{"@context":{"@language":"en","Affiliation":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","AggregatedSourceRepository":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","Citation":"https:\/\/open.library.ubc.ca\/terms#identifierCitation","CopyrightHolder":"https:\/\/open.library.ubc.ca\/terms#rightsCopyright","Creator":"http:\/\/purl.org\/dc\/terms\/creator","DateAvailable":"http:\/\/purl.org\/dc\/terms\/issued","DateIssued":"http:\/\/purl.org\/dc\/terms\/issued","Description":"http:\/\/purl.org\/dc\/terms\/description","DigitalResourceOriginalRecord":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","FullText":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","Genre":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","IsShownAt":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","Language":"http:\/\/purl.org\/dc\/terms\/language","PeerReviewStatus":"https:\/\/open.library.ubc.ca\/terms#peerReviewStatus","Provider":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","Publisher":"http:\/\/purl.org\/dc\/terms\/publisher","PublisherDOI":"https:\/\/open.library.ubc.ca\/terms#publisherDOI","Rights":"http:\/\/purl.org\/dc\/terms\/rights","RightsURI":"https:\/\/open.library.ubc.ca\/terms#rightsURI","ScholarlyLevel":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","Subject":"http:\/\/purl.org\/dc\/terms\/subject","Title":"http:\/\/purl.org\/dc\/terms\/title","Type":"http:\/\/purl.org\/dc\/terms\/type","URI":"https:\/\/open.library.ubc.ca\/terms#identifierURI","SortDate":"http:\/\/purl.org\/dc\/terms\/date"},"Affiliation":[{"@value":"Other UBC","@language":"en"}],"AggregatedSourceRepository":[{"@value":"DSpace","@language":"en"}],"Citation":[{"@value":"Journal of High Energy Physics. 2022 Jun 17;2022(6):97","@language":"en"}],"CopyrightHolder":[{"@value":"The Author(s)","@language":"en"}],"Creator":[{"@value":"ATLAS Collaboration","@language":"en"}],"DateAvailable":[{"@value":"2023-01-03T19:10:54Z","@language":"en"}],"DateIssued":[{"@value":"2022-06-17","@language":"en"}],"Description":[{"@value":"Abstract\r\n                     \r\n              The associated production of a Higgs boson and a top-quark pair is measured in events characterised by the presence of one or two electrons or muons. The Higgs boson decay into a b-quark pair is used. The analysed data, corresponding to an integrated luminosity of 139 fb\u22121, were collected in proton-proton collisions at the Large Hadron Collider between 2015 and 2018 at a centre-of-mass energy of \r\n                \r\n                  \r\n                \r\n                \r\n                  \r\n                    s\r\n                  \r\n                \r\n                $$ \\sqrt{s} $$\r\n               = 13 TeV. The measured signal strength, defined as the ratio of the measured signal yield to that predicted by the Standard Model, is \r\n                \r\n                  \r\n                \r\n                \r\n                  \r\n                    0.35\r\n                    \r\n                      \u2212\r\n                      0.34\r\n                    \r\n                    \r\n                      +\r\n                      0.36\r\n                    \r\n                  \r\n                \r\n                $$ {0.35}_{-0.34}^{+0.36} $$\r\n              . This result is compatible with the Standard Model prediction and corresponds to an observed (expected) significance of 1.0 (2.7) standard deviations. The signal strength is also measured differentially in bins of the Higgs boson transverse momentum in the simplified template cross-section framework, including a bin for specially selected boosted Higgs bosons with transverse momentum above 300 GeV.","@language":"en"}],"DigitalResourceOriginalRecord":[{"@value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/83538?expand=metadata","@language":"en"}],"FullText":[{"@value":"JHEP06(2022)097Published for SISSA by SpringerReceived: November 15, 2021Revised: February 26, 2022Accepted: May 16, 2022Published: June 17, 2022Measurement of Higgs boson decay into b-quarks inassociated production with a top-quark pair in ppcollisions at \u221as = 13TeV with the ATLAS detectorThe ATLAS collaborationE-mail: atlas.publications@cern.chAbstract: The associated production of a Higgs boson and a top-quark pair is measuredin events characterised by the presence of one or two electrons or muons. The Higgs bosondecay into a b-quark pair is used. The analysed data, corresponding to an integratedluminosity of 139 fb\u22121, were collected in proton-proton collisions at the Large HadronCollider between 2015 and 2018 at a centre-of-mass energy of\u221as = 13TeV. The measuredsignal strength, defined as the ratio of the measured signal yield to that predicted by theStandard Model, is 0.35+0.36\u22120.34. This result is compatible with the Standard Model predictionand corresponds to an observed (expected) significance of 1.0 (2.7) standard deviations.The signal strength is also measured differentially in bins of the Higgs boson transversemomentum in the simplified template cross-section framework, including a bin for speciallyselected boosted Higgs bosons with transverse momentum above 300GeV.Keywords: Hadron-Hadron ScatteringArXiv ePrint: 2111.06712Open Access, Copyright CERN,for the benefit of the ATLAS Collaboration.Article funded by SCOAP3.https:\/\/doi.org\/10.1007\/JHEP06(2022)097JHEP06(2022)097Contents1 Introduction 12 ATLAS detector 33 Signal and background modelling 43.1 Signal modelling 53.2 tt\u00af+ jets background 53.3 Other backgrounds 74 Object and event selection 85 Analysis strategy 105.1 Analysis regions 115.2 Multivariate analysis 126 Systematic uncertainties 146.1 Experimental uncertainties 156.2 Theoretical modelling uncertainties 167 Results 198 Conclusion 33A Input variables to the classification BDTs 34The ATLAS collaboration 461 IntroductionThe Higgs boson [1\u20133] was discovered by the ATLAS and CMS collaborations [4, 5] in 2012,with a mass of around 125GeV [6]. Since then, the analysis of proton-proton (pp) collisiondata at centre-of-mass energies of 7 TeV, 8TeV and 13TeV delivered by the Large HadronCollider (LHC) [7] has led to more detailed measurements of its properties and couplings,testing the predictions of the Standard Model (SM). Of particular interest is the Yukawacoupling to the top quark, the heaviest elementary particle in the SM, which could be verysensitive to effects of physics beyond the SM (BSM) [8].Both the ATLAS and CMS collaborations have observed the interaction of the Higgsboson with third-generation fermions of the SM. The coupling to \u03c4 -leptons was measuredin the observation of H \u2192 \u03c4\u03c4 decays [9\u201311], while the observation of the decay of the Higgsboson into b-quark pairs provided direct evidence for the Yukawa coupling to down-typequarks [12, 13]. The interaction of the Higgs boson with the top quark was indirectlyconstrained (assuming no BSM phenomena in the loops) from measurements of gluon-gluon fusion Higgs production and decay to \u03b3\u03b3 [9], before being directly measured in theobservation of Higgs boson production in association with a pair of top quarks (tt\u00afH) [14, 15].\u2013 1 \u2013JHEP06(2022)097ggt(a) tt\u00afH(bb\u00af) t-channelt(b) tt\u00afH(bb\u00af) s-channelggt(c) tt\u00af+ bb\u00afFigure 1. Representative tree-level Feynman diagrams for the production of a Higgs boson inassociation with a top-quark pair (tt\u00afH) in (a) the t-channel and (b) the s-channel and the subsequentdecay of the Higgs boson into bb\u00af, and for (c) the main tt\u00af+ bb\u00af background.The tt\u00afH production mode, in which the top quark couples to the Higgs boson at treelevel, is the most favourable to extract direct information on the top-quark Yukawa couplingwithout assumptions about the potential presence of BSM physics [16\u201319].Although this production mode contributes only around 1% of the total Higgs bosonproduction cross-section at the LHC [20], the top quarks in the final state offer a distinctivesignature and allow access to many Higgs boson decay modes. The decay into two b-quarks (H \u2192 bb\u00af) is the most probable, with a SM branching fraction of about 58% [20].Furthermore, in the H \u2192 bb\u00af decay mode the reconstruction of the Higgs boson kinematicsis possible, which allows the extraction of additional information about the structure ofthe top-Higgs interaction [21\u201324]. This analysis therefore aims to select events with aHiggs boson produced in association with a pair of top quarks and decaying into a pair ofb-quarks(tt\u00afH(bb\u00af)), in which one or both top quarks decay semileptonically, producing anelectron or a muon, collectively referred to as leptons (`).1 With many final-state particles,the main challenges are the low efficiency to reconstruct and identify all of them, thelarge combinatorial ambiguities when trying to match the observed objects to the decayproducts of the Higgs boson and top quarks, and the large background of tt\u00af+ jets processes(in particular when these jets originate from b- or c-quarks), which have a much largerproduction cross-section than the signal. Representative Feynman diagrams for the tt\u00afH(bb\u00af)signal and dominant tt\u00af+ bb\u00af background are shown in figure 1.The ATLAS Collaboration searched for tt\u00afH(bb\u00af) production at\u221as = 8TeV in final stateswith at least one [25] or no lepton [26], and at\u221as = 13TeV with at least one lepton in thefinal state with data collected in 2015 and 2016, corresponding to an integrated luminosity of36.1 fb\u22121 [27]. A combined signal strength (defined as the ratio of the measured signal yieldto that predicted by the Standard Model) of 0.84+0.64\u22120.61 was measured, with an observed (ex-pected) significance of 1.4 (1.6) standard deviations. This result was combined with analysesof Higgs boson decays into massive vector bosons, \u03c4 -leptons, or photons to reach observationof the tt\u00afH production mode [14]. The CMS Collaboration searched for the same processes us-ing 35.9 fb\u22121 of data collected at\u221as = 13TeV in 2016, in events with at least one lepton [28]or no lepton [29], and measured a signal strength of 0.72\u00b1 0.45 and 0.9\u00b1 1.5, respectively.These results also contributed to the observation of the tt\u00afH production mode [15].In this paper, a measurement of the tt\u00afH production cross-section in the H \u2192 bb\u00af channelis performed using the LHC Run 2 pp collision data collected by the ATLAS detector,1Electrons and muons from the decay of a \u03c4 -lepton itself originating from a W boson are included.\u2013 2 \u2013JHEP06(2022)097corresponding to an integrated luminosity of 139 fb\u22121 at\u221as = 13TeV. Events with eitherone lepton or two leptons are analysed separately in exclusive single-lepton or dileptoncategories depending on the number of leptons, the number of jets and the number of jetsidentified as originating from b-hadrons (b-jets). In the single-lepton channel (but not inthe dilepton channel because the expected number of events is small), a specific category,referred to as \u2018boosted\u2019 in the following, is designed to select events in which the Higgs bosonis produced with high transverse momentum (pT). The non-boosted categories, where theHiggs boson decay products are less collimated, are referred to as \u2018resolved\u2019. Machine-learningalgorithms are used to classify events into signal-rich categories, which are analysed togetherwith the signal-depleted ones in a combined profile likelihood fit. The output distributions ofthese multivariate algorithms are used as the main discriminant to extract the signal. Thissignal extraction fit simultaneously determines the event yields for the signal and for themost important background components, while constraining the overall background modelwithin the assigned systematic uncertainties. In addition, making use of the possibility toreconstruct the Higgs boson kinematics in the H \u2192 bb\u00af channel, the cross-section is measuredas a function of the Higgs boson \u2018truth\u2019 transverse momentum p\u02c6HT , as obtained from MonteCarlo simulation, in the simplified template cross-sections (STXS) formalism [20], whichaims to reduce the theory dependencies that are folded into the measurements.Several aspects of the analysis have been improved relative to the previous version [27].The full Run 2 dataset is analysed, with improved reconstruction algorithms and detectorcalibrations, which in turn enhance the performance of the b-tagging algorithm. This allowsthe use of tighter selection criteria to reject events in poorly modelled regions of phasespace, and to define analysis regions differential in Higgs boson pT. An enhanced model isadopted for the tt\u00af+ jets background, with updated event generator versions, a higher-orderprecision prediction for the tt\u00af+ bb\u00af process, an increased number of simulated events, andan improved assessment of uncertainties. Finally, a new deep neural network improves theperformance of the boosted channel.This paper is organised as follows. The ATLAS detector is described in section 2.The signal and background modelling is presented in section 3. Section 4 summarises theselection criteria for reconstructed objects and events, while section 5 describes the analysisstrategy. Systematic uncertainties are discussed in section 6. Results are presented insection 7, and the conclusions in section 8.2 ATLAS detectorThe ATLAS experiment [30\u201333] at the LHC is a multipurpose particle detector with aforward-backward symmetric cylindrical geometry and a near 4pi coverage in solid angle.22ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) inthe centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre ofthe LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, \u03c6) are used in the transverse plane,\u03c6 being the azimuthal angle around the z-axis. The rapidity is defined as y = (1\/2) ln[(E + pz)\/(E \u2212 pz)]where E is the energy and pz is the longitudinal component of the momentum along the beam pipe. Thepseudorapidity is defined in terms of the polar angle \u03b8 as \u03b7 = \u2212 ln tan(\u03b8\/2). Angular distance is measuredin units of \u2206R \u2261\u221a(\u2206\u03b7)2 + (\u2206\u03c6)2.\u2013 3 \u2013JHEP06(2022)097It consists of an inner tracking detector surrounded by a thin superconducting solenoidproviding a 2 T axial magnetic field, electromagnetic and hadron calorimeters, and amuon spectrometer. The inner tracking detector (ID) covers the pseudorapidity range|\u03b7| < 2.5. It consists of silicon pixel, silicon microstrip, and transition radiation trackingdetectors. Lead\/liquid-argon (LAr) sampling calorimeters provide electromagnetic (EM)energy measurements with high granularity. A steel\/scintillator-tile hadron calorimetercovers the central pseudorapidity range |\u03b7| < 1.7. The endcap and forward regions areinstrumented with LAr calorimeters for both the EM and hadronic energy measurements upto |\u03b7| = 4.9. The muon spectrometer surrounds the calorimeters and is based on three largeair-core toroidal superconducting magnets with eight coils each. The field integral of thetoroids ranges between 2 and 6 T m across most of the detector. The muon spectrometerincludes a system of precision chambers for tracking and fast detectors for triggering. Atwo-level trigger system is used to select events. The first-level trigger is implemented inhardware and uses a subset of the detector information to accept events at a rate below100 kHz. This is followed by a software-based trigger that reduces the accepted event rateto 1 kHz on average depending on the data-taking conditions [34]. An extensive softwaresuite [35] is used in the reconstruction and analysis of real and simulated data, in detectoroperations, and in the trigger and data acquisition systems of the experiment.3 Signal and background modellingSimulated event samples are used to model the tt\u00afH signal and the background processes.The numbers of selected events and the distribution shapes of variables used to discriminatebetween signal and background enter into the signal extraction fit, which also constrainsthe modelling of the background processes. The Monte Carlo (MC) samples were producedusing either the full ATLAS detector simulation [36] based on Geant4 [37] or a fastersimulation where the full Geant4 simulation of the calorimeter response is replaced bya detailed parameterisation of the shower shapes [36]. For the observables used in theanalysis, the two simulations were found to give similar modelling. To simulate the effectsof multiple interactions in the same and neighbouring bunch crossings (pile-up), additionalinteractions were generated using Pythia 8.186 [38] with a set of tuned parameters calledthe A3 tune [39] and overlaid onto the simulated hard-scatter event. Simulated events arereweighted to match the pile-up conditions observed in the full Run 2 dataset, with a meannumber of pp interactions per bunch crossing of 34. All simulated events are processedthrough the same reconstruction algorithms and analysis chain as the data.In all samples where the parton shower (PS), hadronisation, and multi-parton interac-tions (MPI) were generated with either Pythia 8 or Herwig 7 [40, 41], the decays of b- andc-hadrons were simulated using the EvtGen 1.6.0 program [42]. The b-quark mass was setto mb = 4.80GeV (4.50GeV) for samples using Pythia 8 (Herwig 7). For Pythia 8, theA14 tune [43] and the NNPDF2.3lo parton distribution function (PDF) set [44] were used.For Herwig 7, the H7UE tune [41] was used with the MMHT2014lo PDF set [45]. TheHiggs boson mass was set to mH = 125.0GeV, and the top-quark mass to mt = 172.5GeV.The precision of the matrix element (ME) generators is next-to-leading order (NLO) in\u2013 4 \u2013JHEP06(2022)097quantum chromodynamics (QCD) for most samples. Some samples are normalised tohigher precision in QCD (next-to-next-to-leading order, NNLO, or next-to-next-to-leadinglogarithm, NNLL) or with electroweak (EW) corrections. A summary of all generatedsamples is presented in table 1, which includes both the samples used for nominal predictionsand other samples used to assess systematic uncertainties. Further details are provided inthe following subsections.3.1 Signal modellingIn tt\u00afH events the production and decays were modelled in the five-flavour scheme using thePowhegBox [61\u201365] generator at NLO in QCD with the NNPDF3.0nlo [66] PDF set.The hdamp parameter3 was set to 0.75\u00d7(mt+mt\u00af+mH) = 352.5GeV, and the functional formof the renormalisation and factorisation scales were both set to 3\u221amT(t) \u00b7mT(t\u00af) \u00b7mT(H)(where mT =\u221am2 + p2T is the transverse mass of a particle). The events were showered byPythia 8 and all Higgs boson decay modes are considered. The samples are normalised tothe fixed-order cross-section calculation, \u03c3tt\u00afH = 507+35\u221250 fb, which includes NLO QCD andEW corrections [20] for a Higgs boson mass of 125GeV.3.2 tt\u00af + jets backgroundSimulated tt\u00af+ jets events are categorised according to the flavour of additional jets in theevent, using the procedure described in ref. [25]. For this purpose, jets are reconstructedfrom stable particles (mean lifetime \u03c4 > 3\u00d7 10\u221211 s) using the anti-kt algorithm [67, 68],and the number of b- or c-hadrons within \u2206R = 0.4 of the jet axis is considered (withpT > 5GeV for the leading b- or c-hadron around the jet), excluding particles produced bythe top-quark decay. Events are labelled as tt\u00af+\u22651b if at least one b-flavour jet is identified,or else as tt\u00af+\u22651c if at least one c-flavour jet is identified, and otherwise as tt\u00af+ light. Wherenecessary, the tt\u00af+\u22651b events are further separated into tt\u00af+ 1b (where exactly one jet ismatched to at least one b-hadron) and tt\u00af+\u22652b (all remaining events).To accurately model the dominant tt\u00af+\u22651b background, a sample with tt\u00af+ bb\u00af MEs wasproduced at NLO QCD accuracy in the four-flavour scheme with the PowhegBoxRes [69]generator and OpenLoops [70, 71], using a pre-release of the implementation of this processin PowhegBoxRes provided by the authors [72], with the NNPDF3.0nlo nf4 [66] PDFset, and using Pythia 8 for the PS and hadronisation. The factorisation scale was set to0.5\u00d7\u03a3i=t,t\u00af,b,b\u00af,jmT(i) (where j stands for extra partons), the renormalisation scale was set to4\u221amT(t) \u00b7mT(t\u00af) \u00b7mT(b) \u00b7mT(b\u00af), and the hdamp parameter was set to 0.5\u00d7 \u03a3i=t,t\u00af,b,b\u00afmT(i).The mass of the two b-quarks produced in the ME in association with the two top quarks wasset to the same value as the mass of the b-quarks from the top-quark decays, mb = 4.95GeV.Inclusive tt\u00af+ jets events were also generated with tt\u00af MEs in the five-flavour schemeusing the PowhegBox v2 generator at NLO in QCD, using Pythia 8 for the PS andhadronisation. Here, the hdamp parameter was set to 1.5 mt [60], and the functional form of3The hdamp parameter controls the pT of the first additional emission beyond the leading-order Feynmandiagram in the PS and therefore regulates the high-pT emission against which the tt\u00afH system recoils.\u2013 5 \u2013JHEP06(2022)097Process ME generator ME PDF PS NormalisationHiggs bosontt\u00afH PowhegBox v2 NNPDF3.0nlo Pythia 8.230 NLO+NLO (EW) [20]PowhegBox v2 NNPDF3.0nlo Herwig 7.04 NLO+NLO (EW) [20]MadGraph5_aMC@NLO 2.6.0 NNPDF3.0nlo Pythia 8.230 NLO+NLO (EW) [20]tHjb MadGraph5_aMC@NLO 2.6.2 NNPDF3.0nlo nf4 Pythia 8.230 \u2013tWH MadGraph5_aMC@NLO 2.6.2 [DR] NNPDF3.0nlo Pythia 8.235 \u2013tt\u00af+ jets and single-toptt\u00af PowhegBox v2 NNPDF3.0nlo Pythia 8.230 NNLO+NNLL [46\u201352]PowhegBox v2 NNPDF3.0nlo Herwig 7.04 NNLO+NNLL [46\u201352]MadGraph5_aMC@NLO 2.6.0 NNPDF3.0nlo Pythia 8.230 NNLO+NNLL [46\u201352]tt\u00af+ bb\u00af PowhegBoxRes NNPDF3.0nlo nf4 Pythia 8.230 \u2013Sherpa 2.2.1 NNPDF3.0nnlo nf4 Sherpa \u2013tW PowhegBox v2 [DR] NNPDF3.0nlo Pythia 8.230 NLO+NNLL [53, 54]PowhegBox v2 [DS] NNPDF3.0nlo Pythia 8.230 NLO+NNLL [53, 54]PowhegBox v2 [DR] NNPDF3.0nlo Herwig 7.04 NLO+NNLL [53, 54]MadGraph5_aMC@NLO 2.6.2 [DR] CT10nlo Pythia 8.230 NLO+NNLL [53, 54]t-channel PowhegBox v2 NNPDF3.0nlo nf4 Pythia 8.230 NLO [55, 56]PowhegBox v2 NNPDF3.0nlo nf4 Herwig 7.04 NLO [55, 56]MadGraph5_aMC@NLO 2.6.2 NNPDF3.0nlo nf4 Pythia 8.230 NLO [55, 56]s-channel PowhegBox v2 NNPDF3.0nlo Pythia 8.230 NLO [55, 56]PowhegBox v2 NNPDF3.0nlo Herwig 7.04 NLO [55, 56]MadGraph5_aMC@NLO 2.6.2 NNPDF3.0nlo Pythia 8.230 NLO [55, 56]OtherW+ jets Sherpa 2.2.1 (NLO [2j], LO [4j]) NNPDF3.0nnlo Sherpa NNLO [57]Z+ jets Sherpa 2.2.1 (NLO [2j], LO [4j]) NNPDF3.0nnlo Sherpa NNLO [57]V V (had.) Sherpa 2.2.1 NNPDF3.0nnlo Sherpa \u2013V V (lep.) Sherpa 2.2.2 NNPDF3.0nnlo Sherpa \u2013V V (lep.) + jj Sherpa 2.2.2 (LO [EW]) NNPDF3.0nnlo Sherpa \u2013tt\u00afW MadGraph5_aMC@NLO 2.3.3 NNPDF3.0nlo Pythia 8.210 NLO+NLO (EW) [20]Sherpa 2.0.0 (LO [2j]) NNPDF3.0nnlo Sherpa NLO+NLO (EW) [20]tt\u00af`` MadGraph5_aMC@NLO 2.3.3 NNPDF3.0nlo Pythia 8.210 NLO+NLO (EW) [20]Sherpa 2.0.0 (LO [1j]) NNPDF3.0nnlo Sherpa NLO+NLO (EW) [20]tt\u00afZ (qq, \u03bd\u03bd) MadGraph5_aMC@NLO 2.3.3 NNPDF3.0nlo Pythia 8.210 NLO+NLO (EW) [20]Sherpa 2.0.0 (LO [2j]) NNPDF3.0nnlo Sherpa NLO+NLO (EW) [20]tt\u00aftt\u00af MadGraph5_aMC@NLO 2.3.3 NNPDF3.1nlo Pythia 8.230 NLO+NLO (EW) [58]tZq MadGraph5_aMC@NLO 2.3.3 (LO) CTEQ6L1 Pythia 8.212 \u2013tWZ MadGraph5_aMC@NLO 2.3.3 [DR] NNPDF3.0nlo Pythia 8.230 \u2013Table 1. Table summarising the generator set-ups for samples used in this analysis. The first rowfor each sample details the nominal settings used for this process in the analysis. Any additionalrows describe samples which are used to evaluate the modelling and performance of the analysis. Foroverlap between tt\u00af and tW -like diagrams, samples using the diagram removal scheme [59] are listedas [DR] and samples using the diagram subtraction scheme [59, 60] are listed as [DS]. The precisionof the ME generator is NLO in QCD if no additional information is provided in parentheses. Thehigher-order cross-section used to normalise these samples is listed in the last column and refers tothe order of QCD processes if no additional information is provided. If no information is present inthis column, no higher-order K-factor is applied to this process. For the V V Sherpa samples, \u2018lep.\u2019(\u2018had.\u2019) means that both bosons decay leptonically (one decays leptonically and one hadronically).\u2013 6 \u2013JHEP06(2022)097the renormalisation and factorisation scales was set to mT(t).4 The tt\u00af+\u22651c and tt\u00af+ lightevents using this prediction are combined with the previously described tt\u00af+\u22651b sample toform the nominal tt\u00af model, while the tt\u00af+\u22651b events from this five-flavour scheme are usedonly to assign modelling uncertainties.3.3 Other backgroundsThe QCD V+ jets processes (i.e.W+ jets and Z+ jets) were simulated with the Sherpa 2.2.1generator [73]. In this set-up, NLO-accurate MEs for up to two partons, and leading-order(LO) accurate MEs for up to four partons were calculated with the Comix [74] andOpenLoops libraries. They were matched with the Sherpa parton shower [75] by usingthe MEPS@NLO prescription [76\u201379] with the set of tuned parameters developed bythe Sherpa authors and based on the NNPDF3.0nnlo set of PDFs [66]. A data-drivencorrection to the normalisation was derived for Z+ jets predictions containing at leasttwo heavy-flavour jets (where heavy-flavour means jets originating from b-hadrons andc-hadrons). Events are selected, with objects passing the selection discussed in section 4, indedicated control regions in data defined by requiring at least two jets and two opposite-electric-charge same-flavour leptons (e+e\u2212 or \u00b5+\u00b5\u2212) with an invariant mass inside theZ-boson mass window 83\u201399GeV. A 25% yield increase is found to improve the modellingof Z+ jets events with at least two heavy-flavour jets, consistent with ref. [80].Diboson events were simulated with the Sherpa 2.2.1 and 2.2.2 generators. In this set-up multiple MEs were matched and merged with the Sherpa PS based on Catani-Seymourdipole factorisation [74, 75] using the MEPS@NLO prescription. For semileptonicallyand fully leptonically decaying diboson event samples, as well as loop-induced dibosonevent samples, the virtual QCD correction for MEs at NLO accuracy was provided by theOpenLoops library. For EW V V jj production, the calculation was performed in the G\u00b5scheme, ensuring an optimal description of pure EW interactions at the EW scale [81\u201383].All samples were generated using the NNPDF3.0nnlo PDF set, along with the dedicatedset of tuned PS parameters developed by the Sherpa authors.The associated production of a single top quark and a Higgs boson is treated asbackground. Samples for two subprocesses, tHjb and tWH production, were generatedusing the MadGraph5_aMC@NLO 2.6.2 generator at NLO in QCD. The functionalform of the renormalisation and factorisation scale was set to 0.5\u00d7\u2211imT(i), where thesum runs over all the particles generated from the ME calculation. For tHjb (tWH), eventswere generated in the four-flavour (five-flavour) scheme using the NNPDF3.0nlo nf4(NNPDF3.0nlo) PDF set, and the diagram removal scheme was employed to handle theinterference with tt\u00afH in the tWH sample [59, 84].Single-top t-channel, s-channel, and tW production were modelled using thePowhegBox v2 [85\u201387] generator at NLO in QCD. For t-channel production, eventswere generated in the four-flavour scheme with the NNPDF3.0nlo nf4 PDF set, and thefunctional form of the renormalisation and factorisation scales was set to mT(b) followingthe recommendation of ref. [85]. For s-channel and tW production, events were generated4This scale is calculated in the tt\u00af rest-frame and hence the pT values of the top quark and top antiquarkare equivalent.\u2013 7 \u2013JHEP06(2022)097in the five-flavour scheme with the NNPDF3.0nlo PDF set, and the functional form of therenormalisation and factorisation scales was set to the top-quark mass. For tW production,the diagram removal scheme [59] was employed to handle the interference with tt\u00af produc-tion [60]. An additional sample which applies the diagram subtraction scheme [59, 60] wasused to assess an uncertainty in the modelling of this interference.The production and decay of a top-quark pair in association with a vector bo-son (i.e. tt\u00afW or tt\u00afZ), referred to collectively as tt\u00afV , was modelled using the Mad-Graph5_aMC@NLO 2.3.3 generator at NLO in QCD. The functional form of therenormalisation and factorisation scale was set to 0.5\u00d7\u2211imT(i), where the sum runs overall the particles generated from the ME calculation.For events with four top quarks (tt\u00aftt\u00af) the production and decays were modelled using theMadGraph5_aMC@NLO 2.3.3 generator at NLO in QCD with the NNPDF3.1nlo [66]PDF set. The functional form of the renormalisation and factorisation scales was setto 0.25 \u00d7\u2211imT(i), where the sum runs over all the particles generated from the MEcalculation, following the recommendation of ref. [58].The tZq events were generated using the MadGraph5_aMC@NLO 2.3.3 generatorin the four-flavour scheme at LO in QCD, with the CTEQ6L1 [88] PDF set. Followingthe recommendations of ref. [85], the renormalisation and factorisation scales were set to4\u00d7mT(b), where the b-quark is the one coming from the gluon splitting.The tWZ sample was produced using the MadGraph5_aMC@NLO 2.3.3 generatorat NLO in QCD with the NNPDF3.0nlo PDF set. The renormalisation and factorisationscales were set to the top-quark mass. The diagram removal scheme was employed to handlethe interference between tWZ and tt\u00afZ.In the single-lepton channel, a negligible contribution is found from events with a jet ora photon misidentified as a lepton, or from events with a non-prompt lepton. In the dileptonchannel, the contribution from events with non-prompt leptons is estimated from simulatedevents (from tt\u00af, tt\u00afZ, tt\u00afW , diboson, Z+ jets, W+ jets, tW , s-channel and t-channel singletop production) where at least one of the reconstructed leptons does not match a truelepton in the event record. These events are collectively referred to as fakes.4 Object and event selectionEvents are selected from pp collisions at\u221as = 13TeV recorded by the ATLAS detectorbetween 2015 and 2018, corresponding to an integrated luminosity of 139 fb\u22121. Only eventsfor which the LHC beams were in stable-collision mode and all relevant subsystems wereoperational are considered [89]. Events are required to have at least one primary vertex withtwo or more tracks with pT > 0.5GeV. If more than one vertex is found, the hard-scatteringprimary vertex is selected as the one with the highest sum of squared transverse momentaof associated tracks [90].Events were recorded using single-lepton triggers with either a low pT threshold and alepton isolation requirement, or a higher threshold but a looser identification criterion andwithout any isolation requirement. The lowest pT threshold in the single-muon trigger was20 (26)GeV [91] for data taken in 2015 (2016\u20132018), while in the single-electron trigger itwas 24 (26)GeV [92]).\u2013 8 \u2013JHEP06(2022)097Electrons are reconstructed from tracks in the ID associated with topological clustersof energy depositions in the calorimeter [93] and are required to have pT > 10GeV and|\u03b7| < 2.47. Candidates in the calorimeter barrel-endcap transition region (1.37 < |\u03b7| < 1.52)are excluded. Electrons must satisfy the Medium likelihood identification criterion. Muoncandidates are identified by matching ID tracks to full tracks or track segments reconstructedin the muon spectrometer, using the Loose identification criterion [94]. Muons are requiredto have pT > 10GeV and |\u03b7| < 2.5. Lepton tracks must match the primary vertex of theevent, i.e. they have to satisfy |z0 sin(\u03b8)| < 0.5 mm and |d0\/\u03c3(d0)| < 5 (3) for electrons(muons), where z0 is the longitudinal impact parameter relative to the primary vertex andd0 (with uncertainty \u03c3(d0)) is the transverse impact parameter relative to the beam line.Jets are reconstructed from noise-suppressed topological clusters of calorimeter energydepositions [95] calibrated at the electromagnetic scale [96], using the anti-kt algorithmwith a radius parameter of 0.4. These are referred to as small-R jets. The average energycontribution from pile-up is subtracted according to the jet area and jets are calibrated asdescribed in ref. [96] with a series of simulation-based corrections and in situ techniques.Jets are required to satisfy pT > 25GeV and |\u03b7| < 2.5. The effect of pile-up is reduced byan algorithm requiring that the calorimeter-based jets are consistent with originating fromthe primary vertex using tracking information [97].Jets containing b-hadrons are identified (b-tagged) with the MV2c10 multivariatealgorithm [98], which combines information about the transverse impact parameters ofdisplaced tracks and the topological properties of secondary and tertiary decay verticesreconstructed within the jet. Four working points, defined by different thresholds forthe MV2c10 discriminant, are used in this analysis, corresponding to efficiencies rangingfrom 85% to 60% for b-jets with pT > 20GeV as determined in simulated tt\u00af events. Thecorresponding rejection rates are in the range 2\u201322 for c-jets (containing c-hadrons and nob-hadrons) and 27\u20131150 for light-jets. Jets are then assigned a pseudo-continuous b-taggingvalue [98] according to the tightest working point they satisfy. Correction factors are appliedto the simulated events to compensate for differences between data and simulation in theb-tagging efficiency for b-, c-, and light-jets [98\u2013100].Hadronically decaying \u03c4 -leptons (\u03c4had) are distinguished from jets using their trackmultiplicity and a multivariate discriminant based on calorimetric shower shapes andtracking information [101]. They are required to have pT > 25GeV and |\u03b7| < 2.5, and topass the Medium \u03c4 -identification working point.An overlap removal procedure is applied to prevent double-counting of objects. Theclosest jet within \u2206Ry =\u221a(\u2206y)2 + (\u2206\u03c6)2 = 0.2 of a selected electron is removed. Ifthe nearest jet surviving that selection is within \u2206Ry = 0.4 of the electron, the electronis discarded. Muons are usually removed if they are separated from the nearest jet by\u2206Ry < 0.4, since this reduces the background from heavy-flavour decays inside jets. However,if this jet has fewer than three associated tracks, the muon is kept and the jet is removedinstead; this avoids an inefficiency for high-energy muons undergoing significant energy lossin the calorimeter. A \u03c4had candidate is rejected if it is separated by \u2206Ry < 0.2 from anyselected electron or muon. No overlap removal is performed between jets and \u03c4had candidates.\u2013 9 \u2013JHEP06(2022)097The missing transverse momentum (with magnitude EmissT ) is reconstructed as thenegative vector sum of the pT of all the selected electrons, muons, \u03c4had and jets describedabove, with an extra \u2018soft term\u2019 built from additional tracks associated with the primaryvertex, to make it resilient to pile-up contamination [102]. The missing transverse momentumis not used for event selection but is included in the inputs to the multivariate discriminantsthat are built in the most sensitive analysis categories (see section 5.2).For the boosted category, the small-R jets are reclustered [103] using the anti-ktalgorithm with a radius parameter of 1.0, resulting in a collection of large-R jets (referred toas RC jets). These RC jets are required to have a reconstructed invariant mass higher than50GeV, pT > 200GeV and at least two small-R constituent jets. RC jets are used as inputto a deep neural network (DNN), described in section 5.2, to identify high-pT (boosted)top-quark and Higgs boson candidates decaying into collimated hadronic final states. Theevent is flagged as containing a boosted Higgs boson candidate if one of the RC jets has ahigh probability of originating from a Higgs boson, as estimated by the DNN.Events are required to have exactly one lepton in the single-lepton channels and exactlytwo leptons with opposite electric charge in the dilepton channel. At least one of theleptons must have pT > 27GeV and match a corresponding object at trigger level. In theee and \u00b5\u00b5 channels, the dilepton invariant mass must be above 15GeV and outside theZ-boson mass window 83\u201399GeV. To maintain orthogonality with other tt\u00afH channels [104],events are vetoed if they contain one or more (two or more) \u03c4had candidates in the dilepton(single-lepton) channel. Leptons are further required to satisfy additional identification andisolation criteria to increase background rejection: electrons (muons) must pass the Tight(Medium) identification criterion and the Gradient (FixedCutTightTrackOnly) isolationcriteria [93, 105].5 Analysis strategyIn order to target the tt\u00afH(bb\u00af) final state, events are categorised into mutually exclusiveregions defined by the number of leptons, the number of jets, the number of b-tagged jets atdifferent b-tagging efficiencies (60%, 70%, 77%, or 85%) and the number of boosted Higgsboson candidates (see section 5.1).The analysis regions with higher signal-to-background ratio are referred to as \u2018signalregions\u2019 (SR). Higgs boson candidates are reconstructed using a DNN in the boostedcategory, and boosted decision trees (BDT) referred to as \u2018reconstruction BDTs\u2019 in theresolved categories, aiming at associating the reconstructed jets to the final state partons.Kinematic variables of these Higgs boson candidates, such as their transverse momentumpHT , are computed and used together with several other variables as input to other BDTs.These, referred to as \u2018classification BDTs\u2019, are then employed to separate signal frombackground, in each of the SRs (see section 5.2). The remaining analysis regions, depletedin signal, are referred to as \u2018control regions\u2019 (CR). They provide stringent constraints on thenormalisation of the backgrounds and on systematic uncertainties when used in a combinedfit with the signal regions.\u2013 10 \u2013JHEP06(2022)0975.1 Analysis regionsTable 2 defines the 16 regions into which the events are classified: 11 SRs (dilepton SR\u22654j\u22654b,single-lepton SR\u22656j\u22654b and SRboosted, split according to the reconstructed pHT into four, fiveand two regions, respectively), and five CRs.In the single-lepton channel, events are assigned to the boosted category if they containat least four jets b-tagged at the 85% working point, one boosted Higgs boson candidate, andat least two jets not belonging to the boosted Higgs boson candidate which are b-tagged atthe 77% working point. The boosted Higgs boson candidate must satisfy pT > 300GeV, havean invariant mass in the range 100\u2013140GeV, a DNN score P (H) > 0.6 (see section 5.2), andexactly two jet constituents b-tagged at the 85% working point. If more than one candidateis identified, the one with a mass closest to the Higgs boson mass is chosen. The selectedRC jet is used to determine the kinematic properties of the boosted Higgs boson candidate(reconstructed pHT , mbb\u00af, etc.). All other selected events belong to the resolved categories.The analysis regions are further split according to the reconstructed pHT to allow theextraction of multiple signal parameters, sensitive to new physics effects. The pHT rangesare the same as used to define STXS bins with \u2018truth\u2019 p\u02c6HT (where the \u2018truth\u2019 p\u02c6HT is takenfrom the MC event record before the Higgs boson decays), and were chosen to minimise thecorrelation among signal strengths in different STXS bins. The split goes as follows:\u2022 The single-lepton resolved signal region, SR\u22656j\u22654b, is split in five reconstructed pHTregions: 0\u2013120GeV, 120\u2013200GeV, 200\u2013300GeV, 300\u2013450GeV and \u2265 450GeV.\u2022 The dilepton signal region, SR\u22654j\u22654b, is split in a similar manner, with the two highestpHT bins merged because only a small number of events are expected.\u2022 The boosted signal region, SRboosted, is split into two reconstructed pHT regions:300\u2013450GeV and \u2265 450GeV.\u2022 Control regions are inclusive in reconstructed pHT to maintain the constraints on thebackground composition.After these selections are applied, tt\u00af + heavy-flavour jets dominate the backgroundcomposition and the tt\u00afH selection efficiency is 1.2%. Although all Higgs boson decaymodes are considered, H \u2192 bb\u00af events account for at least 94% of tt\u00afH events selected inthe signal regions. In the SRs the shape and normalisation of a multivariate discriminantdistribution is used in the statistical analysis, except in the highest reconstructed pHT bin ofthe single-lepton resolved analysis, where only the event yield is used.In the dilepton CRs, only the event yield is used to correct the amount of tt\u00af + \u22651cpredicted from the inclusive tt\u00af+ jets sample. In the single-lepton channel the distributionshape and normalisation of the average \u2206R for all possible combinations of b-tagged jetpairs, \u2206Ravgbb , is used in the CRs to help better constrain the background contributions andcorrect their shape. Control regions have different ratios of tt\u00af + \u22651b to tt\u00af + \u22651c events:regions labelled \u2018hi\u2019, referring to a higher b-tagging probability, are enriched in tt\u00af+\u22651b,while in regions labelled \u2018lo\u2019, the proportion of tt\u00af+\u22651c events is increased. The different\u2013 11 \u2013JHEP06(2022)097RegionDilepton Single-leptonSR\u22654j\u22654b CR\u22654j3b hi CR\u22654j3b lo CR3j3b hi SR\u22656j\u22654b CR5j\u22654b hi CR5j\u22654b lo SRboosted#leptons = 2 = 1#jets \u2265 4 = 3 \u2265 6 = 5 \u2265 4#b-tag@85% \u2013 \u2265 4@77% \u2013 \u2013 \u2265 2\u2020@70% \u2265 4 = 3 \u2265 4 \u2014@60% \u2014 = 3 < 3 = 3 \u2014 \u2265 4 < 4 \u2014#boosted cand. \u2013 0 \u2265 1Fit input BDT Yield BDT\/Yield \u2206Ravgbb BDTTable 2. Definition of the analysis regions, split according to the number of leptons, jets, andb-tagged jets using different working points, and the number of boosted Higgs candidates. ForSRboosted, b-tagged jets flagged with \u2020 are extra b-jets not part of the boosted Higgs boson candidate.All SRs are further split in reconstructed pHT as described in the text. The last row specifies the typeof input to the fit used in each region: normalisation only (Yield) or shape and normalisation of theclassification BDT or \u2206Ravgbb distribution. In the highest bin (pHT \u2265 450GeV) of the single-leptonresolved analysis, only the event yield is used.proportions of tt\u00af+\u22651b and tt\u00af+\u22651c in the control regions allow the signal extraction fit tobetter constrain the relative fractions of these processes in the signal regions.5.2 Multivariate analysisMultivariate classifiers are used in two parts of this analysis: identifying Higgs bosoncandidate objects and classifying tt\u00afH signal events. In all SRs of the resolved categories, themultivariate classifiers are constructed analogously to the reconstruction and classificationBDTs used in the previous analysis [27] and trained with TMVA [106]. The training forthe reconstruction BDTs is identical to this previous analysis, matching reconstructedjets to the partons emitted from top-quark and Higgs boson decays. For this purpose,W -boson, top-quark and Higgs boson candidates are built from combinations of jets andleptons. The b-tagging information is used to discard combinations containing jet-partonassignments inconsistent with the correct parton candidate flavour. The combination of jetswith the highest reconstruction BDT score is selected, allowing the computation of kinematicproperties of the Higgs boson candidate (reconstructed pHT , mbb\u00af, etc.) by summing thefour-momenta of the two jets associated with the Higgs boson candidate in this combination.In the boosted channel the Higgs boson candidate is found using a DNN with a three-node softmax output layer trained with Keras [107] and a TensorFlow backend [108] on asample of RC jets from signal events. The DNN quantifies the probability that an RC jetis originated from a Higgs boson (labelled P (H) and shown in figure 2), top quark or anyother process (mostly multijet production). The most important DNN input variables foridentifying a Higgs boson candidate jet are built from the small-R jet constituent masses\u2013 12 \u2013JHEP06(2022)0970.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1Higgs candidate DNN-tagger P(H)0.50.7511.25 Data \/ Pred. 0100200300400500EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSR [300,450) GeV\u2208 HTpPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1Higgs candidate DNN-tagger P(H)0.50.7511.25 Data \/ Pred. 01020304050607080EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSR) GeV\u221e [450,\u2208 HTpPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)Figure 2. Comparison between data and prediction for the DNN P (H) output for the Higgs bosoncandidate prior to any fit to the data in the single-lepton boosted channel for (a) 300 \u2264 pHT < 450GeVand (b) pHT \u2265 450GeV. The dashed line shows the tt\u00afH signal distribution normalised to the totalbackground prediction. The uncertainty band includes all uncertainties and their correlations.and pseudo-continuous b-tagging values, while substructure variables [109] also contribute.The Higgs boson jets are correctly identified 76% of the time, while top-quark jets areidentified in 67% of the cases, in signal events.The performance of the reconstruction in the three channels is reported in figure 3,which shows the migration matrix for Higgs boson candidates between reconstructed pHTbins and \u2018truth\u2019 p\u02c6HT bins, for all reconstructed Higgs boson candidates. When applying thereconstruction BDT or the DNN (which are trained with tt\u00afH events to match b-jets to aHiggs boson candidate) to the tt\u00af+\u22651b sample, the two selected jets do not originate fromthe top-quark decay products about half (two-thirds) of the time in the lower (higher) pHTbin. The number of events with both jets originating from top-quark decays is negligible.The classification BDTs have been retrained since the previous analysis to profit fromthe improved background modelling and from the increased number of simulated events,using the signal and components of the nominal background model presented in this paper.The dilepton BDT is trained only against tt\u00af + bb\u00af events (as it constitutes most of thebackground), the single-lepton resolved BDT is trained against tt\u00af+ jets events (becausett\u00af+\u22651c and tt\u00af+ light events also contribute) and the single-lepton boosted BDT is trainedagainst all background processes.These BDTs are built by combining kinematic variables, such as invariant masses andangular separations of pairs of reconstructed jets and leptons, outputs of the reconstructiondiscriminants, as well as the pseudo-continuous b-tagging discriminant of selected jets.The complete lists of input variables can be found in appendix A. The reconstructiondiscriminants provide their own output value as well as variables derived from the selectedcombination of jets with the highest reconstruction BDT score in the resolved channels.\u2013 13 \u2013JHEP06(2022)09775 20 4 1 71 22 6 125 58 16 2 29 53 16 210 14 63 12 1 16 18 54 11 14 4 11 73 8 11 12 19 51 7 97 34 3 3 11 80 9 9 9 21 52 25 75 [0,120) [GeV]\u2208 HT, p4b\u22654j\u2265SR [120,200) [GeV]\u2208 HT, p4b\u22654j\u2265SR [200,300) [GeV]\u2208 HT, p4b\u22654j\u2265SR [300,450) [GeV]\u2208 HT, p4b\u22654j\u2265SR) [GeV]\u221e [450,\u2208 HT, p4b\u22654j\u2265SR [0,120) [GeV]\u2208 HT, p4b\u22656j\u2265SR [120,200) [GeV]\u2208 HT, p4b\u22656j\u2265SR [200,300) [GeV]\u2208 HT, p4b\u22656j\u2265SR [300,450) [GeV]\u2208 HT, p4b\u22656j\u2265SR) [GeV]\u221e [450,\u2208 HT, p4b\u22656j\u2265SR [300,450) [GeV]\u2208 HT, pboostedSR) [GeV]\u221e [450,\u2208 HT, pboostedSR [0,120) [GeV]   \u2208 HTp [120,200) [GeV]\u2208 HTp [200,300) [GeV]\u2208 HTp [300,450) [GeV]\u2208 HTp) [GeV]   \u221e [450,\u2208 HTp0102030405060708090100Purity [%]ATLAS HtSimulation t =13 TeVsFigure 3. Performance of the Higgs boson reconstruction algorithms. For each row of \u2018truth\u2019 p\u02c6HT ,the matrix shows (in percentages) the fraction of all Higgs boson candidates with reconstructed pHTin the various bins of the dilepton (left), single-lepton resolved (middle) and boosted (right) channels.In the single-lepton resolved channel, a likelihood discriminant method that combinesthe signal and background probabilities of all possible jet combinations in each event isalso used as input to the classification BDT [27]. In the boosted channel, informationfrom the DNN is used as input to the classification BDT, including the Higgs probability,P (H). Distributions of the output of these BDT classifiers serve as SR inputs to the signalextraction fit.The most important variables entering the dilepton BDT include the reconstructionBDT score for the Higgs boson candidate identified using Higgs boson information, theaverage \u2206\u03b7 between b-tagged jets, the minimum invariant mass of a b-tagged jet pair,the \u2206R between the Higgs candidate and the tt\u00af candidate system, and the number ofb-tagged jet pairs with an invariant mass within 30GeV of 125GeV. The most importantvariables entering the single-lepton resolved BDT include the likelihood discriminant, theaverage \u2206R for all possible combinations of two b-tagged jets (\u2206Ravgbb ), the invariant massof the two b-tagged jets with the smallest \u2206R, the reconstruction BDT score for the Higgsboson candidate identified using Higgs boson information, and the \u2206R between the twohighest-pT b-tagged jets. The most important variables entering the single-lepton boostedBDT include the DNN P (H) output for the Higgs boson candidate, the sum of b-taggingdiscriminants of small-R jets from Higgs, hadronic top and leptonic top candidates, thehadronic top candidate\u2019s invariant mass, the small-R jet multiplicity and, in the sum ofb-tagging discriminants, the fraction due to all jets not associated with the Higgs or hadronictop candidates.6 Systematic uncertaintiesMany sources of systematic uncertainty affect this analysis, impacting the categorisation ofevents as well as the shape and normalisation of the final discriminants used in the signalextraction fit. All sources of experimental uncertainty considered, with the exception of the\u2013 14 \u2013JHEP06(2022)097uncertainty in the luminosity, affect both the normalisations and shapes of distributionsin all the simulated event samples. Uncertainties related to the theoretical modelling ofthe signal and background processes affect both the normalisations and shapes of thedistributions, with the exception of cross-section and normalisation uncertainties which onlyaffect the overall yield of the considered sample. Nonetheless, the normalisation uncertaintiesmodify the relative fractions of the different samples leading to a shape uncertainty in thedistribution of the final discriminant for the total prediction in the different analysis regions.A single independent nuisance parameter is assigned to each source of systematicuncertainty. Some of the systematic uncertainties, in particular most of the experimentaluncertainties, are decomposed into several independent sources, as specified in the following.Each individual source then has a correlated effect across all the channels, analysis regions,and signal and background samples. Modelling uncertainties are typically broken down intocomponents which target specific physics effects in the event generation, such as scale varia-tions or changing the hadronisation model, and are uncorrelated between different samples.6.1 Experimental uncertaintiesThe uncertainty in the combined 2015\u20132018 integrated luminosity is 1.7% [110], obtainedusing the LUCID-2 detector [111] for the primary luminosity measurement. An uncertaintyassociated with the modelling of pile-up in the simulation is included to cover the differencebetween the predicted and measured inelastic cross-section values [112].The jet energy scale uncertainty is derived by combining information from test-beamdata, LHC collision data and simulation, and the jet energy resolution uncertainty isobtained by combining dijet pT-balance measurements and simulation [96]. Additionalconsiderations related to jet flavour, pile-up corrections, \u03b7 dependence and high-pT jets areincluded. These uncertainties are further propagated into the single-lepton boosted analysisby applying the reclustering described in section 4 with systematically varied inputs. Atotal of 40 independent contributions are considered. While the uncertainties are not large,varying between 1% and 5% per jet (depending on the jet pT), the effects are amplified bythe large number of jets considered in the final state. The efficiency to identify and removejets from pile-up is measured with Z \u2192 \u00b5+\u00b5\u2212 events in data using techniques similar tothose used in ref. [97]. All small-R jet constituent uncertainties are propagated to RC jets.The efficiency to correctly tag b-jets is measured using dileptonic tt\u00af events [98]. Themis-tag rate for c-jets is measured using single-lepton tt\u00af events, exploiting the c-jets fromthe hadronic W -boson decays using techniques similar to those in ref. [99]. The mis-tag ratefor light-jets is measured using a negative-tag method similar to that in ref. [100] appliedto Z+ jets events. The uncertainty in tagging b-jets is 2%\u201310% depending on the workingpoint and jet pT. The uncertainty in mis-tagging c-jets (light-jets) is 10%\u201325% (15%\u201350%)depending on the working point and jet pT. For the calibration of the four working pointsused in this analysis, a large number of uncertainty components are considered, yielding 45,20, and 20 uncorrelated sources of uncertainty for b-, c- and light-jets, respectively.Uncertainties associated with leptons arise from the trigger, reconstruction, identifica-tion, and isolation, as well as the lepton momentum scale and resolution. Efficiencies aremeasured and scale and resolution calibrations are performed using leptons in Z \u2192 `+`\u2212\u2013 15 \u2013JHEP06(2022)097and J\/\u03c8 \u2192 `+`\u2212 events [93, 105]. Systematic uncertainties in these measurements accountfor 22 independent sources but have only a small impact on the final result.All uncertainties related to the energy scales or resolution of the reconstructed objectsare propagated to the calculation of the missing transverse momentum. Three additionaluncertainties associated with the scale and resolution of the soft term are also included. Asthe missing transverse momentum is only used for event reconstruction and not for eventselection, these uncertainties have a minimal impact.6.2 Theoretical modelling uncertaintiesUncertainties in the predicted SM tt\u00afH signal cross-section are evaluated with a particularfocus on the impact on STXS bins. An uncertainty of \u00b13.6% from varying the PDF and \u03b1s inthe fixed-order calculation is applied [20, 113\u2013117]. The effect of PDF variations on the shapeof the distributions considered in this analysis is found to be negligible. Uncertainties in theHiggs boson branching fractions are also considered, and amount to 2.2% for the bb\u00af decaymode [20]. An uncertainty related to the amount of initial-state radiation (ISR) is estimatedby simultaneously varying the renormalisation and factorisation scales in the ME and \u03b1ISRsin the PS [118], while an uncertainty related to final-state radiation (FSR) is estimated byvarying \u03b1FSRs in the PS. The nominal PowhegBox+Pythia 8 sample is also comparedwith the PowhegBox+Herwig 7 sample to assess an uncertainty related to the choice ofPS and hadronisation model, and with the MadGraph5_aMC@NLO+Pythia 8 sampleto assess an uncertainty arising from changing the NLO matching procedure (sample detailsare given in table 1). Uncertainties due to missing higher-order terms in the perturbativeQCD calculations affecting the total cross-section and event migration between STXS binsare estimated by varying the renormalisation and factorisation scales independently by afactor of two, as well as evaluating the ISR and FSR uncertainties. The largest effect wasfound to originate from the ISR uncertainty, corresponding to a 9.2% variation of the totalcross-section, leading to an uncertainty of 10%\u201317% in bin migrations estimated using theStewart-Tackmann procedure [119]. All signal uncertainties are correlated across STXSbins, with the exception of bin migration uncertainties.The systematic uncertainties affecting the tt\u00af + jets background modelling are sum-marised in table 3. An uncertainty of 6% is assumed for the inclusive tt\u00af productioncross-section predicted at NNLO+NNLL, including effects from varying the factorisationand renormalisation scales, the PDFs, \u03b1s, and the top-quark mass [46\u201352]. This uncertaintyis applied to tt\u00af+ light samples only, since this component is dominant in tt\u00af production inthe full phase-space. An uncertainty of 100% in the normalisation of tt\u00af + \u22651c events isapplied, motivated by the fitted value of this normalisation in the previous analysis [27]. Thenormalisation of tt\u00af+\u22651b is allowed to float freely in the signal extraction fit. The tt\u00af+\u22651b,tt\u00af+\u22651c and tt\u00af+ light processes are affected by different types of uncertainties: tt\u00af+ lightprofits from relatively precise measurements in data; tt\u00af+\u22651b and tt\u00af+\u22651c can have similaror different diagrams depending on the precision of the ME and the flavour scheme used forthe PDF, and the different masses of the c- and b-quarks contribute to additional differencesbetween these two processes. For these reasons, all uncertainties in the tt\u00af+ jets background\u2013 16 \u2013JHEP06(2022)097Uncertainty source Description Componentstt\u00af cross-section \u00b16% tt\u00af+ lighttt\u00af+\u22651b normalisation Free-floating tt\u00af+\u22651btt\u00af+\u22651c normalisation \u00b1100% tt\u00af+\u22651cNLO matching MadGraph5_aMC@NLO+Pythia 8 vs PowhegBox+Pythia 8 AllPS & hadronisation PowhegBox+Herwig 7 vs PowhegBox+Pythia 8 AllISR Varying \u03b1ISRs (PS), \u00b5r&\u00b5f (ME)in PowhegBoxRes+Pythia 8 tt\u00af+\u22651bin PowhegBox+Pythia 8 tt\u00af+\u22651c, tt\u00af+ lightFSR Varying \u03b1FSRs (PS)in PowhegBoxRes+Pythia 8 tt\u00af+\u22651bin PowhegBox+Pythia 8 tt\u00af+\u22651c, tt\u00af+ lighttt\u00af+\u22651b fractions PowhegBox+Herwig 7 vs PowhegBox+Pythia 8 tt\u00af+ 1b, tt\u00af+\u22652bpbbT shape Shape mismodelling measured from data tt\u00af+\u22651bTable 3. Summary of the sources of systematic uncertainty for tt\u00af+ jets modelling. The systematicuncertainties listed in the second section of the table are evaluated in such a way as to have noimpact on the normalisation of the three tt\u00af + \u22651b, tt\u00af + \u22651c, and tt\u00af + light components in thephase-space selected in this analysis. The last column of the table indicates the tt\u00af+ jets componentsto which a systematic uncertainty is assigned. All systematic uncertainty sources are treated asuncorrelated across the three components.modelling are assigned independent nuisance parameters for the tt\u00af + \u22651b, tt\u00af + \u22651c andtt\u00af+ light processes. The effect of PDF uncertainties was found to be negligible.Systematic uncertainties in the acceptance and the distribution shapes are extractedfrom comparisons between the nominal prediction and different samples or settings. Thefraction of tt\u00af+\u22651b events in the selected phase-space in all alternative samples is reweightedto match the fraction in the nominal sample. This is to allow the normalisation of tt\u00af+\u22651bto be driven solely by the free-floating parameter in the signal extraction fit to data. Thesystematic uncertainties related to varying the amount of ISR, the amount of FSR, the PSand hadronisation model, and the NLO matching procedure are estimated using the sameprocedure as for tt\u00afH, comparing the nominal prediction with alternative samples. In thespecific case of tt\u00af+\u22651b, relative uncertainties are used to estimate the effect of changing thePS and hadronisation model or the NLO matching procedure by comparing predictions fromthe NLO tt\u00af generators (see table 3). These comparisons are made using predictions in whichthe additional b-quarks were generated at leading-log precision from gluon splitting. A checkwas performed, comparing the nominal tt\u00af+\u22651b prediction with a smaller tt\u00af+ bb\u00af sampleproduced with Sherpa 2.2.1. The size of the difference estimated from tt\u00af samples wasobserved to be generally the same as or larger than the difference between the two tt\u00af+bb\u00af NLOpredictions. The impact of these uncertainties on the final results is reported in section 7.Special consideration is given to the correlation of modelling uncertainties acrossdifferent pHT bins, in order to provide the fit with enough flexibility to cover backgroundmismodelling without biasing the signal extraction. The tt\u00af+\u22651b NLO matching uncertaintyis shown to depend on pHT and is therefore decorrelated across pT bins in the SRs. The NLOmatching uncertainty and the PS and hadronisation uncertainties for tt\u00af+\u22651b are further\u2013 17 \u2013JHEP06(2022)0970 100 200 300 400 500 600 [GeV]THiggs boson candidate p0.50.7511.25 Data \/ Pred. 020040060080010001200EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SRPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)0 100 200 300 400 500 600 [GeV]THiggs boson candidate p0.50.7511.25 Data \/ Pred. 0200040006000800010000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SRPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)300 350 400 450 500 550 600 [GeV]THiggs boson candidate p0.50.7511.25 Data \/ Pred. 02004006008001000EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSRPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(c)Figure 4. Pre-fit distributions of the reconstructed Higgs boson candidate pHT for the (a) dileptonSR\u22654j\u22654b, (b) single-lepton resolved SR\u22656j\u22654b and (c) single-lepton boosted SRboosted signal regions. Thedashed line shows the tt\u00afH signal distribution normalised to the total background prediction. Theuncertainty band includes all uncertainties and their correlations, except for the uncertainty in thek(tt\u00af+\u22651b) normalisation factor which is not defined pre-fit. The last bin includes the overflow.decorrelated between the single-lepton and dilepton channels in order to avoid the transferof constraints from the single-lepton resolved channel to the dilepton channel, which isless sensitive to the high-pHT regime and produces less additional radiation. The pre-fitdistributions of the reconstructed pHT are shown in figure 4. An additional uncertainty isderived for the tt\u00af + \u22651b sample to cover the mismodelling observed in this distribution.After removing the overall normalisation difference by scaling the tt\u00af + \u22651b backgroundin the dilepton SR\u22654j\u22654b (single-lepton SR\u22656j\u22654b), a weight is computed in each reconstructedpHT bin of the dilepton SR\u22654j\u22654b (single-lepton SR\u22656j\u22654b), which corrects the predicted tt\u00af+\u22651bcontribution so that the data and background model yields agree in each pHT bin. Thederived weights are not applied to the nominal sample: instead they define the +1\u03c3 variationof an additional uncertainty in the tt\u00af + \u22651b sample in each reconstructed pHT bin. Theweights derived in the single-lepton resolved channel are also applied in the boosted channel.This uncertainty enters the signal extraction fit as a single nuisance parameter (pbbT shape),correlated across all channels, such that a pull of +1\u03c3 corresponds to applying this weight,effectively correcting the reconstructed pHT spectrum.To account for variations in the tt\u00af + \u22651b subcomponent fractions found in differentpredictions, an additional nuisance parameter is introduced to cover the largest discrepancybetween two models for the fraction of tt\u00af + 1b and tt\u00af + \u22652b. The 1\u03c3 variation of thisnuisance parameter corresponds to reducing the amount of tt\u00af+\u22652b by 13% and increasingthe amount of tt\u00af+ 1b by 22%. This uncertainty is correlated across all regions, and impactseach region differently due to the varying compositions of tt\u00af+\u22651b.An uncertainty of 5% is considered for the cross-sections of the three single-top pro-duction modes [55, 56, 120, 121]. Uncertainties associated with the PS and hadronisationmodel, and with the NLO matching scheme, are evaluated by comparing, for each process,\u2013 18 \u2013JHEP06(2022)097the nominal PowhegBox+Pythia 8 sample with a sample produced using PowhegBox+Herwig 7 and MadGraph5_aMC@NLO+Pythia 8, respectively. The uncertaintyassociated with the interference between tW and tt\u00af production at NLO [59] is assessedby comparing the nominal PowhegBox+Pythia 8 sample produced using the diagramremoval scheme with the alternative sample produced with the same generator but usingthe diagram subtraction scheme.An uncertainty of 40% is assumed for the W+ jets cross-section, with an additional 30%normalisation uncertainty used for W+ heavy-flavour jets, taken as uncorrelated betweenevents with two and more than two heavy-flavour jets. These uncertainties are based onvariations of the factorisation and renormalisation scales and of the Sherpa matchingparameters. An uncertainty of 35% is applied to the Z+ jets normalisation, uncorrelatedacross jet bins, to account for both the variations of the scales and Sherpa matchingparameters and the uncertainty in the extraction from data of the correction factor for theheavy-flavour component.The uncertainty in the tt\u00afV NLO cross-section prediction is 15% [122], split into PDF andscale uncertainties as for tt\u00afH . An additional tt\u00afV modelling uncertainty, related to the choiceof PS and hadronisation model and NLO matching scheme is assessed by comparing thenominal MadGraph5_aMC@NLO+Pythia 8 samples with alternative ones generatedwith Sherpa.The uncertainty in the diboson background is assumed to be 50%, which includesuncertainties in the inclusive cross-section and additional jet production [123]. A 50%normalisation uncertainty is considered for the four-top background, covering effects fromvarying the factorisation and renormalisation scales, the PDFs and \u03b1s [124].The small backgrounds from tZq and tWZ are each assigned cross-section uncertainties;for tZq two uncertainties are used, 7.9% accounting for factorisation and renormalisationscale variations and 0.9% accounting for PDFs, and for tWZ a single uncertainty of 50%is used [124]. Uncertainties in the associated production of a single top quark and aHiggs boson include factorisation and renormalisation scale variations as well as PDFuncertainties: they amount to +6.5\/\u2212 14.9% (+6.5\/\u2212 6.7%) and \u00b13.7% (\u00b16.3%) for tHjb(tWH), respectively [20]. Finally, a 25% normalisation uncertainty is considered for thefake lepton background in the dilepton channel.7 ResultsThe distributions of the classification BDT in the signal regions, and the event yield orthe \u2206Ravgbb distributions in the dilepton or single-lepton control regions, respectively, arecombined in a profile likelihood fit to extract the signal, while simultaneously determiningthe yields and constraining the normalisation and shape of differential distributions of themost important background components. The binning of these distributions is optimised tomaximise the analysis sensitivity while keeping the total MC statistical uncertainty in eachbin to a level adjusted to avoid biases due to fluctuations in the predicted number of events.The statistical analysis is based on a binned likelihood function L(\u00b5, \u03b8) constructedas a product of Poisson probability terms over all bins considered in the analysis. The\u2013 19 \u2013JHEP06(2022)097likelihood function depends on the signal-strength parameter \u00b5, defined as \u00b5 = \u03c3\/\u03c3SM(where \u03c3 is the measured cross-section and \u03c3SM is the Standard Model prediction), and aset of nuisance parameters \u03b8 which characterise the effects of systematic uncertainties inthe signal and background expectations. They are implemented in the likelihood functionas Gaussian or Poisson priors, with the exception of the unconstrained normalisation factork(tt\u00af + \u22651b) for the tt\u00af + \u22651b background, of which no prior knowledge from theory orsubsidiary measurements is assumed. The statistical uncertainty in the prediction, whichincorporates the statistical uncertainty arising from the limited number of simulated events,is included in the likelihood in the form of additional nuisance parameters, one for each ofthe considered bins. In the statistical analysis, the number of events expected in a givenbin depends on \u00b5 and \u03b8. The nuisance parameters \u03b8 adjust the expectations for signaland background according to the corresponding systematic uncertainties, and their fittedvalues correspond to the amount that best fits the data. The test statistic t\u00b5 is definedas the profile likelihood ratio: t\u00b5 = \u22122 ln(L(\u00b5, \u02c6\u02c6\u03b8\u00b5)\/L(\u00b5\u02c6, \u03b8\u02c6)), where \u00b5\u02c6 and \u03b8\u02c6 are the valuesof the parameters that maximise the likelihood function, and \u02c6\u02c6\u03b8\u00b5 are the values of thenuisance parameters that maximise the likelihood function for a given value of \u00b5 [125]. Thistest statistic, as implemented in the RooStat framework [126, 127], is used to assess thecompatibility of the observed data with the background-only hypothesis (i.e. for \u00b5 = 0) andto make statistical inferences about \u00b5. The uncertainty in the best-fit value of the signalstrength is extracted by finding the values of \u00b5 that correspond to varying t\u00b5 by one unit.Tables 4 and 5 show the observed and predicted signal and background event yields inall SRs and CRs before and after the inclusive fit to data. Post-fit values are summarisedin figure 5, where the precision increases post-fit due to profiling and the uncertaintiescan be observed to increase as a function of pHT , ranging from 2% to 12%. The SR BDTdistributions are presented in figures 6 and 7, while the \u2206Ravgbb distributions in the single-lepton resolved CRs are shown in figure 8. All distributions are compatible with the data.The normalisation factor for the tt\u00af+\u22651b background is found to be k(tt\u00af+\u22651b) = 1.28\u00b10.08.The best-fit \u00b5 value is\u00b5 = 0.35\u00b1 0.20 (stat.)+0.30\u22120.28 (syst.) = 0.35+0.36\u22120.34,corresponding to an observed (expected) significance of 1.0 (2.7) standard deviations withrespect to the background-only hypothesis. This observed inclusive \u00b5 and its uncertainty,common to all channels, cannot affect the shape of the signal distributions but only theirnormalisation, leading to post-fit signal yield uncertainties very close to 100%.The statistical uncertainty is obtained by repeating the fit to data after fixing allnuisance parameters to their post-fit values, with the exception of the free normalisationfactors in the fit: k(tt\u00af + \u22651b) and \u00b5. The total systematic uncertainty is obtained bysubtracting the statistical variance from the total variance, i.e. \u03c3syst =\u221a\u03c32tot \u2212 \u03c32stat. Theexpected significance is computed from a fit to a pseudo-dataset, built using the pulls fromthe nominal fit when fixing \u00b5 = 1. Figure 9 shows the event yield in data compared withthe post-fit prediction for all events entering the analysis selection, grouped and ordered bythe signal-to-background ratio of the corresponding final-discriminant bins.\u2013 20 \u2013JHEP06(2022)097SR\u22654j\u22654b, pHT \u2208 [0,120) GeV SR\u22654j\u22654b, pHT \u2208 [120,200) GeV SR\u22654j\u22654b, pHT \u2208 [200,300) GeV SR\u22654j\u22654b, pHT \u2208 [300,\u221e) GeVPre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fittt\u00afH 33.6\u00b1 4.1 12\u00b1 12 15.6\u00b1 1.8 5.5\u00b1 5.3 7.71\u00b1 0.89 2.7\u00b1 2.6 3.72\u00b1 0.44 1.3\u00b1 1.3tH 0.249\u00b1 0.065 0.249\u00b1 0.064 0.148\u00b1 0.063 0.146\u00b1 0.061 0.043\u00b1 0.032 0.043\u00b1 0.031 0.031\u00b1 0.027 0.031\u00b1 0.025tt\u00af+\u22651b 432\u00b1 59 546\u00b1 24 203\u00b1 27 263\u00b1 12 92\u00b1 14 116.9\u00b1 8.8 42\u00b1 15 37.9\u00b1 6.0tt\u00af+\u22651c 27\u00b1 29 48.5\u00b1 9.1 11\u00b1 12 16.3\u00b1 5.0 4.0\u00b1 4.2 6.5\u00b1 1.4 1.9\u00b1 2.1 3.69\u00b1 0.96tt\u00af + Z 12.5\u00b1 2.0 12.6\u00b1 2.0 7.4\u00b1 1.6 7.6\u00b1 1.6 4.18\u00b1 0.72 4.15\u00b1 0.70 2.05\u00b1 0.45 2.06\u00b1 0.44tt\u00af + W 0.75\u00b1 0.31 0.75\u00b1 0.31 0.38\u00b1 0.12 0.40\u00b1 0.11 0.27\u00b1 0.12 0.27\u00b1 0.11 0.124\u00b1 0.068 0.127\u00b1 0.068tt\u00af + light 3.6\u00b1 4.9 4.8\u00b1 6.2 0.97\u00b1 0.96 0.92\u00b1 0.74 0.46\u00b1 0.65 0.41\u00b1 0.47 0.22\u00b1 0.31 0.22\u00b1 0.25tt\u00aftt\u00af 3.1\u00b1 1.5 3.0\u00b1 1.5 2.4\u00b1 1.2 2.3\u00b1 1.2 1.38\u00b1 0.70 1.36\u00b1 0.69 0.81\u00b1 0.41 0.79\u00b1 0.40Fakes 3.7\u00b1 1.1 3.7\u00b1 1.1 1.33\u00b1 0.51 1.33\u00b1 0.51 0.40\u00b1 0.23 0.40\u00b1 0.23 0.57\u00b1 0.30 0.57\u00b1 0.30Other sources 19.1\u00b1 6.9 19.3\u00b1 7.0 7.1\u00b1 4.4 7.7\u00b1 4.3 4.3\u00b1 4.0 4.5\u00b1 4.1 2.0\u00b1 1.5 2.0\u00b1 1.5Total 536\u00b1 71 651\u00b1 21 249\u00b1 32 305\u00b1 11 114\u00b1 16 137.2\u00b1 8.1 53\u00b1 15 48.7\u00b1 5.7Data 647 306 135 48CR3j3b hi CR\u22654j3b hi CR\u22654j3b loPre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fittt\u00afH 25.2\u00b1 3.1 8.8\u00b1 8.6 117\u00b1 13 42\u00b1 41 76.4\u00b1 8.4 27\u00b1 27tH 1.26\u00b1 0.15 1.23\u00b1 0.15 2.06\u00b1 0.39 2.02\u00b1 0.38 1.19\u00b1 0.53 1.12\u00b1 0.50tt\u00af+\u22651b 1900\u00b1 510 2010\u00b1 130 2810\u00b1 300 4070\u00b1 210 1730\u00b1 210 2550\u00b1 160tt\u00af+\u22651c 350\u00b1 360 550\u00b1 130 700\u00b1 710 1190\u00b1 240 1500\u00b1 1500 2550\u00b1 470tt\u00af + Z 11.1\u00b1 1.8 10.8\u00b1 1.7 57.1\u00b1 7.4 57.5\u00b1 7.3 51.7\u00b1 7.0 52.3\u00b1 6.7tt\u00af + W 1.88\u00b1 0.59 1.84\u00b1 0.55 10.8\u00b1 1.6 10.9\u00b1 1.6 21.5\u00b1 3.7 21.8\u00b1 3.4tt\u00af + light 128\u00b1 74 119\u00b1 61 200\u00b1 120 210\u00b1 120 850\u00b1 350 900\u00b1 340tt\u00aftt\u00af 0.047\u00b1 0.026 0.044\u00b1 0.024 12.3\u00b1 6.1 12.0\u00b1 6.1 8.9\u00b1 4.5 8.8\u00b1 4.4Fakes 6.3\u00b1 1.8 6.4\u00b1 1.8 47\u00b1 12 47\u00b1 12 56\u00b1 14 56\u00b1 14Other sources 125\u00b1 35 125\u00b1 34 211\u00b1 62 211\u00b1 62 251\u00b1 73 257\u00b1 73Total 2540\u00b1 630 2835\u00b1 54 4160\u00b1 810 5855\u00b1 79 4500\u00b1 1600 6431\u00b1 83Data 2827 5865 6429Table 4. Pre-fit and post-fit event yields in the dilepton channel (top) signal regions and (bottom)control regions. Post-fit yields are after the inclusive fit in all channels. All uncertainties are included,taking into account correlations in the post-fit case. The k(tt\u00af + \u22651b) uncertainty is not definedpre-fit and therefore only included in the post-fit uncertainties. For the tt\u00afH signal, the pre-fit yieldvalues correspond to the theoretical prediction and corresponding uncertainties, while the post-fityield and uncertainties correspond to those in the inclusive signal-strength measurement. \u2018Othersources\u2019 refers to s-channel, t-channel, tW , tWZ, tZq, Z+ jets and diboson events.The global goodness of fit, including all input variables to the classification BDTs andto a fit using the saturated model [128], is 92%, validating the good post-fit modellingachieved. Some of the most important variables used in the classification BDTs are shownin figure 10 for the dilepton channel, figure 11 for the single-lepton resolved channel andfigure 12 for the single-lepton boosted channel. The probability of obtaining a level ofagreement worse than observed between the fitted predictions and data was evaluated bycalculating the \u03c72 for a given number of degrees of freedom and integrating the cumulativeprobability distribution to +\u221e. For all channels combined, the mean probability is 80% forclassification BDT outputs and 60% for classification BDT training variables in all SRs.Post-fit distributions of the reconstructed Higgs boson candidate mass in the three SRs areshown in figure 13.\u2013 21 \u2013JHEP06(2022)097SR\u22656j\u22654b, pHT \u2208 [0,120) GeV SR\u22656j\u22654b, pHT \u2208 [120,200) GeV SR\u22656j\u22654b, pHT \u2208 [200,300) GeV SR\u22656j\u22654b, pHT \u2208 [300,450) GeV SR\u22656j\u22654b, pHT \u2208 [450,\u221e) GeVPre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fittt\u00afH 213\u00b129 76\u00b173 113\u00b115 40\u00b139 59.9\u00b17.8 21\u00b121 13.9\u00b12.0 4.8\u00b14.7 3.09\u00b10.49 1.0\u00b11.0tH 3.01\u00b10.47 3.03\u00b10.44 1.81\u00b10.44 1.83\u00b10.40 1.24\u00b10.26 1.28\u00b10.25 0.26\u00b10.17 0.25\u00b10.14 0.000\u00b10.041 0.000\u00b10.041tt\u00af+\u22651b 3160\u00b1490 4530\u00b1160 1530\u00b1210 2050\u00b180 720\u00b1130 865\u00b147 215\u00b161 240\u00b122 55\u00b127 44.7\u00b18.5tt\u00af+\u22651c 510\u00b1540 930\u00b1190 220\u00b1230 401\u00b183 96\u00b1100 176\u00b136 26\u00b127 45\u00b110 6.9\u00b17.5 12.8\u00b13.2tt\u00af + W 7.0\u00b11.2 7.1\u00b11.1 4.31\u00b10.90 4.37\u00b10.84 2.47\u00b10.52 2.49\u00b10.46 1.05\u00b10.32 1.06\u00b10.30 0.47\u00b10.15 0.47\u00b10.14tt\u00af + Z 77\u00b111 77\u00b110 44.6\u00b16.6 45.6\u00b16.3 30.1\u00b14.9 30.8\u00b14.8 11.5\u00b12.4 11.6\u00b12.3 2.05\u00b10.64 2.11\u00b10.64tt\u00af + light 180\u00b1120 220\u00b1130 85\u00b158 97\u00b158 37\u00b123 43\u00b125 10.5\u00b17.7 11.6\u00b17.8 2.2\u00b12.1 2.7\u00b12.3Single top tW 71\u00b140 76\u00b142 40\u00b126 42\u00b127 17.9\u00b17.6 18.3\u00b17.7 8.5\u00b17.9 9.5\u00b19.0 6.0\u00b15.3 6.0\u00b15.4tt\u00aftt\u00af 15.9\u00b18.0 15.7\u00b17.9 12.6\u00b16.3 12.5\u00b16.3 7.7\u00b13.9 7.6\u00b13.8 2.7\u00b11.4 2.7\u00b11.3 0.98\u00b10.50 0.95\u00b10.49Other top sources 46\u00b124 45\u00b124 23\u00b116 24\u00b116 13\u00b110 12.7\u00b19.6 4.3\u00b12.8 4.4\u00b12.6 1.08\u00b10.54 1.06\u00b10.54V & V V + jets 60\u00b124 60\u00b123 29\u00b111 29\u00b111 19.7\u00b18.3 19.7\u00b17.9 7.8\u00b13.4 7.8\u00b13.2 1.90\u00b10.88 1.84\u00b10.82Total 4350\u00b1810 6045\u00b182 2100\u00b1360 2745\u00b148 1000\u00b1180 1198\u00b133 301\u00b172 339\u00b116 80\u00b129 73.7\u00b17.2Data 6047 2742 1199 331 75SRboosted, pHT \u2208 [300,450) GeV SRboosted, pHT \u2208 [450,\u221e) GeV CR5j\u22654b lo CR5j\u22654b hiPre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fit Pre-fit Post-fittt\u00afH 35.1\u00b14.1 12\u00b112 8.5\u00b11.1 3.0\u00b12.9 61.7\u00b18.1 21\u00b121 62.1\u00b18.6 22\u00b121tH 1.31\u00b10.31 1.31\u00b10.29 0.42\u00b10.10 0.42\u00b10.10 3.15\u00b10.41 3.15\u00b10.40 3.16\u00b10.42 3.18\u00b10.42tt\u00af+\u22651b 246\u00b149 295\u00b123 55\u00b124 52.5\u00b19.3 1370\u00b1160 1581\u00b189 1000\u00b1150 1118\u00b151tt\u00af+\u22651c 84\u00b190 156\u00b135 21\u00b123 38\u00b110 390\u00b1410 650\u00b1140 56\u00b159 93\u00b123tt\u00af + W 1.86\u00b10.39 1.88\u00b10.35 0.55\u00b10.18 0.56\u00b10.17 2.53\u00b10.53 2.58\u00b10.45 0.54\u00b10.13 0.53\u00b10.12tt\u00af + Z 10.7\u00b12.1 10.9\u00b12.1 2.21\u00b10.60 2.32\u00b10.59 26.4\u00b13.7 26.0\u00b13.5 23.5\u00b13.4 23.0\u00b13.2tt\u00af + light 56\u00b126 61\u00b125 17\u00b110 16.2\u00b17.3 260\u00b1120 270\u00b195 22\u00b116 20\u00b114Single top tW 13.1\u00b18.0 13.7\u00b18.3 6.1\u00b15.8 5.3\u00b14.6 58\u00b132 59\u00b132 27\u00b120 28\u00b120tt\u00aftt\u00af 1.76\u00b10.89 1.74\u00b10.88 0.42\u00b10.22 0.41\u00b10.21 0.33\u00b10.17 0.31\u00b10.16 0.24\u00b10.13 0.23\u00b10.12Other top sources 4.3\u00b13.2 4.4\u00b13.1 0.80\u00b10.78 0.82\u00b10.77 41\u00b116 41\u00b116 27\u00b111 26\u00b110V & V V + jets 12.4\u00b15.7 12.4\u00b15.4 4.3\u00b12.3 4.2\u00b12.0 42\u00b116 41\u00b115 24.2\u00b18.8 24.2\u00b18.5Total 470\u00b1120 571\u00b122 117\u00b138 123.8\u00b19.7 2260\u00b1490 2694\u00b153 1250\u00b1170 1359\u00b136Data 581 118 2696 1362Table 5. Pre-fit and post-fit event yields in the single-lepton (top) resolved signal regions and(bottom) boosted signal regions and control regions. Post-fit yields are after the inclusive fit in allchannels. All uncertainties are included, taking into account correlations in the post-fit case. Thek(tt\u00af+\u22651b) uncertainty is not defined pre-fit and therefore only included in the post-fit uncertainties.For the tt\u00afH signal, the pre-fit yield values correspond to the theoretical prediction and correspondinguncertainties, while the post-fit yield and uncertainties correspond to those in the inclusive signal-strength measurement. \u2018Other top sources\u2019 refers to s-channel, t-channel, tWZ and tZq events.The probability of the obtained signal strength being compatible with the SM predictionis 8.5%, estimated by redoing the fit while fixing \u00b5 = 1. The measured signal strength iscompatible with that obtained previously [27] from part of the dataset, and the impact ofsystematic uncertainties has been reduced by about a factor of two. The main improvementscome from improved theoretical knowledge in tt\u00af+\u22651b modelling, from the much larger sizeof simulated event samples for systematic uncertainty estimation as well as from the refinedb-tagging scale factors and jet energy scale and resolution measurements.\u2013 22 \u2013JHEP06(2022)097 [0,120) GeV\u2208 HT, p4j\u22654b\u2265SR [120,200) GeV\u2208 HT, p4j\u22654b\u2265SR [200,300) GeV\u2208 HT, p4j\u22654b\u2265SR) GeV\u221e [300,\u2208 HT, p4j\u22654b\u2265SR 3j3b hiCR 4j\u22653b hiCR 4j\u22653b loCR0.80.911.1 Data \/ Pred. 110210310410510EventsATLAS  -1 = 13 TeV, 139 fbsDileptonPost-FitData Htt 1b\u2265 + tttH 1c\u2265 + tt  + Vtt + lighttt Other Uncertainty(a) [0,120) GeV\u2208 HT, p4b\u22656j\u2265SR [120,200) GeV\u2208 HT, p4b\u22656j\u2265SR [200,300) GeV\u2208 HT, p4b\u22656j\u2265SR [300,450) GeV\u2208 HT, p4b\u22656j\u2265SR) GeV\u221e [450,\u2208 HT, p4b\u22656j\u2265SR [300,450) GeV\u2208 HT, pboosted   SR) GeV\u221e [450,\u2208 HT, pboosted   SR4b lo\u22655jCR4b hi\u22655jCR0.80.911.1 Data \/ Pred. 110210310410510EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonPost-FitData Htt 1b\u2265 + tttH 1c\u2265 + tt  + Vtt + lighttt Other Uncertainty(b)Figure 5. Comparison of predicted and observed event yields in each of the control and signalregions in the (a) dilepton and (b) single-lepton channels after the fit to the data. The uncertaintyband includes all uncertainties and their correlations.1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.50.7511.25 Data \/ Pred. 050100150200250300350400450EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SR [0,120) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.50.7511.25 Data \/ Pred. 050100150200250300350EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SR [120,200) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.50.7511.25 Data \/ Pred. 020406080100120140160180EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SR [200,300) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(c)1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.50.7511.25 Data \/ Pred. 010203040506070EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SR) GeV\u221e [300,\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(d)Figure 6. Comparison between data and prediction for the BDT discriminant in the dileptonSRs after the inclusive fit to the data, for (a) 0 \u2264 pHT < 120GeV, (b) 120 \u2264 pHT < 200GeV, (c)200 \u2264 pHT < 300GeV and (d) pHT \u2265 300GeV. The tt\u00afH signal yield (solid red) is normalised to thefitted \u00b5 value from the inclusive fit. The dashed line shows the tt\u00afH signal distribution normalisedto the total background prediction. The uncertainty band includes all uncertainties and theircorrelations.The fit is repeated with three independent signal strengths, one each for the dilepton,single-lepton resolved and single-lepton boosted channels. Figure 14 shows the \u00b5 valueobtained for each channel and the single signal strength from the previous fit. In this fit thenormalisation factor for the tt\u00af+\u22651b background is found to be k(tt\u00af+\u22651b) = 1.27\u00b1 0.08,compatible with the value in the single \u00b5 fit. The probability of obtaining a discrepancybetween these three \u00b5 values equal to or larger than the one observed is 90%.The measurement is largely dominated by systematic uncertainties. Their contributionsto the fit to \u00b5 are reported in table 6. The dominant impact comes from the modelling ofthe tt\u00af + \u22651b background, followed by the signal modelling, tW modelling and b-taggingefficiency uncertainties. The largest observed pull on systematic uncertainties, as shown infigure 15, is seen in the tt\u00af+\u22651b ISR uncertainty, and is about 1.2\u03c3, mostly driven by the\u2013 23 \u2013JHEP06(2022)0971\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.750.87511.125 Data \/ Pred. 05001000150020002500300035004000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR [0,120) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.750.87511.125 Data \/ Pred. 0200400600800100012001400160018002000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR [120,200) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.750.87511.125 Data \/ Pred. 0100200300400500600700800EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR [200,300) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(c)1\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Classification BDT0.750.87511.125 Data \/ Pred. 050100150200250300350EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR [300,450) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(d)    0.750.87511.125 Data \/ Pred. 020406080100120140EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR) GeV\u221e [450,\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(e)0.05\u2212 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Classification BDT0.750.87511.125 Data \/ Pred. 050100150200250EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboosted   SR [300,450) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(f)0.05\u2212 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Classification BDT0.750.87511.125 Data \/ Pred. 0102030405060708090EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboosted   SR) GeV\u221e [450,\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(g)Figure 7. Comparison between data and prediction for the BDT discriminant in the single-leptonSRs after the inclusive fit to the data. The resolved channel is shown for (a) 0 \u2264 pHT < 120GeV,(b) 120 \u2264 pHT < 200GeV, (c) 200 \u2264 pHT < 300GeV, (d) 300 \u2264 pHT < 450GeV and (e) pHT \u2265 450GeV(yield only). The boosted channel is shown for (f) 300 \u2264 pHT < 450GeV and (g) pHT \u2265 450GeV. Thett\u00afH signal yield (solid red) is normalised to the fitted \u00b5 value from the inclusive fit. The dashed lineshows the tt\u00afH signal distribution normalised to the total background prediction. The uncertaintyband includes all uncertainties and their correlations.1.6 1.8 2 2.2 2.4 2.6 2.8 3avgbbR\u22060.50.7511.251.5Data \/ Pred. 010002000300040005000600070008000Events \/ bin-widthATLAS-1 = 13 TeV, 139 fbsSingle lepton4b lo\u22655jCRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)1.6 1.8 2 2.2 2.4 2.6 2.8 3avgbbR\u22060.50.7511.251.5Data \/ Pred. 0500100015002000250030003500Events \/ bin-widthATLAS-1 = 13 TeV, 139 fbsSingle lepton4b hi\u22655jCRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)Figure 8. Comparison between data and prediction for \u2206Ravgbb after the inclusive fit to the data inthe single-lepton (a) CR5j\u22654b lo and (b) CR5j\u22654b hi control regions. The tt\u00afH signal yield (solid red)is normalised to the fitted \u00b5 value from the inclusive fit. The dashed line shows the tt\u00afH signaldistribution normalised to the total background prediction. The uncertainty band includes alluncertainties and their correlations. The first (last) bin includes the underflow (overflow).\u2013 24 \u2013JHEP06(2022)0972.6\u2212 2.4\u2212 2.2\u2212 2\u2212 1.8\u2212 1.6\u2212 1.4\u2212 1.2\u2212 1\u2212 0.8\u2212(S\/B)10log0.811.21.41.6Data \/ Bkgd=1.0) + BkgdSM\u00b5H (tt=0.35) + Bkgdfit\u00b5H (tt210310410510Events \/ 0.2Data=1.0)SM\u00b5H (tt=0.35)fit\u00b5H (ttBackgroundBkgd Unc.ATLAS  -1 = 13 TeV, 139 fbs) CombinedbH(bttSingle lepton and DileptonPost-FitFigure 9. Post-fit yields of signal (S) and total background (B) as a function of log(S\/B), comparedwith data. Final-discriminant bins in all dilepton and single-lepton analysis regions are combinedinto bins of log(S\/B), with the signal normalised to the SM prediction used for the computationof log(S\/B). The signal is then shown normalised to the best-fit value and the SM prediction.The lower frame reports the ratio of data to background, and this is compared with the expectedtt\u00afH-signal-plus-background yield divided by the background-only yield for the best-fit signal strength(solid red line) and the SM prediction (dashed orange line).renormalisation scale. This pull indicates that the data favours a softer renormalisation scalein the ME calculation, as also suggested in ref. [129] where a lower scale in tt\u00afbb\u00af calculationsgives better agreement with tt\u00afbb\u00afj calculations. This effect was shown to not affect the BDTshapes in each individual region, while correcting a mismodelling in the distribution of thenumber of jets in the event (by adjusting the amount of additional radiation), which affectsthe categorisation of events. The distributions of the number of jets in the three SRs areshown in figure 16 pre-fit and post-fit. Decorrelating this uncertainty between the dileptonand single-lepton channels leads to very similar fitted \u00b5 values and nuisance parameter pulls.Another large pull is on the reconstructed pbbT shape uncertainty in the tt\u00af + \u22651bbackground, as expected from the pre-fit mismodelling (see figure 4) and how this uncertaintyis defined: a +1\u03c3 variation corresponds to correcting the reconstructed pHT shape suchthat it agrees between data and the background model (see section 6.2). The sensitivity ofthe result to this uncertainty was tested by replacing the data-driven mismodelling withdecorrelated free-floating tt\u00af+\u22651b normalisation factors across the STXS bins and analysisregions, and no bias was observed on the fitted signal strength. The reconstructed pHTdistributions display good post-fit agreement, as shown in figure 17 (to be compared withthe pre-fit discrepancies shown in figure 4).\u2013 25 \u2013JHEP06(2022)0971\u2212 0.8\u2212 0.6\u2212 0.4\u2212 0.2\u2212 0 0.2 0.4 0.6 0.8 1Reconstruction BDT0.750.87511.125 Data \/ Pred. 020406080100120140160180200220240EventsATLAS  -1 = 13 TeV, 139 fbsDilepton [0,120)\u2208 HT, p4j\u22654b\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)0 0.5 1 1.5 2 2.5bbavg\u03b7\u22060.750.87511.1251.25Data \/ Pred. 0100200300400500600700800900Events \/ bin-widthATLAS-1 = 13 TeV, 139 fbsDilepton [0,120)\u2208 HT, p4j\u22654b\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)Figure 10. Comparison between data and prediction for (a) the reconstruction BDT score for theHiggs boson candidate identified using Higgs boson information, and (b) the average \u2206\u03b7 betweenb-tagged jets, after the inclusive fit to the data in the dilepton resolved channel for 0 \u2264 pHT < 120GeV.The tt\u00afH signal yield (solid red) is normalised to the fitted \u00b5 value from the inclusive fit. Thedashed line shows the tt\u00afH signal distribution normalised to the total background prediction. Theuncertainty band includes all uncertainties and their correlations.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Likelihood discriminant0.750.87511.125 Data \/ Pred. 05001000150020002500EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR [0,120) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)1.6 1.8 2 2.2 2.4 2.6 2.8 3avgbbR\u22060.750.87511.125 Data \/ Pred. 05001000150020002500EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SR [0,120) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)Figure 11. Comparison between data and prediction for (a) the likelihood discriminant, and (b)the average \u2206R for all possible combinations of b-tagged jet pairs, after the inclusive fit to thedata in the single-lepton resolved channel for 0 \u2264 pHT < 120GeV. The tt\u00afH signal yield (solid red)is normalised to the fitted \u00b5 value from the inclusive fit. The dashed line shows the tt\u00afH signaldistribution normalised to the total background prediction. The uncertainty band includes alluncertainties and their correlations.\u2013 26 \u2013JHEP06(2022)0970.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1Higgs candidate DNN-tagger P(H)0.50.7511.25 Data \/ Pred. 0100200300400500EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSR [300,450) GeV\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1Higgs candidate DNN-tagger P(H)0.50.7511.25 Data \/ Pred. 01020304050607080EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSR) GeV\u221e [450,\u2208 HTpPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)Figure 12. Comparison between data and prediction for the DNN P (H) output for the Higgsboson candidate after the inclusive fit to the data in the single-lepton boosted channel for (a)300 \u2264 pHT < 450GeV and (b) pHT \u2265 450GeV. The tt\u00afH signal yield (solid red) is normalised to thefitted \u00b5 value from the inclusive fit. The dashed line shows the tt\u00afH signal distribution normalisedto the total background prediction. The uncertainty band includes all uncertainties and theircorrelations.50 100 150 200 250 300Higgs boson candidate mass [GeV]0.50.7511.25 Data \/ Pred. 0100200300400500600700800EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)50 100 150 200 250 300 350 400Higgs boson candidate mass [GeV]0.50.7511.25 Data \/ Pred. 01000200030004000500060007000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)100 105 110 115 120 125 130 135 140Higgs boson candidate mass [GeV]0.50.7511.25 Data \/ Pred. 020406080100120140160180200EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(c)Figure 13. Post-fit distributions of the reconstructed Higgs boson candidate mass for the (a)dilepton SR\u22654j\u22654b, (b) single-lepton resolved SR\u22656j\u22654b and (c) single-lepton boosted SRboosted signalregions. The tt\u00afH signal yield (solid red) is normalised to the fitted \u00b5 value from the inclusive fit.The dashed line shows the tt\u00afH signal distribution normalised to the total background prediction.The uncertainty band includes all uncertainties and their correlations. The first (last) bin includesthe underflow (overflow).\u2013 27 \u2013JHEP06(2022)0972\u2212 0 2 4 6 8 10SMHtt\u03c3\/Htt\u03c3= Htt\u00b5InclusiveDileptonl+jets boostedl+jets resolved 0.41\u2212+0.430.30     0.21\u2212+0.22                          0.34\u2212+0.37      (         )0.57\u2212+0.610.32     0.42\u2212+0.45                          0.38\u2212+0.41      (         )0.65\u2212+0.690.60     0.39\u2212+0.40                          0.52\u2212+0.56      (         )0.34\u2212+0.360.35     0.20\u2212+0.20                          0.28\u2212+0.30      (         )  Tot. ( Stat.  Syst.)Total Stat.ATLASSM compatibility: 8.5%=125 GeVH, m-1=13 TeV, 139 fbsFigure 14. Fitted values of the tt\u00afH signal-strength parameter in the individual channels and inthe inclusive signal strength measurement.Additionally, a large pull is observed in the tt\u00af+\u22651b NLO matching uncertainty in thedilepton channel in the 0 \u2264 pHT < 120GeV bin, as shown in figure 15 where it is rankedsixth for the impact on this measurement. Due to the large number of nuisance parametersentering into the likelihood fit, there is a non-negligible probability that any one of thett\u00af + \u22651b NLO matching uncertainties could be pulled to a value at least as extreme asthat observed from a purely statistical standpoint. The probability is calculated to be17%. Despite the large differences between models (see section 6.2), the varying tt\u00af+\u22651bcomposition in the CRs and SRs allows the tt\u00af + \u22651b subcomponent fraction systematicuncertainty to be constrained. In general the fit mostly constrains the tt\u00af+\u22651b modellinguncertainties and the normalisation of the tt\u00af+\u22651c background, which is pulled to 0.6\u03c3 andagrees very well with the previous publication [27]. To further validate the robustness of thefit, a pseudo-data sample was built from simulated events by replacing the nominal tt\u00af+\u22651bbackground with the alternative tt\u00af+ bb\u00af MC sample generated with Sherpa 2.2.1. The fitto this pseudo-data sample did not reveal any significant bias in the signal extraction, thesignal strength being compatible with unity within uncertainties.The fit is also performed with multiple signal strengths corresponding to the five \u2018truth\u2019p\u02c6HT bins. Figure 18 shows the values obtained. The normalisation factor is found to bek(tt\u00af + \u22651b) = 1.28 \u00b1 0.08, in perfect agreement with the single \u00b5 fit value. The globalgoodness of fit is 88%, summarising the good post-fit modelling obtained. The probabilitythat the obtained signal strengths are compatible with the SM predictions is 45%, estimatedby redoing the fit while fixing the \u00b5 value to 1 in the five bins. Overall, pulls and constraintssimilar to those in the inclusive measurement are observed. The measurement is dominatedby systematic uncertainties in the lowest bin of \u2018truth\u2019 p\u02c6HT (mostly from the tt\u00af + \u22651bbackground modelling), and by statistical uncertainties in the upper three bins.Cross-section upper limits are also derived in the STXS framework. In this case, thelikelihood function is slightly different from the one used to extract signal strengths: theeffects of signal scale and PDF uncertainties on the predicted cross-section are not included\u2013 28 \u2013JHEP06(2022)097Uncertainty source \u2206\u00b5Process modellingtt\u00afH modelling +0.13 \u22120.05tt\u00af+\u22651b modellingtt\u00af+\u22651b NLO matching +0.21 \u22120.20tt\u00af+\u22651b fractions +0.12 \u22120.12tt\u00af+\u22651b FSR +0.10 \u22120.11tt\u00af+\u22651b PS & hadronisation +0.09 \u22120.08tt\u00af+\u22651b pbbT shape +0.04 \u22120.04tt\u00af+\u22651b ISR +0.04 \u22120.04tt\u00af+\u22651c modelling +0.03 \u22120.04tt\u00af+ light modelling +0.03 \u22120.03tW modelling +0.08 \u22120.07Background-model statistical uncertainty +0.04 \u22120.05b-tagging efficiency and mis-tag ratesb-tagging efficiency +0.03 \u22120.02c-mis-tag rates +0.03 \u22120.03l-mis-tag rates +0.02 \u22120.02Jet energy scale and resolutionb-jet energy scale +0.00 \u22120.01Jet energy scale (flavour) +0.01 \u22120.01Jet energy scale (pile-up) +0.00 \u22120.01Jet energy scale (remaining) +0.01 \u22120.01Jet energy resolution +0.02 \u22120.02Luminosity +0.01 \u22120.00Other sources +0.03 \u22120.03Total systematic uncertainty +0.30 \u22120.28tt\u00af+\u22651b normalisation +0.04 \u22120.07Total statistical uncertainty +0.20 \u22120.20Total uncertainty +0.36 \u22120.34Table 6. Breakdown of the contributions to the uncertainties in \u00b5. The contributions from thedifferent sources of uncertainty are evaluated after the fit. The \u2206\u00b5 values are obtained by repeatingthe fit after having fixed a certain set of nuisance parameters corresponding to a group of systematicuncertainties, and then evaluating (\u2206\u00b5)2 by subtracting the resulting squared uncertainty of \u00b5 fromits squared uncertainty found in the full fit. The same procedure is followed when quoting the effectof the tt\u00af+\u22651b normalisation. The total uncertainty is different from the sum in quadrature of thedifferent components due to correlations between nuisance parameters existing in the fit.\u2013 29 \u2013JHEP06(2022)0972\u2212 1.5\u2212 1\u2212 0.5\u2212 0 0.5 1 1.5 2\u03b8\u2206)\/0\u03b8-\u03b8(+light: PS & hadronisationtttW: NLO matchingH: cross-section (QCD scale)tt1b: ISR\u2265+tt) GeV\u221e [450,\u2208 HT1b: NLO match. ljets p\u2265+tt [300,450) GeV\u2208 HT1b: NLO match. ljets p\u2265+ttH: PS & hadronisationtttW: diagram subtraction shapebbT1b: p\u2265+tt [120,200) GeV\u2208 HT1b: NLO match. dilep p\u2265+tt1b)\u2265+tk(tH: NLO matchingtttW: PS & hadronisation1b: NLO match. CR ljets\u2265+tt [0,120) GeV\u2208 HT1b: NLO match. dilep p\u2265+tt1b: PS & hadronisation dilep\u2265+tt1b: FSR\u2265+tt1b fraction\u2265+tt [120,200) GeV\u2208 HT1b: NLO match. ljets p\u2265+tt [0,120) GeV\u2208 HT1b: NLO match. ljets p\u2265+tt0.4\u2212 0.3\u2212 0.2\u2212 0.1\u2212 0 0.1 0.2 0.3 0.4\u00b5\u2206:\u00b5Pre-fit impact on \u03b8\u2206+\u03b8 = \u03b8 \u03b8\u2206-\u03b8 = \u03b8:\u00b5Post-fit impact on \u03b8\u2206+\u03b8 = \u03b8 \u03b8\u2206-\u03b8 = \u03b8Nuis. Param. PullATLAS  -1 = 13 TeV, 139 fbsCombinedFigure 15. Ranking of the 20 nuisance parameters with the largest post-fit impact on \u00b5 in the fit.Nuisance parameters corresponding to statistical uncertainties in the simulated event samples arenot included. The empty blue rectangles correspond to the pre-fit impact on \u00b5 and the filled blueones to the post-fit impact on \u00b5, both referring to the upper scale. The impact of each nuisanceparameter, \u2206\u00b5, is computed by comparing the nominal best-fit value of \u00b5 with the result of the fitwhen fixing the considered nuisance parameter to its best-fit value, \u03b8\u02c6, shifted by its pre-fit (post-fit)uncertainties \u00b1\u2206\u03b8 (\u00b1\u2206\u03b8\u02c6). The black points show the pulls of the nuisance parameters relative totheir nominal values, \u03b80. These pulls and their relative post-fit errors, \u2206\u03b8\u02c6\/\u2206\u03b8, refer to the lowerscale. The \u2018ljets\u2019 (\u2018dilep\u2019) label refers to the single-lepton (dilepton) channel.because, while affecting the signal-strength measurements, they do not affect the cross-section measurements. Scale effects are still present in the statistical model though, via theISR uncertainty, but with no impact on the overall cross-section. The inclusive cross-sectionof 507 fb is used to calculate these limits, scaled by the fraction of events in each p\u02c6HT bin toestablish the fiducial cross-section for each STXS bin. The measured 95% confidence level(CL) cross-section upper limits in each STXS bin are shown in figure 19, where the hatcheduncertainty bands correspond to the theoretical uncertainty in the fiducial cross-sectionprediction in each bin.\u2013 30 \u2013JHEP06(2022)0974 5 6 7 8 9\u2265Number of jets0.20.611.4 Data \/ Pred. 0100200300400500600700EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SRPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)6 7 8 9 10 11 12 13\u2265Number of jets0.20.611.4 Data \/ Pred. 010002000300040005000600070008000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SRPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)4 5 6 7 8 9 10 11 12\u2265Number of small-R jets0.20.611.4 Data \/ Pred. 050100150200250300350EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSRPre-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(c)4 5 6 7 8 9\u2265Number of jets0.20.611.4 Data \/ Pred. 0100200300400500600700EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(d)6 7 8 9 10 11 12 13\u2265Number of jets0.20.611.4 Data \/ Pred. 010002000300040005000600070008000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(e)4 5 6 7 8 9 10 11 12\u2265Number of small-R jets0.20.611.4 Data \/ Pred. 050100150200250300350EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(f)Figure 16. Pre-fit (top) and post-fit (bottom) distributions of the number of jets in the (a, d)dilepton SR\u22654j\u22654b, (b, e) single-lepton resolved SR\u22656j\u22654b and (c, f) single-lepton boosted SRboosted signalregions. The tt\u00afH signal yield (solid red) is normalised to the Standard Model expectation (the fitted\u00b5 value from the inclusive fit) in the pre-fit (post-fit) distributions. The dashed line shows the tt\u00afHsignal distribution normalised to the total background prediction. The uncertainty band includes alluncertainties and their correlations, except in the pre-fit distributions where the uncertainty in thek(tt\u00af+\u22651b) normalisation factor is not defined.\u2013 31 \u2013JHEP06(2022)0970 100 200 300 400 500 600 [GeV]THiggs boson candidate p0.50.7511.25 Data \/ Pred. 020040060080010001200EventsATLAS  -1 = 13 TeV, 139 fbsDilepton4j\u22654b\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(a)0 100 200 300 400 500 600 [GeV]THiggs boson candidate p0.50.7511.25 Data \/ Pred. 0200040006000800010000EventsATLAS  -1 = 13 TeV, 139 fbsSingle lepton4b\u22656j\u2265SRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(b)300 350 400 450 500 550 600 [GeV]THiggs boson candidate p0.50.7511.25 Data \/ Pred. 02004006008001000EventsATLAS  -1 = 13 TeV, 139 fbsSingle leptonboostedSRPost-FitData HttH *tt 1b\u2265 + tttH 1c\u2265 + tt + Vtt  + lightttOther Uncertainty*: normalised to total Bkg.(c)Figure 17. Post-fit distributions of the reconstructed Higgs boson candidate pHT for the (a) dileptonSR\u22654j\u22654b, (b) single-lepton resolved SR\u22656j\u22654b and (c) single-lepton boosted SRboosted signal regions. Thett\u00afH signal yield (solid red) is normalised to the fitted \u00b5 value from the inclusive fit. The dashed lineshows the tt\u00afH signal distribution normalised to the total background prediction. The uncertaintyband includes all uncertainties and their correlations. The last bin includes the overflow.2\u2212 0 2 4 6 8 10 12SMHtt\u03c3\/Htt\u03c3= Htt\u00b5Inclusive                     ) [GeV]   \u221e [450,\u2208HTp, Htt\u00b5 [300,450) [GeV]\u2208HTp, Htt\u00b5 [200,300) [GeV]\u2208HTp, Htt\u00b5 [120,200) [GeV]\u2208HTp, Htt\u00b5 [0,120) [GeV]   \u2208HTp, Htt\u00b5 0.99\u2212+1.040.86     0.47\u2212+0.48                          0.87\u2212+0.92      (         )1.02\u2212+1.03-0.18     0.69\u2212+0.71                          0.75\u2212+0.75        (       )0.86\u2212+0.901.05     0.68\u2212+0.70                          0.53\u2212+0.57      (         )0.72\u2212+0.74-0.19     0.55\u2212+0.58                          0.47\u2212+0.45      (         )1.39\u2212+1.47-0.10     0.91\u2212+1.06                          1.05\u2212+1.03      (         )0.34\u2212+0.360.35     0.20\u2212+0.20                          0.28\u2212+0.30      (         )  Tot. ( Stat.  Syst.)Total Stat.ATLASSM compatibility: 45%=125 GeVH, m-1=13 TeV, 139 fbsFigure 18. Signal-strength measurements in the individual STXS p\u02c6HT bins, as well as the inclusivesignal strength.\u2013 32 \u2013JHEP06(2022)09710 210310 410 [fb]Htt\u03c395% CL upper limit on Inclusive                ) [GeV]   \u221e [450,\u2208 HTp [300,450) [GeV]\u2208 HTp [200,300) [GeV]\u2208 HTp [120,200) [GeV]\u2208 HTp [0,120) [GeV]   \u2208 HTp =1)\u00b5Expected (\u03c3 1\u00b1Expected \u03c3 2\u00b1Expected ObservedSM predictionATLAS =125 GeVH, m-1=13 TeV, 139 fbsFigure 19. 95% CL simplified template cross-section upper limits in the individual STXS p\u02c6HTbins, as well as the inclusive limit. The observed limits are shown (solid black lines), together withthe expected limits both in the background-only hypothesis (dotted black lines) and in the SMhypothesis (dotted red lines). In the case of the expected limits in the background-only hypothesis,one- and two-standard-deviation uncertainty bands are also shown. The hatched uncertainty bandscorrespond to the theory uncertainty in the fiducial cross-section prediction in each bin.8 ConclusionMeasurements of a Standard Model Higgs boson produced in association with a pair of topquarks and decaying into a pair of b-quarks are performed. The results are based on theRun 2 dataset of pp collision data collected at\u221as = 13TeV by the ATLAS detector at theLHC, corresponding to an integrated luminosity of 139 fb\u22121. The event selection targetstop-quark pair decays to a final state containing one or two leptons. Machine-learningtechniques are used to discriminate between signal and background events, the latter beingdominated by tt\u00af + jets production. The measured signal strength is 0.35+0.36\u22120.34, correspondingto an observed (expected) significance of 1.0 (2.7) standard deviations. The probabilityof the obtained signal strength being compatible with the SM prediction is 8.5%. Themeasurement uncertainty is dominated by systematic uncertainties, despite significantimprovement relative to the previous measurement [27], especially regarding theoreticalknowledge of the tt\u00af+\u22651b background process, which still drives the sensitivity. To furthertest the Standard Model, the first differential measurement of the tt\u00afH signal strengthwas performed in five bins of Higgs boson transverse momentum in the STXS framework,including a bin for specially selected boosted Higgs bosons with transverse momentumabove 300GeV. Upper limits on the STXS cross-sections are also derived. Observed resultsare compatible with Standard Model expectations within uncertainties.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.\u2013 33 \u2013JHEP06(2022)097We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia;BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP,Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC,China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark;IN2P3-CNRS and CEA-DRF\/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG,Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center,Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN,Norway; MEiN, Poland; FCT, Portugal; MNE\/IFA, Romania; JINR; MES of Russia andNRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZ\u0160, Slovenia;DSI\/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI,SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey;STFC, United Kingdom; DOE and NSF, United States of America. In addition, individualgroups and members have received support from BCKDF, CANARIE, Compute Canadaand CRC, Canada; COST, ERC, ERDF, Horizon 2020 and Marie Sk\u0142odowska-Curie Actions,European Union; Investissements d\u2019Avenir Labex, Investissements d\u2019Avenir Idex and ANR,France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmesco-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; NorwegianFinancial Mechanism 2014\u20132021, Norway; NCN and NAWA, Poland; La Caixa BankingFoundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenTProgrammes Generalitat Valenciana, Spain; G\u00f6ran Gustafssons Stiftelse, Sweden; TheRoyal Society and Leverhulme Trust, United Kingdom.The crucial computing support from all WLCG partners is acknowledged gratefully,in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF(Denmark, Norway, Sweden), CC-IN2P3 (France), KIT\/GridKA (Germany), INFN-CNAF(Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.),the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributorsof computing resources are listed in ref. [130].A Input variables to the classification BDTsIn this appendix, the full list of variables used as inputs to the classification BDT, describedin section 5.2, in each of the signal regions is reported. Variables are listed separately intable 7 for the dilepton channel, in table 8 for the resolved single-lepton channel and intable 9 for the single-lepton boosted channel. Variables from the reconstruction BDT exploitthe chosen jet-parton assignment (see section 5.2). The b-tagging discriminant assigned toeach jet and the large-R jets used to build the Higgs-boson and top-quark candidates inthe boosted category are defined in section 4. Some kinematic and topological variables arebuilt considering only b-tagged jets in the event. In the resolved and boosted single-leptonchannels, b-tagged jets are defined as the four jets with the largest value of the b-taggingdiscriminant. If two jets have the same b-tagging discriminant value, they are ordered bydecreasing jet pT value. In the dilepton channel, jets are required to pass the 70% workingpoint to be b-tagged.\u2013 34 \u2013JHEP06(2022)097Variable DefinitionGeneral kinematic variablesmminbb Minimum invariant mass of a b-tagged jet pairmmin \u2206Rbb Invariant mass of the b-tagged jet pair with minimum \u2206Rmmax pTjj Invariant mass of the jet pair with maximum pTmmax pTbb Invariant mass of the b-tagged jet pair with maximum pT\u2206\u03b7avgbb Average \u2206\u03b7 for all b-tagged jet pairsNHiggs 30bbNumber of b-tagged jet pairs with invariant mass within30GeV of the Higgs boson massVariables from reconstruction BDTBDT outputs Output of the reco. BDT w\/ Higgs info. for the combina-tion selected by the reco. BDTs w\/ or w\/o Higgs info. \u2021mHiggsbb Higgs candidate mass\u2206RH,tt\u00af \u2206R between Higgs candidate and tt\u00af candidate system \u2020\u2206RminH,` Minimum \u2206R between Higgs candidate and lepton\u2206RminH,bMinimum \u2206R between Higgs candidate and b-jet fromtopTable 7. Variables used in the classification BDTs in the dilepton channel. For variables dependingon b-tagged jets, only jets b-tagged using the 70% working point are considered. For variables fromthe reconstruction BDT, those with a \u2020 are from the BDT using Higgs boson information, thosewith no sign are from the BDT without Higgs boson information while for those with a \u2021 bothversions are used.\u2013 35 \u2013JHEP06(2022)097Variable DefinitionGeneral kinematic variables\u2206Ravgbb Average \u2206R for all b-tagged jet pairs\u2206Rmax pTbb \u2206R between the two b-tagged jets with the largest vector sum pT\u2206\u03b7maxjj Maximum \u2206\u03b7 between any two jetsmmin \u2206Rbb Mass of the combination of two b-tagged jets with the smallest \u2206RNHiggs 30bbNumber of b-tagged jet pairs with invariant mass within 30GeV ofthe Higgs boson massAplanarity 1.5\u03bb2, where \u03bb2 is the second eigenvalue of the momentum tensor[Phys. Rev. D 48 (1993) R3953] built with all jetsH1 Second Fox-Wolfram moment computed using all jets and the leptonVariables from reconstruction BDTBDT output Output of the reconstruction BDT \u2020mHiggsbb Higgs candidate massmH,blep top Mass of Higgs candidate and b-jet from leptonic top candidate\u2206RHiggsbb \u2206R between b-jets from the Higgs candidate\u2206RH,tt\u00af \u2206R between Higgs candidate and tt\u00af candidate system \u2020\u2206RH,lep top \u2206R between Higgs candidate and leptonic top candidateVariables from likelihood calculationsLHD Likelihood discriminantVariables from b-taggingwHiggsb-tagSum of b-tagging discriminants of jets from best Higgs candidatefrom the reconstruction BDTB3jet 3rd largest jet b-tagging discriminantB4jet 4th largest jet b-tagging discriminantB5jet 5th largest jet b-tagging discriminantTable 8. Input variables to the classification BDTs in the single-lepton resolved channel. Forvariables depending on b-tagged jets, jets are sorted by their pseudo-continuous b-tag score, andby their pT when they have the same pseudo-continuous b-tag score. For variables from thereconstruction BDT, those with a \u2020 are from the BDT using Higgs boson information, those with no\u2020 are from the BDT without Higgs boson information.\u2013 36 \u2013JHEP06(2022)097Variable DefinitionmHiggsbb Higgs candidate masspHT Higgs candidate transverse momentum\u03b7Higgslep \u03b7 of the Higgs candidate relative to the leptonP (H) DNN Higgs probability for the Higgs candidatemhad top Hadronic top candidate massphad topT Hadronic top candidate transverse momentum\u03b7lephad top \u03b7 of the hadronic top candidate relative to the leptonBihad top ith largest jet b-tagging discriminant associated to the hadronic top candidatemlep top Leptonic top candidate massplep topT Leptonic top candidate transverse momentumBlep top b-tagging discriminant of the jet associated to the leptonic top candidatenjets Small-R jets multiplicity\u2206RH,had top \u2206R between the Higgs and the hadronic top candidates\u2206RH,lep top \u2206R between the Higgs and the leptonic top candidates\u2206Rhad top,lep top \u2206R between the hadronic top and the leptonic top candidatesptt\u00afHT tt\u00afH system transverse momentumptt\u00afT tt\u00af system transverse momentumwsumb-tagSum of b-tagging discriminants of jets from Higgs, hadronic and leptonictop candidateswadd jetb-tagFraction of the sum of b-tagging discriminants of all jets not associated toHiggs or hadronic top candidatesTable 9. 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Fabbri21b,21a, G. Facini173, V. Fadeyev141, R.M. Fakhrutdinov118,S. Falciano70a, P.J. Falke22, S. Falke34, J. Faltova138, Y. Fan13a, Y. Fang13a, G. Fanourakis42,M. Fanti66a,66b, M. Faraj58c, A. Farbin7, A. Farilla72a, E.M. Farina68a,68b, T. Farooque103,S.M. Farrington48, P. Farthouat34, F. Fassi33e, D. Fassouliotis8, M. Faucci Giannelli71a,71b,W.J. Fawcett30, L. Fayard62, O.L. Fedin133,p, G. Fedotov133, M. Feickert168, L. Feligioni98,A. Fell145, C. Feng58b, M. Feng13b, M.J. Fenton166, A.B. Fenyuk118, L. Ferencz44,S.W. Ferguson41, J. Ferrando44, A. Ferrari167, P. Ferrari115, R. Ferrari68a, D. Ferrere52,C. Ferretti102, F. Fiedler96, A. Filip\u010di\u010d89, F. Filthaut114, M.C.N. Fiolhais135a,135c,a, L. Fiorini169,F. Fischer147, W.C. Fisher103, T. Fitschen19, I. Fleck147, P. Fleischmann102, T. Flick177,B.M. Flierl110, L. Flores132, M. Flores31d, L.R. Flores Castillo60a, F.M. Follega73a,73b, N. Fomin15,J.H. Foo162, B.C. Forland63, A. Formica140, F.A. F\u00f6rster12, A.C. Forti97, E. Fortin98, M.G. Foti130,\u2013 48 \u2013JHEP06(2022)097L. Fountas8, D. Fournier62, H. Fox87, P. Francavilla69a,69b, S. Francescato57, M. Franchini21b,21a,S. Franchino59a, D. Francis34, L. Franco4, L. Franconi18, M. Franklin57, G. Frattari70a,70b,A.C. Freegard90, P.M. Freeman19, W.S. Freund78b, E.M. Freundlich45, D. Froidevaux34,J.A. Frost130, Y. Fu58a, M. Fujimoto122, E. Fullana Torregrosa169, J. Fuster169, A. Gabrielli21b,21a,A. Gabrielli34, P. Gadow44, G. Gagliardi53b,53a, L.G. Gagnon16, G.E. Gallardo130, E.J. Gallas130,B.J. Gallop139, R. Gamboa Goni90, K.K. Gan123, S. Ganguly159, J. Gao58a, Y. Gao48,Y.S. Gao29,m, F.M. Garay Walls142a, C. Garc\u00eda169, J.E. Garc\u00eda Navarro169, J.A. Garc\u00eda Pascual13a,M. Garcia-Sciveres16, R.W. Gardner35, D. Garg75, R.B. Garg149, S. Gargiulo50, C.A. Garner162,V. Garonne129, S.J. Gasiorowski144, P. Gaspar78b, G. Gaudio68a, P. Gauzzi70a,70b,I.L. Gavrilenko107, A. Gavrilyuk119, C. Gay170, G. Gaycken44, E.N. Gazis9, A.A. Geanta25b,C.M. Gee141, C.N.P. Gee139, J. Geisen94, M. Geisen96, C. Gemme53b, M.H. Genest56,S. Gentile70a,70b, S. George91, W.F. George19, T. Geralis42, L.O. Gerlach51, P. Gessinger-Befurt34,M. Ghasemi Bostanabad171, A. Ghosh166, A. Ghosh75, B. Giacobbe21b, S. Giagu70a,70b,N. Giangiacomi162, P. Giannetti69a, A. Giannini67a,67b, S.M. Gibson91, M. Gignac141, D.T. Gil81b,B.J. Gilbert37, D. Gillberg32, G. Gilles115, N.E.K. Gillwald44, D.M. Gingrich2,aj,M.P. Giordani64a,64c, P.F. Giraud140, G. Giugliarelli64a,64c, D. Giugni66a, F. Giuli71a,71b,I. Gkialas8,h, P. Gkountoumis9, L.K. Gladilin109, C. Glasman95, P.C.F. Glaysher44,G.R. Gledhill127, M. Glisic127, I. Gnesi39b,d, M. Goblirsch-Kolb24, D. Godin106, S. Goldfarb101,T. Golling52, D. Golubkov118, J.P. Gombas103, A. Gomes135a,135b, R. Goncalves Gama51,R. Gon\u00e7alo135a,135c, G. Gonella127, L. Gonella19, A. Gongadze77, F. Gonnella19, J.L. Gonski37,S. Gonz\u00e1lez de la Hoz169, S. Gonzalez Fernandez12, R. Gonzalez Lopez88, C. Gonzalez Renteria16,R. Gonzalez Suarez167, S. Gonzalez-Sevilla52, G.R. Gonzalvo Rodriguez169,R.Y. Gonz\u00e1lez Andana142a, L. Goossens34, N.A. Gorasia19, P.A. Gorbounov119, H.A. Gordon27,B. Gorini34, E. Gorini65a,65b, A. Gori\u0161ek89, A.T. Goshaw47, M.I. Gostkin77, C.A. Gottardo114,M. Gouighri33b, V. Goumarre44, A.G. Goussiou144, N. Govender31c, C. Goy4,I. Grabowska-Bold81a, K. Graham32, E. Gramstad129, S. Grancagnolo17, M. Grandi152,V. Gratchev133, P.M. Gravila25f, F.G. Gravili65a,65b, H.M. Gray16, C. Grefe22, I.M. Gregor44,P. Grenier149, K. Grevtsov44, C. Grieco12, N.A. Grieser124, A.A. Grillo141, K. Grimm29,l,S. Grinstein12,v, J.-F. Grivaz62, S. Groh96, E. Gross175, J. Grosse-Knetter51, C. Grud102,A. Grummer113, J.C. Grundy130, L. Guan102, W. Guan176, C. Gubbels170, J. Guenther34,J.G.R. Guerrero Rojas169, F. Guescini111, D. Guest17, R. Gugel96, A. Guida44, T. Guillemin4,S. Guindon34, F. Guo13a, J. Guo58c, L. Guo62, Y. Guo102, Z. Guo98, R. Gupta44, S. Gurbuz22,G. Gustavino124, M. Guth52, P. Gutierrez124, L.F. Gutierrez Zagazeta132, C. Gutschow92,C. Guyot140, C. Gwenlan130, C.B. Gwilliam88, E.S. Haaland129, A. Haas121, M. Habedank44,C. Haber16, H.K. Hadavand7, A. Hadef96, S. Hadzic111, M. Haleem172, J. Haley125, J.J. Hall145,G. Halladjian103, G.D. Hallewell98, L. Halser18, K. Hamano171, H. Hamdaoui33e, M. Hamer22,G.N. Hamity48, K. Han58a, L. Han13c, L. Han58a, S. Han16, Y.F. Han162, K. Hanagaki79,t,M. Hance141, M.D. Hank35, R. Hankache97, E. Hansen94, J.B. Hansen38, J.D. Hansen38,M.C. Hansen22, P.H. Hansen38, K. Hara164, T. Harenberg177, S. Harkusha104, Y.T. Harris130,P.F. Harrison173, N.M. Hartman149, N.M. Hartmann110, Y. Hasegawa146, A. Hasib48,S. Hassani140, S. Haug18, R. Hauser103, M. Havranek137, C.M. Hawkes19, R.J. Hawkings34,S. Hayashida112, D. Hayden103, C. Hayes102, R.L. Hayes170, C.P. Hays130, J.M. Hays90,H.S. Hayward88, S.J. Haywood139, F. He58a, Y. He160, Y. He131, M.P. Heath48, V. Hedberg94,A.L. Heggelund129, N.D. Hehir90, C. Heidegger50, K.K. Heidegger50, W.D. Heidorn76,J. Heilman32, S. Heim44, T. Heim16, B. Heinemann44,ah, J.G. Heinlein132, J.J. Heinrich127,L. Heinrich34, J. Hejbal136, L. Helary44, A. Held121, C.M. Helling141, S. Hellman43a,43b,C. Helsens34, R.C.W. Henderson87, L. Henkelmann30, A.M. Henriques Correia34, H. Herde149,Y. Hern\u00e1ndez Jim\u00e9nez151, H. Herr96, M.G. Herrmann110, T. Herrmann46, G. Herten50,\u2013 49 \u2013JHEP06(2022)097R. Hertenberger110, L. Hervas34, N.P. Hessey163a, H. Hibi80, S. Higashino79,E. Hig\u00f3n-Rodriguez169, K.H. Hiller44, S.J. Hillier19, M. Hils46, I. Hinchliffe16, F. Hinterkeuser22,M. Hirose128, S. Hirose164, D. Hirschbuehl177, B. Hiti89, O. Hladik136, J. Hobbs151, R. Hobincu25e,N. Hod175, M.C. Hodgkinson145, B.H. Hodkinson30, A. Hoecker34, J. Hofer44, D. Hohn50,T. Holm22, T.R. Holmes35, M. Holzbock111, L.B.A.H. Hommels30, B.P. Honan97, J. Hong58c,T.M. Hong134, Y. Hong51, J.C. Honig50, A. H\u00f6nle111, B.H. Hooberman168, W.H. Hopkins5,Y. Horii112, L.A. Horyn35, S. Hou154, J. Howarth55, J. Hoya86, M. Hrabovsky126, A. Hrynevich105,T. Hryn\u2019ova4, P.J. Hsu61, S.-C. Hsu144, Q. Hu37, S. Hu58c, Y.F. Hu13a,13d,al, D.P. Huang92,X. Huang13c, Y. Huang58a, Y. Huang13a, Z. Hubacek137, F. Hubaut98, M. Huebner22,F. Huegging22, T.B. Huffman130, M. Huhtinen34, S.K. Huiberts15, R. Hulsken56, N. Huseynov77,z,J. Huston103, J. Huth57, R. Hyneman149, S. Hyrych26a, G. Iacobucci52, G. Iakovidis27,I. Ibragimov147, L. Iconomidou-Fayard62, P. Iengo34, R. Iguchi159, T. Iizawa52, Y. Ikegami79,A. Ilg18, N. Ilic162, H. Imam33a, T. Ingebretsen Carlson43a,43b, G. Introzzi68a,68b, M. Iodice72a,V. Ippolito70a,70b, M. Ishino159, W. Islam176, C. Issever17,44, S. Istin11c,am, J.M. Iturbe Ponce60a,R. Iuppa73a,73b, A. Ivina175, J.M. Izen41, V. Izzo67a, P. Jacka136, P. Jackson1, R.M. Jacobs44,B.P. Jaeger148, C.S. Jagfeld110, G. J\u00e4kel177, K. Jakobs50, T. Jakoubek175, J. Jamieson55,K.W. Janas81a, G. Jarlskog94, A.E. Jaspan88, T. Jav\u016frek34, M. Javurkova99, F. Jeanneau140,L. Jeanty127, J. Jejelava155a,aa, P. Jenni50,e, S. J\u00e9z\u00e9quel4, J. Jia151, Z. Jia13c, Y. Jiang58a,S. Jiggins48, J. Jimenez Pena111, S. Jin13c, A. Jinaru25b, O. Jinnouchi160, H. Jivan31f,P. Johansson145, K.A. Johns6, C.A. Johnson63, D.M. Jones30, E. Jones173, R.W.L. Jones87,T.J. Jones88, J. Jovicevic14, X. Ju16, J.J. Junggeburth34, A. Juste Rozas12,v, S. Kabana142d,A. Kaczmarska82, M. Kado70a,70b, H. Kagan123, M. Kagan149, A. Kahn37, A. Kahn132,C. Kahra96, T. Kaji174, E. Kajomovitz156, C.W. Kalderon27, A. Kamenshchikov118, M. Kaneda159,N.J. Kang141, S. Kang76, Y. Kano112, D. Kar31f, K. Karava130, M.J. Kareem163b, I. Karkanias158,S.N. Karpov77, Z.M. Karpova77, V. Kartvelishvili87, A.N. Karyukhin118, E. Kasimi158, C. Kato58d,J. Katzy44, K. Kawade146, K. Kawagoe85, T. Kawaguchi112, T. Kawamoto140, G. Kawamura51,E.F. Kay171, F.I. Kaya165, S. Kazakos12, V.F. Kazanin117b,117a, Y. Ke151, J.M. Keaveney31a,R. Keeler171, J.S. Keller32, A.S. Kelly92, D. Kelsey152, J.J. Kempster19, J. Kendrick19,K.E. Kennedy37, O. Kepka136, S. Kersten177, B.P. Ker\u0161evan89, S. Ketabchi Haghighat162,M. Khandoga131, A. Khanov125, A.G. Kharlamov117b,117a, T. Kharlamova117b,117a,E.E. Khoda144, T.J. Khoo17, G. Khoriauli172, E. Khramov77, J. Khubua155b, S. Kido80,M. Kiehn34, A. Kilgallon127, E. Kim160, Y.K. Kim35, N. Kimura92, A. Kirchhoff51,D. Kirchmeier46, C. Kirfel22, J. Kirk139, A.E. Kiryunin111, T. Kishimoto159, D.P. Kisliuk162,C. Kitsaki9, O. Kivernyk22, T. Klapdor-Kleingrothaus50, M. Klassen59a, C. Klein32, L. Klein172,M.H. Klein102, M. Klein88, U. Klein88, P. Klimek34, A. Klimentov27, F. Klimpel111, T. Klingl22,T. Klioutchnikova34, F.F. Klitzner110, P. Kluit115, S. Kluth111, E. Kneringer74, T.M. Knight162,A. Knue50, D. Kobayashi85, R. Kobayashi83, M. Kobel46, M. Kocian149, T. Kodama159,P. Kodys138, D.M. Koeck152, P.T. Koenig22, T. Koffas32, N.M. K\u00f6hler34, M. Kolb140, I. Koletsou4,T. Komarek126, K. K\u00f6neke50, A.X.Y. Kong1, T. Kono122, V. Konstantinides92, N. Konstantinidis92,B. Konya94, R. Kopeliansky63, S. Koperny81a, K. Korcyl82, K. Kordas158, G. Koren157, A. Korn92,S. Korn51, I. Korolkov12, E.V. Korolkova145, N. Korotkova109, B. Kortman115, O. Kortner111,S. Kortner111, W.H. Kostecka116, V.V. Kostyukhin147,161, A. Kotsokechagia62, A. Kotwal47,A. Koulouris34, A. Kourkoumeli-Charalampidi68a,68b, C. Kourkoumelis8, E. Kourlitis5,O. Kovanda152, R. Kowalewski171, W. Kozanecki140, A.S. Kozhin118, V.A. Kramarenko109,G. Kramberger89, P. Kramer96, D. Krasnopevtsev58a, M.W. Krasny131, A. Krasznahorkay34,J.A. Kremer96, J. Kretzschmar88, K. Kreul17, P. Krieger162, F. Krieter110, S. Krishnamurthy99,A. Krishnan59b, M. Krivos138, K. Krizka16, K. Kroeninger45, H. Kroha111, J. Kroll136, J. Kroll132,K.S. Krowpman103, U. Kruchonak77, H. Kr\u00fcger22, N. Krumnack76, M.C. Kruse47, J.A. Krzysiak82,\u2013 50 \u2013JHEP06(2022)097A. Kubota160, O. Kuchinskaia161, S. Kuday3a, D. Kuechler44, J.T. Kuechler44, S. Kuehn34,T. Kuhl44, V. Kukhtin77, Y. Kulchitsky104,ad, S. Kuleshov142c, M. Kumar31f, N. Kumari98,M. Kuna56, A. Kupco136, T. Kupfer45, O. Kuprash50, H. Kurashige80, L.L. Kurchaninov163a,Y.A. Kurochkin104, A. Kurova108, M.G. Kurth13a,13d, E.S. Kuwertz34, M. Kuze160, A.K. Kvam144,J. Kvita126, T. Kwan100, K.W. Kwok60a, C. Lacasta169, F. Lacava70a,70b, H. Lacker17,D. Lacour131, N.N. Lad92, E. Ladygin77, R. Lafaye4, B. Laforge131, T. Lagouri142d, S. Lai51,I.K. Lakomiec81a, N. Lalloue56, J.E. Lambert124, S. Lammers63, W. Lampl6, C. Lampoudis158,E. Lan\u00e7on27, U. Landgraf50, M.P.J. Landon90, V.S. Lang50, J.C. Lange51, R.J. Langenberg99,A.J. Lankford166, F. Lanni27, K. Lantzsch22, A. Lanza68a, A. Lapertosa53b,53a, J.F. Laporte140,T. Lari66a, F. Lasagni Manghi21b, M. Lassnig34, V. Latonova136, T.S. Lau60a, A. Laudrain96,A. Laurier32, M. Lavorgna67a,67b, S.D. Lawlor91, Z. Lawrence97, M. Lazzaroni66a,66b, B. Le97,B. Leban89, A. Lebedev76, M. LeBlanc34, T. LeCompte5, F. Ledroit-Guillon56, A.C.A. Lee92,G.R. Lee15, L. Lee57, S.C. Lee154, S. Lee76, L.L. Leeuw31c, B. Lefebvre163a, H.P. Lefebvre91,M. Lefebvre171, C. Leggett16, K. Lehmann148, N. Lehmann18, G. Lehmann Miotto34,W.A. Leight44, A. Leisos158,u, M.A.L. Leite78c, C.E. Leitgeb44, R. Leitner138, K.J.C. Leney40,T. Lenz22, S. Leone69a, C. Leonidopoulos48, A. Leopold150, C. Leroy106, R. Les103, C.G. Lester30,M. Levchenko133, J. Lev\u00eaque4, D. Levin102, L.J. Levinson175, D.J. Lewis19, B. Li13b, B. Li58b,C. Li58a, C-Q. Li58c,58d, H. Li58a, H. Li58b, H. Li58b, J. Li58c, K. Li144, L. Li58c, M. Li13a,13d,Q.Y. Li58a, S. Li58d,58c,c, T. Li58b, X. Li44, Y. Li44, Z. Li58b, Z. Li130, Z. Li100, Z. Li88,Z. Liang13a, M. Liberatore44, B. Liberti71a, K. Lie60c, J. Lieber Marin78b, K. Lin103, R.A. Linck63,R.E. Lindley6, J.H. Lindon2, A. Linss44, E. Lipeles132, A. Lipniacka15, T.M. Liss168,ai, A. Lister170,J.D. Little7, B. Liu13a, B.X. Liu148, J.B. Liu58a, J.K.K. Liu35, K. Liu58d,58c, M. Liu58a,M.Y. Liu58a, P. Liu13a, Q. Liu58d,144,58c, X. Liu58a, Y. Liu44, Y. Liu13c,13d, Y.L. Liu102,Y.W. Liu58a, M. Livan68a,68b, J. Llorente Merino148, S.L. Lloyd90, E.M. Lobodzinska44, P. Loch6,S. Loffredo71a,71b, T. Lohse17, K. Lohwasser145, M. Lokajicek136, J.D. Long168, I. Longarini70a,70b,L. Longo34, R. Longo168, I. Lopez Paz12, A. Lopez Solis44, J. Lorenz110, N. Lorenzo Martinez4,A.M. Lory110, A. L\u00f6sle50, X. Lou43a,43b, X. Lou13a, A. Lounis62, J. Love5, P.A. Love87,J.J. Lozano Bahilo169, G. Lu13a, M. Lu58a, S. Lu132, Y.J. Lu61, H.J. Lubatti144, C. Luci70a,70b,F.L. Lucio Alves13c, A. Lucotte56, F. Luehring63, I. Luise151, L. Luminari70a, O. Lundberg150,B. Lund-Jensen150, N.A. Luongo127, M.S. Lutz157, D. Lynn27, H. Lyons88, R. Lysak136,E. Lytken94, F. Lyu13a, V. Lyubushkin77, T. Lyubushkina77, H. Ma27, L.L. Ma58b, Y. Ma92,D.M. Mac Donell171, G. Maccarrone49, C.M. Macdonald145, J.C. MacDonald145, R. Madar36,W.F. Mader46, M. Madugoda Ralalage Don125, N. Madysa46, J. Maeda80, T. Maeno27,M. Maerker46, V. Magerl50, J. Magro64a,64c, D.J. Mahon37, C. Maidantchik78b,A. Maio135a,135b,135d, K. Maj81a, O. Majersky26a, S. Majewski127, N. Makovec62, V. Maksimovic14,B. Malaescu131, Pa. Malecki82, V.P. Maleev133, F. Malek56, D. Malito39b,39a, U. Mallik75,C. Malone30, S. Maltezos9, S. Malyukov77, J. Mamuzic169, G. Mancini49, J.P. Mandalia90,I. Mandi\u010789, L. Manhaes de Andrade Filho78a, I.M. Maniatis158, M. Manisha140,J. Manjarres Ramos46, K.H. Mankinen94, A. Mann110, A. Manousos74, B. Mansoulie140,I. Manthos158, S. Manzoni115, A. Marantis158,u, G. Marchiori131, M. Marcisovsky136,L. Marcoccia71a,71b, C. Marcon94, M. Marjanovic124, Z. Marshall16, S. Marti-Garcia169,T.A. Martin173, V.J. Martin48, B. Martin dit Latour15, L. Martinelli70a,70b, M. Martinez12,v,P. Martinez Agullo169, V.I. Martinez Outschoorn99, S. Martin-Haugh139, V.S. Martoiu25b,A.C. Martyniuk92, A. Marzin34, S.R. Maschek111, L. Masetti96, T. Mashimo159, J. Masik97,A.L. Maslennikov117b,117a, L. Massa21b, P. Massarotti67a,67b, P. Mastrandrea69a,69b,A. Mastroberardino39b,39a, T. Masubuchi159, D. Matakias27, T. Mathisen167, A. Matic110,N. Matsuzawa159, J. Maurer25b, B. Ma\u010dek89, D.A. Maximov117b,117a, R. Mazini154, I. Maznas158,S.M. Mazza141, C. Mc Ginn27, J.P. Mc Gowan100, S.P. Mc Kee102, T.G. McCarthy111,\u2013 51 \u2013JHEP06(2022)097W.P. McCormack16, E.F. McDonald101, A.E. McDougall115, J.A. Mcfayden152, G. Mchedlidze155b,M.A. McKay40, K.D. McLean171, S.J. McMahon139, P.C. McNamara101, R.A. McPherson171,y,J.E. Mdhluli31f, Z.A. Meadows99, S. Meehan34, T. Megy36, S. Mehlhase110, A. Mehta88,B. Meirose41, D. Melini156, B.R. Mellado Garcia31f, A.H. Melo51, F. Meloni44, A. Melzer22,E.D. Mendes Gouveia135a, A.M. Mendes Jacques Da Costa19, H.Y. Meng162, L. Meng34,S. Menke111, M. Mentink34, E. Meoni39b,39a, C. Merlassino130, P. Mermod52,*, L. Merola67a,67b,C. Meroni66a, G. Merz102, O. Meshkov107,109, J.K.R. Meshreki147, J. Metcalfe5, A.S. Mete5,C. Meyer63, J-P. Meyer140, M. Michetti17, R.P. Middleton139, L. Mijovi\u010748, G. Mikenberg175,M. Mikestikova136, M. Miku\u017e89, H. Mildner145, A. Milic162, C.D. Milke40, D.W. Miller35,L.S. Miller32, A. Milov175, D.A. Milstead43a,43b, T. Min13c, A.A. Minaenko118, I.A. Minashvili155b,L. Mince55, A.I. Mincer121, B. Mindur81a, M. Mineev77, Y. Minegishi159, Y. Mino83, L.M. Mir12,M. Miralles Lopez169, M. Mironova130, T. Mitani174, V.A. Mitsou169, M. Mittal58c, O. Miu162,P.S. Miyagawa90, Y. Miyazaki85, A. Mizukami79, J.U. Mj\u00f6rnmark94, T. Mkrtchyan59a,M. Mlynarikova116, T. Moa43a,43b, S. Mobius51, K. Mochizuki106, P. Moder44, P. Mogg110,A.F. Mohammed13a, S. Mohapatra37, G. Mokgatitswane31f, B. Mondal147, S. Mondal137,K. M\u00f6nig44, E. Monnier98, L. Monsonis Romero169, A. Montalbano148, J. Montejo Berlingen34,M. Montella123, F. Monticelli86, N. Morange62, A.L. Moreira De Carvalho135a,M. Moreno Ll\u00e1cer169, C. Moreno Martinez12, P. Morettini53b, S. Morgenstern173, D. Mori148,M. Morii57, M. Morinaga159, V. Morisbak129, A.K. Morley34, A.P. Morris92, L. Morvaj34,P. Moschovakos34, B. Moser115, M. Mosidze155b, T. Moskalets50, P. Moskvitina114, J. Moss29,n,E.J.W. Moyse99, S. Muanza98, J. Mueller134, R. Mueller18, D. Muenstermann87, G.A. Mullier94,J.J. Mullin132, D.P. Mungo66a,66b, J.L. Munoz Martinez12, F.J. Munoz Sanchez97, M. Murin97,P. Murin26b, W.J. Murray173,139, A. Murrone66a,66b, J.M. Muse124, M. Mu\u0161kinja16, C. Mwewa27,A.G. Myagkov118,ae, A.J. Myers7, A.A. Myers134, G. Myers63, M. Myska137, B.P. Nachman16,O. Nackenhorst45, A.Nag Nag46, K. Nagai130, K. Nagano79, J.L. Nagle27, E. Nagy98, A.M. Nairz34,Y. Nakahama112, K. Nakamura79, H. Nanjo128, F. Napolitano59a, R. Narayan40,E.A. Narayanan113, I. Naryshkin133, M. Naseri32, C. Nass22, T. Naumann44, G. Navarro20a,J. Navarro-Gonzalez169, R. Nayak157, P.Y. Nechaeva107, F. Nechansky44, T.J. Neep19,A. Negri68a,68b, M. Negrini21b, C. Nellist114, C. Nelson100, K. Nelson102, S. Nemecek136,M. Nessi34,f, M.S. Neubauer168, F. Neuhaus96, J. Neundorf44, R. Newhouse170, P.R. Newman19,C.W. Ng134, Y.S. Ng17, Y.W.Y. Ng166, B. Ngair33e, H.D.N. Nguyen106, R.B. Nickerson130,R. Nicolaidou140, D.S. Nielsen38, J. Nielsen141, M. Niemeyer51, N. Nikiforou10, V. Nikolaenko118,ae,I. Nikolic-Audit131, K. Nikolopoulos19, P. Nilsson27, H.R. Nindhito52, A. Nisati70a, N. Nishu2,R. Nisius111, T. Nitta174, T. Nobe159, D.L. Noel30, Y. Noguchi83, I. Nomidis131, M.A. Nomura27,M.B. Norfolk145, R.R.B. Norisam92, J. Novak89, T. Novak44, O. Novgorodova46, L. Novotny137,R. Novotny113, L. Nozka126, K. Ntekas166, E. Nurse92, F.G. Oakham32,aj, J. Ocariz131, A. Ochi80,I. Ochoa135a, J.P. Ochoa-Ricoux142a, S. Oda85, S. Odaka79, S. Oerdek167, A. Ogrodnik81a,A. Oh97, C.C. Ohm150, H. Oide160, R. Oishi159, M.L. Ojeda44, Y. Okazaki83, M.W. O\u2019Keefe88,Y. Okumura159, A. Olariu25b, L.F. Oleiro Seabra135a, S.A. Olivares Pino142d,D. Oliveira Damazio27, D. Oliveira Goncalves78a, J.L. Oliver166, M.J.R. Olsson166, A. Olszewski82,J. Olszowska82, \u00d6.O. \u00d6ncel22, D.C. O\u2019Neil148, A.P. O\u2019neill130, A. Onofre135a,135e, P.U.E. Onyisi10,R.G. Oreamuno Madriz116, M.J. Oreglia35, G.E. Orellana86, D. Orestano72a,72b, N. Orlando12,R.S. Orr162, V. O\u2019Shea55, R. Ospanov58a, G. Otero y Garzon28, H. Otono85, P.S. Ott59a,G.J. Ottino16, M. Ouchrif33d, J. Ouellette27, F. Ould-Saada129, A. Ouraou140,*, Q. Ouyang13a,M. Owen55, R.E. Owen139, K.Y. Oyulmaz11c, V.E. Ozcan11c, N. Ozturk7, S. Ozturk11c,J. Pacalt126, H.A. Pacey30, K. Pachal47, A. Pacheco Pages12, C. Padilla Aranda12,S. Pagan Griso16, G. Palacino63, S. Palazzo48, S. Palestini34, M. Palka81b, P. Palni81a,D.K. Panchal10, C.E. Pandini52, J.G. Panduro Vazquez91, P. Pani44, G. Panizzo64a,64c,\u2013 52 \u2013JHEP06(2022)097L. Paolozzi52, C. Papadatos106, S. Parajuli40, A. Paramonov5, C. Paraskevopoulos9,D. Paredes Hernandez60b, S.R. Paredes Saenz130, B. Parida175, T.H. Park162, A.J. Parker29,M.A. Parker30, F. Parodi53b,53a, E.W. Parrish116, J.A. Parsons37, U. Parzefall50,L. Pascual Dominguez157, V.R. Pascuzzi16, F. Pasquali115, E. Pasqualucci70a, S. Passaggio53b,F. Pastore91, P. Pasuwan43a,43b, J.R. Pater97, A. Pathak176, J. Patton88, T. Pauly34,J. Pearkes149, M. Pedersen129, L. Pedraza Diaz114, R. Pedro135a, T. Peiffer51,S.V. Peleganchuk117b,117a, O. Penc136, C. Peng60b, H. Peng58a, M. Penzin161, B.S. Peralva78a,A.P. Pereira Peixoto135a, L. Pereira Sanchez43a,43b, D.V. Perepelitsa27, E. Perez Codina163a,M. Perganti9, L. Perini66a,66b, H. Pernegger34, S. Perrella34, A. Perrevoort115, K. Peters44,R.F.Y. Peters97, B.A. Petersen34, T.C. Petersen38, E. Petit98, V. Petousis137, C. Petridou158,P. Petroff62, F. Petrucci72a,72b, A. Petrukhin147, M. Pettee178, N.E. Pettersson34, K. Petukhova138,A. Peyaud140, R. Pezoa142e, L. Pezzotti34, G. Pezzullo178, T. Pham101, P.W. Phillips139,M.W. Phipps168, G. Piacquadio151, E. Pianori16, F. Piazza66a,66b, A. Picazio99, R. Piegaia28,D. Pietreanu25b, J.E. Pilcher35, A.D. Pilkington97, M. Pinamonti64a,64c, J.L. Pinfold2,C. Pitman Donaldson92, D.A. Pizzi32, L. Pizzimento71a,71b, A. Pizzini115, M.-A. Pleier27,V. Plesanovs50, V. Pleskot138, E. Plotnikova77, P. Podberezko117b,117a, R. Poettgen94, R. Poggi52,L. Poggioli131, I. Pogrebnyak103, D. Pohl22, I. Pokharel51, G. Polesello68a, A. Poley148,163a,A. Policicchio70a,70b, R. Polifka138, A. Polini21b, C.S. Pollard130, Z.B. Pollock123,V. Polychronakos27, D. Ponomarenko108, L. Pontecorvo34, S. Popa25a, G.A. Popeneciu25d,L. Portales4, D.M. Portillo Quintero163a, S. Pospisil137, P. Postolache25c, K. Potamianos130,I.N. Potrap77, C.J. Potter30, H. Potti1, T. Poulsen44, J. Poveda169, T.D. Powell145, G. Pownall44,M.E. Pozo Astigarraga34, A. Prades Ibanez169, P. Pralavorio98, M.M. Prapa42, S. Prell76,D. Price97, M. Primavera65a, M.A. Principe Martin95, M.L. Proffitt144, N. Proklova108,K. Prokofiev60c, F. Prokoshin77, S. Protopopescu27, J. Proudfoot5, M. Przybycien81a,D. Pudzha133, P. Puzo62, D. Pyatiizbyantseva108, J. Qian102, Y. Qin97, T. Qiu90, A. Quadt51,M. Queitsch-Maitland34, G. Rabanal Bolanos57, F. Ragusa66a,66b, J.A. Raine52, S. Rajagopalan27,K. Ran13a,13d, D.F. Rassloff59a, D.M. Rauch44, S. Rave96, B. Ravina55, I. Ravinovich175,M. Raymond34, A.L. Read129, N.P. Readioff145, D.M. Rebuzzi68a,68b, G. Redlinger27, K. Reeves41,D. Reikher157, A. Reiss96, A. Rej147, C. Rembser34, A. Renardi44, M. Renda25b, M.B. Rendel111,A.G. Rennie55, S. Resconi66a, M. Ressegotti53b,53a, E.D. Resseguie16, S. Rettie92, B. Reynolds123,E. Reynolds19, M. Rezaei Estabragh177, O.L. Rezanova117b,117a, P. Reznicek138, E. Ricci73a,73b,R. Richter111, S. Richter44, E. Richter-Was81b, M. Ridel131, P. Rieck111, P. Riedler34, O. Rifki44,M. Rijssenbeek151, A. Rimoldi68a,68b, M. Rimoldi44, L. Rinaldi21b,21a, T.T. Rinn168,M.P. Rinnagel110, G. Ripellino150, I. Riu12, P. Rivadeneira44, J.C. Rivera Vergara171,F. Rizatdinova125, E. Rizvi90, C. Rizzi52, B.A. Roberts173, B.R. Roberts16, S.H. Robertson100,y,M. Robin44, D. Robinson30, C.M. Robles Gajardo142e, M. Robles Manzano96, A. Robson55,A. Rocchi71a,71b, C. Roda69a,69b, S. Rodriguez Bosca59a, A. Rodriguez Rodriguez50,A.M. Rodr\u00edguez Vera163b, S. Roe34, A.R. Roepe124, J. Roggel177, O. R\u00f8hne129, R.A. Rojas171,B. Roland50, C.P.A. Roland63, J. Roloff27, A. Romaniouk108, M. Romano21b,A.C. Romero Hernandez168, N. Rompotis88, M. Ronzani121, L. Roos131, S. Rosati70a,B.J. Rosser132, E. Rossi162, E. Rossi4, E. Rossi67a,67b, L.P. Rossi53b, L. Rossini44, R. Rosten123,M. Rotaru25b, B. Rottler50, D. Rousseau62, D. Rousso30, G. Rovelli68a,68b, A. Roy10,A. Rozanov98, Y. Rozen156, X. Ruan31f, A.J. Ruby88, T.A. Ruggeri1, F. R\u00fchr50,A. Ruiz-Martinez169, A. Rummler34, Z. Rurikova50, N.A. Rusakovich77, H.L. Russell34,L. Rustige36, J.P. Rutherfoord6, E.M. R\u00fcttinger145, M. Rybar138, E.B. Rye129, A. Ryzhov118,J.A. Sabater Iglesias44, P. Sabatini169, L. Sabetta70a,70b, H.F-W. Sadrozinski141, R. Sadykov77,F. Safai Tehrani70a, B. Safarzadeh Samani152, M. Safdari149, S. Saha100, M. Sahinsoy111,A. Sahu177, M. Saimpert140, M. Saito159, T. Saito159, D. Salamani34, G. Salamanna72a,72b,\u2013 53 \u2013JHEP06(2022)097A. Salnikov149, J. Salt169, A. Salvador Salas12, D. Salvatore39b,39a, F. Salvatore152, A. Salzburger34,D. Sammel50, D. Sampsonidis158, D. Sampsonidou58d,58c, J. S\u00e1nchez169, A. Sanchez Pineda4,V. Sanchez Sebastian169, H. Sandaker129, C.O. Sander44, I.G. Sanderswood87, J.A. Sandesara99,M. Sandhoff177, C. Sandoval20b, D.P.C. Sankey139, M. Sannino53b,53a, A. Sansoni49, C. Santoni36,H. Santos135a,135b, S.N. Santpur16, A. Santra175, K.A. Saoucha145, A. Sapronov77,J.G. Saraiva135a,135d, J. Sardain98, O. Sasaki79, K. Sato164, C. Sauer59b, F. Sauerburger50,E. Sauvan4, P. Savard162,aj, R. Sawada159, C. Sawyer139, L. Sawyer93, I. Sayago Galvan169,C. Sbarra21b, A. Sbrizzi21b,21a, T. Scanlon92, J. Schaarschmidt144, P. Schacht111, D. Schaefer35,U. Sch\u00e4fer96, A.C. Schaffer62, D. Schaile110, R.D. Schamberger151, E. Schanet110, C. Scharf17,N. Scharmberg97, V.A. Schegelsky133, D. Scheirich138, F. Schenck17, M. Schernau166,C. Schiavi53b,53a, L.K. Schildgen22, Z.M. Schillaci24, E.J. Schioppa65a,65b, M. Schioppa39b,39a,B. Schlag96, K.E. Schleicher50, S. Schlenker34, K. Schmieden96, C. Schmitt96, S. Schmitt44,L. Schoeffel140, A. Schoening59b, P.G. Scholer50, E. Schopf130, M. Schott96, J. Schovancova34,S. Schramm52, F. Schroeder177, H-C. Schultz-Coulon59a, M. Schumacher50, B.A. Schumm141,Ph. Schune140, A. Schwartzman149, T.A. Schwarz102, Ph. Schwemling140, R. Schwienhorst103,A. Sciandra141, G. Sciolla24, F. Scuri69a, F. Scutti101, C.D. Sebastiani88, K. Sedlaczek45,P. Seema17, S.C. Seidel113, A. Seiden141, B.D. Seidlitz27, T. Seiss35, C. Seitz44, J.M. Seixas78b,G. Sekhniaidze67a, S.J. Sekula40, L. Selem4, N. Semprini-Cesari21b,21a, S. Sen47, C. Serfon27,L. Serin62, L. Serkin64a,64b, M. Sessa72a,72b, H. Severini124, S. Sevova149, F. Sforza53b,53a,A. Sfyrla52, E. Shabalina51, R. Shaheen150, J.D. Shahinian132, N.W. Shaikh43a,43b,D. Shaked Renous175, L.Y. Shan13a, M. Shapiro16, A. Sharma34, A.S. Sharma1, S. Sharma44,P.B. Shatalov119, K. Shaw152, S.M. Shaw97, P. Sherwood92, L. Shi92, C.O. Shimmin178,Y. Shimogama174, J.D. Shinner91, I.P.J. Shipsey130, S. Shirabe52, M. Shiyakova77, J. Shlomi175,M.J. Shochet35, J. Shojaii101, D.R. Shope150, S. Shrestha123, E.M. Shrif31f, M.J. Shroff171,E. Shulga175, P. Sicho136, A.M. Sickles168, E. Sideras Haddad31f, O. Sidiropoulou34, A. Sidoti21b,F. Siegert46, Dj. Sijacki14, J.M. Silva19, M.V. Silva Oliveira34, S.B. Silverstein43a, S. Simion62,R. Simoniello34, N.D. Simpson94, S. Simsek11b, P. Sinervo162, V. Sinetckii109, S. Singh148,S. Singh162, S. Sinha44, S. Sinha31f, M. Sioli21b,21a, I. Siral127, S.Yu. Sivoklokov109, J. Sj\u00f6lin43a,43b,A. Skaf51, E. Skorda94, P. Skubic124, M. Slawinska82, K. Sliwa165, V. Smakhtin175, B.H. Smart139,J. Smiesko138, S.Yu. Smirnov108, Y. Smirnov108, L.N. Smirnova109,r, O. Smirnova94, E.A. Smith35,H.A. Smith130, M. Smizanska87, K. Smolek137, A. Smykiewicz82, A.A. Snesarev107, H.L. Snoek115,S. Snyder27, R. Sobie171,y, A. Soffer157, F. Sohns51, C.A. Solans Sanchez34, E.Yu. Soldatov108,U. Soldevila169, A.A. Solodkov118, S. Solomon50, A. Soloshenko77, O.V. Solovyanov118,V. Solovyev133, P. Sommer145, H. Son165, A. Sonay12, W.Y. Song163b, A. Sopczak137, A.L. Sopio92,F. Sopkova26b, S. Sottocornola68a,68b, R. Soualah120c, A.M. Soukharev117b,117a, Z. Soumaimi33e,D. South44, S. Spagnolo65a,65b, M. Spalla111, M. Spangenberg173, F. Span\u00f291, D. Sperlich50,T.M. Spieker59a, G. Spigo34, M. Spina152, D.P. Spiteri55, M. Spousta138, A. Stabile66a,66b,R. Stamen59a, M. Stamenkovic115, A. Stampekis19, M. Standke22, E. Stanecka82, B. Stanislaus34,M.M. Stanitzki44, M. Stankaityte130, B. Stapf44, E.A. Starchenko118, G.H. Stark141, J. Stark98,D.M. Starko163b, P. Staroba136, P. Starovoitov59a, S. St\u00e4rz100, R. Staszewski82, G. Stavropoulos42,P. Steinberg27, A.L. Steinhebel127, B. Stelzer148,163a, H.J. Stelzer134, O. Stelzer-Chilton163a,H. Stenzel54, T.J. Stevenson152, G.A. Stewart34, M.C. Stockton34, G. Stoicea25b, M. Stolarski135a,S. Stonjek111, A. Straessner46, J. Strandberg150, S. Strandberg43a,43b, M. Strauss124, T. Strebler98,P. Strizenec26b, R. Str\u00f6hmer172, D.M. Strom127, L.R. Strom44, R. Stroynowski40, A. Strubig43a,43b,S.A. Stucci27, B. Stugu15, J. Stupak124, N.A. Styles44, D. Su149, S. Su58a, W. Su58d,144,58c,X. Su58a, K. Sugizaki159, V.V. Sulin107, M.J. Sullivan88, D.M.S. Sultan52, L. Sultanaliyeva107,S. Sultansoy3c, T. Sumida83, S. Sun102, S. Sun176, X. Sun97, O. Sunneborn Gudnadottir167,C.J.E. Suster153, M.R. Sutton152, M. Svatos136, M. Swiatlowski163a, T. Swirski172, I. Sykora26a,\u2013 54 \u2013JHEP06(2022)097M. Sykora138, T. Sykora138, D. Ta96, K. Tackmann44,w, A. Taffard166, R. Tafirout163a,R.H.M. Taibah131, R. Takashima84, K. Takeda80, T. Takeshita146, E.P. Takeva48, Y. Takubo79,M. Talby98, A.A. Talyshev117b,117a, K.C. Tam60b, N.M. Tamir157, A. Tanaka159, J. Tanaka159,R. Tanaka62, J. Tang58c, Z. Tao170, S. Tapia Araya76, S. Tapprogge96,A. Tarek Abouelfadl Mohamed103, S. Tarem156, K. Tariq58b, G. Tarna25b, G.F. Tartarelli66a,P. Tas138, M. Tasevsky136, E. Tassi39b,39a, G. Tateno159, Y. Tayalati33e, G.N. Taylor101,W. Taylor163b, H. Teagle88, A.S. Tee176, R. Teixeira De Lima149, P. Teixeira-Dias91,H. Ten Kate34, J.J. Teoh115, K. Terashi159, J. Terron95, S. Terzo12, M. Testa49, R.J. Teuscher162,y,N. Themistokleous48, T. Theveneaux-Pelzer17, O. Thielmann177, D.W. Thomas91, J.P. Thomas19,E.A. Thompson44, P.D. Thompson19, E. Thomson132, E.J. Thorpe90, Y. Tian51,V. Tikhomirov107,af, Yu.A. Tikhonov117b,117a, S. Timoshenko108, E.X.L. Ting1, P. Tipton178,S. Tisserant98, S.H. Tlou31f, A. Tnourji36, K. Todome21b,21a, S. Todorova-Nova138, S. Todt46,M. Togawa79, J. Tojo85, S. Tok\u00e1r26a, K. Tokushuku79, E. Tolley123, R. Tombs30, M. Tomoto79,112,L. Tompkins149, P. Tornambe99, E. Torrence127, H. Torres46, E. Torr\u00f3 Pastor169, M. Toscani28,C. Tosciri35, J. Toth98,x, D.R. Tovey145, A. Traeet15, C.J. Treado121, T. Trefzger172, A. Tricoli27,I.M. Trigger163a, S. Trincaz-Duvoid131, D.A. Trischuk170, W. Trischuk162, B. Trocm\u00e956,A. Trofymov62, C. Troncon66a, F. Trovato152, L. Truong31c, M. Trzebinski82, A. Trzupek82,F. Tsai151, M. Tsai102, A. Tsiamis158, P.V. Tsiareshka104, A. Tsirigotis158,u, V. Tsiskaridze151,E.G. Tskhadadze155a, M. Tsopoulou158, Y. Tsujikawa83, I.I. Tsukerman119, V. Tsulaia16,S. Tsuno79, O. Tsur156, D. Tsybychev151, Y. Tu60b, A. Tudorache25b, V. Tudorache25b,A.N. Tuna34, S. Turchikhin77, I. Turk Cakir3a, R.J. Turner19, R. Turra66a, P.M. Tuts37,S. Tzamarias158, P. Tzanis9, E. Tzovara96, K. Uchida159, F. Ukegawa164, P.A. Ulloa Poblete142b,G. Unal34, M. Unal10, A. Undrus27, G. Unel166, F.C. Ungaro101, K. Uno159, J. Urban26b,P. Urquijo101, G. Usai7, R. Ushioda160, M. Usman106, Z. Uysal11d, V. Vacek137, B. Vachon100,K.O.H. Vadla129, T. Vafeiadis34, C. Valderanis110, E. Valdes Santurio43a,43b, M. Valente163a,S. Valentinetti21b,21a, A. Valero169, R.A. Vallance19, A. Vallier98, J.A. Valls Ferrer169,T.R. Van Daalen144, P. Van Gemmeren5, S. Van Stroud92, I. Van Vulpen115, M. Vanadia71a,71b,W. Vandelli34, M. Vandenbroucke140, E.R. Vandewall125, D. Vannicola157, L. Vannoli53b,53a,R. Vari70a, E.W. Varnes6, C. Varni16, T. Varol154, D. Varouchas62, K.E. Varvell153, M.E. Vasile25b,L. Vaslin36, G.A. Vasquez171, F. Vazeille36, D. Vazquez Furelos12, T. Vazquez Schroeder34,J. Veatch51, V. Vecchio97, M.J. Veen115, I. Veliscek130, L.M. Veloce162, F. Veloso135a,135c,S. Veneziano70a, A. Ventura65a,65b, A. Verbytskyi111, M. Verducci69a,69b, C. Vergis22,M. Verissimo De Araujo78b, W. Verkerke115, A.T. Vermeulen115, J.C. Vermeulen115, C. Vernieri149,P.J. Verschuuren91, M. Vessella99, M.L. Vesterbacka121, M.C. Vetterli148,aj, A. Vgenopoulos158,N. Viaux Maira142e, T. Vickey145, O.E. Vickey Boeriu145, G.H.A. Viehhauser130, L. Vigani59b,M. Villa21b,21a, M. Villaplana Perez169, E.M. Villhauer48, E. Vilucchi49, M.G. Vincter32,G.S. Virdee19, A. Vishwakarma48, C. Vittori21b,21a, I. Vivarelli152, V. Vladimirov173,E. Voevodina111, M. Vogel177, P. Vokac137, J. Von Ahnen44, E. Von Toerne22, B. Vormwald34,V. Vorobel138, K. Vorobev108, M. Vos169, J.H. Vossebeld88, M. Vozak97, L. Vozdecky90,N. Vranjes14, M. Vranjes Milosavljevic14, V. Vrba137,*, M. Vreeswijk115, N.K. Vu98,R. Vuillermet34, O.V. Vujinovic96, I. Vukotic35, S. Wada164, C. Wagner99, W. Wagner177,S. Wahdan177, H. Wahlberg86, R. Wakasa164, M. Wakida112, V.M. Walbrecht111, J. Walder139,R. Walker110, S.D. Walker91, W. Walkowiak147, A.M. Wang57, A.Z. Wang176, C. Wang58a,C. Wang58c, H. Wang16, J. Wang60a, P. Wang40, R.-J. Wang96, R. Wang57, R. Wang116,S.M. Wang154, S. Wang58b, T. Wang58a, W.T. Wang75, W.X. Wang58a, X. Wang13c, X. Wang168,X. Wang58c, Y. Wang58a, Z. Wang102, Z. Wang102, C. Wanotayaroj34, A. Warburton100,C.P. Ward30, R.J. Ward19, N. Warrack55, A.T. Watson19, M.F. Watson19, G. Watts144,B.M. Waugh92, A.F. Webb10, C. Weber27, M.S. Weber18, S.A. Weber32, S.M. Weber59a, C. Wei58a,\u2013 55 \u2013JHEP06(2022)097Y. Wei130, A.R. Weidberg130, J. Weingarten45, M. Weirich96, C. Weiser50, T. Wenaus27,B. Wendland45, T. Wengler34, S. Wenig34, N. Wermes22, M. Wessels59a, K. Whalen127,A.M. Wharton87, A.S. White57, A. White7, M.J. White1, D. Whiteson166, L. Wickremasinghe128,W. Wiedenmann176, C. Wiel46, M. Wielers139, N. Wieseotte96, C. Wiglesworth38,L.A.M. Wiik-Fuchs50, D.J. Wilbern124, H.G. Wilkens34, L.J. Wilkins91, D.M. Williams37,H.H. Williams132, S. Williams30, S. Willocq99, P.J. Windischhofer130, I. Wingerter-Seez4,E. Winkels152, F. Winklmeier127, B.T. Winter50, M. Wittgen149, M. Wobisch93, A. Wolf96,R. W\u00f6lker130, J. Wollrath166, M.W. Wolter82, H. Wolters135a,135c, V.W.S. Wong170,A.F. Wongel44, S.D. Worm44, B.K. Wosiek82, K.W. Wo\u017aniak82, K. Wraight55, J. Wu13a,13d,S.L. Wu176, X. Wu52, Y. Wu58a, Z. Wu140,58a, J. Wuerzinger130, T.R. Wyatt97, B.M. Wynne48,S. Xella38, L. Xia13c, M. Xia13b, J. Xiang60c, X. Xiao102, M. Xie58a, X. Xie58a, I. Xiotidis152,D. Xu13a, H. Xu58a, H. Xu58a, L. Xu58a, R. Xu132, T. Xu58a, W. Xu102, Y. Xu13b, Z. Xu58b,Z. Xu149, B. Yabsley153, S. Yacoob31a, N. Yamaguchi85, Y. Yamaguchi160, M. Yamatani159,H. Yamauchi164, T. Yamazaki16, Y. Yamazaki80, J. Yan58c, S. Yan130, Z. Yan23, H.J. Yang58c,58d,H.T. Yang16, S. Yang58a, T. Yang60c, X. Yang58a, X. Yang13a, Y. Yang159, Z. Yang102,58a,W-M. Yao16, Y.C. Yap44, H. Ye13c, J. Ye40, S. Ye27, I. Yeletskikh77, M.R. Yexley87, P. Yin37,K. Yorita174, K. Yoshihara76, C.J.S. Young50, C. Young149, M. Yuan102, R. Yuan58b,i, X. Yue59a,M. Zaazoua33e, B. Zabinski82, G. Zacharis9, E. Zaid48, A.M. Zaitsev118,ae, T. Zakareishvili155b,N. Zakharchuk32, S. Zambito34, D. Zanzi50, O. Zaplatilek137, S.V. Zei\u00dfner45, C. Zeitnitz177,J.C. Zeng168, D.T. Zenger Jr24, O. Zenin118, T. \u017deni\u016126a, S. Zenz90, S. Zerradi33a, D. Zerwas62,B. Zhang13c, D.F. Zhang145, G. Zhang13b, J. Zhang5, K. Zhang13a, L. Zhang13c, M. Zhang168,R. Zhang176, S. Zhang102, X. Zhang58c, X. Zhang58b, Z. Zhang62, P. Zhao47, T. Zhao58b,Y. Zhao141, Z. Zhao58a, A. Zhemchugov77, Z. Zheng149, D. Zhong168, B. Zhou102, C. Zhou176,H. Zhou6, N. Zhou58c, Y. Zhou6, C.G. Zhu58b, C. Zhu13a,13d, H.L. Zhu58a, H. Zhu13a, J. Zhu102,Y. Zhu58a, X. Zhuang13a, K. Zhukov107, V. Zhulanov117b,117a, D. Zieminska63, N.I. Zimine77,S. Zimmermann50,*, J. Zinsser59b, M. Ziolkowski147, L. \u017divkovi\u010714, A. Zoccoli21b,21a, K. Zoch52,T.G. Zorbas145, O. Zormpa42, W. Zou37, L. Zwalinski34.1 Department of Physics, University of Adelaide, Adelaide; Australia2 Department of Physics, University of Alberta, Edmonton AB; Canada3 (a)Department of Physics, Ankara University, Ankara;(b)Istanbul Aydin University, Application andResearch Center for Advanced Studies, Istanbul;(c)Division of Physics, TOBB University ofEconomics and Technology, Ankara; Turkey4 LAPP, Univ. Savoie Mont Blanc, CNRS\/IN2P3, Annecy; France5 High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America6 Department of Physics, University of Arizona, Tucson AZ; United States of America7 Department of Physics, University of Texas at Arlington, Arlington TX; United States of America8 Physics Department, National and Kapodistrian University of Athens, Athens; Greece9 Physics Department, National Technical University of Athens, Zografou; Greece10 Department of Physics, University of Texas at Austin, Austin TX; United States of America11 (a)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul;(b)Istanbul BilgiUniversity, Faculty of Engineering and Natural Sciences, Istanbul;(c)Department of Physics, BogaziciUniversity, Istanbul;(d)Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey12 Institut de F\u00edsica d\u2019Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona;Spain13 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b)Physics Department,Tsinghua University, Beijing;(c)Department of Physics, Nanjing University, Nanjing;(d)University ofChinese Academy of Science (UCAS), Beijing; China14 Institute of Physics, University of Belgrade, Belgrade; Serbia15 Department for Physics and Technology, University of Bergen, Bergen; Norway\u2013 56 \u2013JHEP06(2022)09716 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA;United States of America17 Institut f\u00fcr Physik, Humboldt Universit\u00e4t zu Berlin, Berlin; Germany18 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, Universityof Bern, Bern; Switzerland19 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom20 (a)Facultad de Ciencias y Centro de Investigaci\u00f3nes, Universidad Antonio Nari\u00f1o,Bogot\u00e1;(b)Departamento de F\u00edsica, Universidad Nacional de Colombia, Bogot\u00e1; Colombia21 (a)Dipartimento di Fisica e Astronomia A. Righi, Universit\u00e0 di Bologna, Bologna;(b)INFN Sezione diBologna; Italy22 Physikalisches Institut, Universit\u00e4t Bonn, Bonn; Germany23 Department of Physics, Boston University, Boston MA; United States of America24 Department of Physics, Brandeis University, Waltham MA; United States of America25 (a)Transilvania University of Brasov, Brasov;(b)Horia Hulubei National Institute of Physics andNuclear Engineering, Bucharest;(c)Department of Physics, Alexandru Ioan Cuza University of Iasi,Iasi;(d)National Institute for Research and Development of Isotopic and Molecular Technologies,Physics Department, Cluj-Napoca;(e)University Politehnica Bucharest, Bucharest;(f)West Universityin Timisoara, Timisoara; Romania26 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava;(b)Departmentof Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice;Slovak Republic27 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America28 Departamento de F\u00edsica (FCEN) and IFIBA, Universidad de Buenos Aires and CONICET, BuenosAires; Argentina29 California State University, CA; United States of America30 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom31 (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)School of Physics,University of the Witwatersrand, Johannesburg; South Africa32 Department of Physics, Carleton University, Ottawa ON; Canada33 (a)Facult\u00e9 des Sciences Ain Chock, R\u00e9seau Universitaire de Physique des Hautes Energies \u2014Universit\u00e9 Hassan II, Casablanca;(b)Facult\u00e9 des Sciences, Universit\u00e9 Ibn-Tofail, K\u00e9nitra;(c)Facult\u00e9des Sciences Semlalia, Universit\u00e9 Cadi Ayyad, LPHEA-Marrakech;(d)LPMR, Facult\u00e9 des Sciences,Universit\u00e9 Mohamed Premier, Oujda;(e)Facult\u00e9 des sciences, Universit\u00e9 Mohammed V,Rabat;(f)Mohammed VI Polytechnic University, Ben Guerir; Morocco34 CERN, Geneva; Switzerland35 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America36 LPC, Universit\u00e9 Clermont Auvergne, CNRS\/IN2P3, Clermont-Ferrand; France37 Nevis Laboratory, Columbia University, Irvington NY; United States of America38 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark39 (a)Dipartimento di Fisica, Universit\u00e0 della Calabria, Rende;(b)INFN Gruppo Collegato di Cosenza,Laboratori Nazionali di Frascati; Italy40 Physics Department, Southern Methodist University, Dallas TX; United States of America41 Physics Department, University of Texas at Dallas, Richardson TX; United States of America42 National Centre for Scientific Research \u201cDemokritos\u201d, Agia Paraskevi; Greece43 (a)Department of Physics, Stockholm University;(b)Oskar Klein Centre, Stockholm; Sweden44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany45 Fakult\u00e4t Physik, Technische Universit\u00e4t Dortmund, Dortmund; Germany46 Institut f\u00fcr Kern- und Teilchenphysik, Technische Universit\u00e4t Dresden, Dresden; Germany47 Department of Physics, Duke University, Durham NC; United States of America48 SUPA \u2014 School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom\u2013 57 \u2013JHEP06(2022)09749 INFN e Laboratori Nazionali di Frascati, Frascati; Italy50 Physikalisches Institut, Albert-Ludwigs-Universit\u00e4t Freiburg, Freiburg; Germany51 II. Physikalisches Institut, Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen; Germany52 D\u00e9partement de Physique Nucl\u00e9aire et Corpusculaire, Universit\u00e9 de Gen\u00e8ve, Gen\u00e8ve; Switzerland53 (a)Dipartimento di Fisica, Universit\u00e0 di Genova, Genova;(b)INFN Sezione di Genova; Italy54 II. Physikalisches Institut, Justus-Liebig-Universit\u00e4t Giessen, Giessen; Germany55 SUPA \u2014 School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom56 LPSC, Universit\u00e9 Grenoble Alpes, CNRS\/IN2P3, Grenoble INP, Grenoble; France57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United Statesof America58 (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 InterdisciplinaryScience and Key Laboratory of Particle Physics and Particle Irradiation (MOE), ShandongUniversity, Qingdao;(c)School of Physics and Astronomy, Shanghai Jiao Tong University, KeyLaboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC, Shanghai;(d)Tsung-Dao LeeInstitute, Shanghai; China59 (a)Kirchhoff-Institut f\u00fcr Physik, Ruprecht-Karls-Universit\u00e4t Heidelberg, Heidelberg;(b)PhysikalischesInstitut, Ruprecht-Karls-Universit\u00e4t Heidelberg, Heidelberg; Germany60 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., HongKong;(b)Department of Physics, University of Hong Kong, Hong Kong;(c)Department of Physics andInstitute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay,Kowloon, Hong Kong; China61 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan62 IJCLab, Universit\u00e9 Paris-Saclay, CNRS\/IN2P3, 91405, Orsay; France63 Department of Physics, Indiana University, Bloomington IN; United States of America64 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b)ICTP, Trieste;(c)DipartimentoPolitecnico di Ingegneria e Architettura, Universit\u00e0 di Udine, Udine; Italy65 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Universit\u00e0 del Salento, Lecce; Italy66 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Universit\u00e0 di Milano, Milano; Italy67 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Universit\u00e0 di Napoli, Napoli; Italy68 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Universit\u00e0 di Pavia, Pavia; Italy69 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Universit\u00e0 di Pisa, Pisa; Italy70 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Universit\u00e0 di Roma, Roma; Italy71 (a)INFN Sezione di Roma Tor Vergata;(b)Dipartimento di Fisica, Universit\u00e0 di Roma Tor Vergata,Roma; Italy72 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Universit\u00e0 Roma Tre, Roma;Italy73 (a)INFN-TIFPA;(b)Universit\u00e0 degli Studi di Trento, Trento; Italy74 Institut f\u00fcr Astro- und Teilchenphysik, Leopold-Franzens-Universit\u00e4t, Innsbruck; Austria75 University of Iowa, Iowa City IA; United States of America76 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America77 Joint Institute for Nuclear Research, Dubna; Russia78 (a)Departamento de Engenharia El\u00e9trica, Universidade Federal de Juiz de Fora (UFJF), Juiz deFora;(b)Universidade Federal do Rio De Janeiro COPPE\/EE\/IF, Rio de Janeiro;(c)Instituto deF\u00edsica, Universidade de S\u00e3o Paulo, S\u00e3o Paulo; Brazil79 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan80 Graduate School of Science, Kobe University, Kobe; Japan81 (a)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science,Krakow;(b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland82 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland83 Faculty of Science, Kyoto University, Kyoto; Japan84 Kyoto University of Education, Kyoto; Japan\u2013 58 \u2013JHEP06(2022)09785 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University,Fukuoka; Japan86 Instituto de F\u00edsica La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina87 Physics Department, Lancaster University, Lancaster; United Kingdom88 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom89 Department of Experimental Particle Physics, Jo\u017eef Stefan Institute and Department of Physics,University of Ljubljana, Ljubljana; Slovenia90 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom91 Department of Physics, Royal Holloway University of London, Egham; United Kingdom92 Department of Physics and Astronomy, University College London, London; United Kingdom93 Louisiana Tech University, Ruston LA; United States of America94 Fysiska institutionen, Lunds universitet, Lund; Sweden95 Departamento de F\u00edsica Teorica C-15 and CIAFF, Universidad Aut\u00f3noma de Madrid, Madrid; Spain96 Institut f\u00fcr Physik, Universit\u00e4t Mainz, Mainz; Germany97 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom98 CPPM, Aix-Marseille Universit\u00e9, CNRS\/IN2P3, Marseille; France99 Department of Physics, University of Massachusetts, Amherst MA; United States of America100 Department of Physics, McGill University, Montreal QC; Canada101 School of Physics, University of Melbourne, Victoria; Australia102 Department of Physics, University of Michigan, Ann Arbor MI; United States of America103 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United Statesof America104 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus105 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus106 Group of Particle Physics, University of Montreal, Montreal QC; Canada107 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia108 National Research Nuclear University MEPhI, Moscow; Russia109 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow;Russia110 Fakult\u00e4t f\u00fcr Physik, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, M\u00fcnchen; Germany111 Max-Planck-Institut f\u00fcr Physik (Werner-Heisenberg-Institut), M\u00fcnchen; Germany112 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan113 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States ofAmerica114 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University\/Nikhef, Nijmegen;Netherlands115 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam;Netherlands116 Department of Physics, Northern Illinois University, DeKalb IL; United States of America117 (a)Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk;(b)Novosibirsk StateUniversity Novosibirsk; Russia118 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino;Russia119 Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National ResearchCentre \u201cKurchatov Institute\u201d, Moscow; Russia120 (a)New York University Abu Dhabi, Abu Dhabi;(b)United Arab Emirates University, AlAin;(c)University of Sharjah, Sharjah; United Arab Emirates121 Department of Physics, New York University, New York NY; United States of America122 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan123 Ohio State University, Columbus OH; United States of America124 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK;United States of America125 Department of Physics, Oklahoma State University, Stillwater OK; United States of America\u2013 59 \u2013JHEP06(2022)097126 Palack\u00fd University, Joint Laboratory of Optics, Olomouc; Czech Republic127 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America128 Graduate School of Science, Osaka University, Osaka; Japan129 Department of Physics, University of Oslo, Oslo; Norway130 Department of Physics, Oxford University, Oxford; United Kingdom131 LPNHE, Sorbonne Universit\u00e9, Universit\u00e9 Paris Cit\u00e9, CNRS\/IN2P3, Paris; France132 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America133 Konstantinov Nuclear Physics Institute of National Research Centre \u201cKurchatov Institute\u201d, PNPI, St.Petersburg; Russia134 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States ofAmerica135 (a)Laborat\u00f3rio de Instrumenta\u00e7\u00e3o e F\u00edsica Experimental de Part\u00edculas \u2014 LIP,Lisboa;(b)Departamento de F\u00edsica, Faculdade de Ci\u00eancias, Universidade de Lisboa,Lisboa;(c)Departamento de F\u00edsica, Universidade de Coimbra, Coimbra;(d)Centro de F\u00edsica Nuclear daUniversidade de Lisboa, Lisboa;(e)Departamento de F\u00edsica, Universidade do Minho,Braga;(f)Departamento de F\u00edsica Te\u00f3rica y del Cosmos, Universidad de Granada, Granada(Spain);(g)Instituto Superior T\u00e9cnico, Universidade de Lisboa, Lisboa; Portugal136 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic137 Czech Technical University in Prague, Prague; Czech Republic138 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic139 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom140 IRFU, CEA, Universit\u00e9 Paris-Saclay, Gif-sur-Yvette; France141 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA;United States of America142 (a)Departamento de F\u00edsica, Pontificia Universidad Cat\u00f3lica de Chile, Santiago;(b)Instituto deInvestigaci\u00f3n Multidisciplinario en Ciencia y Tecnolog\u00eda, y Departamento de F\u00edsica, Universidad deLa Serena;(c)Universidad Andres Bello, Department of Physics, Santiago;(d)Instituto de AltaInvestigaci\u00f3n, Universidad de Tarapac\u00e1, Arica;(e)Departamento de F\u00edsica, Universidad T\u00e9cnicaFederico Santa Mar\u00eda, Valpara\u00edso; Chile143 Universidade Federal de S\u00e3o Jo\u00e3o del Rei (UFSJ), S\u00e3o Jo\u00e3o del Rei; Brazil144 Department of Physics, University of Washington, Seattle WA; United States of America145 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom146 Department of Physics, Shinshu University, Nagano; Japan147 Department Physik, Universit\u00e4t Siegen, Siegen; Germany148 Department of Physics, Simon Fraser University, Burnaby BC; Canada149 SLAC National Accelerator Laboratory, Stanford CA; United States of America150 Department of Physics, Royal Institute of Technology, Stockholm; Sweden151 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States ofAmerica152 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom153 School of Physics, University of Sydney, Sydney; Australia154 Institute of Physics, Academia Sinica, Taipei; Taiwan155 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;(b)HighEnergy Physics Institute, Tbilisi State University, Tbilisi; Georgia156 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel157 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel158 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece159 International Center for Elementary Particle Physics and Department of Physics, University ofTokyo, Tokyo; Japan160 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan161 Tomsk State University, Tomsk; Russia162 Department of Physics, University of Toronto, Toronto ON; Canada\u2013 60 \u2013JHEP06(2022)097163 (a)TRIUMF, Vancouver BC;(b)Department of Physics and Astronomy, York University, Toronto ON;Canada164 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure andApplied Sciences, University of Tsukuba, Tsukuba; Japan165 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America166 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States ofAmerica167 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden168 Department of Physics, University of Illinois, Urbana IL; United States of America169 Instituto de F\u00edsica Corpuscular (IFIC), Centro Mixto Universidad de Valencia \u2014 CSIC, Valencia;Spain170 Department of Physics, University of British Columbia, Vancouver BC; Canada171 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada172 Fakult\u00e4t f\u00fcr Physik und Astronomie, Julius-Maximilians-Universit\u00e4t W\u00fcrzburg, W\u00fcrzburg; Germany173 Department of Physics, University of Warwick, Coventry; United Kingdom174 Waseda University, Tokyo; Japan175 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel176 Department of Physics, University of Wisconsin, Madison WI; United States of America177 Fakult\u00e4t f\u00fcr Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universit\u00e4tWuppertal, Wuppertal; Germany178 Department of Physics, Yale University, New Haven CT; United States of Americaa Also at Borough of Manhattan Community College, City University of New York, New York NY;United States of Americab Also at Bruno Kessler Foundation, Trento; Italyc Also at Center for High Energy Physics, Peking University; Chinad Also at Centro Studi e Ricerche Enrico Fermi; Italye Also at CERN, Geneva; Switzerlandf Also at D\u00e9partement de Physique Nucl\u00e9aire et Corpusculaire, Universit\u00e9 de Gen\u00e8ve, Gen\u00e8ve;Switzerlandg Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spainh Also at Department of Financial and Management Engineering, University of the Aegean, Chios;Greecei Also at Department of Physics and Astronomy, Michigan State University, East Lansing MI; UnitedStates of Americaj Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY; UnitedStates of Americak Also at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israell Also at Department of Physics, California State University, East Bay; United States of Americam Also at Department of Physics, California State University, Fresno; United States of American Also at Department of Physics, California State University, Sacramento; United States of Americao Also at Department of Physics, King\u2019s College London, London; United Kingdomp Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russiaq Also at Department of Physics, University of Fribourg, Fribourg; Switzerlandr Also at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russias Also at Faculty of Physics, Sofia University, \u2018St. Kliment Ohridski\u2019, Sofia; Bulgariat Also at Graduate School of Science, Osaka University, Osaka; Japanu Also at Hellenic Open University, Patras; Greecev Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spainw Also at Institut f\u00fcr Experimentalphysik, Universit\u00e4t Hamburg, Hamburg; Germanyx Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest;Hungaryy Also at Institute of Particle Physics (IPP); Canada\u2013 61 \u2013JHEP06(2022)097z Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijanaa Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgiaab Also at Instituto de Fisica Teorica, IFT-UAM\/CSIC, Madrid; Spainac Also at Istanbul University, Dept. of Physics, Istanbul; Turkeyad Also at Joint Institute for Nuclear Research, Dubna; Russiaae Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russiaaf Also at National Research Nuclear University MEPhI, Moscow; Russiaag Also at Physics Department, An-Najah National University, Nablus; Palestineah Also at Physikalisches Institut, Albert-Ludwigs-Universit\u00e4t Freiburg, Freiburg; Germanyai Also at The City College of New York, New York NY; United States of Americaaj Also at TRIUMF, Vancouver BC; Canadaak Also at Universit\u00e0 di Napoli Parthenope, Napoli; Italyal Also at University of Chinese Academy of Sciences (UCAS), Beijing; Chinaam Also at Yeditepe University, Physics Department, Istanbul; Turkey\u2217 Deceased\u2013 62 \u2013","@language":"en"}],"Genre":[{"@value":"Article","@language":"en"}],"IsShownAt":[{"@value":"10.14288\/1.0422932","@language":"en"}],"Language":[{"@value":"eng","@language":"en"}],"PeerReviewStatus":[{"@value":"Reviewed","@language":"en"}],"Provider":[{"@value":"Vancouver : University of British Columbia Library","@language":"en"}],"Publisher":[{"@value":"Springer Berlin Heidelberg","@language":"en"}],"PublisherDOI":[{"@value":"10.1007\/JHEP06(2022)097","@language":"en"}],"Rights":[{"@value":"Attribution 4.0 International (CC BY 4.0)","@language":"en"}],"RightsURI":[{"@value":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/","@language":"en"}],"ScholarlyLevel":[{"@value":"Faculty","@language":"en"}],"Subject":[{"@value":"Hadron-Hadron Scattering","@language":"en"}],"Title":[{"@value":"Measurement of Higgs boson decay into b-quarks in associated production with a top-quark pair in pp collisions at \r\n              \r\n                \r\n              \r\n              \r\n                \r\n                  s\r\n                \r\n              \r\n              $$ \\sqrt{s} $$\r\n             = 13 TeV with the ATLAS detector","@language":"en"}],"Type":[{"@value":"Text","@language":"en"}],"URI":[{"@value":"http:\/\/hdl.handle.net\/2429\/83538","@language":"en"}],"SortDate":[{"@value":"2022-06-17 AD","@language":"en"}],"@id":"doi:10.14288\/1.0422932"}