, ϕ being the azimuthal angle around the z-axis.The pseudorapidity η is defined in terms of the polar angle θ by η = − ln tan(θ/2). Rapidity is defined as y =0.5 ln[(E + pz)/(E − pz)] where E denotes the energy and pz is the component of the momentum along thebeam direction. The angular distance ∆R is defined as√(∆η)2 + (∆ϕ)2.– 3 –JHEP12(2023)167structures within |η| < 1.7, and two copper/LAr hadron endcap calorimeters. The solidangle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modulesoptimised for electromagnetic and hadronic energy measurements respectively.The muon spectrometer (MS) comprises separate trigger and high-precision trackingchambers measuring the deflection of muons in a magnetic field generated by thesuperconducting air-core toroidal magnets. The field integral of the toroids ranges between 2.0and 6.0Tm across most of the detector. Three layers of precision chambers, each consistingof layers of monitored drift tubes, cover the region |η| < 2.7, complemented by cathode-stripchambers in the forward region, where the background is highest. The muon trigger systemcovers the range |η| < 2.4 with resistive-plate chambers in the barrel, and thin-gap chambersin the endcap regions.Interesting events are selected by the first-level trigger system implemented in customhardware, followed by selections made by algorithms implemented in software in the high-leveltrigger [40]. The first-level trigger accepts events from the 40MHz bunch crossings at arate below 100 kHz, which the high-level trigger further reduces in order to record eventsto disk at about 1 kHz.An extensive software suite [41] is used in data simulation, in the reconstruction andanalysis of real and simulated data, in detector operations, and in the trigger and dataacquisition systems of the experiment.3 Data and simulated eventsThe analyses presented in this paper use 139 fb−1 of pp collision data provided by theLHC and collected between 2015 and 2018 by the ATLAS detector, at a centre-of-massenergy of 13TeV and with an interval of 25 ns between consecutive crossings of protonbunches. The average number of interactions per bunch crossing (pile-up) was ⟨µ⟩ = 20in 2015–2016, ⟨µ⟩ = 38 in 2017 and ⟨µ⟩ = 37 in 2018. The uncertainty in the combined2015–2018 integrated luminosity is 1.7% [42], obtained using the LUCID-2 detector [43] forthe primary luminosity measurements.Signal selection efficiencies and SM backgrounds are evaluated using Monte Carlo (MC)simulated event samples. The signal Monte Carlo samples were processed with a completesimulation of the response of the detector provided by Geant 4 [44], or fast simulation [45]which relies on a parameterisation of the calorimeter response [46]. A varying number ofinelastic pp interactions was overlaid on the hard-scattering event for all simulated eventsto model the multiple proton-proton interactions in the same and nearby bunch crossings.The pile-up events were generated with Pythia 8.186 [47] using the NNPDF2.3LO partondistribution function (PDF) set [48] and the A3 set of tuned parameters (tune) [49]. Thesimulated events were processed with the same trigger, reconstruction and identificationalgorithms used for data. Dedicated correction factors are applied to simulation to accountfor differences between data and MC simulated events.The simulated backgrounds considered in the analyses are: tt¯ pair production; single-topproduction (s-channel, t-channel, and associated Wt production); W/Z+jets production; tt¯production with an electroweak boson (tt¯+ V ); Higgs boson production (tt¯ +h, V h); diboson(WW , WZ, ZZ) and multiboson (V V V where V = W,Z) production. The simulated– 4 –JHEP12(2023)167ggF and VBF Higgs samples are not used as these processes are taken into account in thediboson samples. A further overlap removal is applied to avoid double counting between V hand diboson samples. Different MC event generators were used depending on the simulatedprocesses. All simulated background processes were normalised to the best available theoreticalcalculation of their respective cross-sections. The samples for W and Z boson productionassociated with jets (W/Z+jets) were simulated using Sherpa. The modelling includesup to two partons at next-to-leading order (NLO), normalised to next-to-next-to-leadingorder (NNLO) for the inclusive cross-section, and five partons at leading order (LO) usingComix [50] and OpenLoops [51, 52] and merged with the Sherpa parton shower [53] accordingto the ME+PS@NLO prescription [54–57] using the set of tuned parameters developed bythe Sherpa authors. Sherpa 2.2.1 is used in the C1N2-Wh analysis and Sherpa 2.2.11 isused in the C1N2-WZ and C1C1-WW analyses. The event generators, the parton showerand hadronisation routines, and the underlying-event parameter tunes and PDF sets usedin simulating the SM background processes, along with the accuracy of the theoreticalcross-sections, are all summarised in table 1.For all MC samples showered with Pythia, the EvtGen v1.2.0 [58] program was used tosimulate the properties of the bottom- and charm-hadron decays. Systematic uncertaintiesassociated with the different background-specific configurations of the MC generators areestimated by using MC samples produced without detector simulation. The uncertaintiesinclude variations of the renormalisation and factorisation scales, the CKKW-L [59] matchingscale, and different PDF sets and fragmentation/hadronisation models. A detailed discussionof the uncertainties related to the MC modelling is presented in section 7.The SUSY signal samples were simulated using MadGraph5_aMC@NLO v2.6.2 [60]and Pythia 8.230 with the A14 [61] set of tuned parameters for the modelling of the partonshowering (PS), hadronisation and underlying event. The matrix element (ME) calculationwas performed at tree level and includes the emission of up to two additional partons. TheME-PS matching was performed using the CKKW-L prescription, with a matching scale setto one quarter of the chargino and next-to-lightest neutralino mass. The NNPDF2.3LO [48]PDF set was used.Signal cross-sections are calculated at NLO accuracy in the strong coupling constant,adding the resummation of soft gluon emission at next-to-leading-logarithmic accuracy(NLO+NLL) [62–66]. The nominal cross-section and its uncertainty are taken as the midpointand half-width of an envelope of cross-section predictions using different PDF sets andfactorisation and renormalisation scales, as described in ref. [67]. The simplified modelsconsidered for electroweakino production rely on two parameters: for χ˜+1 χ˜−1 , they are themasses of the χ˜±1 and the χ˜01; for χ˜±1 χ˜02, they are the masses of χ˜±1 / χ˜02 (considered to bedegenerate) and χ˜01. They also depend on the branching ratio B of the SUSY particlesdecays: χ˜+1 decays into Wχ˜01 with B = 100% whilst two separate sets of signal samples wereproduced for the χ˜±1 χ˜02 process, with χ˜02 decaying into a χ˜01 and either a Z boson or a Higgsboson. In each case, a 100% branching ratio was assumed for the χ˜02 decay. The productioncross-section of the process χ˜+1 χ˜−1 (χ˜±1 χ˜02) decreases from 903 fb (1807 fb) to 0.62 fb (1.34 fb)with increasing m(χ˜±1 /χ˜02) from 200 to 1000GeV.– 5 –JHEP12(2023)167Process Generator Parton shower and Tune PDF Cross-sectionhadronisationtt¯ PowhegBox v2 [68–71] Pythia 8.230 [47] A14 [61] NNPDF2.3LO [48] NNLO+NNLL [72]Single top PowhegBox v2 [73–75] Pythia 8.230 A14 NNPDF2.3LO NLO+NNLL [76]W/Z+jets Sherpa 2.2.1 & 2.2.11 [77] Sherpa 2.2.1 & 2.2.11 Sherpa standard NNPDF3.0NNLO NNLO [78]Diboson Sherpa 2.2.1 [77] & 2.2.2 Sherpa 2.2.1 & 2.2.2 Sherpa standard NNPDF3.0NNLO NLO [78]Multiboson Sherpa 2.2.1 & 2.2.2 Sherpa 2.2.1 & 2.2.2 Sherpa standard NNPDF3.0NNLO NLO [78]tt¯+ V MadGraph5_aMC@NLO v2.3.3 Pythia 8.210 A14 NNPDF2.3LO NLO [79]tt¯ + h PowhegBox v2 Pythia 8.230 AZNLO [80] CTEQ6L1 [81] NLO [82]V h PowhegBox v2 Pythia 8.212 A14 NNPDF2.3LO NLO [82]Table 1. Simulated background MC samples used in this analysis with the corresponding matrixelement and parton shower generators, underlying-event tune, PDF set, and cross-section order in αs.4 Event reconstructionEvents are selected if they have at least one reconstructed vertex with two or more associatedtracks each with the transverse momentum pT > 500 MeV. If multiple vertices are associatedwith an event the primary vertex (PV) is defined as the one with the highest scalar sumof the squared transverse momenta of the associated tracks [83]. A set of baseline qualitycriteria are applied to reject events with non-collision backgrounds or detector noise [84].Candidate jets and leptons have two levels of classification: ‘baseline’ and ‘signal’. Baselineobjects have a lower purity but higher acceptance and are used for the computation of themissing transverse momentum and solving possible reconstruction ambiguities. Signal objectsare a subset of baseline objects and are used in the definition of the regions of interestof the searches.All electron candidates are reconstructed from energy deposits in the electromagneticcalorimeter that are matched to charged-particle tracks in the ID [85]. Baseline electroncandidates are required to have pT > 7GeV and |η| < 2.47, excluding the transition regionbetween the barrel and endcap calorimeters (1.3 < |η| < 1.52), and satisfy the identificationrequirements of the ‘loose’ operating point provided by a likelihood-based algorithm, describedin ref. [85]. The longitudinal impact parameter z0 relative to the PV is required to satisfy|z0 sin θ| < 0.5 mm. Discrimination between electrons and converted photons is achievedby observing the number of hits in the innermost pixel layer. Signal electrons are requiredto satisfy stricter identification criteria: they are required to satisfy a ‘tight’ likelihoodoperating point selection and the significance of the transverse impact parameter d0 mustsatisfy |d0/σ(d0)| < 5. Signal electron candidates are further refined using a multivariatelikelihood discriminant, in order to discriminate against electron candidates coming fromhadronic jets, photon conversions and heavy-flavor hadron decays. Electron candidates withpT < 75GeV use a looser selection on the likelihood output value (PLVLoose working point),otherwise the candidates are required to satisfy more stringent selection(PLVTight isolationworking point), an analogue procedure has been used in ref. [86].Muon candidates are reconstructed from matching tracks in the ID and muonspectrometer, refined through a global fit that uses the hits from both subdetectors [87].Baseline muon candidates are required to have pT > 6GeV and |η| < 2.7, z0 is required tosatisfy |z0 sin θ| < 0.5 mm and the ‘medium’ identification criteria. Signal muon candidates– 6 –JHEP12(2023)167are required to satify stricter requirements on pseudorapidity and impact parameter, |η| < 2.5and |d0/σ(d0)| < 3. Signal muon candidates are required to satisfy the PLVLoose isolationworking point if they have pT < 75 GeV, and the PflowTightVarRad isolation working pointotherwise [88]. Finally a veto is applied on signal muons to reject events with a poorlymeasured charge-to-momentum ratio σ(q/p)(q/p) > 0.4.Jets are reconstructed from three-dimensional topological energy clusters [89] in thecalorimeters using the anti-kt algorithm with a radius parameter R = 0.4 [90]. Baselinejet candidates are required to have |η| < 4.5 and pT > 20GeV. Signal jets are requiredto have |η| < 2.8 and pT > 30GeV. To suppress jets from pile-up interactions, signal jetcandidates with |η| < 2.4 and pT < 60GeV are required to be matched to the PV throughthe jet vertex tagger (JVT), a tagging algorithm that identifies jets originating from thePV using track information [91, 92], using the tight working point. Additionally, jets arecalibrated following the criteria in ref. [93], which, among other things, includes correctionsto the jet energy and resolution.To exploit the high pT phase space, large-R jets are used for the C1C1-WW and C1N2-WZ models to reconstruct highly boosted W and Z bosons by utilising the substructureof collimated objects. Large-R jets are reconstructed with the same algorithm (anti-kt)as standard jets, but with a large radius parameter of R = 1.0. To reduce the pile-upcontributions to the large-R jets, a jet trimming algorithm [94] is employed, with the Rsuband fcut parameters set to 0.2 and 0.05, respectively, to refine the jet reconstruction, removinglow pT radiation and allowing the parton sub-jets inside the large-R jets to be identified,Large-R jets with pT > 200 GeV and |η| < 2.0 are calibrated using ATLAS prescriptions [95],and are identified as possible W or Z candidates using dedicated taggers designed to identifyW and Z bosons at 50% tagging efficiency [96, 97].Jets originating from the hadronisation of a b-quark are identified (b-tagged) via amultivariate algorithm that combines information from the impact parameters of displacedtracks and topological properties of secondary and tertiary decay vertices reconstructedwithin the jet. The b-tagging relies on the DL1r tagger [98]. The full distribution of thetagger score is used in a procedure referred to as pseudo-continuous b-tagging, allowing amore fine-grained calibration of the b-tagged jets. The score is divided into five bins definedby fixed b-tagging efficiency working points and the distribution edge points (interpretedas the working points at 100% and 0% efficiency). The b-tagged jets are defined using aworking point providing a 77% efficiency for b-hadron identification in tt¯ simulated events.The variable quantifying the likelihood of a jet to be b-tagged (b-quantile) according to thepseudo-continuous b-tagging procedure is used in the analysis targeting the C1N2-Wh model.To resolve the reconstruction ambiguities between electrons, muons, and jets, an overlapremoval procedure is applied to baseline objects. First, any electron sharing the same IDtrack with a muon is rejected. If it shares the same ID track with another electron, the onewith lower pT is discarded. Next, jets are rejected if they lie within ∆R = 0.2 of a muon or ifthe muon is matched to the jet through ghost association [99]. Subsequently, electrons withina cone of size ∆R = min(0.4, 0.04 + 10 GeV /pT) around a jet are removed. Lastly, muonswithin a cone, defined in the same way as for electrons, around any remaining jet are removed.– 7 –JHEP12(2023)167The missing transverse momentum pmissT , and its magnitude EmissT , are reconstructed byusing the set of reconstructed and fully calibrated baseline objects, i.e., electrons, muons,photons and jets described above. Baseline photons [100] are defined as those that satisfypT > 25GeV, |η| < 2.37, and the tight identification criteria. The determination of themissing transverse momentum also includes a soft term consisting of tracks that are notassociated with any reconstructed object. In the searches described here, the tight workingpoint is used for the missing transverse momentum [101, 102].5 Analysis strategy and event selectionThree sets of signal regions (SRs) are defined in the analyses, with each set targeting one ofthe three models considered for electroweakino production and decay. All event selectionsdefined for these regions require that the events were recorded with single lepton (electron andmuon) triggers [103, 104]. The offline lepton pT thresholds are set to ensure that the selectedevents are in the plateau region of the corresponding trigger efficiency distribution. Theoffline trigger pT threshold values increased over the years due to the increase in luminosity,going from 25 (21)GeV to 27 (27.3)GeV for electron (muon) events.Signal signatures have one leptonically decaying W boson, one hadronically decaying Wor Z boson or a Higgs boson decaying into b-quarks, and missing transverse momentum dueto the χ˜01 and neutrinos escaping detection. Hence events are required to have exactly onesignal electron or muon, one to three (two to three) signal jets, allowing for an additionaljet from initial- or final-state radiation and large (moderate) EmissT , for the C1C1-WW andC1N2-WZ (C1N2-Wh) scenarios.A main feature of this analysis is that in the C1C1-WW and C1N2-WZ cases all eventsare additionally required to contain at least one large-R jet. This complements previousresults [27] and allows boosted W or Z boson decays to be probed. Different boson taggingschemes are employed for different signal scenarios: W tagging is applied for the χ˜+1 χ˜−1scenario, while Z tagging is applied for the χ˜±1 χ˜02 scenario. On the other hand, final stateswith small mass-splitting between the chargino and the LSP (still above the Higgs bosonmass) are targeted in the case of the C1N2-Wh models. This complements the analysispresented in ref. [35], optimised for large mass-splittings and small LSP masses. No significantboost is expected for the Higgs boson, and two b-tagged jets are required to identify theHiggs boson candidate. A BDT, described in the following, is used as the final discriminant.The SRs for the three analyses are defined through selections that suppress backgroundcontributions and maximize the sensitivity to signal. The numbers of residual SM events arethen estimated with the aid of MC simulated samples and using a profile likelihood fit [105]as detailed in section 6. Normalisation factors of the MC samples corresponding to the SMprocesses expected to contribute the most to the event yields in the SRs are left free tofloat. A set of control regions (CR), specific for each analysis, are designed to aid in the SMbackgrounds evaluation. The likelihood (one for each analysis) is finally built as the productof Poissonian terms for each CR and, when assessing the discovery (model-independent) orexclusion (model-dependent) sensitivity to new physics model, SR.– 8 –JHEP12(2023)167A set of kinematic variables is built from the physics objects introduced in the previoussection and used to define the event selection for the SRs and CRs. They are describedin the following.• The transverse mass, mT, is defined from the lepton transverse momentum pℓT and themissing transverse momentum pmissT asmT =√2pℓTEmissT (1− cos[∆ϕ(pℓT,pmissT )]),where ∆ϕ(pℓT,pmissT ) is the azimuthal angle between pℓT and pmissT . For W+jets andsemileptonic tt¯ events in which one on-shell W boson decays leptonically, this observablehas an upper endpoint at the W boson mass, while for signal events the mT distributionextends significantly above mW . A requirement is placed on the upper value of∆ϕ(pℓT,pmissT ) for the analysis targeting the C1C1-WW and C1N2-WZ models to rejectbackground with a high momentum lepton and soft jets, where the angle between leptonand pmissT can be large in ϕ.• The missing transverse energy significance, σEmissT [106], is defined as the log-likelihood(L) ratio of measuring the total observed transverse momentum to the likelihood of thenull hypothesis,σEmissT=√√√√2 ln [maxpinvT ̸=0L (EmissT |pinvT )maxpinvT =0L (EmissT |pinvT )]. (5.1)A high value indicates that the measured EmissT value is not compatible with resolutioneffects alone and suggests that the event is more likely to contain objects escapingdetection, which happens more in the signal events than the background events.• Throughout the analyses, variables denoted by mαβ are invariant masses of particles αand β. In particular, the invariant mass of the two leading (highest pT) jets, mjj, isrequired to be in a range around the W or Z mass as signal events are expected to emitan on-shell W or Z boson and have a mass peak in the mjj distribution. The invariantmass of the two jets with highest b-tag weight and consistent with being a b-jet, mbb¯, isrequired to be close to the Higgs boson mass for C1N2-Wh models. The invariant massof the lepton and the jets with highest b-tag weight is denoted by mℓbi , with i = 1, 2and referring to the first, second leading b-jet, respectively. This observable providesgood discrimination against tt¯ and single-top background events.• The effective mass, meff , is defined as the scalar sum of the lepton transverse momentum,the signal jets’ transverse momenta, and the missing transverse momentum,meff = pℓT +∑jetspT + EmissT . (5.2)In the design of SRs targeting exclusion sensitivity for the C1C1-WW and C1N2-WZmodels, two meff regions are constructed to target low and high signal mass differencesbetween the χ˜±1 /χ˜02 and the χ˜01.– 9 –JHEP12(2023)167• The amT2 [107] is referred to as the asymmetric stransverse mass. The stransverse mass,mT2 is a generalization of the transverse mass applied to signatures where pair-producedparent particles decay semi-invisibly. The amT2 is used if the parent particles decaywith different (asymmetric) masses of the invisible particles. For the C1N2-Wh models,where the visible parts of the signal decay are the two b-jets and the lepton and theinvisible parts are the neutralinos and the neutrino, amT2 is used as a discriminant toreject tt¯ contributions. The jets are ordered by their b-tag weight as j1 and j2, so thatthe amT2 is defined as:amT2 = min(mT2(ℓ+ j1, j2),mT2(ℓ+ j2, j1)), (5.3)where mT2 is defined as min[max(m2T(pα, p),m2T(pβ , q))], with pα either pℓ + pj1 orpℓ + pj2 and pβ either pj2 + pℓ or pj1 + pℓ, as the momenta of the visible parts ofthe decay branches, and p and q are the possible transverse momenta of the invisibleparticles in the branches. The minimisation is conducted by selecting values for p andq such that their vector sum is equal to the missing transverse momentum.• The variable mCT [108], referred as contransverse mass, has similar properties to thestransverse mass and is defined as:m2CT = (ET (α) + ET (β))2 − (pT (α)− pT (β))2, (5.4)where α and β are defined as above and pT (α) and pT (β) are their transverse components.Similarly to amT2, the mCT variable is also used for the C1N2-Wh models to rejecttop-quark SM background contributions, since for signal, the two b-jets arise from thesame particle (the Higgs boson), while for tt¯ and single top-quark production they arisefrom two different particles.SRs are then constructed through selections on these quantities or, for the C1N2-Whmodels, using a BDT. Their definition follows two approaches: the exclusion SRs are designedfor setting model-dependent exclusion limits (‘excl.’); the discovery SRs are constructedfor model-independent limits and null-hypothesis tests (‘disc.’ for discovery). Once SRsare defined, the signal and background yield estimates are computed. The strategy isdetailed in section 6. The systematic uncertainties, fit and results are then discussed inthe following sections.5.1 C1C1-WW and C1N2-WZ SRs definitionAn overview of the SR definitions targeting the C1C1-WW and C1N2-WZ models is providedin table 2. The main difference between chargino-chargino and chargino-neutralino signalscenarios is the large-R jet boson-tagging type. Three separate classes of SRs are defined foreach scenario, using mT to target regions sensitive to the increasing mass differences betweenthe χ˜±1 (and its mass-degenerate χ˜02 wino partner) and the χ˜01. These regions are labelledas SRLM, SRMM and SRHM to indicate low (LM), medium (MM) and high (HM) massdifferences, respectively. The requirements on mT make the three regions mutually exclusive.For both the C1C1-WW and C1N2-WZ models, each LM, MM and HM exclusion SR isfurther split into two meff bins, thus providing six exclusion SR bins in total per model for– 10 –JHEP12(2023)167Variable C1C1-WW model C1N2-WZ modelSRLM SRMM SRHM SRLM SRMM SRHMNlep (pT > 25GeV) 1Njet (pT > 30GeV) 1–3Nlarge−Rjet (pT > 250GeV) ≥ 1EmissT [GeV] > 200∆ϕ(ℓ,EmissT ) < 2.6Large-R jet type W tagged Z taggedmT [GeV] 120–200 200–300 > 300 120–200 200–300 > 300Exclusion SRmeff [GeV] (excl.) [600–850, > 850] [600–850, > 850]mjj[GeV] (excl.) [70–90, – ] [80–100, – ]σEmissT(excl.) [> 12, > 15] [> 12, > 12]Discovery SRmeff [GeV] (disc.) > 600 > 600 > 850 > 600 > 850 > 850mjj[GeV] (disc.) — — — 80–100 — —σEmissT(disc.) > 15 > 15 > 15 > 12 > 12 > 12Table 2. Overview of the selection criteria for the exclusion SRs and the discovery SRs used inC1C1-WW and C1N2-WZ models. For exclusion SRs, they are further divided into two meff bins.The selection on mjj and σEmissT varies for low and high meff bins. For discovery SRs, one SR is definedper mT region. The symbol ‘–’ indicates no additional requirement.a simultaneous two-dimensional fit in mT and meff . The multi-bin approach enhances thesensitivity to a range of SUSY scenarios with different properties. The missing transverseenergy significance is optimized separately for low and high meff bins. In the low meff bin,the mjj reconstructed from two resolved jets is required to be close to the mass of the W orZ boson. This is to improve the sensitivity in a semi-boosted regime where the large-R jetwould catch most of the boson decay products but often two jets are resolved. The high meffbin is to target a fully boosted topology hence there is no additional mass constraint on theresolved jets. For the χ˜±1 χ˜02 model, the acceptance times efficiency is 0.37% in SRHM for a600GeV χ˜±1 /χ˜02 mass and massless χ˜01. For the χ˜+1 χ˜−1 model, the acceptance times efficiencyis 0.31% in SRMM for a 600GeV χ˜±1 mass and massless χ˜01.The discovery SRs are defined such that the various meff bins are merged for each ofthe three SRs per model, and selections on mjj and σEmissT are optimized for the best signalsensitivity at a benchmark point for each meff bin.5.2 C1N2-Wh SR definitionThe analysis targets scenarios characterised by ∆m(χ˜±1 , χ˜01) of at least mh, where selections onindividual variables are expected to be sub-optimal to separate the signal from SM production– 11 –JHEP12(2023)167processes due to the similar kinematic properties of SUSY and SM background events.Consequently, a multivariate approach, where the discriminating power of multiple observablesis exploited at once, is expected to increase the sensitivity. A BDT is implemented in theanalysis by making use of the XGBoost (XGB) [109] framework. The training procedureuses events that satisfy an initial selection that requires exactly one signal lepton withpT > 27GeV, two to three jets with pT > 30GeV, exactly two b-tagged jets, EmissT > 50GeV,σEmissT> 5 and mbb¯ in the range of 50–200GeV. A set of object-based and event-basedvariables (30 in total) are used in the training. Object-based variables include the pT , η andϕ of the lepton and the jets, and the b-quantile of the jets. Event-based observables includembb¯, amT2, mCT, mT, σEmissT , mℓbi,i=1,2 and radial distances between pairs of visible objects.Events are classified in five different categories: three corresponding to the mainbackgrounds processes (tt¯, single-top and W+jets), one including all remaining minorbackground processes (Z+jets, diboson, rare processes), and one grouping together thesignal samples in the region mh < ∆m(χ˜±1 , χ˜01) < 200GeV. The grouping of multiple signalsamples increases the statistical power and is enabled by the similarity of the kinematicproperties of the SUSY models of interest. A one-versus-rest multi-classification procedurewas used, wherein each class is fitted against all the other classes producing output scorescontaining the predicted probability of an event being in that class. This method is moreeffective in discriminating the signal from the dominant backgrounds than using a binarysignal versus background classifier. This also has the additional benefit of having background-processes classification scores that can be used to increase the purity of different backgroundswhilst building control and validation regions.The output score wsig denotes the signal class output score and is used in the definitionof the SRs. The scores of the background classes are used in the definition of CRs andvalidation regions (VR). Tools to interpret the BDT learning process are used to identifythe most relevant observables. The mbb¯ variable has the most predictive power for signal, asexpected since it is used to identify the Higgs boson candidate. The mT and the amT2 are,on the other hand, the most predictive variables for W+jets and tt¯ events, respectively.The final selection for the analysis targeting the C1N2-Wh model requires wsig > 0.91(where the BDT score is defined between 0 and 1) and more stringent requirements on theinvariant mass of the two b-tagged jets, 95 <mbb¯ < 140GeV, and on the missing transverseenergy significance, σEmissT > 8. As an example, the acceptance times efficiency is around0.1% for a 350GeV χ˜±1 /χ˜02 mass and a 150GeV χ˜01 mass. When assessing the exclusionsensitivity for the signal-plus-background hypothesis, four wsig bins are used in the likelihoodfit: [0.91, 0.928), [0.928, 0.948), [0.948, 0.964), [0.964, 1], referred to as SRXGB Bin 1–4.Three discovery signal regions are defined, integrated over the signal score with increasingthresholds on it, namely [0.928,1], [0.948,1] and [0.964, 1].6 Background estimationDominant SM background sources in the SRs depend on the analysis. For analyses targetingthe C1C1-WW and C1N2-WZ models, W+jets and diboson production events constitute themain background (46%–73% and 16%–39%, respectively, depending on the SR). Subdominant– 12 –JHEP12(2023)167SM background contributions originate from Z+jets, single-top, multiboson, tt¯ + V , tt¯ +hand V h. For the analysis targeting C1N2-Wh models, tt¯ and single-top (almost exclusivelyWt production) processes are dominant (about 30%–35% each across all bins), followedby W+jets (15%–20%). Remaining contributions originate from Z+jets, diboson, tt¯ + Vand Higgs boson production processes.The main background contributions are estimated by using partially data-driventechniques through the set of CRs designed to be mutually exclusive and non-overlappingwith the SRs (across and within the three analyses), and characterised by negligible expectedsignal contributions for the models of interest. The expected background yield in each SR isdetermined in the profile likelihood fit using the ‘background-only fit’ approach [105]. Withthis fit, the normalisation of the major backgrounds is adjusted to match the data in CRswith negligible signal contamination. A probability density function is defined for each CR.The inputs are the observed event yield and the predicted background yield from simulation,with Poisson statistical uncertainties and systematic uncertainties (detailed in section 7) asnuisance parameters. The nuisance parameters are constrained by Gaussian distributions withwidths corresponding to the sizes of the uncertainties. Systematic uncertainties account forbin-to-bin correlations, with normalisation and nuisance parameters correlated in all regions.The product of all the probability density functions forms the likelihood, which is maximisedby adjusting the normalisation and nuisance parameters. The resulting normalisation factorsare then used to correct the expected yields of the corresponding backgrounds in the variousSRs. The extrapolation of the adjusted normalisation and nuisance parameters to the SRs ischecked in VRs, which kinematically resemble the SRs but are expected to have low signalcontamination, and do not overlap with either CRs or SRs.6.1 C1C1-WW and C1N2-WZ control and validation regionsFor the diboson background, single-lepton processes (ℓvvv) and dilepton processes (ℓℓvv)contribute equally to the backgrounds in the signal regions. The ℓvvv (ℓℓvv) process is markedas diboson1l (diboson2l) in the following yield tables and kinematic figures. The dibosonℓℓvv entering in the SRs are events with two real leptons present in the decay chain, whereone lepton fails the signal lepton requirement, or escapes detection. The ℓℓvv background isestimated and validated in the two-lepton control and validation regions. The crucial variableto enrich the diboson background contribution in the corresponding CR is the dileptoninvariant mass, which is required to be consistent with the SM Z boson mass. Furtherselection criteria for EmissT , σEmissT , and ∆ϕ(pℓT,pmissT ) are defined similarly to the SRs, butwith less stringent bounds, to enhance the number of events. An additional veto on the mjjvariable minimizes the potential overlap with a complementary chargino and neutralino searchwith two leptons and two jets in the final states performed by ATLAS [28], to allow futurestatistical combinations of different channels targeting the same SUSY production processes.In addition to the these selections, the control region DB2LCR requires mT in the range of50–200GeV and the validation region DB2LVR requires mT in the range of 200–350GeV .The single-lepton diboson process ℓvvv has one lepton and missing energy in the finalstate, the kinematic behaviour of which is identical to W+jets background. A set of sharedcontrol and validation regions, the WDB1L regions, are designed for these two processes. The– 13 –JHEP12(2023)167Variable DB2LCR VRNlep (pT > 25GeV) 2Njet (pT > 30GeV) 1–3Nb−jet (pT > 30GeV) 0EmissT [GeV] > 200∆ϕ(ℓ,EmissT ) < 2.9mℓℓ [GeV] 70–100mjjveto [GeV] 75–95σEmissT > 12 > 10mT [GeV] 50–200 200–350Table 3. Overview of the CR and VR definitions for diboson ℓℓvv backgrounds. The Nlep variableprovides the orthogonality to the SR.Variable WDB1L and TCR VR1 VR2Nlep (pT > 25GeV) 1Njet (pT > 30GeV) 1–3Nb−jet (pT > 30GeV) 0 for WDB1L; > 0 for TopNlarge−Rjet (pT > 250GeV) ≥ 1EmissT [GeV] > 200∆ϕ(ℓ,EmissT ) < 2.9Large-R jet type W-taggedmeff [GeV] [600–850, > 850]σEmissT < 12 < 12 > 12mT [GeV] 50–80 > 80 50–120Table 4. Overview of the CR and VR definitions for W+jets, diboson ℓvvv and tt¯ backgrounds.They share the same CR and VR definitions except for number of b-tagged jets requirement. The mTvariable provides the orthogonality to the SR.CR is defined with a selection similar to the SRs, but with mT in the range of 50–80GeVand with inverted σEmissT requirements. A b-jet veto is applied to reduce heavy flavourcontamination. Two sets of VRs are defined: the VR1 validates the extrapolation from theCR to the SRs in mT, and the VR2 validates the extrapolation from the CR to the SRs inσEmissTand mT. The control and validation regions share the same meff binning as the signalregions. The tt¯ control and validation regions, namely TCR, TVR1, and TVR2, have thesame selections as the WDB1L regions, except for the requirement of at least one b-tagged jet.A summary of all CR and VR selection criteria is reported in tables 3 and 4. TheW+jets (ℓvvv) purity is 42%-56% (13%-21%) in WDB1LCR. The tt¯ purity is 58%–77% inTCR and ℓℓvv purity is 58% in DB2LCR.– 14 –JHEP12(2023)167Variable RegionsEmissT [GeV] > 50Nlep (pT > 27GeV) 1Njet (pT > 30GeV) 2–3Nb−jet (pT > 30GeV) 2mbb [GeV] ∈ [50, 200]σEmissT> 5CRtt¯ (CRttXGB) CR single-top (CRstXGB) CR W+jets (CRWXGB)wsig ∈ [0.2, 0.3] ∈ [0, 0.2] ∈ [0.0, 0.2]wtt¯ > 0.73 — —wst < 0.2 > 0.45 < 0.2wW+jets < 0.4 — > 0.65VR tt¯ (VRttXGB) VR single-top (VRstXGB) VR W+jets (VRWXGB)wsig ∈ [0.4, 0.9] ∈ [0.2, 0.9] ∈ [0.2, 0.9]wtt¯ > 0.4 — —wst < 0.2 > 0.2 < 0.2wW+jets < 0.4 — > 0.4Table 5. Definition of the CRs and VRs used to estimate tt¯, single-top and W+jets backgroundprocesses.Sub-dominant background processes, such as Z+jets, single-top, multiboson, tt¯ +V , tt¯+h and V h, which have no dedicated control regions, are normalised to the cross-sectionsindicated in table 1. Similarly to the dominant backgrounds, their expected yields in the SRsare subject to statistical and systematic uncertainties. Backgrounds with misidentified (fake)leptons such as jets misreconstructed as a lepton, and events with leptons originating from ajet produced by heavy-flavour quarks or from photon conversions, are estimated by using amatrix method as described in ref. [110], and found to be negligible in all regions.6.2 C1N2-Wh control and validation regionsThe multi-class BDT approach results in a classifier-output score for each of the backgroundcategories. Only the three categories representing the dominant backgrounds (tt¯, single-top,and W+jets associated production) are considered, and selections on their output scores(wtt¯, wst and wW+jets, respectively) are applied to define the CRs after the initial commonselection. Table 5 shows the definition of the CRs. The selections on the output scoresare defined to maximize the purity of the CR for the targeted background. To reduce thecontamination from signals of interest to a negligible level, the wsig score is also requiredto be low. The purity of the CRs obtained with these selections are 95%, 56% and 72%for tt¯, single-top and W+jets, respectively. In the case of the single-top CR, most of theremaining events arise from tt¯ production.VRs are defined to validate the extrapolation from CRs. Each have events selected withtightened scores towards their respective background class and a higher signal classificationscore that approaches the SRs range. The requirements on the signal score are such thatthe validation regions are orthogonal to both the CRs (by the lower bound) and the SR (by– 15 –JHEP12(2023)167the upper bound). A selection on the background classification scores is maintained in VRsto isolate the extrapolation from each of the control regions and to reduce potential signalcontamination to reasonable levels (<10% for all models). The definition of the validationregions are also given in table 5.Similarly to the C1C1-WW and C1N2-WZ models analyses, sub-dominant backgroundprocesses, such as Z+jets, diboson, multiboson, tt¯ +V , tt¯ +h and V h, are normalised totheir respective cross-sections and their expected yields in the SRs are subject to statisticaland systematic uncertainties. Background contributions from misidentified leptons are alsoevaluated with the matrix method and found negligible.7 Systematic uncertaintiesThe background yield in the SRs is subject to theoretical and experimental systematicuncertainties. The source of these systematic uncertainties for all simulated signal andbackground processes are evaluated and presented in this section.The experimental uncertainties are related to the jet energy scale (JES), jet energyresolution (JER), b-tagging, EmissT modelling, lepton reconstruction and identification, pile-up,and JVT. The dominant uncertainties across all analyses and SRs arise from JES and JER,which are parameterized as a function of the pT and η of the jet, the pile-up conditions,and the jet flavour composition [111]. The uncertainties arising from the large-R jet-bosontagging are grouped into JES and JER systematic uncertainties, for the C1C1-WW andC1N2-WZ SRs. The impact of uncertainties on the efficiencies and mis-tag rates of theb-tagging algorithm is relevant for the C1N2-Wh SRs and is estimated by varying, as afunction of pT, η and jet flavour, the scale factors used to correct the MC simulation, in arange that reflects the uncertainty in their measurement [112, 113]. The EmissT modellingsystematic uncertainties are estimated by propagating the uncertainties in the energy andmomentum scale of each of the objects entering the calculation, and the uncertainties inthe soft term’s resolution and scale [102]. The evaluation of the lepton reconstruction andidentification uncertainties is performed using Z → ℓ+ℓ−, J/ψ → ℓ+ℓ− samples [87, 114].The procedure of pile-up reweighting is applied to the simulation to match the number ofreconstructed vertices to the data. The pile-up uncertainty is estimated by performing areweighting in which ⟨µ⟩ is varied by ±4%.Uncertainties in the modelling of the SM background processes from MC simulationare profiled for dominant backgrounds in dedicated control regions, where the systematicuncertainties only have an impact on the extrapolation factors, while for sub-dominantbackgrounds they are entirely estimated from simulation and affect the inclusive cross-sectionfor each process and the acceptance of the analysis selection in all regions. They are assumedto be fully correlated across signal regions within the same analysis, but uncorrelated betweendifferent processes.Theoretical uncertainties in the tt¯ and single-top backgrounds are dominant for theanalysis targeting C1N2-Wh models, but also relevant for the others. They take into accountuncertainties due to modelling of the hard-scattering, evaluated through a comparisonbetween the nominal PowhegBox +Pythia 8 sample and the alternative aMC@NLO+Pythia 8 sample, and uncertainties arising from the parton shower and hadronisation– 16 –JHEP12(2023)167models, derived from comparisons between samples generated with PowhegBox +Pythia8 and PowhegBox +Herwig 7 [115]. Variations of the renormalisation and factorisationscales (scaled up and down by a factor of two), the initial- and final-state radiation parametersand PDF sets are also considered. The uncertainty assigned to the interference betweensingle-top Wt and tt¯ production [116] is obtained by comparing diagram removal (DR) anddiagram subtraction (DS) samples, modelled by PowhegBox +Pythia 8 for the C1C1-WWand C1N2-WZ channels. In the case of the C1N2-Wh analysis, the Wt predictions of the DSsample in some of the CRs and VRs are significantly lower than those of the nominal sampleand the data, such that a systematic uncertainty estimation by comparison is not possible.A conservative 35% uncertainty is assumed for the uncertainty in the interference betweenthe Wt and tt¯ processes, following studies reported in ref. [35] and ref. [117].The diboson modelling uncertanties are among the dominant uncertainties in the C1C1-WW and C1N2-WZ channels, and are studied separately for the single-lepton and the dileptonprocesses. They are evaluated by studying the envelope of the variations of the renormalisationand factorisation scales. Variations of the renormalisation and factorisation scales are alsoapplied to W/Z+jets, multiboson, tt¯ +V , tt¯ +h, and V h. The PDF uncertainties areconsidered following the PDF4LHC15 recommendations [118].For W/Z+jets, the resummation (QSF) and matching scale (CKKW-L) [119] for theW/Z+jets are estimated by varying the scale parameters up and down for the Sherpagenerator. Further, for Sherpa 2.2.11 W/Z+jets samples, the electroweak NLO correctionuncertainties are assigned to account for the impact of applying different correction methods.An overall 5% systematic uncertainty in the inclusive cross-section is assigned for the Z+jetssamples [120] and similar cross-section uncertainties, 5%–10%, are also assigned for othersub-dominant background contributions.The variations of the parameters corresponding to the factorisation, renormalisation andCKKW-L matching scales in aMC@NLO +Pythia 8 samples provide the uncertaintiesfor the two simplified signal models considered.The dominant systematic uncertainties in the background estimates for the signal regionsare presented in tables 6, 7 and 8. Theoretical and experimental uncertainties are shown foreach of the dominant background contribution. The uncertainties in the scale factor fits to thecontrol regions are listed as ‘Normalisation of dominant backgrounds’. For the SRs targetingthe C1C1-WW and C1N2-WZ models, they contribute around 6%–7% across regions. For theanalysis targeting the C1N2-Wh model, they are around 9%–16% and are dominated by tt¯ andsingle-top backgrounds. The largest individual experimental uncertainty amounts to 4%–17%depending on the SR for C1C1-WW and C1N2-WZ. For the C1N2-Wh analysis, the dominantexperimental uncertainty arises from the JER (12%–20%) followed by the JES and b-tagging.The MC statistical uncertainties contribute up to 19% depending on the SR and analysis.– 17 –JHEP12(2023)167C1C1-WW model SRLM SRMM SRHMTotal background expectation 22.0 9.2 15.7Total background systematic uncertainty ±3.2 ±2.5 ±3.0Theoretical systematic uncertaintiestt¯ ±1.1 ±0.25 ±0.15Single top ±0.31 ±0.08 ±0.35W+jets ±0.4 ±0.15 ±0.32Diboson ±0.29 ±0.24 ±0.26Other backgrounds ±0.10 ±0.07 ±0.08MC statistical uncertaintiesMC statistical uncertainty ±2.1 ±1.6 ±2.1Uncertainties in the background normalisationNormalisation of dominant backgrounds ±1.5 ±0.6 ±1.1Experimental systematic uncertaintiesJet energy resolution ±1.1 ±1.0 ±1.4Jet energy scale ±1.7 ±1.5 ±1.1EmissT ±0.5 ±0.26 ±0.6Lepton uncertainties ±0.4 ±0.10 ±0.5Pile-up/JVT ±0.10 ±0.21 ±0.23Table 6. Breakdown of the dominant systematic uncertainties in background estimates in the variousexclusion signal regions for the C1C1-WW model. The individual uncertainties can be correlated, anddo not necessarily add up in quadrature to the total background uncertainty.– 18 –JHEP12(2023)167C1N2-WZ model SRLM SRMM SRHMTotal background expectation 29 12.7 17.0Total background systematic uncertainty ±4 ±2.5 ±2.9Theoretical systematic uncertaintiestt¯ ±0.9 ±0.29 ±0.20Single top ±1.1 ±0.24 ±0.5W+jets ±0.6 ±0.22 ±0.4Diboson ±0.5 ±0.24 ±0.6Other backgrounds ±0.15 ±0.18 ±0.09MC statistical uncertaintiesMC statistical uncertainty ±2.5 ±1.4 ±2.1Uncertainties in the background normalisationNormalisation of dominant backgrounds ±2.0 ±0.8 ±1.2Experimental systematic uncertaintiesJet energy resolution ±1.1 ±1.3 ±0.8Jet energy scale ±1.3 ±1.0 ±1.3EmissT ±0.5 ±0.6 ±0.07Lepton uncertainties ±0.34 ±0.23 ±0.20Pile-up/JVT ±0.06 ±0.8 ±0.11Table 7. Breakdown of the dominant systematic uncertainties in background estimates in the variousexclusion signal regions for the C1N2-WZ model. The individual uncertainties can be correlated, anddo not necessarily add up in quadrature to the total background uncertainty.– 19 –JHEP12(2023)167C1N2-Wh model SRXGB Bin 1 SRXGB Bin 2 SRXGB Bin 3 SRXGB Bin 4[0.91, 0.928) [0.928, 0.948) [0.948, 0.964) [0.964, 1]Total background expectation 9.4 5.7 4.2 2.2Total background systematic uncertainty ±2.1 ±2.0 ±1.4 ±0.7Theoretical systematic uncertaintiestt¯ ±1.1 ±0.7 ±0.5 ±0.10Single top ±1.2 ±0.9 ±0.9 ±0.4W+jets ±0.17 ±0.14 ±0.12 ±0.04Other backgrounds ±0.14 ±0.13 ±0.13 ±0.10MC statistical uncertaintiesMC statistical uncertainty ±1.0 ±0.8 ±0.7 ±0.4Uncertainties in the background normalisationNormalisation of dominant backgrounds ±1.3 ±0.9 ±0.5 ±0.19Experimental systematic uncertaintiesJet energy resolution ±1.1 ±1.2 ±0.6 ±0.4Jet energy scale ±0.5 ±0.31 ±0.33 ±0.07b-tagging ±0.12 ±0.8 ±0.05 ±0.06Pile-up/JVT ±0.4 ±0.5 ±0.29 ±0.09Lepton and EmissT uncertainties ±0.05 ±0.4 ±0.14 ±0.12Table 8. Breakdown of the dominant systematic uncertainties in background estimates in the variousexclusion signal regions for the C1N2-Wh model. The individual uncertainties can be correlated, anddo not necessarily add up in quadrature to the total background uncertainty. The category ‘Others’refers to diboson, tt¯+W/Z, Higgs boson and Z+jets events.– 20 –JHEP12(2023)1678 ResultsThe observed event yield in each of the exclusion signal regions for the three analyses issummarized in tables 9, 10 and 11 for the C1C1-WW, C1N2-WZ and C1N2-Wh models,respectively. Yields in data are reported with the corresponding SM predictions obtainedfrom the background-only fit, where the predicted post-fit level of background is comparedwith the observed yields in the corresponding VRs and SRs. Two distinct fits are run, one forthe C1C1-WW and C1N2-WZ analyses, and one for the analysis targeting C1N2-Wh models.In the former case, the normalisation factors to be applied to the MC predictions of the mainSM backgrounds are 0.81+0.10−0.09 for tt¯, 1.05+0.09−0.09 for W+jets and diboson 1L, and 1.22+0.18−0.18 fordiboson 2L. In the latter case, the normalisation factors are found to be 1.00± 0.29 for tt¯,0.95± 0.19 for single top and 1.30± 0.05 for W+jets production. The large uncertainties inthe tt¯ and single top normalisation factors are related to their large theoretical systematicuncertainties. Overall the normalisation factors are found to be consistent across analysesfor each background process within uncertainties. For the W+jets normalisation factors,differences are expected because of the different requirement on the number of b-tagged jets.MC predictions for the production of a W or Z boson and b-jets are consistently below data inSM cross-section measurements, and in agreement if no b-jets are required in the event [121].The agreement between the observed and expected event yields in control, validationand exclusion signal regions is illustrated in figures 2 and 3. No significant excesses areobserved in data above the SM prediction.Figures 4–5 present the post-fit mT, σEmissT , and meff distributions for the C1C1-WWand C1N2-WZ analyses compared with the data in the selected control and validation regions.The data and the background expectation in all validation regions agree well within aroundtwo standard deviations. Therefore no further systematic uncertainty is applied to thebackground estimate in the signal regions. Figure 6 shows the post-fit meff distributions inSRLM, SRMM, and SRHM for both the C1C1-WW and C1N2-WZ models. The uncertaintybands include all statistical and systematic uncertainties. The dashed lines represent thebenchmark signal points.For the C1N2-Wh analysis, EmissT and output signal score distributions in tt¯ and singletop validation regions are shown in figure 7. The data and the background expectation inall validation regions agree well within around one standard deviations. Figure 8 shows thepost-fit distributions for EmissT , amT2, mbb¯ and σEmissT in the inclusive SR, i.e., considering allSR bins. Events selected by the BDT as compatible with the signal of interest (high wsig score)have a moderate to large EmissT and amT2, mbb¯ close to the Higgs boson mass and large σEmissT .– 21 –JHEP12(2023)167C1C1-WW model SRLM Bin 0 Bin 1[600, 850]GeV > 850GeVObserved events 23 16 7Total SM background events 22.0± 3.2 15.8± 2.8 6.2± 1.0tt¯ 2.6± 1.3 2.2± 1.2 0.36± 0.21W+jets 13.6± 2.7 10.1± 2.3 3.5± 0.8Z+jets 0.10+0.16−0.10 0.04+0.13−0.04 0.054± 0.034Single-top 0.5± 0.4 0.19+0.25−0.19 0.26± 0.18Diboson2l 1.7± 0.5 1.3± 0.4 0.43± 0.12Diboson1l 3.0± 0.7 1.5± 0.5 1.41± 0.30tt¯ +V 0.50± 0.14 0.30± 0.10 0.20± 0.06tt¯ +h 0.008± 0.005 0.005± 0.005 0.003± 0.001Multiboson 0.005± 0.002 − 0.005± 0.002C1C1-WW model SRMM Bin 0 Bin 1[600, 850]GeV > 850GeVObserved events 11 7 4Total SM background events 9.2± 2.5 6.4± 2.2 2.7± 0.9tt¯ 0.60± 0.32 0.44± 0.25 0.16± 0.10W+jets 5.3± 2.1 4.0± 1.9 1.3± 0.5Z+jets 0.11± 0.04 0.09± 0.04 0.020± 0.010Single-top 0.12± 0.09 0.09± 0.07 0.03+0.03−0.07Diboson2l 1.4± 0.4 0.89± 0.32 0.49± 0.21Diboson1l 1.4± 0.4 0.80± 0.28 0.64± 0.25tt¯ +V 0.29± 0.12 0.16± 0.09 0.13± 0.07tt¯ +h 0.004± 0.002 0.004± 0.002 −Multiboson − − −C1C1-WW model SRHM Bin 0 Bin 1[600, 850]GeV > 850GeVObserved events 16 4 12Total SM background events 15.7± 3.0 4.8± 1.3 10.8± 2.5tt¯ 0.36± 0.18 0.19± 0.11 0.17± 0.09W+jets 11.3± 2.5 3.3± 1.1 8.0± 2.2Z+jets 0.17± 0.07 0.14± 0.06 0.029± 0.018Single-top 0.49± 0.34 0.23± 0.17 0.26± 0.20Diboson2l 1.5± 0.4 0.54± 0.17 0.99± 0.28Diboson1l 1.5± 0.4 0.39± 0.14 1.09± 0.31tt¯ +V 0.33± 0.12 0.037± 0.035 0.29± 0.11tt¯ +h 0.003± 0.003 − 0.003± 0.003Multiboson 0.004± 0.001 − 0.004± 0.001Table 9. Observed event yields and the background expectation obtained from a background fit inthe C1C1-WW model SRs with an integrated luminosity of 139 fb−1. The first column with numbersstands for the yields in all bins. The second and third columns correspond to the low and high bins inmeff . Uncertainties reported for the fitted background estimates combine statistical and systematicuncertainties.– 22 –JHEP12(2023)167C1N2-WZ model SRLM Bin 0 Bin 1[600, 850]GeV > 850GeVObserved events 26 16 10Total SM background events 29± 4 15.6± 2.8 13.0± 1.8tt¯ 2.7± 1.0 1.6± 0.7 1.1± 0.4W+jets 18.1± 2.8 10.6± 2.1 7.5± 1.2Z+jets 0.06+0.60−0.06 0.03+0.66−0.03 0.03+0.06−0.03Single-top 0.6+1.0−0.6 0.16+0.34−0.16 0.5+0.8−0.5Diboson2l 2.4± 0.6 1.4± 0.4 0.96± 0.28Diboson1l 3.9± 0.9 1.3± 0.4 2.5± 0.6tt¯ +V 0.74± 0.22 0.37± 0.13 0.37± 0.11tt¯ +h 0.021± 0.006 0.011± 0.004 0.010± 0.004Multiboson 0.020± 0.005 − 0.020± 0.005C1N2-WZ model SRMM Bin 0 Bin 1[600, 850]GeV > 850GeVObserved events 22 13 9Total SM background events 12.7± 2.5 7.0± 2.2 5.7± 1.3tt¯ 0.9± 0.4 0.52± 0.23 0.41± 0.20W+jets 7.3± 2.0 4.4± 1.9 2.9± 0.8Z+jets 0.30± 0.23 0.12± 0.12 0.18± 0.15Single-top 0.14+0.24−0.14 0.09+0.16−0.09 0.05+0.11−0.05Diboson2l 1.5± 0.4 0.66± 0.28 0.85± 0.25Diboson1l 1.9± 0.5 0.94± 0.27 0.95± 0.30tt¯ +V 0.54± 0.17 0.25± 0.10 0.28± 0.10tt¯ +h 0.026+0.029−0.026 0.005± 0.004 0.022+0.025−0.022Multiboson 0.008± 0.002 0.004± 0.001 0.004± 0.001C1N2-WZ model SRHM Bin 0 Bin 1[600, 850]GeV > 850GeVObserved events 26 5 21Total SM background events 17.0± 2.8 3.3± 1.4 13.7± 2.3tt¯ 0.63± 0.28 0.23± 0.15 0.40± 0.19W+jets 11.6± 2.1 2.3± 1.1 9.3± 1.6Z+jets 0.045± 0.022 0.076± 0.021 −Single-top 1.3± 0.7 0.014+0.066−0.014 1.2± 0.7Diboson2l 1.5± 0.7 0.30± 0.18 1.2± 0.5Diboson1l 1.6± 0.4 0.35± 0.15 1.22± 0.30tt¯ +V 0.42± 0.14 0.019+0.023−0.019 0.40± 0.14tt¯ +h 0.003± 0.003 − 0.003± 0.003Multiboson 0.006+0.011−0.006 − 0.006+0.011−0.006Table 10. Observed event yields and the background expectation obtained from a background fit inthe C1N2-WZ model SRs with an integrated luminosity of 139 fb−1. The first column with numbersstands for the yields in all bins. The second and third columns correspond to the low and high bins inmeff . Uncertainties reported for the fitted background estimates combine statistical and systematicuncertainties.– 23 –JHEP12(2023)167Yields SRXGB Bin 1 SRXGB Bin 2 SRXGB Bin 3 SRXGB Bin 4[0.91, 0.928) [0.928, 0.948) [0.948, 0.964) [0.964, 1]Observed events 5 9 6 5Total SM background events 9.4± 2.1 5.7± 2.0 4.2± 1.4 2.2± 0.8tt¯ 3.8± 1.1 2.3± 1.4 1.3± 0.7 0.30+0.32−0.30Single-top 3.1± 1.6 2.5± 1.2 1.8± 1.0 0.8± 0.5W+jets 1.7± 0.6 0.5± 0.4 0.8± 0.2 0.8± 0.2Diboson 0.4± 0.2 0.14± 0.09 0.09± 0.07 0.05± 0.02tt¯+h, Wh 0.24± 0.22 0.18± 0.05 0.14± 0.02 0.15± 0.03tt¯ +W/Z 0.13± 0.06 0.04+0.05−0.04 0.08± 0.06 0.05± 0.02Others 0.04+0.05−0.04 0.05± 0.04 0.01± 0.01 0.02± 0.01Table 11. Observed event yields and the background expectation obtained from a background fit inthe C1N2-Wh model SR with an integrated luminosity of 139 fb−1. Each column corresponds to abin in wsig score. Uncertainties reported for the fitted background estimates combine statistical andsystematic uncertainties. The category ‘Others’ refers to Z+jets and multiboson events.– 24 –JHEP12(2023)167Signal channel Observed events Total SM background ⟨ϵσ⟩95obs[fb] S95obs S95exp CLB p0 ZC1C1-WW modelSRLM (disc.) 16 11.6± 1.6 0.09 13.0 8.8+4.3−1.5 0.84 0.14 1.09SRMM (disc.) 9 9.8± 2.0 0.06 7.9 9.0+5.4−1.4 0.42 0.50 0.00SRHM (disc.) 12 10.8± 2.5 0.07 10.4 9.4+4.1−3.0 0.60 0.39 0.29C1N2-WZ modelSRLM (disc.) 17 18.4± 2.9 0.08 11.5 13.7+4.0−4.5 0.40 0.50 0.00SRMM (disc.) 9 5.7± 1.3 0.07 10.2 6.8+3.1−0.9 0.87 0.13 1.11SRHM (disc.) 21 13.7± 2.3 0.13 17.5 10.5+4.4−2.4 0.92 0.06 1.54C1N2-Wh modelSR (inclusive) 25 21.4± 4.2 0.12 16.2 13.4+5.5−4.0 0.70 0.29 0.57SR Bin 2–4 20 12.0± 3.0 0.13 17.9 10.7+4.7−3.1 0.92 0.06 1.58SR Bin 3–4 11 6.3± 1.6 0.09 12.0 7.4+3.5−2.3 0.89 0.08 1.42SR Bin 4 5 2.2± 1.0 0.06 8.0 5.1+2.7−1.6 0.86 0.08 1.37Table 12. The number of observed events, total SM background, 95% CL upper limits on the visiblecross-section (⟨ϵσ⟩95obs) and on the number of signal events (S95obs ). The fifth column (S95exp) shows the95% CL upper limit on the number of signal events, given the expected number (with ±1 standarddeviation excursions on the expectation) of background events. The last three columns indicate theCLB value that provides a measure of compatibility of the observed data with the 95% CL signalstrength hypothesis relative to fluctuations of the background, the discovery p-value (p0) that measurescompatibility of the observed data with the background-only (zero signal strength) hypothesis relativeto fluctuations of the background and the corresponding Gaussian significance (Z). Larger valuesindicate greater relative compatibility. The p0 is not calculated in signal regions with a deficit relativeto the nominal background prediction and here the p0 value is capped at 0.50.Table 12 summarises the observed (S95obs) and expected (S95exp) 95% confidence level (CL)upper limits on the number of signal events and on the observed visible cross-section, ⟨ϵσ⟩95obs,for each SR(disc.). The discovery SRs are used to test for the presence of any beyond-the-Standard-Model (BSM) physics processes. Upper limits on contributions from new physicsprocesses are estimated by using the ‘model-independent fit’, where a generic BSM processis assumed to contribute only to the SR and not to the CRs, thus giving a conservativebackground estimate in the SR. When normalised to the integrated luminosity of the datasample, the results can be interpreted as corresponding to observed upper limits ⟨ϵσ⟩95obs,defined as the product of the production cross-section, the acceptance, and the selectionefficiency of a BSM signal. The p0 value and the CLB value are also provided. The formerrepresents the probability of the SM background alone to fluctuate to the observed numberof events or higher, and latter provides the confidence level observed for the background-onlyhypothesis. The limits are validated by comparing pseudo-experiments and using asymptoticformulae, and found to be comparable. All limits presented in this paper are calculatedusing asymptotic formulae.– 25 –JHEP12(2023)167DB2LCRWDB1LCR bin0WDB1LCR bin1TCR bin0TCR bin1DB2LVRWDB1LVR1 bin0WDB1LVR1 bin1WDB1LVR2 bin0WDB1LVR2 bin1TVR1 bin0TVR1 bin1TVR2 bin0TVR2 bin1110210310410510Number of eventsData Diboson1lTotal SM Diboson2lW+jets Single toptt OthersATLAS missT + jets + Eµ1e/-1 = 13 TeV, 139 fbsDB2LCRWDB1LCR bin0WDB1LCR bin1TCR bin0TCR bin1DB2LVRWDB1LVR1 bin0WDB1LVR1 bin1WDB1LVR2 bin0WDB1LVR2 bin1TVR1 bin0TVR1 bin1TVR2 bin0TVR2 bin12−02Significance(a)SRLM WZ bin0SRLM WZ bin1SRMM WZ bin0SRMM WZ bin1SRHM WZ bin0SRHM WZ bin1SRLM WW bin0SRLM WW bin1SRMM WW bin0SRMM WW bin1SRHM WW bin0SRHM WW bin1110210310Number of eventsData Diboson1lTotal SM Diboson2lW+jets Single toptt OthersATLAS missT + jets + Eµ1e/-1 = 13 TeV, 139 fbsSRLM WZ bin0SRLM WZ bin1SRMM WZ bin0SRMM WZ bin1SRHM WZ bin0SRHM WZ bin1SRLM WW bin0SRLM WW bin1SRMM WW bin0SRMM WW bin1SRHM WW bin0SRHM WW bin12−02Significance(b)Figure 2. Comparison of the observed and expected event yields in (a) the control and validationregions, and (b) the exclusion signal regions for the C1C1-WW and C1N2-WZ analyses. Uncertaintiesin the background estimates include both the statistical and systematic uncertainties. The bottompanel shows the significance [122] of the differences between the observed and expected yields.– 26 –JHEP12(2023)167CRttXGBCRstXGBCRWXGBVRttXGBVRstXGBVRWXGBSRXGB Bin 1SRXGB Bin 2SRXGB Bin 3SRXGB Bin 42−02Significance 110210310410510610Number of eventstt Single-topW+jets HiggsDiboson Z+jetsVtt SM TotalData ATLAS-1 = 13 TeV, 139 fbsmissT + 2 b-jets + Eµ1e/Figure 3. Comparison of the observed and expected event yields in control, validation regions, andexclusion SR bins for the C1N2-Wh analysis. Uncertainties in the background estimates include boththe statistical and systematic uncertainties. The bottom panel shows the significance [122] of thedifferences between the observed and expected yields for control regions, validation regions and in thesignal region in bins of wsig.Model-dependent exclusion limits at 95% CL are placed on the signal model. Theselimits are shown as a function of the masses of the SUSY particles in figure 9 for C1C1-WWand C1N2-WZ models, and figure 10 for C1N2-Wh models. A likelihood similar to theone used in the background-only fit, but with additional terms for the SRs, is used for thecalculation. The exclusion SRs are included in the fit and are used to constrain normalisationand nuisance parameters. A signal is allowed in this likelihood in both the CRs and SRs. TheVRs are not used in the fit. The CLs method [123] is used to derive the CL of the exclusionfor a particular signal model; signal models with a CLs value below 0.05 are excluded at95% CL. The uncertainties in the observed limit are calculated by varying the cross-sectionfor the signal up and down by its uncertainty. For the C1C1-WW model, the χ˜±1 mass ofabout 260–520GeV is excluded for a massless χ˜01, which complements the previous ATLASlimits in a 0-lepton channel [30] and a 2-lepton channel [29]. For the C1N2-WZ model, therange of 260–420GeV in m(χ˜±1 /χ˜02) for a massless χ˜01 is excluded. The limit in the highm(χ˜±1 /χ˜02) region is dominated by the high meff bin of SR-HM. Combining the low and highmeff bins of SRMM for C1N2-WZ model leads to a signal significance of around 2.1 standard– 27 –JHEP12(2023)1671−10110210310410510Events / 10 GeV-1 = 13 TeV, 139 fbsATLASWDB1LCR/VR1efflow mDataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers50 70 90 110 130 150 [GeV]Tm012Data / SM(a)1−10110210310410510Events / 4 -1 = 13 TeV, 139 fbsATLASWDB1LCR/VR2effhigh mDataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers0 4 8 12 16 20 24missTEσ012Data / SM(b)1−10110210310410510610Events / 250 GeV-1 = 13 TeV, 139 fbsATLASWDB1LCReffall mDataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers600 850 1100 1350 1600 [GeV]effm012Data / SM(c)Figure 4. The post-fit (a) mT distribution in WDB1L CR and VR1 for low meff bin, (b) σEmissTdistribution in WDB1L CR and VR2 for high meff bin and (c) meff distribution in WDB1L CR. Thevertical dashed line separates the control and validation regions. The uncertainty bands plotted includeall statistical and systematic uncertainties. The overflow events, where present, are included in the lastbin. The lower panels of the plots show the ratio of the observed data to the total background prediction.deviations. This differences between observed and expected events in bins with small numbersof events lead to an observed limit weaker than the expected one. Similar differences arisein the exclusion limit of C1N2-Wh models. In this case, limits are shown as a function ofthe mass of the chargino and next-to-lightest neutralino and the mass difference betweenthat and the LSP, and are compared with previous ATLAS results on the same data sample.The presented mass range is chosen to illustrate the improved region only. While the lownumbers of events and the large systematic uncertainties in the most constraining bins inwsig reduce the expected sensitivity, the BDT approach exceed previous constraints at low∆m(χ˜±1 /χ˜02, χ˜01) by up to 40GeV in the range of 200–260GeV and 280–470GeV in m(χ˜±1 /χ˜02).– 28 –JHEP12(2023)1671−10110210310410510Events / 10 GeV-1 = 13 TeV, 139 fbsATLASTCR/VR1efflow mDataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers50 70 90 110 130 150 [GeV]Tm012Data / SM(a)1−10110210310410510Events / 4 -1 = 13 TeV, 139 fbsATLASTCR/VR2effhigh mDataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers0 4 8 12 16 20 24missTEσ012Data / SM(b)1−10110210310410510610Events / 250 GeV-1 = 13 TeV, 139 fbsATLASTCReffall mDataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers600 850 1100 1350 1600 [GeV]effm012Data / SM(c)Figure 5. The post-fit (a) mT distribution in tt¯ CR and VR1 for low meff bin, (b) σEmissT distributionin tt¯ CR and VR2 for high meff bin and (c) meff distribution in tt¯ CR. The vertical dashed lineseparates the control and validation regions. The uncertainty bands plotted include all statistical andsystematic uncertainties. The overflow events, where present, are included in the last bin. The lowerpanels of the plots show the ratio of the observed data to the total background prediction.– 29 –JHEP12(2023)167600 850 1100 1350 1600 [GeV]effm5101520253035Events / 250 GeV-1 = 13 TeV, 139 fbsATLASSRLMC1C1-WW DataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers)=(400,0) GeV10χ∼,1±χ∼m()=(300,100) GeV10χ∼,1±χ∼m((a)600 850 1100 1350 1600 [GeV]effm5101520253035Events / 250 GeV-1 = 13 TeV, 139 fbsATLASSRLMC1N2-WZ DataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers)=(400,0) GeV10χ∼,20χ∼/1±χ∼m()=(300,100) GeV10χ∼,20χ∼/1±χ∼m((b)600 850 1100 1350 1600 [GeV]effm2468101214161820Events / 250 GeV-1 = 13 TeV, 139 fbsATLASSRMMC1C1-WW DataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers)=(400,0) GeV10χ∼,1±χ∼m()=(300,100) GeV10χ∼,1±χ∼m((c)600 850 1100 1350 1600 [GeV]effm51015202530Events / 250 GeV-1 = 13 TeV, 139 fbsATLASSRMMC1N2-WZ DataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers)=(400,0) GeV10χ∼,20χ∼/1±χ∼m()=(300,100) GeV10χ∼,20χ∼/1±χ∼m((d)600 850 1100 1350 1600 [GeV]effm2468101214161820Events / 250 GeV-1 = 13 TeV, 139 fbsATLASSRHMC1C1-WW DataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers)=(400,0) GeV10χ∼,1±χ∼m()=(600,0) GeV10χ∼,1±χ∼m((e)600 850 1100 1350 1600 [GeV]effm51015202530Events / 250 GeV-1 = 13 TeV, 139 fbsATLASSRHMC1N2-WZ DataTotal SMW+jetsttDiboson1lDiboson2lSingle topOthers)=(400,0) GeV10χ∼,20χ∼/1±χ∼m()=(600,0) GeV10χ∼,20χ∼/1±χ∼m((f)Figure 6. The post-fit meff distributions in the exclusion signal regions (a, b) SRLM, (c, d) SRMM,and (e, f) SRHM for the C1C1-WW and C1N2-WZ models. The uncertainty bands plotted include allstatistical and systematic uncertainties. The dashed lines represent the benchmark signal samples.The overflow events, where present, are included in the last bin.– 30 –JHEP12(2023)167100 200 300 400 500 600 [GeV]missTE00.511.52Data/SM110210310410510Number of events / 110 GeVtt Single-topW+jets HiggsDiboson Z+jetsVtt SM TotalDataATLAS -1 = 13 TeV, 139 fbsVRttXGB(a)100 200 300 400 500 600 [GeV]missTE00.511.52Data/SM110210310410510Number of events / 110 GeVtt Single-topW+jets HiggsZ+jets DibosonVtt SM TotalDataATLAS -1 = 13 TeV, 139 fbsVRsRXG (b)0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9XGB Signal Score00.511.52Data/SM110210310410510Number of events / 0.10 tt Single-topW+jets HiggsDiboson Z+jetsVtt SM TotalDataATLAS-1 = 13 TeV, 139 fbsVRttXGB(c)0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1XGB Signal Score00.511.52Data/SM110210310410510Number of events / 0.20 tt Single-topW+jets HiggsZ+jets DibosonVtt SM TotalDataATLAS -1 = 13 TeV, 139 fbsVRsRXG (d)Figure 7. Post-fit EmissT and signal output score distributions: (a) and (c) in tt¯, (b) and (d) insingle-top VRs. The uncertainty bands plotted include all statistical and systematic uncertainties.The overflow events, where present, are included in the last bin. The lower panels of the plots showthe ratio of the observed data to the total background prediction.– 31 –JHEP12(2023)167100 200 300 400 500 600 [GeV]missTE110210Number of events / 110 GeVSingle-top ttW+jets HiggsDiboson VttZ+jets SM TotalData)= (350,150) GeV01 ∼,02 ∼/ 1 ∼m()= (400,200) GeV01 ∼,02 ∼/ 1 ∼m( TL S -1 = 13 TeV, 139 fbsSRinclC1N2 Wh(a)6 8 10 12 14 16 18 20 22missTE 110210Number of events / 3.20 Single-top ttW+jets HiggsDiboson VttZ+jets SM TotalData)= (350,150) GeV01χ∼,02χ∼/ 1χ∼m()= (400,200) GeV01χ∼,02χ∼/ 1χ∼m( TL S -1 = 13 TeV, 139 fbsSRinclC1N2 Wh(b)100 200 300 400 500 600 700 [GeV]T2am110210Number of events / 120 GeVSingle-top ttW+jets HiggsDiboson VttZ+jets SM TotalData)= (350,150) GeV01 ∼,02 ∼/ 1 ∼m()= (400,200) GeV01 ∼,02 ∼/ 1 ∼m( TL S -1 = 13 TeV, 139 fbsSRinclC1N2 Wh(c)80 90 100 110 120 130 140 150 [GeV]bbm110210Number of events / 7 GeVSingle-top ttW+jets HiggsDiboson VttZ+jets SM TotalData)= (350,150) GeV01 ∼,02 ∼/ 1 ∼m()= (400,200) GeV01 ∼,02 ∼/ 1 ∼m( TL S-1 = 13 TeV, 139 fbsSRinclC1N2 Wh(d)0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1XGB Signal Score012Data/SM110210Number of events / 0.02 Single-top ttW+jets HiggsDiboson VttZ+jets SM TotalData)= (350,150) GeV01 ∼,02 ∼/ 1 ∼m()= (400,200) GeV01 ∼,02 ∼/ 1 ∼m( TL S -1 = 13 TeV, 139 fbsSRinclC1N2 Wh(e)Figure 8. Post-fit distributions in the SRs (not binned in signal output score) for representativekinematic observables (a) EmissT , (b) σEmissT , (c) amT2, (d) mbb¯ and (e) the signal output score. Tworepresentative SUSY signal models are overlaid for illustration. The uncertainty bands plotted includeall statistical and systematic uncertainties. The overflow events, where present, are included in thelast bin.– 32 –JHEP12(2023)167200 300 400 500 600 700 800 900 1000) [GeV]1±χ∼m(050100150200250300350400) [GeV]10χ∼m() + 80.4 GeV10χ∼) < m(1±χ∼m()expσ1 ±Expected Limit ()SUSYtheoryσ1 ±Observed Limit (q q→, W ν l→, W 10χ∼10χ∼ WW → 1±χ∼1±χ∼, All limits at 95% CL-1=13 TeV, 139 fbsATLAS(a)200 300 400 500 600 700 800 900 1000) [GeV]20χ∼)/m(1±χ∼m(050100150200250300350400) [GeV]10χ∼m() + 91.2 GeV10χ∼) < m(20χ∼/1±χ∼m()expσ1 ±Expected Limit ()SUSYtheoryσ1 ±Observed Limit (q q→, Z ν l→, W 10χ∼10χ∼ WZ → 20χ∼1±χ∼, All limits at 95% CL-1=13 TeV, 139 fbsATLAS(b)Figure 9. Model-dependent exclusion contour at 95% CL on (a) the chargino pair production and(b) the production of a chargino and a next-to-lightest neutralino. The observed limit is given by thesolid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text.Expected limits are given by the dashed line with uncertainties shown by the shaded band.– 33 –JHEP12(2023)167150 200 250 300 350 400 450 [GeV]02χ∼/±1χ∼m100120140160180200220240260280)[GeV]0 1χ∼ ,0 2χ∼ /± 1χ∼ m(∆h) = m01χ∼,02χ∼/±1χ∼ m(∆02χ∼/±1χ∼) = m01χ∼,02χ∼/±1χ∼ m(∆ATLAS), BDT-based expσ1 ±Expected Limit (), BDT-based SUSYtheoryσ1 ±Observed Limit (Expected Limit, ATLAS 13 TeV, arXiv:1909.09226Observed Limit, ATLAS 13 TeV, arXiv:1909.09226b b→, h ν l→, W 01χ∼01χ∼ Wh → 02χ∼±1χ∼, All limits at 95% CL-1=13 TeV, 139 fbsFigure 10. Model-dependent exclusion contour at 95% CL on the chargino and a next-to-lightestneutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. Theobserved limit is given by the solid line with the signal cross-section uncertainties shown by the dottedlines as indicated in the text. The area above the red observed limit line corresponds to the excludedregion. Expected limits are given by the dashed line with uncertainties shown by the shaded band.The observed and expected limit contours from the previous ATLAS analysis of the same data sampleusing standard cut-and-count methods are also shown for comparison.– 34 –JHEP12(2023)1679 ConclusionThe results of three searches for electroweakino pair production pp→ χ˜±1 χ˜02/χ˜+1 χ˜−1 in whichthe chargino (χ˜±1 ) decays into a W boson and the lightest neutralino (χ˜01), while the heavierneutralino (χ˜02) decays into either a Z or a Higgs boson and a second χ˜01 are presented.The searches are performed in events with one isolated lepton, jets and missing transversemomentum, using pp collisions provided by the LHC at a centre-of-mass energy of 13 TeV.Data collected with the ATLAS detector between 2015 and 2018 are used, corresponding to anintegrated luminosity of 139 fb−1. No significant deviation from the expected Standard Modelbackground is observed, and limits are set on the direct production of the electroweakinosin simplified models. Searches exploiting large radius jets to identify hadronically decayingW and Z bosons complement the previous ATLAS limits. In the χ˜+1 χ˜−1 model, masses ofχ˜±1 ranging from 260 to 520GeV are excluded at 95% confidence level for a massless χ˜01.In the χ˜±1 χ˜02 model with χ˜02 decaying via a Z boson, masses of χ˜±1 /χ˜02 ranging from 260 to420GeV are excluded at 95% CL for a massless χ˜01. Utilizing a BDT discriminant, the searchtargeting χ˜±1 χ˜02 models with χ˜02 decaying via a Higgs boson and mass-splitting between themass-degenerate χ˜±1 and χ˜02 as small as the Higgs boson mass and the LSP exceed previousconstraints by up to 40GeV in the range of 200–260GeV and 280–470GeV in m(χ˜±1 /χ˜02).AcknowledgmentsWe thank CERN for the very successful operation of the LHC, as well as the support stafffrom 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; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece;RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXTand JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland;FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ,Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden;SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK,Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition,individual groups and members have received support from BCKDF, CANARIE, ComputeCanada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic;COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Skłodowska-CurieActions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idexand ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeiaprogrammes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA,Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; LaCaixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEOand GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden;The Royal Society and Leverhulme Trust, United Kingdom.– 35 –JHEP12(2023)167The crucial computing support from all WLCG partners is acknowledged gratefully, inparticular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark,Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), theTier-2 facilities worldwide and large non-WLCG resource providers. 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Dungs 49,K. Dunne 47a,47b, A. Duperrin 102, H. Duran Yildiz 3a, M. Düren 58, A. Durglishvili 149b,B.L. Dwyer 115, G.I. Dyckes 17a, M. Dyndal 86a, B.S. Dziedzic 87, Z.O. Earnshaw 146,G.H. Eberwein 126, B. Eckerova 28a, S. Eggebrecht 55, E. Egidio Purcino De Souza 127,L.F. Ehrke 56, G. Eigen 16, K. Einsweiler 17a, T. Ekelof 161, P.A. Ekman 98, S. El Farkh 35b,Y. El Ghazali 35b, H. El Jarrari 36, A. El Moussaouy 108, V. Ellajosyula 161, M. Ellert 161,F. Ellinghaus 171, N. Ellis 36, J. Elmsheuser 29, M. Elsing 36, D. Emeliyanov 134, Y. Enari 153,I. Ene 17a, S. Epari 13, J. Erdmann 49, P.A. Erland 87, M. Errenst 171, M. Escalier 66,C. Escobar 163, E. Etzion 151, G. Evans 130a, H. Evans 68, L.S. Evans 95, M.O. Evans 146,A. Ezhilov 37, S. Ezzarqtouni 35a, F. Fabbri 59, L. Fabbri 23b,23a, G. Facini 96,V. Fadeyev 136, R.M. Fakhrutdinov 37, S. Falciano 75a, L.F. Falda Ulhoa Coelho 36,P.J. Falke 24, J. Faltova 133, C. Fan 162, Y. Fan 14a, Y. Fang 14a,14e, M. Fanti 71a,71b,M. Faraj 69a,69b, Z. Farazpay 97, A. Farbin 8, A. Farilla 77a, T. Farooque 107,S.M. Farrington 52, F. Fassi 35e, D. Fassouliotis 9, M. Faucci Giannelli 76a,76b, W.J. Fawcett 32,L. Fayard 66, P. Federic 133, P. Federicova 131, O.L. Fedin 37,a, G. Fedotov 37,M. Feickert 170, L. Feligioni 102, D.E. Fellers 123, C. Feng 62b, M. Feng 14b, Z. Feng 114,M.J. Fenton 160, A.B. Fenyuk37, L. Ferencz 48, R.A.M. Ferguson 91, S.I. Fernandez Luengo 137f ,P. Fernandez Martinez 13, M.J.V. Fernoux 102, J. Ferrando 48, A. Ferrari 161, P. Ferrari 114,113,R. Ferrari 73a, D. Ferrere 56, C. Ferretti 106, F. Fiedler 100, P. Fiedler 132, A. Filipčič 93,E.K. Filmer 1, F. Filthaut 113, M.C.N. Fiolhais 130a,130c,c, L. Fiorini 163, W.C. Fisher 107,T. Fitschen 101, P.M. Fitzhugh135, I. Fleck 141, P. Fleischmann 106, T. Flick 171,M. Flores 33d,ac, L.R. Flores Castillo 64a, L. Flores Sanz De Acedo 36, F.M. Follega 78a,78b,N. Fomin 16, J.H. Foo 155, B.C. Forland68, A. Formica 135, A.C. Forti 101, E. Fortin 36,A.W. Fortman 61, M.G. Foti 17a, L. Fountas 9,j , D. Fournier 66, H. Fox 91,P. Francavilla 74a,74b, S. Francescato 61, S. Franchellucci 56, M. Franchini 23b,23a,S. Franchino 63a, D. Francis36, L. Franco 113, V. Franco Lima 36, L. Franconi 48,M. Franklin 61, G. Frattari 26, A.C. Freegard 94, W.S. Freund 83b, Y.Y. Frid 151, J. Friend 59,N. Fritzsche 50, A. Froch 54, D. Froidevaux 36, J.A. Frost 126, Y. Fu 62a,S. Fuenzalida Garrido 137f , M. Fujimoto 102, K.Y. Fung 64a, E. Furtado De Simas Filho 83b,M. Furukawa 153, J. Fuster 163, A. Gabrielli 23b,23a, A. Gabrielli 155, P. Gadow 36,G. Gagliardi 57b,57a, L.G. Gagnon 17a, E.J. Gallas 126, B.J. Gallop 134, K.K. Gan 119,S. Ganguly 153, Y. Gao 52, F.M. Garay Walls 137a,137b, B. Garcia29, C. García 163,A. Garcia Alonso 114, A.G. Garcia Caffaro 172, J.E. García Navarro 163, M. Garcia-Sciveres 17a,G.L. Gardner 128, R.W. Gardner 39, N. Garelli 158, D. Garg 80, R.B. Garg 143,n,J.M. Gargan52, C.A. Garner155, C.M. Garvey 33a, P. Gaspar 83b, V.K. Gassmann158,G. Gaudio 73a, V. Gautam13, P. Gauzzi 75a,75b, I.L. Gavrilenko 37, A. Gavrilyuk 37,– 47 –JHEP12(2023)167C. Gay 164, G. Gaycken 48, E.N. Gazis 10, A.A. Geanta 27b, C.M. Gee 136, A. Gekow119,C. Gemme 57b, M.H. Genest 60, S. Gentile 75a,75b, A.D. Gentry 112, S. George 95,W.F. George 20, T. Geralis 46, P. Gessinger-Befurt 36, M.E. Geyik 171, M. Ghani 167,M. Ghneimat 141, K. Ghorbanian 94, A. Ghosal 141, A. Ghosh 160, A. Ghosh 7,B. Giacobbe 23b, S. Giagu 75a,75b, T. Giani 114, P. Giannetti 74a, A. Giannini 62a,S.M. Gibson 95, M. Gignac 136, D.T. Gil 86b, A.K. Gilbert 86a, B.J. Gilbert 41,D. Gillberg 34, G. Gilles 114, N.E.K. Gillwald 48, L. Ginabat 127, D.M. Gingrich 2,af ,M.P. Giordani 69a,69c, P.F. Giraud 135, G. Giugliarelli 69a,69c, D. Giugni 71a, F. Giuli 36,I. Gkialas 9,j , L.K. Gladilin 37, C. Glasman 99, G.R. Gledhill 123, G. Glemža 48, M. Glisic123,I. Gnesi 43b,f , Y. Go 29,ai, M. Goblirsch-Kolb 36, B. Gocke 49, D. Godin108, B. Gokturk 21a,S. Goldfarb 105, T. Golling 56, M.G.D. Gololo33g, D. Golubkov 37, J.P. Gombas 107,A. Gomes 130a,130b, G. Gomes Da Silva 141, A.J. Gomez Delegido 163, R. Gonçalo 130a,130c,G. Gonella 123, L. Gonella 20, A. Gongadze 149c, F. Gonnella 20, J.L. Gonski 41,R.Y. González Andana 52, S. González de la Hoz 163, S. Gonzalez Fernandez 13,R. Gonzalez Lopez 92, C. Gonzalez Renteria 17a, M.V. Gonzalez Rodrigues 48,R. Gonzalez Suarez 161, S. Gonzalez-Sevilla 56, G.R. Gonzalvo Rodriguez 163, L. Goossens 36,B. Gorini 36, E. Gorini 70a,70b, A. Gorišek 93, T.C. Gosart 128, A.T. Goshaw 51,M.I. Gostkin 38, S. Goswami 121, C.A. Gottardo 36, S.A. Gotz 109, M. Gouighri 35b,V. Goumarre 48, A.G. Goussiou 138, N. Govender 33c, I. Grabowska-Bold 86a, K. Graham 34,E. Gramstad 125, S. Grancagnolo 70a,70b, M. Grandi 146, C.M. Grant1,135, P.M. Gravila 27f ,F.G. Gravili 70a,70b, H.M. Gray 17a, M. Greco 70a,70b, C. Grefe 24, I.M. Gregor 48,P. Grenier 143, S.G. Grewe110, C. Grieco 13, A.A. Grillo 136, K. Grimm 31, S. Grinstein 13,s,J.-F. Grivaz 66, E. Gross 169, J. Grosse-Knetter 55, C. Grud106, J.C. Grundy 126, L. Guan 106,W. Guan 29, C. Gubbels 164, J.G.R. Guerrero Rojas 163, G. Guerrieri 69a,69c, F. Guescini 110,R. Gugel 100, J.A.M. Guhit 106, A. Guida 18, E. Guilloton 167,134, S. Guindon 36,F. Guo 14a,14e, J. Guo 62c, L. Guo 48, Y. Guo 106, R. Gupta 48, R. Gupta 129, S. Gurbuz 24,S.S. Gurdasani 54, G. Gustavino 36, M. Guth 56, P. Gutierrez 120, L.F. Gutierrez Zagazeta 128,M. Gutsche 50, C. Gutschow 96, C. Gwenlan 126, C.B. Gwilliam 92, E.S. Haaland 125,A. Haas 117, M. Habedank 48, C. Haber 17a, H.K. Hadavand 8, A. Hadef 50, S. Hadzic 110,A.I. Hagan91, J.J. Hahn 141, E.H. Haines 96, M. Haleem 166, J. Haley 121, J.J. Hall 139,G.D. Hallewell 102, L. Halser 19, K. Hamano 165, M. Hamer 24, G.N. Hamity 52,E.J. Hampshire 95, J. Han 62b, K. Han 62a, L. Han 14c, L. Han 62a, S. Han 17a,Y.F. Han 155, K. Hanagaki 84, M. Hance 136, D.A. Hangal 41,ab, H. Hanif 142, M.D. Hank 128,R. Hankache 101, J.B. Hansen 42, J.D. Hansen 42, P.H. Hansen 42, K. Hara 157, D. Harada 56,T. Harenberg 171, S. Harkusha 37, M.L. Harris 103, Y.T. Harris 126, J. Harrison 13,N.M. Harrison 119, P.F. Harrison167, N.M. Hartman 110, N.M. Hartmann 109, Y. Hasegawa 140,R. Hauser 107, C.M. Hawkes 20, R.J. Hawkings 36, Y. Hayashi 153, S. Hayashida 111,D. Hayden 107, C. Hayes 106, R.L. Hayes 114, C.P. Hays 126, J.M. Hays 94, H.S. Hayward 92,F. He 62a, M. He 14a,14e, Y. He 154, Y. He 48, N.B. Heatley 94, V. Hedberg 98,A.L. Heggelund 125, N.D. Hehir 94, C. Heidegger 54, K.K. Heidegger 54, W.D. Heidorn 81,J. Heilman 34, S. Heim 48, T. Heim 17a, J.G. Heinlein 128, J.J. Heinrich 123,L. Heinrich 110,ad, J. Hejbal 131, L. Helary 48, A. Held 170, S. Hellesund 16, C.M. Helling 164,S. Hellman 47a,47b, R.C.W. Henderson91, L. Henkelmann 32, A.M. Henriques Correia36,– 48 –JHEP12(2023)167H. Herde 98, Y. Hernández Jiménez 145, L.M. Herrmann 24, T. Herrmann 50, G. Herten 54,R. Hertenberger 109, L. Hervas 36, M.E. Hesping 100, N.P. Hessey 156a, H. Hibi 85, E. Hill 155,S.J. Hillier 20, J.R. Hinds 107, F. Hinterkeuser 24, M. Hirose 124, S. Hirose 157,D. Hirschbuehl 171, T.G. Hitchings 101, B. Hiti 93, J. Hobbs 145, R. Hobincu 27e, N. Hod 169,M.C. Hodgkinson 139, B.H. Hodkinson 32, A. Hoecker 36, D.D. Hofer 106, J. Hofer 48,T. Holm 24, M. Holzbock 110, L.B.A.H. Hommels 32, B.P. Honan 101, J. Hong 62c,T.M. Hong 129, B.H. Hooberman 162, W.H. Hopkins 6, Y. Horii 111, S. Hou 148,A.S. Howard 93, J. Howarth 59, J. Hoya 6, M. Hrabovsky 122, A. Hrynevich 48, T. Hryn’ova 4,P.J. Hsu 65, S.-C. Hsu 138, Q. Hu 62a, Y.F. Hu 14a,14e, S. Huang 64b, X. Huang 14c,X. Huang 14a,14e, Y. Huang 139, Y. Huang 14a, Z. Huang 101, Z. Hubacek 132, M. Huebner 24,F. Huegging 24, T.B. Huffman 126, C.A. Hugli 48, M. Huhtinen 36, S.K. Huiberts 16,R. Hulsken 104, N. Huseynov 12, J. Huston 107, J. Huth 61, R. Hyneman 143, G. Iacobucci 56,G. Iakovidis 29, I. Ibragimov 141, L. Iconomidou-Fayard 66, P. Iengo 72a,72b, R. Iguchi 153,T. Iizawa 126, Y. Ikegami 84, N. Ilic 155, H. Imam 35a, M. Ince Lezki 56,T. Ingebretsen Carlson 47a,47b, G. Introzzi 73a,73b, M. Iodice 77a, V. Ippolito 75a,75b,R.K. Irwin 92, M. Ishino 153, W. Islam 170, C. Issever 18,48, S. Istin 21a,ak, H. Ito 168,J.M. Iturbe Ponce 64a, R. Iuppa 78a,78b, A. Ivina 169, J.M. Izen 45, V. Izzo 72a,P. Jacka 131,132, P. Jackson 1, R.M. Jacobs 48, B.P. Jaeger 142, C.S. Jagfeld 109, G. Jain 156a,P. Jain 54, K. Jakobs 54, T. Jakoubek 169, J. Jamieson 59, K.W. Janas 86a, M. Javurkova 103,F. Jeanneau 135, L. Jeanty 123, J. Jejelava 149a,z, P. Jenni 54,g, C.E. Jessiman 34,S. Jézéquel 4, C. Jia62b, J. Jia 145, X. Jia 61, X. Jia 14a,14e, Z. Jia 14c, S. Jiggins 48,J. Jimenez Pena 13, S. Jin 14c, A. Jinaru 27b, O. Jinnouchi 154, P. Johansson 139,K.A. Johns 7, J.W. Johnson 136, D.M. Jones 32, E. Jones 48, P. Jones 32, R.W.L. Jones 91,T.J. Jones 92, H.L. Joos 55,36, R. Joshi 119, J. Jovicevic 15, X. Ju 17a, J.J. Junggeburth 103,T. Junkermann 63a, A. Juste Rozas 13,s, M.K. Juzek 87, S. Kabana 137e, A. Kaczmarska 87,M. Kado 110, H. Kagan 119, M. Kagan 143, A. Kahn41, A. Kahn 128, C. Kahra 100,T. Kaji 153, E. Kajomovitz 150, N. Kakati 169, I. Kalaitzidou 54, C.W. Kalderon 29,A. Kamenshchikov 155, N.J. Kang 136, D. Kar 33g, K. Karava 126, M.J. Kareem 156b,E. Karentzos 54, I. Karkanias 152, O. Karkout 114, S.N. Karpov 38, Z.M. Karpova 38,V. Kartvelishvili 91, A.N. Karyukhin 37, E. Kasimi 152, J. Katzy 48, S. Kaur 34,K. Kawade 140, M.P. Kawale 120, C. Kawamoto 88, T. Kawamoto 62a, E.F. Kay 36,F.I. Kaya 158, S. Kazakos 107, V.F. Kazanin 37, Y. Ke 145, J.M. Keaveney 33a, R. Keeler 165,G.V. Kehris 61, J.S. Keller 34, A.S. Kelly96, J.J. Kempster 146, K.E. Kennedy 41,P.D. Kennedy 100, O. Kepka 131, B.P. Kerridge 167, S. Kersten 171, B.P. Kerševan 93,S. Keshri 66, L. Keszeghova 28a, S. Ketabchi Haghighat 155, R.A. Khan129, M. Khandoga 127,A. Khanov 121, A.G. Kharlamov 37, T. Kharlamova 37, E.E. Khoda 138, M. Kholodenko 37,T.J. Khoo 18, G. Khoriauli 166, J. Khubua 149b, Y.A.R. Khwaira 66, A. Kilgallon 123,D.W. Kim 47a,47b, Y.K. Kim 39, N. Kimura 96, M.K. Kingston 55, A. Kirchhoff 55,C. Kirfel 24, F. Kirfel 24, J. Kirk 134, A.E. Kiryunin 110, C. Kitsaki 10, O. Kivernyk 24,M. Klassen 63a, C. Klein 34, L. Klein 166, M.H. Klein 106, M. Klein 92, S.B. Klein 56,U. Klein 92, P. Klimek 36, A. Klimentov 29, T. Klioutchnikova 36, P. Kluit 114, S. Kluth 110,E. Kneringer 79, T.M. Knight 155, A. Knue 49, R. Kobayashi 88, D. Kobylianskii 169,S.F. Koch 126, M. Kocian 143, P. Kodyš 133, D.M. Koeck 123, P.T. Koenig 24, T. Koffas 34,– 49 –JHEP12(2023)167O. Kolay 50, I. Koletsou 4, T. Komarek 122, K. Köneke 54, A.X.Y. Kong 1, T. Kono 118,N. Konstantinidis 96, P. Kontaxakis 56, B. Konya 98, R. Kopeliansky 68, S. Koperny 86a,K. Korcyl 87, K. Kordas 152,e, G. Koren 151, A. Korn 96, S. Korn 55, I. Korolkov 13,N. Korotkova 37, B. Kortman 114, O. Kortner 110, S. Kortner 110, W.H. Kostecka 115,V.V. Kostyukhin 141, A. Kotsokechagia 135, A. Kotwal 51, A. Koulouris 36,A. Kourkoumeli-Charalampidi 73a,73b, C. Kourkoumelis 9, E. Kourlitis 110,ad, O. Kovanda 146,R. Kowalewski 165, W. Kozanecki 135, A.S. Kozhin 37, V.A. Kramarenko 37, G. Kramberger 93,P. Kramer 100, M.W. Krasny 127, A. Krasznahorkay 36, J.W. Kraus 171, J.A. Kremer 48,T. Kresse 50, J. Kretzschmar 92, K. Kreul 18, P. Krieger 155, S. Krishnamurthy 103,M. Krivos 133, K. Krizka 20, K. Kroeninger 49, H. Kroha 110, J. Kroll 131, J. Kroll 128,K.S. Krowpman 107, U. Kruchonak 38, H. Krüger 24, N. Krumnack81, M.C. Kruse 51,O. Kuchinskaia 37, S. Kuday 3a, S. Kuehn 36, R. Kuesters 54, T. Kuhl 48, V. Kukhtin 38,Y. Kulchitsky 37,a, S. Kuleshov 137d,137b, M. Kumar 33g, N. Kumari 48, A. Kupco 131,T. Kupfer49, A. Kupich 37, O. Kuprash 54, H. Kurashige 85, L.L. Kurchaninov 156a,O. Kurdysh 66, Y.A. Kurochkin 37, A. Kurova 37, M. Kuze 154, A.K. Kvam 103, J. Kvita 122,T. Kwan 104, N.G. Kyriacou 106, L.A.O. Laatu 102, C. Lacasta 163, F. Lacava 75a,75b,H. Lacker 18, D. Lacour 127, N.N. Lad 96, E. Ladygin 38, B. Laforge 127, T. Lagouri 137e,F.Z. Lahbabi 35a, S. Lai 55, I.K. Lakomiec 86a, N. Lalloue 60, J.E. Lambert 165,S. Lammers 68, W. Lampl 7, C. Lampoudis 152,e, A.N. Lancaster 115, E. Lançon 29,U. Landgraf 54, M.P.J. Landon 94, V.S. Lang 54, R.J. Langenberg 103, O.K.B. Langrekken 125,A.J. Lankford 160, F. Lanni 36, K. Lantzsch 24, A. Lanza 73a, A. Lapertosa 57b,57a,J.F. Laporte 135, T. Lari 71a, F. Lasagni Manghi 23b, M. Lassnig 36, V. Latonova 131,A. Laudrain 100, A. Laurier 150, S.D. Lawlor 139, Z. Lawrence 101, R. Lazaridou167,M. Lazzaroni 71a,71b, B. Le101, E.M. Le Boulicaut 51, B. Leban 93, A. Lebedev 81,M. LeBlanc 101, F. Ledroit-Guillon 60, A.C.A. Lee96, S.C. Lee 148, S. Lee 47a,47b, T.F. Lee 92,L.L. Leeuw 33c, H.P. Lefebvre 95, M. Lefebvre 165, C. Leggett 17a, G. Lehmann Miotto 36,M. Leigh 56, W.A. Leight 103, W. Leinonen 113, A. Leisos 152,r, M.A.L. Leite 83c,C.E. Leitgeb 48, R. Leitner 133, K.J.C. Leney 44, T. Lenz 24, S. Leone 74a,C. Leonidopoulos 52, A. Leopold 144, C. Leroy 108, R. Les 107, C.G. Lester 32,M. Levchenko 37, J. Levêque 4, D. Levin 106, L.J. Levinson 169, M.P. Lewicki 87,D.J. Lewis 4, A. Li 5, B. Li 62b, C. Li62a, C-Q. Li 110, H. Li 62a, H. Li 62b, H. Li 14c,H. Li 14b, H. Li 62b, J. Li 62c, K. Li 138, L. Li 62c, M. Li 14a,14e, Q.Y. Li 62a, S. Li 14a,14e,S. Li 62d,62c,d, T. Li 5, X. Li 104, Z. Li 126, Z. Li 104, Z. Li 92, Z. Li 14a,14e, S. Liang14a,14e,Z. Liang 14a, M. Liberatore 135, B. Liberti 76a, K. Lie 64c, J. Lieber Marin 83b, H. Lien 68,K. Lin 107, R.E. Lindley 7, J.H. Lindon 2, E. Lipeles 128, A. Lipniacka 16, A. Lister 164,J.D. Little 4, B. Liu 14a, B.X. Liu 142, D. Liu 62d,62c, J.B. Liu 62a, J.K.K. Liu 32,K. Liu 62d,62c, M. Liu 62a, M.Y. Liu 62a, P. Liu 14a, Q. Liu 62d,138,62c, X. Liu 62a,Y. Liu 14d,14e, Y.L. Liu 62b, Y.W. Liu 62a, J. Llorente Merino 142, S.L. Lloyd 94,E.M. Lobodzinska 48, P. Loch 7, T. Lohse 18, K. Lohwasser 139, E. Loiacono 48,M. Lokajicek 131,∗, J.D. Lomas 20, J.D. Long 162, I. Longarini 160, L. Longo 70a,70b,R. Longo 162, I. Lopez Paz 67, A. Lopez Solis 48, N. Lorenzo Martinez 4, A.M. Lory 109,G. Löschcke Centeno 146, O. Loseva 37, X. Lou 47a,47b, X. Lou 14a,14e, A. Lounis 66,J. Love 6, P.A. Love 91, G. Lu 14a,14e, M. Lu 80, S. Lu 128, Y.J. Lu 65, H.J. Lubatti 138,– 50 –JHEP12(2023)167C. Luci 75a,75b, F.L. Lucio Alves 14c, A. Lucotte 60, F. Luehring 68, I. Luise 145,O. Lukianchuk 66, O. Lundberg 144, B. Lund-Jensen 144, N.A. Luongo 6, M.S. Lutz 151,A.B. Lux 25, D. Lynn 29, H. Lyons92, R. Lysak 131, E. Lytken 98, V. Lyubushkin 38,T. Lyubushkina 38, M.M. Lyukova 145, H. Ma 29, K. Ma62a, L.L. Ma 62b, W. Ma 62a,Y. Ma 121, D.M. Mac Donell 165, G. Maccarrone 53, J.C. MacDonald 100,P.C. Machado De Abreu Farias 83b, R. Madar 40, W.F. Mader 50, T. Madula 96, J. Maeda 85,T. Maeno 29, H. Maguire 139, V. Maiboroda 135, A. Maio 130a,130b,130d, K. Maj 86a,O. Majersky 48, S. Majewski 123, N. Makovec 66, V. Maksimovic 15, B. Malaescu 127,Pa. Malecki 87, V.P. Maleev 37, F. Malek 60, M. Mali 93, D. Malito 95, U. Mallik 80,S. Maltezos10, S. Malyukov38, J. Mamuzic 13, G. Mancini 53, G. Manco 73a,73b,J.P. Mandalia 94, I. Mandić 93, L. Manhaes de Andrade Filho 83a, I.M. Maniatis 169,J. Manjarres Ramos 102,aa, D.C. Mankad 169, A. Mann 109, B. Mansoulie 135, S. Manzoni 36,L. Mao 62c, X. Mapekula 33c, A. Marantis 152,r, G. Marchiori 5, M. Marcisovsky 131,C. Marcon 71a,71b, M. Marinescu 20, S. Marium 48, M. Marjanovic 120, E.J. Marshall 91,Z. Marshall 17a, S. Marti-Garcia 163, T.A. Martin 167, V.J. Martin 52, B. Martin dit Latour 16,L. Martinelli 75a,75b, M. Martinez 13,s, P. Martinez Agullo 163, V.I. Martinez Outschoorn 103,P. Martinez Suarez 13, S. Martin-Haugh 134, V.S. Martoiu 27b, A.C. Martyniuk 96,A. Marzin 36, D. Mascione 78a,78b, L. Masetti 100, T. Mashimo 153, J. Masik 101,A.L. Maslennikov 37, L. Massa 23b, P. Massarotti 72a,72b, P. Mastrandrea 74a,74b,A. Mastroberardino 43b,43a, T. Masubuchi 153, T. Mathisen 161, J. Matousek 133,N. Matsuzawa153, J. Maurer 27b, B. Maček 93, D.A. Maximov 37, R. Mazini 148, I. Maznas 152,M. Mazza 107, S.M. Mazza 136, E. Mazzeo 71a,71b, C. Mc Ginn 29, J.P. Mc Gowan 104,S.P. Mc Kee 106, E.F. McDonald 105, A.E. McDougall 114, J.A. Mcfayden 146,R.P. McGovern 128, G. Mchedlidze 149b, R.P. Mckenzie 33g, T.C. Mclachlan 48,D.J. Mclaughlin 96, S.J. McMahon 134, C.M. Mcpartland 92, R.A. McPherson 165,w,S. Mehlhase 109, A. Mehta 92, D. Melini 150, B.R. Mellado Garcia 33g, A.H. Melo 55,F. Meloni 48, A.M. Mendes Jacques Da Costa 101, H.Y. Meng 155, L. Meng 91, S. Menke 110,M. Mentink 36, E. Meoni 43b,43a, G. Mercado 115, C. Merlassino 69a,69c, L. Merola 72a,72b,C. Meroni 71a,71b, G. Merz106, O. Meshkov 37, J. Metcalfe 6, A.S. Mete 6, C. Meyer 68,J-P. Meyer 135, R.P. Middleton 134, L. Mijović 52, G. Mikenberg 169, M. Mikestikova 131,M. Mikuž 93, H. Mildner 100, A. Milic 36, C.D. Milke 44, D.W. Miller 39, L.S. Miller 34,A. Milov 169, D.A. Milstead47a,47b, T. Min14c, A.A. Minaenko 37, I.A. Minashvili 149b,L. Mince 59, A.I. Mincer 117, B. Mindur 86a, M. Mineev 38, Y. Mino 88, L.M. Mir 13,M. Miralles Lopez 163, M. Mironova 17a, A. Mishima153, M.C. Missio 113, A. Mitra 167,V.A. Mitsou 163, Y. Mitsumori 111, O. Miu 155, P.S. Miyagawa 94, T. Mkrtchyan 63a,M. Mlinarevic 96, T. Mlinarevic 96, M. Mlynarikova 36, S. Mobius 19, P. Moder 48,P. Mogg 109, A.F. Mohammed 14a,14e, S. Mohapatra 41, G. Mokgatitswane 33g, L. Moleri 169,B. Mondal 141, S. Mondal 132, K. Mönig 48, E. Monnier 102, L. Monsonis Romero163,J. Montejo Berlingen 13, M. Montella 119, F. Montereali 77a,77b, F. Monticelli 90,S. Monzani 69a,69c, N. Morange 66, A.L. Moreira De Carvalho 130a, M. Moreno Llácer 163,C. Moreno Martinez 56, P. Morettini 57b, S. Morgenstern 36, M. Morii 61, M. Morinaga 153,A.K. Morley 36, F. Morodei 75a,75b, L. Morvaj 36, P. Moschovakos 36, B. Moser 36,M. Mosidze149b, T. Moskalets 54, P. Moskvitina 113, J. Moss 31,l, E.J.W. Moyse 103,– 51 –JHEP12(2023)167O. Mtintsilana 33g, S. Muanza 102, J. Mueller 129, D. Muenstermann 91, R. Müller 19,G.A. Mullier 161, A.J. Mullin32, J.J. Mullin128, D.P. Mungo 155, D. Munoz Perez 163,F.J. Munoz Sanchez 101, M. Murin 101, W.J. Murray 167,134, A. Murrone 71a,71b,M. Muškinja 17a, C. Mwewa 29, A.G. Myagkov 37,a, A.J. Myers 8, G. Myers 68,M. Myska 132, B.P. Nachman 17a, O. Nackenhorst 49, A. Nag 50, K. Nagai 126, K. Nagano 84,J.L. Nagle 29,ai, E. Nagy 102, A.M. Nairz 36, Y. Nakahama 84, K. Nakamura 84,K. Nakkalil 5, H. Nanjo 124, R. Narayan 44, E.A. Narayanan 112, I. Naryshkin 37,M. Naseri 34, S. Nasri 159, C. Nass 24, G. Navarro 22a, J. Navarro-Gonzalez 163, R. Nayak 151,A. Nayaz 18, P.Y. Nechaeva 37, F. Nechansky 48, L. Nedic 126, T.J. Neep 20, A. Negri 73a,73b,M. Negrini 23b, C. Nellist 114, C. Nelson 104, K. Nelson 106, S. Nemecek 131, M. Nessi 36,h,M.S. Neubauer 162, F. Neuhaus 100, J. Neundorf 48, R. Newhouse 164, P.R. Newman 20,C.W. Ng 129, Y.W.Y. Ng 48, B. Ngair 35e, H.D.N. Nguyen 108, R.B. Nickerson 126,R. Nicolaidou 135, J. Nielsen 136, M. Niemeyer 55, J. Niermann 55,36, N. Nikiforou 36,V. Nikolaenko 37,a, I. Nikolic-Audit 127, K. Nikolopoulos 20, P. Nilsson 29, I. Ninca 48,H.R. Nindhito 56, G. Ninio 151, A. Nisati 75a, N. Nishu 2, R. Nisius 110, J-E. Nitschke 50,E.K. Nkadimeng 33g, T. Nobe 153, D.L. Noel 32, T. Nommensen 147, M.B. Norfolk 139,R.R.B. Norisam 96, B.J. Norman 34, M. Noury 35a, J. Novak 93, T. Novak 48, L. Novotny 132,R. Novotny 112, L. Nozka 122, K. Ntekas 160, N.M.J. Nunes De Moura Junior 83b, E. Nurse96,J. Ocariz 127, A. Ochi 85, I. Ochoa 130a, S. Oerdek 48, J.T. Offermann 39, A. Ogrodnik 133,A. Oh 101, C.C. Ohm 144, H. Oide 84, R. Oishi 153, M.L. Ojeda 48, M.W. O’Keefe92,Y. Okumura 153, L.F. Oleiro Seabra 130a, S.A. Olivares Pino 137d, D. Oliveira Damazio 29,D. Oliveira Goncalves 83a, J.L. Oliver 160, Ö.O. Öncel 54, A.P. O’Neill 19, A. Onofre 130a,130e,P.U.E. Onyisi 11, M.J. Oreglia 39, G.E. Orellana 90, D. Orestano 77a,77b, N. Orlando 13,R.S. Orr 155, V. O’Shea 59, L.M. Osojnak 128, R. Ospanov 62a, G. Otero y Garzon 30,H. Otono 89, P.S. Ott 63a, G.J. Ottino 17a, M. Ouchrif 35d, J. Ouellette 29, F. Ould-Saada 125,M. Owen 59, R.E. Owen 134, K.Y. Oyulmaz 21a, V.E. Ozcan 21a, F. Ozturk 87, N. Ozturk 8,S. Ozturk 82, H.A. Pacey 126, A. Pacheco Pages 13, C. Padilla Aranda 13, G. Padovano 75a,75b,S. Pagan Griso 17a, G. Palacino 68, A. Palazzo 70a,70b, S. Palestini 36, J. Pan 172, T. Pan 64a,D.K. Panchal 11, C.E. Pandini 114, J.G. Panduro Vazquez 95, H.D. Pandya 1, H. Pang 14b,P. Pani 48, G. Panizzo 69a,69c, L. Paolozzi 56, C. Papadatos 108, S. Parajuli 44,A. Paramonov 6, C. Paraskevopoulos 10, D. Paredes Hernandez 64b, K.R. Park 41,T.H. Park 155, M.A. Parker 32, F. Parodi 57b,57a, E.W. Parrish 115, V.A. Parrish 52,J.A. Parsons 41, U. Parzefall 54, B. Pascual Dias 108, L. Pascual Dominguez 151,E. Pasqualucci 75a, S. Passaggio 57b, F. Pastore 95, P. Pasuwan 47a,47b, P. Patel 87,U.M. Patel 51, J.R. Pater 101, T. Pauly 36, J. Pearkes 143, M. Pedersen 125, R. Pedro 130a,S.V. Peleganchuk 37, O. Penc 36, E.A. Pender 52, K.E. Penski 109, M. Penzin 37,B.S. Peralva 83d, A.P. Pereira Peixoto 60, L. Pereira Sanchez 47a,47b, D.V. Perepelitsa 29,ai,E. Perez Codina 156a, M. Perganti 10, L. Perini 71a,71b,∗, H. Pernegger 36, O. Perrin 40,K. Peters 48, R.F.Y. Peters 101, B.A. Petersen 36, T.C. Petersen 42, E. Petit 102,V. Petousis 132, C. Petridou 152,e, A. Petrukhin 141, M. Pettee 17a, N.E. Pettersson 36,A. Petukhov 37, K. Petukhova 133, R. Pezoa 137f , L. Pezzotti 36, G. Pezzullo 172,T.M. Pham 170, T. Pham 105, P.W. Phillips 134, G. Piacquadio 145, E. Pianori 17a,F. Piazza 123, R. Piegaia 30, D. Pietreanu 27b, A.D. Pilkington 101, M. Pinamonti 69a,69c,– 52 –JHEP12(2023)167J.L. Pinfold 2, B.C. Pinheiro Pereira 130a, A.E. Pinto Pinoargote 100,135, L. Pintucci 69a,69c,K.M. Piper 146, A. Pirttikoski 56, D.A. Pizzi 34, L. Pizzimento 64b, A. Pizzini 114,M.-A. Pleier 29, V. Plesanovs54, V. Pleskot 133, E. Plotnikova38, G. Poddar 4, R. Poettgen 98,L. Poggioli 127, I. Pokharel 55, S. Polacek 133, G. Polesello 73a, A. Poley 142,156a,R. Polifka 132, A. Polini 23b, C.S. Pollard 167, Z.B. Pollock 119, V. Polychronakos 29,E. Pompa Pacchi 75a,75b, D. Ponomarenko 113, L. Pontecorvo 36, S. Popa 27a,G.A. Popeneciu 27d, A. Poreba 36, D.M. Portillo Quintero 156a, S. Pospisil 132,M.A. Postill 139, P. Postolache 27c, K. Potamianos 167, P.A. Potepa 86a, I.N. Potrap 38,C.J. Potter 32, H. Potti 1, T. Poulsen 48, J. Poveda 163, M.E. Pozo Astigarraga 36,A. Prades Ibanez 163, J. Pretel 54, D. Price 101, M. Primavera 70a, M.A. Principe Martin 99,R. Privara 122, T. Procter 59, M.L. Proffitt 138, N. Proklova 128, K. Prokofiev 64c,G. Proto 110, S. Protopopescu 29, J. Proudfoot 6, M. Przybycien 86a, W.W. Przygoda 86b,J.E. Puddefoot 139, D. Pudzha 37, D. Pyatiizbyantseva 37, J. Qian 106, D. Qichen 101,Y. Qin 101, T. Qiu 52, A. Quadt 55, M. Queitsch-Maitland 101, G. Quetant 56, R.P. Quinn 164,G. Rabanal Bolanos 61, D. Rafanoharana 54, F. Ragusa 71a,71b, J.L. Rainbolt 39, J.A. Raine 56,S. Rajagopalan 29, E. Ramakoti 37, I.A. Ramirez-Berend 34, K. Ran 48,14e, N.P. Rapheeha 33g,H. Rasheed 27b, V. Raskina 127, D.F. Rassloff 63a, S. Rave 100, B. Ravina 55,I. Ravinovich 169, M. Raymond 36, A.L. Read 125, N.P. Readioff 139, D.M. Rebuzzi 73a,73b,G. Redlinger 29, A.S. Reed 110, K. Reeves 26, J.A. Reidelsturz 171, D. Reikher 151, A. Rej 49,C. Rembser 36, A. Renardi 48, M. Renda 27b, M.B. Rendel110, F. Renner 48, A.G. Rennie 160,A.L. Rescia 48, S. Resconi 71a, M. Ressegotti 57b,57a, S. Rettie 36, J.G. Reyes Rivera 107,E. Reynolds 17a, O.L. Rezanova 37, P. Reznicek 133, N. Ribaric 91, E. Ricci 78a,78b,R. Richter 110, S. Richter 47a,47b, E. Richter-Was 86b, M. Ridel 127, S. Ridouani 35d,P. Rieck 117, P. Riedler 36, E.M. Riefel 47a,47b, J.O. Rieger 114, M. Rijssenbeek 145,A. Rimoldi 73a,73b, M. Rimoldi 36, L. Rinaldi 23b,23a, T.T. Rinn 29, M.P. Rinnagel 109,G. Ripellino 161, I. Riu 13, P. Rivadeneira 48, J.C. Rivera Vergara 165, F. Rizatdinova 121,E. Rizvi 94, B.A. Roberts 167, B.R. Roberts 17a, S.H. Robertson 104,w, D. Robinson 32,C.M. Robles Gajardo137f , M. Robles Manzano 100, A. Robson 59, A. Rocchi 76a,76b,C. Roda 74a,74b, S. Rodriguez Bosca 63a, Y. Rodriguez Garcia 22a, A. Rodriguez Rodriguez 54,A.M. Rodríguez Vera 156b, S. Roe36, J.T. Roemer 160, A.R. Roepe-Gier 136, J. Roggel 171,O. Røhne 125, R.A. Rojas 103, C.P.A. Roland 127, J. Roloff 29, A. Romaniouk 37,E. Romano 73a,73b, M. Romano 23b, A.C. Romero Hernandez 162, N. Rompotis 92, L. Roos 127,S. Rosati 75a, B.J. Rosser 39, E. Rossi 126, E. Rossi 72a,72b, L.P. Rossi 57b, L. Rossini 54,R. Rosten 119, M. Rotaru 27b, B. Rottler 54, C. Rougier 102,aa, D. Rousseau 66, D. Rousso 32,A. Roy 162, S. Roy-Garand 155, A. Rozanov 102, Z.M.A. Rozario 59, Y. Rozen 150,X. Ruan 33g, A. Rubio Jimenez 163, A.J. Ruby 92, V.H. Ruelas Rivera 18, T.A. Ruggeri 1,A. Ruggiero 126, A. Ruiz-Martinez 163, A. Rummler 36, Z. Rurikova 54, N.A. Rusakovich 38,H.L. Russell 165, G. Russo 75a,75b, J.P. Rutherfoord 7, S. Rutherford Colmenares 32,K. Rybacki91, M. Rybar 133, E.B. Rye 125, A. Ryzhov 44, J.A. Sabater Iglesias 56,P. Sabatini 163, H.F-W. Sadrozinski 136, F. Safai Tehrani 75a, B. Safarzadeh Samani 134,M. Safdari 143, S. Saha 165, M. Sahinsoy 110, A. Saibel 163, M. Saimpert 135, M. Saito 153,T. Saito 153, D. Salamani 36, A. Salnikov 143, J. Salt 163, A. Salvador Salas 151,D. Salvatore 43b,43a, F. Salvatore 146, A. Salzburger 36, D. Sammel 54, D. Sampsonidis 152,e,– 53 –JHEP12(2023)167D. Sampsonidou 123, J. Sánchez 163, A. Sanchez Pineda 4, V. Sanchez Sebastian 163,H. Sandaker 125, C.O. Sander 48, J.A. Sandesara 103, M. Sandhoff 171, C. Sandoval 22b,D.P.C. Sankey 134, T. Sano 88, A. Sansoni 53, L. Santi 75a,75b, C. Santoni 40,H. Santos 130a,130b, S.N. Santpur 17a, A. Santra 169, K.A. Saoucha 116b, J.G. Saraiva 130a,130d,J. Sardain 7, O. Sasaki 84, K. Sato 157, C. Sauer63b, F. Sauerburger 54, E. Sauvan 4,P. Savard 155,af , R. Sawada 153, C. Sawyer 134, L. Sawyer 97, I. Sayago Galvan163,C. Sbarra 23b, A. Sbrizzi 23b,23a, T. Scanlon 96, J. Schaarschmidt 138, P. Schacht 110,U. Schäfer 100, A.C. Schaffer 66,44, D. Schaile 109, R.D. Schamberger 145, C. Scharf 18,M.M. Schefer 19, V.A. Schegelsky 37, D. Scheirich 133, F. Schenck 18, M. Schernau 160,C. Scheulen 55, C. Schiavi 57b,57a, E.J. Schioppa 70a,70b, M. Schioppa 43b,43a, B. Schlag 143,n,K.E. Schleicher 54, S. Schlenker 36, J. Schmeing 171, M.A. Schmidt 171, K. Schmieden 100,C. Schmitt 100, N. Schmitt 100, S. Schmitt 48, L. Schoeffel 135, A. Schoening 63b,P.G. Scholer 54, E. Schopf 126, M. Schott 100, J. Schovancova 36, S. Schramm 56,F. Schroeder 171, T. Schroer 56, H-C. Schultz-Coulon 63a, M. Schumacher 54,B.A. Schumm 136, Ph. Schune 135, A.J. Schuy 138, H.R. Schwartz 136, A. Schwartzman 143,T.A. Schwarz 106, Ph. Schwemling 135, R. Schwienhorst 107, A. Sciandra 136, G. Sciolla 26,F. Scuri 74a, C.D. Sebastiani 92, K. Sedlaczek 115, P. Seema 18, S.C. Seidel 112, A. Seiden 136,B.D. Seidlitz 41, C. Seitz 48, J.M. Seixas 83b, G. Sekhniaidze 72a, S.J. Sekula 44, L. Selem 60,N. Semprini-Cesari 23b,23a, D. Sengupta 56, V. Senthilkumar 163, L. Serin 66, L. Serkin 69a,69b,M. Sessa 76a,76b, H. Severini 120, F. Sforza 57b,57a, A. Sfyrla 56, E. Shabalina 55,R. Shaheen 144, J.D. Shahinian 128, D. Shaked Renous 169, L.Y. Shan 14a, M. Shapiro 17a,A. Sharma 36, A.S. Sharma 164, P. Sharma 80, S. Sharma 48, P.B. Shatalov 37, K. Shaw 146,S.M. Shaw 101, A. Shcherbakova 37, Q. Shen 62c,5, D.J. Sheppard142, P. Sherwood 96,L. Shi 96, X. Shi 14a, C.O. Shimmin 172, J.D. Shinner 95, I.P.J. Shipsey 126, S. Shirabe 56,h,M. Shiyakova 38,u, J. Shlomi 169, M.J. Shochet 39, J. Shojaii 105, D.R. Shope 125,B. Shrestha 120, S. Shrestha 119,aj , E.M. Shrif 33g, M.J. Shroff 165, P. Sicho 131,A.M. Sickles 162, E. Sideras Haddad 33g, A. Sidoti 23b, F. Siegert 50, Dj. Sijacki 15, F. Sili 90,J.M. Silva 20, M.V. Silva Oliveira 29, S.B. Silverstein 47a, S. Simion66, R. Simoniello 36,E.L. Simpson 59, H. Simpson 146, L.R. Simpson 106, N.D. Simpson98, S. Simsek 82,S. Sindhu 55, P. Sinervo 155, S. Singh 155, S. Sinha 48, S. Sinha 101, M. Sioli 23b,23a,I. Siral 36, E. Sitnikova 48, S.Yu. Sivoklokov 37,∗, J. Sjölin 47a,47b, A. Skaf 55, E. Skorda 20,P. Skubic 120, M. Slawinska 87, V. Smakhtin169, B.H. Smart 134, J. Smiesko 36,S.Yu. Smirnov 37, Y. Smirnov 37, L.N. Smirnova 37,a, O. Smirnova 98, A.C. Smith 41,E.A. Smith 39, H.A. Smith 126, J.L. Smith 92, R. Smith143, M. Smizanska 91, K. Smolek 132,A.A. Snesarev 37, S.R. Snider 155, H.L. Snoek 114, S. Snyder 29, R. Sobie 165,w, A. Soffer 151,C.A. Solans Sanchez 36, E.Yu. Soldatov 37, U. Soldevila 163, A.A. Solodkov 37, S. Solomon 26,A. Soloshenko 38, K. Solovieva 54, O.V. Solovyanov 40, V. Solovyev 37, P. Sommer 36,A. Sonay 13, W.Y. Song 156b, J.M. Sonneveld 114, A. Sopczak 132, A.L. Sopio 96,F. Sopkova 28b, I.R. Sotarriva Alvarez 154, V. Sothilingam63a, O.J. Soto Sandoval 137c,137b,S. Sottocornola 68, R. Soualah 116b, Z. Soumaimi 35e, D. South 48, N. Soybelman 169,S. Spagnolo 70a,70b, M. Spalla 110, D. Sperlich 54, G. Spigo 36, S. Spinali 91, D.P. Spiteri 59,M. Spousta 133, E.J. Staats 34, A. Stabile 71a,71b, R. Stamen 63a, A. Stampekis 20,M. Standke 24, E. Stanecka 87, M.V. Stange 50, B. Stanislaus 17a, M.M. Stanitzki 48,– 54 –JHEP12(2023)167B. Stapf 48, E.A. Starchenko 37, G.H. Stark 136, J. Stark 102,aa, D.M. Starko156b,P. Staroba 131, P. Starovoitov 63a, S. Stärz 104, R. Staszewski 87, G. Stavropoulos 46,J. Steentoft 161, P. Steinberg 29, B. Stelzer 142,156a, H.J. Stelzer 129, O. Stelzer-Chilton 156a,H. Stenzel 58, T.J. Stevenson 146, G.A. Stewart 36, J.R. Stewart 121, M.C. Stockton 36,G. Stoicea 27b, M. Stolarski 130a, S. Stonjek 110, A. Straessner 50, J. Strandberg 144,S. Strandberg 47a,47b, M. Stratmann 171, M. Strauss 120, T. Strebler 102, P. Strizenec 28b,R. Ströhmer 166, D.M. Strom 123, R. Stroynowski 44, A. Strubig 47a,47b, S.A. Stucci 29,B. Stugu 16, J. Stupak 120, N.A. Styles 48, D. Su 143, S. Su 62a, W. Su 62d, X. Su 62a,66,K. Sugizaki 153, V.V. Sulin 37, M.J. Sullivan 92, D.M.S. Sultan 78a,78b, L. Sultanaliyeva 37,S. Sultansoy 3b, T. Sumida 88, S. Sun 106, S. Sun 170, O. Sunneborn Gudnadottir 161,N. Sur 102, M.R. Sutton 146, H. Suzuki 157, M. Svatos 131, M. Swiatlowski 156a,T. Swirski 166, I. Sykora 28a, M. Sykora 133, T. Sykora 133, D. Ta 100, K. Tackmann 48,t,A. Taffard 160, R. Tafirout 156a, J.S. Tafoya Vargas 66, E.P. Takeva 52, Y. Takubo 84,M. Talby 102, A.A. Talyshev 37, K.C. Tam 64b, N.M. Tamir151, A. Tanaka 153, J. Tanaka 153,R. Tanaka 66, M. Tanasini 57b,57a, Z. Tao 164, S. Tapia Araya 137f , S. Tapprogge 100,A. Tarek Abouelfadl Mohamed 107, S. Tarem 150, K. Tariq 14a, G. Tarna 102,27b,G.F. Tartarelli 71a, P. Tas 133, M. Tasevsky 131, E. Tassi 43b,43a, A.C. Tate 162, G. Tateno 153,Y. Tayalati 35e,v, G.N. Taylor 105, W. Taylor 156b, A.S. Tee 170, R. Teixeira De Lima 143,P. Teixeira-Dias 95, J.J. Teoh 155, K. Terashi 153, J. Terron 99, S. Terzo 13, M. Testa 53,R.J. Teuscher 155,w, A. Thaler 79, O. Theiner 56, N. Themistokleous 52,T. Theveneaux-Pelzer 102, O. Thielmann 171, D.W. Thomas95, J.P. Thomas 20,E.A. Thompson 17a, P.D. Thompson 20, E. Thomson 128, Y. Tian 55, V. Tikhomirov 37,a,Yu.A. Tikhonov 37, S. Timoshenko37, D. Timoshyn 133, E.X.L. Ting 1, P. Tipton 172,S.H. Tlou 33g, A. Tnourji 40, K. Todome 154, S. Todorova-Nova 133, S. Todt50, M. Togawa 84,J. Tojo 89, S. Tokár 28a, K. Tokushuku 84, O. Toldaiev 68, R. Tombs 32, M. Tomoto 84,111,L. Tompkins 143,n, K.W. Topolnicki 86b, E. Torrence 123, H. Torres 102,aa, E. Torró Pastor 163,M. Toscani 30, C. Tosciri 39, M. Tost 11, D.R. Tovey 139, A. Traeet16, I.S. Trandafir 27b,T. Trefzger 166, A. Tricoli 29, I.M. Trigger 156a, S. Trincaz-Duvoid 127, D.A. Trischuk 26,B. Trocmé 60, C. Troncon 71a, L. Truong 33c, M. Trzebinski 87, A. Trzupek 87, F. Tsai 145,M. Tsai 106, A. Tsiamis 152,e, P.V. Tsiareshka37, S. Tsigaridas 156a, A. Tsirigotis 152,r,V. Tsiskaridze 155, E.G. Tskhadadze 149a, M. Tsopoulou 152,e, Y. Tsujikawa 88,I.I. Tsukerman 37, V. Tsulaia 17a, S. Tsuno 84, K. Tsuri 118, D. Tsybychev 145, Y. Tu 64b,A. Tudorache 27b, V. Tudorache 27b, A.N. Tuna 61, S. Turchikhin 57b,57a, I. Turk Cakir 3a,R. Turra 71a, T. Turtuvshin 38,x, P.M. Tuts 41, S. Tzamarias 152,e, P. Tzanis 10,E. Tzovara 100, F. Ukegawa 157, P.A. Ulloa Poblete 137c,137b, E.N. Umaka 29, G. Unal 36,M. Unal 11, A. Undrus 29, G. Unel 160, J. Urban 28b, P. Urquijo 105, P. Urrejola 137a,G. Usai 8, R. Ushioda 154, M. Usman 108, Z. Uysal 21b, V. Vacek 132, B. Vachon 104,K.O.H. Vadla 125, T. Vafeiadis 36, A. Vaitkus 96, C. Valderanis 109, E. Valdes Santurio 47a,47b,M. Valente 156a, S. Valentinetti 23b,23a, A. Valero 163, E. Valiente Moreno 163, A. Vallier 102,aa,J.A. Valls Ferrer 163, D.R. Van Arneman 114, T.R. Van Daalen 138, A. Van Der Graaf 49,P. Van Gemmeren 6, M. Van Rijnbach 125,36, S. Van Stroud 96, I. Van Vulpen 114,M. Vanadia 76a,76b, W. Vandelli 36, M. Vandenbroucke 135, E.R. Vandewall 121,D. Vannicola 151, L. Vannoli 57b,57a, R. Vari 75a, E.W. Varnes 7, C. Varni 17b, T. Varol 148,– 55 –JHEP12(2023)167D. Varouchas 66, L. Varriale 163, K.E. Varvell 147, M.E. Vasile 27b, L. Vaslin84,G.A. Vasquez 165, A. Vasyukov 38, F. Vazeille 40, T. Vazquez Schroeder 36, J. Veatch 31,V. Vecchio 101, M.J. Veen 103, I. Veliscek 126, L.M. Veloce 155, F. Veloso 130a,130c,S. Veneziano 75a, A. Ventura 70a,70b, S. Ventura Gonzalez 135, A. Verbytskyi 110,M. Verducci 74a,74b, C. Vergis 24, M. Verissimo De Araujo 83b, W. Verkerke 114,J.C. Vermeulen 114, C. Vernieri 143, M. Vessella 103, M.C. Vetterli 142,af , A. Vgenopoulos 152,e,N. Viaux Maira 137f , T. Vickey 139, O.E. Vickey Boeriu 139, G.H.A. Viehhauser 126,L. Vigani 63b, M. Villa 23b,23a, M. Villaplana Perez 163, E.M. Villhauer52, E. Vilucchi 53,M.G. Vincter 34, G.S. Virdee 20, A. Vishwakarma 52, A. Visibile114, C. Vittori 36,I. Vivarelli 146, E. Voevodina 110, F. Vogel 109, J.C. Voigt 50, P. Vokac 132,Yu. Volkotrub 86a, J. Von Ahnen 48, E. Von Toerne 24, B. Vormwald 36, V. Vorobel 133,K. Vorobev 37, M. Vos 163, K. Voss 141, J.H. Vossebeld 92, M. Vozak 114, L. Vozdecky 94,N. Vranjes 15, M. Vranjes Milosavljevic 15, M. Vreeswijk 114, R. Vuillermet 36,O. Vujinovic 100, I. Vukotic 39, S. Wada 157, C. Wagner103, J.M. Wagner 17a, W. Wagner 171,S. Wahdan 171, H. Wahlberg 90, M. Wakida 111, J. Walder 134, R. Walker 109,W. Walkowiak 141, A. Wall 128, T. Wamorkar 6, A.Z. Wang 136, C. Wang 100, C. Wang 62c,H. Wang 17a, J. Wang 64a, R.-J. Wang 100, R. Wang 61, R. Wang 6, S.M. Wang 148,S. Wang 62b, T. Wang 62a, W.T. Wang 80, W. Wang 14a, X. Wang 14c, X. Wang 162,X. Wang 62c, Y. Wang 62d, Y. Wang 14c, Z. Wang 106, Z. Wang 62d,51,62c, Z. Wang 106,A. Warburton 104, R.J. Ward 20, N. Warrack 59, A.T. Watson 20, H. Watson 59,M.F. Watson 20, E. Watton 59,134, G. Watts 138, B.M. Waugh 96, C. Weber 29,H.A. Weber 18, M.S. Weber 19, S.M. Weber 63a, C. Wei 62a, Y. Wei 126, A.R. Weidberg 126,E.J. Weik 117, J. Weingarten 49, M. Weirich 100, C. Weiser 54, C.J. Wells 48, T. Wenaus 29,B. Wendland 49, T. Wengler 36, N.S. Wenke110, N. Wermes 24, M. Wessels 63a,A.M. Wharton 91, A.S. White 61, A. White 8, M.J. White 1, D. Whiteson 160,L. Wickremasinghe 124, W. Wiedenmann 170, C. Wiel 50, M. Wielers 134, C. Wiglesworth 42,D.J. Wilbern120, H.G. Wilkens 36, D.M. Williams 41, H.H. Williams128, S. Williams 32,S. Willocq 103, B.J. Wilson 101, P.J. Windischhofer 39, F.I. Winkel 30, F. Winklmeier 123,B.T. Winter 54, J.K. Winter 101, M. Wittgen143, M. Wobisch 97, Z. Wolffs 114, J. Wollrath160,M.W. Wolter 87, H. Wolters 130a,130c, A.F. Wongel 48, E.L. Woodward 41, S.D. Worm 48,B.K. Wosiek 87, K.W. Woźniak 87, S. Wozniewski 55, K. Wraight 59, C. Wu 20, J. Wu 14a,14e,M. Wu 64a, M. Wu 113, S.L. Wu 170, X. Wu 56, Y. Wu 62a, Z. Wu 135, J. Wuerzinger 110,ad,T.R. Wyatt 101, B.M. Wynne 52, S. Xella 42, L. Xia 14c, M. Xia 14b, J. Xiang 64c,M. Xie 62a, X. Xie 62a, S. Xin 14a,14e, A. Xiong 123, J. Xiong 17a, D. Xu 14a, H. Xu 62a,L. Xu 62a, R. Xu 128, T. Xu 106, Y. Xu 14b, Z. Xu 52, Z. Xu14c, B. Yabsley 147,S. Yacoob 33a, Y. Yamaguchi 154, E. Yamashita 153, H. Yamauchi 157, T. Yamazaki 17a,Y. Yamazaki 85, J. Yan62c, S. Yan 126, Z. Yan 25, H.J. Yang 62c,62d, H.T. Yang 62a,S. Yang 62a, T. Yang 64c, X. Yang 36, X. Yang 14a, Y. Yang 44, Y. Yang62a, Z. Yang 62a,W-M. Yao 17a, Y.C. Yap 48, H. Ye 14c, H. Ye 55, J. Ye 14a, S. Ye 29, X. Ye 62a, Y. Yeh 96,I. Yeletskikh 38, B.K. Yeo 17b, M.R. Yexley 96, P. Yin 41, K. Yorita 168, S. Younas 27b,C.J.S. Young 36, C. Young 143, C. Yu 14a,14e,ah, Y. Yu 62a, M. Yuan 106, R. Yuan 62b,L. Yue 96, M. Zaazoua 62a, B. Zabinski 87, E. Zaid52, Z.K. Zak 87, T. Zakareishvili 149b,N. Zakharchuk 34, S. Zambito 56, J.A. Zamora Saa 137d,137b, J. Zang 153, D. Zanzi 54,– 56 –JHEP12(2023)167O. Zaplatilek 132, C. Zeitnitz 171, H. Zeng 14a, J.C. Zeng 162, D.T. Zenger Jr 26, O. Zenin 37,T. Ženiš 28a, S. Zenz 94, S. Zerradi 35a, D. Zerwas 66, M. Zhai 14a,14e, B. Zhang 14c,D.F. Zhang 139, J. Zhang 62b, J. Zhang 6, K. Zhang 14a,14e, L. Zhang 14c, P. Zhang14a,14e,R. Zhang 170, S. Zhang 106, S. Zhang 44, T. Zhang 153, X. Zhang 62c, X. Zhang 62b,Y. Zhang 62c,5, Y. Zhang 96, Y. Zhang 14c, Z. Zhang 17a, Z. Zhang 66, H. Zhao 138,T. Zhao 62b, Y. Zhao 136, Z. Zhao 62a, A. Zhemchugov 38, J. Zheng 14c, K. Zheng 162,X. Zheng 62a, Z. Zheng 143, D. Zhong 162, B. Zhou 106, H. Zhou 7, N. Zhou 62c, Y. Zhou7,C.G. Zhu 62b, J. Zhu 106, Y. Zhu 62c, Y. Zhu 62a, X. Zhuang 14a, K. Zhukov 37,V. Zhulanov 37, N.I. Zimine 38, J. Zinsser 63b, M. Ziolkowski 141, L. Živković 15,A. Zoccoli 23b,23a, K. Zoch 61, T.G. Zorbas 139, O. Zormpa 46, W. Zou 41, L. Zwalinski 361Department of Physics, University of Adelaide, Adelaide; Australia2Department of Physics, University of Alberta, Edmonton AB; Canada3 (a)Department of Physics, Ankara University, Ankara;(b)Division of Physics, TOBB University ofEconomics and Technology, Ankara; Türkiye4 LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France5APC, Université Paris Cité, CNRS/IN2P3, Paris; France6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America7Department of Physics, University of Arizona, Tucson AZ; United States of America8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America9 Physics Department, National and Kapodistrian University of Athens, Athens; Greece10 Physics Department, National Technical University of Athens, Zografou; Greece11Department of Physics, University of Texas at Austin, Austin TX; United States of America12 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona;Spain14 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b)Physics Department,Tsinghua University, Beijing;(c)Department of Physics, Nanjing University, Nanjing;(d)School of Science,Shenzhen Campus of Sun Yat-sen University;(e)University of Chinese Academy of Science (UCAS),Beijing; China15 Institute of Physics, University of Belgrade, Belgrade; Serbia16Department for Physics and Technology, University of Bergen, Bergen; Norway17 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA;(b)University of California,Berkeley CA; United States of America18 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany19Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University ofBern, Bern; Switzerland20 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom21 (a)Department of Physics, Bogazici University, Istanbul;(b)Department of Physics Engineering, GaziantepUniversity, Gaziantep;(c)Department of Physics, Istanbul University, Istanbul; Türkiye22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño,Bogotá;(b)Departamento de Física, Universidad Nacional de Colombia, Bogotá; Colombia23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna;(b)INFN Sezione diBologna; Italy24 Physikalisches Institut, Universität Bonn, Bonn; Germany25Department of Physics, Boston University, Boston MA; United States of America26Department of Physics, Brandeis University, Waltham MA; United States of America27 (a)Transilvania University of Brasov, Brasov;(b)Horia Hulubei National Institute of Physics and NuclearEngineering, Bucharest;(c)Department of Physics, Alexandru Ioan Cuza University of Iasi,Iasi;(d)National Institute for Research and Development of Isotopic and Molecular Technologies, PhysicsDepartment, Cluj-Napoca;(e)University Politehnica Bucharest, Bucharest;(f)West University in Timisoara,Timisoara;(g)Faculty of Physics, University of Bucharest, Bucharest; Romania– 57 –JHEP12(2023)16728 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava;(b)Department ofSubnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; SlovakRepublic29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America30Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, yCONICET, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires; Argentina31California State University, CA; United States of America32Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom33 (a)Department of Physics, University of Cape Town, Cape Town;(b)iThemba Labs, WesternCape;(c)Department of Mechanical Engineering Science, University of Johannesburg,Johannesburg;(d)National Institute of Physics, University of the Philippines Diliman(Philippines);(e)University of South Africa, Department of Physics, Pretoria;(f)University of Zululand,KwaDlangezwa;(g)School of Physics, University of the Witwatersrand, Johannesburg; South Africa34Department of Physics, Carleton University, Ottawa ON; Canada35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - UniversitéHassan II, Casablanca;(b)Faculté des Sciences, Université Ibn-Tofail, Kénitra;(c)Faculté des SciencesSemlalia, Université Cadi Ayyad, LPHEA-Marrakech;(d)LPMR, Faculté des Sciences, UniversitéMohamed Premier, Oujda;(e)Faculté des sciences, Université Mohammed V, Rabat;(f)Institute of AppliedPhysics, Mohammed VI Polytechnic University, Ben Guerir; Morocco36CERN, Geneva; Switzerland37Affiliated with an institute covered by a cooperation agreement with CERN38Affiliated with an international laboratory covered by a cooperation agreement with CERN39 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France41Nevis Laboratory, Columbia University, Irvington NY; United States of America42Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark43 (a)Dipartimento di Fisica, Università della Calabria, Rende;(b)INFN Gruppo Collegato di Cosenza,Laboratori Nazionali di Frascati; Italy44 Physics Department, Southern Methodist University, Dallas TX; United States of America45 Physics Department, University of Texas at Dallas, Richardson TX; United States of America46National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece47 (a)Department of Physics, Stockholm University;(b)Oskar Klein Centre, Stockholm; Sweden48Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany49 Fakultät Physik, Technische Universität Dortmund, Dortmund; Germany50 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany51Department of Physics, Duke University, Durham NC; United States of America52 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom53 INFN e Laboratori Nazionali di Frascati, Frascati; Italy54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany56Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland57 (a)Dipartimento di Fisica, Università di Genova, Genova;(b)INFN Sezione di Genova; Italy58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany59 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States ofAmerica62 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics,University of Science and Technology of China, Hefei;(b)Institute of Frontier and Interdisciplinary Scienceand Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University,Qingdao;(c)School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for ParticleAstrophysics and Cosmology (MOE), SKLPPC, Shanghai;(d)Tsung-Dao Lee Institute, Shanghai;(e)Schoolof Physics and Microelectronics, Zhengzhou University; China– 58 –JHEP12(2023)16763 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg;(b)PhysikalischesInstitut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany64 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;(b)Department ofPhysics, University of Hong Kong, Hong Kong;(c)Department of Physics and Institute for Advanced Study,Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China65Department of Physics, National Tsing Hua University, Hsinchu; Taiwan66 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France67Centro Nacional de Microelectrónica (IMB-CNM-CSIC), Barcelona; Spain68Department of Physics, Indiana University, Bloomington IN; United States of America69 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b)ICTP, Trieste;(c)DipartimentoPolitecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy70 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy71 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Università di Milano, Milano; Italy72 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy73 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy74 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy75 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy76 (a)INFN Sezione di Roma Tor Vergata;(b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma;Italy77 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy78 (a)INFN-TIFPA;(b)Università degli Studi di Trento, Trento; Italy79Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria80University of Iowa, Iowa City IA; United States of America81Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America82 Istinye University, Sariyer, Istanbul; Türkiye83 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz deFora;(b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro;(c)Instituto de Física,Universidade de São Paulo, São Paulo;(d)Rio de Janeiro State University, Rio de Janeiro; Brazil84KEK, High Energy Accelerator Research Organization, Tsukuba; Japan85Graduate School of Science, Kobe University, Kobe; Japan86 (a)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow;(b)MarianSmoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland87 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland88 Faculty of Science, Kyoto University, Kyoto; Japan89Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka;Japan90 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina91 Physics Department, Lancaster University, Lancaster; United Kingdom92Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom93Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics,University of Ljubljana, Ljubljana; Slovenia94 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom95Department of Physics, Royal Holloway University of London, Egham; United Kingdom96Department of Physics and Astronomy, University College London, London; United Kingdom97 Louisiana Tech University, Ruston LA; United States of America98 Fysiska institutionen, Lunds universitet, Lund; Sweden99Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain100 Institut für Physik, Universität Mainz, Mainz; Germany101 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom102CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France103Department of Physics, University of Massachusetts, Amherst MA; United States of America104Department of Physics, McGill University, Montreal QC; Canada105 School of Physics, University of Melbourne, Victoria; Australia– 59 –JHEP12(2023)167106Department of Physics, University of Michigan, Ann Arbor MI; United States of America107Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States ofAmerica108Group of Particle Physics, University of Montreal, Montreal QC; Canada109 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany110Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany111Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan112Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States ofAmerica113 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen;Netherlands114Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands115Department of Physics, Northern Illinois University, DeKalb IL; United States of America116 (a)New York University Abu Dhabi, Abu Dhabi;(b)University of Sharjah, Sharjah; United Arab Emirates117Department of Physics, New York University, New York NY; United States of America118Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan119Ohio State University, Columbus OH; United States of America120Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; UnitedStates of America121Department of Physics, Oklahoma State University, Stillwater OK; United States of America122 Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic123 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America124Graduate School of Science, Osaka University, Osaka; Japan125Department of Physics, University of Oslo, Oslo; Norway126Department of Physics, Oxford University, Oxford; United Kingdom127 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France128Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America129Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America130 (a)Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa;(b)Departamento deFísica, Faculdade de Ciências, Universidade de Lisboa, Lisboa;(c)Departamento de Física, Universidade deCoimbra, Coimbra;(d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa;(e)Departamento deFísica, Universidade do Minho, Braga;(f)Departamento de Física Teórica y del Cosmos, Universidad deGranada, Granada (Spain);(g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa,Lisboa; Portugal131 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic132Czech Technical University in Prague, Prague; Czech Republic133Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic134 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom135 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France136 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; UnitedStates of America137 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago;(b)Millennium Institute forSubatomic physics at high energy frontier (SAPHIR), Santiago;(c)Instituto de InvestigaciónMultidisciplinario en Ciencia y Tecnología, y Departamento de Física, Universidad de LaSerena;(d)Universidad Andres Bello, Department of Physics, Santiago;(e)Instituto de Alta Investigación,Universidad de Tarapacá, Arica;(f)Departamento de Física, Universidad Técnica Federico Santa María,Valparaíso; Chile138Department of Physics, University of Washington, Seattle WA; United States of America139Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom140Department of Physics, Shinshu University, Nagano; Japan141Department Physik, Universität Siegen, Siegen; Germany142Department of Physics, Simon Fraser University, Burnaby BC; Canada143 SLAC National Accelerator Laboratory, Stanford CA; United States of America– 60 –JHEP12(2023)167144Department of Physics, Royal Institute of Technology, Stockholm; Sweden145Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States ofAmerica146Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom147 School of Physics, University of Sydney, Sydney; Australia148 Institute of Physics, Academia Sinica, Taipei; Taiwan149 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;(b)High EnergyPhysics Institute, Tbilisi State University, Tbilisi;(c)University of Georgia, Tbilisi; Georgia150Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel151Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel152Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece153 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo,Tokyo; Japan154Department of Physics, Tokyo Institute of Technology, Tokyo; Japan155Department of Physics, University of Toronto, Toronto ON; Canada156 (a)TRIUMF, Vancouver BC;(b)Department of Physics and Astronomy, York University, Toronto ON;Canada157Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and AppliedSciences, University of Tsukuba, Tsukuba; Japan158Department of Physics and Astronomy, Tufts University, Medford MA; United States of America159United Arab Emirates University, Al Ain; United Arab Emirates160Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States ofAmerica161Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden162Department of Physics, University of Illinois, Urbana IL; United States of America163 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain164Department of Physics, University of British Columbia, Vancouver BC; Canada165Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada166 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany167Department of Physics, University of Warwick, Coventry; United Kingdom168Waseda University, Tokyo; Japan169Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel170Department of Physics, University of Wisconsin, Madison WI; United States of America171 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal,Wuppertal; Germany172Department of Physics, Yale University, New Haven CT; United States of AmericaaAlso Affiliated with an institute covered by a cooperation agreement with CERNbAlso at An-Najah National University, Nablus; PalestinecAlso at Borough of Manhattan Community College, City University of New York, New York NY; UnitedStates of AmericadAlso at Center for High Energy Physics, Peking University; ChinaeAlso at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki; Greecef Also at Centro Studi e Ricerche Enrico Fermi; ItalygAlso at CERN, Geneva; SwitzerlandhAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; SwitzerlandiAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spainj Also at Department of Financial and Management Engineering, University of the Aegean, Chios; GreecekAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; IsraellAlso at Department of Physics, California State University, Sacramento; United States of AmericamAlso at Department of Physics, King’s College London, London; United KingdomnAlso at Department of Physics, Stanford University, Stanford CA; United States of AmericaoAlso at Department of Physics, University of Fribourg, Fribourg; SwitzerlandpAlso at Department of Physics, University of Thessaly; Greece– 61 –JHEP12(2023)167qAlso at Department of Physics, Westmont College, Santa Barbara; United States of AmericarAlso at Hellenic Open University, Patras; GreecesAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; SpaintAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; GermanyuAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy ofSciences, Sofia; BulgariavAlso at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; MoroccowAlso at Institute of Particle Physics (IPP); CanadaxAlso at Institute of Physics and Technology, Ulaanbaatar; MongoliayAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku; AzerbaijanzAlso at Institute of Theoretical Physics, Ilia State University, Tbilisi; GeorgiaaaAlso at L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse; FranceabAlso at Lawrence Livermore National Laboratory, Livermore; United States of AmericaacAlso at National Institute of Physics, University of the Philippines Diliman (Philippines); PhilippinesadAlso at Technical University of Munich, Munich; GermanyaeAlso at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; Chinaaf Also at TRIUMF, Vancouver BC; CanadaagAlso at Università di Napoli Parthenope, Napoli; ItalyahAlso at University of Chinese Academy of Sciences (UCAS), Beijing; ChinaaiAlso at University of Colorado Boulder, Department of Physics, Colorado; United States of Americaaj Also at Washington College, Chestertown, MD; United States of AmericaakAlso at Yeditepe University, Physics Department, Istanbul; Türkiye∗Deceased– 62 –
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Hadron-Hadron Scattering
Supersymmetry
Search for direct production of electroweakinos in fnal states with one lepton, jets and missing transverse momentum in pp collisions at √s = 13 TeV with the ATLAS detector
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