Measurements of Higgs boson production cross-sections in the H ->tau(+) tau(-) decay channel in pp collisions at root s=13 TeV with the ATLAS detector

JHEP08(2022)175 Published for SISSA by Springer Received:January 21, 2022 Revised:April 13, 2022 Accepted:June 21, 2022 Published:August 18, 2022 Measurements of Higgs boson production cross-sections in the H→τ+τ−decay channel in pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: Measurements of the production cross-sections of the Standard Model (SM) Higgs boson ( H ) decaying into a pair of τ -leptons are presented. The measurements use data collected with the ATLAS detector from pp collisions produced at the Large Hadron Collider at a centre-of-mass energy of √s = 13 TeV , corresponding to an integrated luminosity of 139 fb−1 . Leptonic ( τ→`ν`ντ ) and hadronic ( τ→hadronsντ ) decays of the τ -lepton are considered. All measurements account for the branching ratio of H→ττ and are performed with a requirement |yH|< 2 . 5, where yH is the true Higgs boson rapidity. The cross-section of the pp →H→ττ process is measured to be 2 . 94 ± 0 . 21 (stat)+ 0.37 −0.32(syst) pb, in agreement with the SM prediction of 3.17 ±0.09 pb . Inclusive cross-sections are determined separately for the four dominant production modes: 2 . 65 ± 0 . 41 (stat)+ 0.91 −0.67(syst) pb for gluon-gluon fusion, 0 . 197 ± 0 . 028 (stat)+ 0.032 −0.026(syst) pb for vectorboson fusion, 0 . 115 ± 0 . 058 (stat)+ 0.042 −0.040(syst) pb for vector-boson associated production, and 0 . 033 ± 0 . 031 (stat)+ 0.022 −0.017(syst) pb for top-quark pair associated production. Measurements in exclusive regions of the phase space, using the simplified template cross-section framework, are also performed. All results are in agreement with the SM predictions. Keywords: Hadron-Hadron Scattering , Higgs Physics, Tau Physics ArXiv ePrint: 2201.08269 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP08(2022)175 JHEP08(2022)175 Contents 1 Introduction 1 2 The ATLAS detector 3 3 Data and simulated event samples 4 3.1 Higgs boson simulation samples 5 3.2 Background processes simulation samples 6 4 Object and event selection 8 4.1 Object reconstruction 8 4.2 Event selection 12 4.3 Event categorisation 14 5 Background modelling 20 5.1 Z→ττ background modelling using Z→`` events 22 5.2 Data-driven estimate of misidentified τprocesses 26 6 Systematic uncertainties 29 6.1 Experimental uncertainties 29 6.2 Background theoretical uncertainties 30 6.3 Signal theoretical uncertainties 31 7 Statistical analysis 32 8 Results 36 9 Conclusion 46 A Distributions of mMMC τ τ and fit projection in each signal region 48 The ATLAS collaboration 64 1 Introduction A particle consistent with the Standard Model (SM) Higgs boson [ 1 – 6 ] was discovered in 2012 by the ATLAS and CMS collaborations [ 7 , 8 ] from the analysis of proton-proton ( pp ) collisions produced by the Large Hadron Collider (LHC) [ 9 ]. Since then, the analysis of data collected at centre-of-mass energies of 7TeV , 8TeV and 13TeV in Runs 1 and 2 of the LHC 1 1Run 1 signifies the LHC data-taking period in the years 2010–2012 and Run 2 the one in 2015–2018. – 1 – JHEP08(2022)175 has led to the precise measurement of the Higgs boson mass, mH = 125.09 GeV [ 10 ], and to the observation and measurement of the four main production modes (gluon-gluon fusion, vector-boson fusion, and associated production with either a weak gauge boson or a pair of top quarks) and of several decay channels of the Higgs boson predicted by the SM [ 11 – 25 ]. The decay into a τ+τ− pair 2 has the largest branching fraction of all leptonic Higgs boson decays ( 6.3% [ 26 , 27 ] for a mass of mH = 125.09 GeV ). The large number of Higgs boson decays into ττ produced at the LHC ( ≈ 500 · 10 3 during Run 2) offers a unique opportunity to study the Yukawa mechanism in detail. Measurements in this final state are, however, complicated at the experimental level, as the presence of two to four neutrinos 3 in the final state significantly degrades the resolution of the measured Higgs boson fourmomentum, rendering the separation between the signal and the large background from Z→ττ events difficult. This effect can be mitigated through the dedicated study of the Higgs production modes where the event topology differs drastically from that of Z +jets events, the two most sensitive being the production of the Higgs boson through vectorboson fusion (VBF) and its production through gluon-gluon fusion (ggF) with Higgs boson produced with a large transverse momentum. The first evidence of the ττ decay of the Higgs boson was obtained by the ATLAS [ 28 ] and CMS [ 29 ] collaborations using data collected at centre-of-mass energies of 7TeV and 8TeV during Run 1 of the LHC. The combination [ 21 ] of these two results led to the first observation of the ττ decay of the Higgs boson. More recent measurements in the H→ττ decay channel are documented in refs. [30–32]. This paper presents measurements of the Higgs boson decaying into a ττ pair with the ATLAS detector, using the full Run 2 LHC dataset. The pp →H→ττ process is measured inclusively, in the four dominant production modes simultaneously, and as a function of key properties of the event. This is achieved with an optimised categorisation of the collected events. Three ττ final states are targeted: two hadronically decaying τ -leptons ( τhad , where the tau decays into hadrons plus a neutrino), denoted τhadτhad ; one leptonically decaying τ -lepton ( τlep ) and one τhad , denoted τlepτhad ; 4 and two τlep with different flavours, denoted τeτµ . The remaining final states, with two same-flavour light leptons ( τeτe and τµτµ ), are not considered due to large uncertainties in Z→ee and Z→µµ contributions to the expected background. The dominant background processes after the event selection are Z→ττ decays, t¯ t production, and processes with at least one jet misreconstructed as a τhad . Smaller contributions to the background arise from events with Z→``5 decays, two weak vector bosons V V (diboson), and H→WW∗ decays. Templates of the estimated invariant mass of the ττ pairs are built for each process in the signal regions (SR) defined by the event selection and categorisation. The templates are used as input to a binned maximumlikelihood fit which allows the yields and kinematics of both the signal and the background processes to be measured. Control regions (CR) enter the fit as event counts and help determine the normalisation of the main backgrounds as well as constrain their uncertainties. 2For simplicity, a τ+τ−pair is denoted by ττ throughout the paper. 3The number of neutrinos depends on the decay modes of the two τ-leptons. 4 The τlepτhad categories can be split into τeτhad and τµτhad when distinguishing the light lepton’s flavour is appropriate. 5In this document, `=e, µ. – 2 – JHEP08(2022)175 This work uses 139fb−1 of pp collision data collected at a centre-of-mass energy of 13TeV , to be compared with 36fb−1 for the previous H→ττ cross-section measurements [ 22 ]. It introduces a new reconstructed-event categorisation designed for the improved stage 1.2 binning [ 33 ] of the simplified template cross-section (STXS) framework [ 27 ]. The treatment of ggF events with Higgs boson produced with a large transverse momentum is refined with three times more categories. Selected events are categorised with requirements on the transverse momentum of the reconstructed Higgs boson candidate ( pT ( H )) and on the potential additional hadronic jets. Two new categories targeting production modes where the Higgs boson is created in association with other objects are added, based on requirements on the kinematics and tagged flavour of the jets in the event. The first targets the production of a Higgs boson in association with a pair of top quarks ( t¯ tH ), where both top quarks and both τ -leptons decay hadronically, complementing the explorations in ref. [ 34 ], and is denoted by tt(0 ` ) H→τhadτhad in the rest of this paper. The second targets the production of a Higgs boson in association with a vector boson V ( W , Z ). This new category, referred to as V(had)H, focuses on events with a hadronic decay of the V boson while the production of Z ( →`` ) H and W ( →`ν ) H events is studied separately [ 35 ]. Finally, the selection of VBF events was also improved by multivariate techniques. In addition to the new extended categorisation, several improvements to the analysis methodology have been implemented: the object selection has been improved, multivariate discriminants have been optimised to enhance the purity of the SRs in the targeted Higgs boson production modes, the number of simulated background events has been increased significantly and the usage of the Z→`` control region has been refined. The latter relies on a new simplified implementation of the embedding technique [ 36 , 37 ] which, instead of replacing the reconstructed electrons and muons from Z→`` events by equivalent simulated τ -lepton decay products, simply rescales their transverse momentum to that of an equivalent τ-lepton. This document is organised as follows. Section 2describes the ATLAS detector. This is followed in section 3by a description of the dataset and Monte Carlo (MC) simulated samples employed in the measurement. Section 4.1 details the reconstruction of the physics objects. The event selection and categorisation is described in section 4.2. In section 5, the estimation of the background processes is discussed with an emphasis on the simplified embedding technique to model Z→ττ processes in section 5.1 and the data-driven estimates of the processes with at least one jet misidentified as an electron, a muon or a τhad in section 5.2. Section 6presents the systematic uncertainties affecting the measurement and their estimation. The details of the signal extraction fit are discussed in section 7, and section 8 presents the results of the measurement. Section 9summarises the conclusions of this work. 2 The ATLAS detector The ATLAS detector [ 38 ] at the LHC covers nearly the entire solid angle around the collision point. 6 It consists of an inner tracking detector surrounded by a thin superconducting 6 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z -axis along the beam pipe. The x -axis points from the IP to the centre – 3 – JHEP08(2022)175 solenoid, electromagnetic and hadron calorimeters, and a muon spectrometer incorporating three large superconducting air-core toroidal magnets. The inner-detector system (ID) is immersed in a 2T axial magnetic field and provides charged-particle tracking in the range |η|< 2 . 5. The high-granularity silicon pixel detector covers the vertex region and typically provides four measurements per track, the first hit normally being in the insertable B-layer installed before Run 2 [ 39 , 40 ]. It is followed by the silicon microstrip tracker, which usually provides eight measurements per track. These silicon detectors are complemented by the transition radiation tracker (TRT), which enables radially extended track reconstruction up to |η| = 2 . 0. The TRT also provides electron identification information based on the fraction of hits (typically 30 in total) above a higher energy-deposit threshold corresponding to transition radiation. The calorimeter system covers the pseudorapidity range |η|< 4 . 9. Within the region |η|< 3 . 2, electromagnetic calorimetry is provided by barrel and endcap high-granularity lead/liquid-argon (LAr) calorimeters, with an additional thin LAr presampler covering |η|< 1 . 8to correct for energy loss in material upstream of the calorimeters. Hadron calorimetry is provided by the steel/scintillator-tile calorimeter, segmented into three barrel structures within |η|< 1 . 7, and two copper/LAr hadron endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic energy measurements respectively. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by the superconducting air-core toroidal magnets. The field integral of the toroids ranges between 2.0 and 6.0Tm across most of the detector. A set of precision chambers covers the region |η|< 2 . 7with three layers of monitored drift tubes, complemented by cathode-strip chambers in the forward region, where the background is highest. The muon trigger system covers the range |η|< 2 . 4with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. Interesting events are selected by the first-level (L1) trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the high-level trigger [ 41 ]. The first-level trigger accepts events from the 40MHz bunch crossings at a rate below 100kHz , which the high-level trigger reduces in order to record events to disk at about 1 kHz. An extensive software suite [ 42 ] is used in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 3 Data and simulated event samples The data used in this analysis were collected using unprescaled single-lepton, dilepton or ττ triggers [ 43 – 46 ] at a centre-of-mass energy of 13 TeV during the 2015–2018 LHC running of the LHC ring, and the y -axis points upwards. Cylindrical coordinates ( r, φ )are used in the transverse plane, φ being the azimuthal angle around the z -axis. The pseudorapidity is defined in terms of the polar angle θas η=−ln tan(θ/2). Angular distance is measured in units of ∆R≡p(∆η)2+ (∆φ)2. – 4 – JHEP08(2022)175 Process Generator PDF set Tune Normalisation ME PS ME PS Higgs boson ggF Powheg Box v2 Pythia 8PDF4LHC15nnlo CTEQ6L1 AZNLO N3LO QCD + NLO EW VBF Powheg Box v2 Pythia 8PDF4LHC15nlo CTEQ6L1 AZNLO NNLO QCD + NLO EW V H Powheg Box v2 Pythia 8PDF4LHC15nlo CTEQ6L1 AZNLO NNLO QCD + NLO EW t¯ tH Powheg Box v2 Pythia 8NNPDF3.0nnlo NNPDF2.3lo A14 NLO QCD + NLO EW tH MadGraph5_Pythia 8CT10 NNPDF2.3lo A14 NLO aMC@NLO b¯ bH Powheg Box v2 Pythia 8NNPDF3.0nnlo NNPDF2.3lo A14 NLO Background V+jets (QCD/EW) Sherpa 2.2.1 NNPDF3.0nnlo Sherpa NNLO for QCD, LO for EW t¯ tPowheg Box v2 Pythia 8NNPDF3.0nnlo NNPDF2.3lo A14 NNLO + NNLL Single top Powheg Box v2 Pythia 8NNPDF3.0nnlo NNPDF2.3lo A14 NLO Diboson Sherpa 2.2.1 NNPDF3.0nnlo Sherpa NLO Table 1. Overview of the MC generators used for the main signal and background samples. The last column, labelled ‘Normalisation’, specifies the order of the cross-section calculation used for the normalisation of the simulated samples. periods. Events are selected for analysis only if they are of good quality and if all the relevant detector components are known to have been in good operating condition [ 47 ], which corresponds to a total integrated luminosity of 139.0fb−1. MC simulated events are used to model most of the backgrounds from SM processes and the H→ττ signal processes. A summary of all the generators used for the simulation of the signal and background processes is shown in table 1. The same event generators as in ref. [ 22 ] were used, but the number of simulated events in each sample was at least quadrupled, which is the factor by which the integrated luminosity grew since the previous publication. In addition, the total number of simulated Z→ττ events was increased by a further factor of approximately four. This computationally expensive task helps to densely populate the phase space where Z→ττ events are produced in association with several jets. All samples of simulated events were processed through the ATLAS detector simulation [ 48 ] based on Geant4 [ 49 ]. The effects of multiple interactions in the same and nearby bunch crossings (pile-up) were modelled by overlaying minimum-bias events, simulated using the soft QCD processes of Pythia 8.186 [ 50 ] with the A3 [ 51 ] set of tuned parameters and NNPDF2.3lo [52] parton distribution functions (PDF). The decays and spin correlations for τ -leptons are handled by Sherpa for the samples it generated, and by Pythia for the other MC event generators. The decays and spin correlations have been included in Pythia version 8.150 [ 53 ], and have been thoroughly validated by comparisons with Tauola [54]. 3.1 Higgs boson simulation samples The main Higgs boson production mode at the LHC is ggF with a total expected crosssection of 48.6pb , followed by VBF ( 3.78pb ), associated V H ( 2.25pb ), associated b¯ bH ( 0.64pb ) and t¯ tH ( 0.51pb ) production. Simulated event samples for these production modes were generated using Powheg Box v2 [ 55 – 59 ]. The tH process was also considered, – 5 – JHEP08(2022)175 but with a cross-section of 0.092pb its expected contribution was found to be negligible. It was simulated with the MadGraph5_aMC@NLO 2.6.2 [60] generator. For the ggF sample the PDF4LHC15nnlo PDF set [ 61 ] was used, while VBF and V H production samples used the PDF4LHC15nlo PDF set. The t¯ tH and b¯ bH events were produced with the NNPDF3.0nlo PDF set [ 62 ], and tH events with the CT10 PDF set [ 63 ]. Parton shower (PS) and non-perturbative effects were modelled with Pythia 8.230 [ 64 ] with parameter values set according to the AZNLO tune [ 65 ], except for t¯ tH , b¯ bH and tH events, which rely on the A14 tune [66]. Higgs boson production via gluon-gluon fusion was simulated at next-to-next-toleading-order (NNLO) accuracy in QCD. The simulation achieves NNLO accuracy for arbitrary inclusive gg →Hobservables by reweighting the Higgs boson rapidity spectrum in Hj-MiNLO [ 67 – 69 ] to that of HNNLO [ 70 ]. The gluon-gluon fusion prediction from the MC simulated samples is normalised to the next-to-next-to-next-to-leading-order (N 3 LO) cross-section in QCD plus electroweak (EW) corrections at next-to-leading order (NLO) [27,71–80]. Higgs boson production via vector-boson fusion was simulated at NLO accuracy in QCD. It is tuned to match calculations with effects due to finite heavy-quark masses and soft-gluon resummations up to next-to-next-to-leading logarithms (NNLL). The prediction from the MC simulated samples is normalised to an approximate-NNLO QCD cross-section with NLO electroweak corrections [81–83]. Higgs boson production in association with a vector boson was simulated at next-toleading order accuracy for V H plus one-jet production. The loop-induced gg →ZH process was generated separately at leading order in QCD. The prediction from the MC simulated sample is normalised to cross-sections calculated at NNLO in QCD with NLO electroweak corrections for pp →V H and at NLO and next-to-leading-logarithm accuracy in QCD for gg →ZH [84–90]. The production of t¯ tH events was simulated at NLO accuracy in QCD. The decays of bottom and charm hadrons were performed by EvtGen 1.6.0 [ 91 ]. The cross-section used to normalise the t¯ tH process is calculated at NLO in QCD and electroweak couplings [ 27 , 92 – 95 ]. The production of b¯ bH and tH events was simulated at NLO. The prediction from the MC simulated samples is normalised to cross-sections calculated at NLO in QCD [96–98]. The normalisation of all Higgs boson samples accounts for the decay branching ratio calculated with HDECAY [ 26 , 99 , 100 ] and Prophecy4f [ 101 – 103 ]. A Higgs boson mass of 125.09GeV is assumed in the calculation of the expected cross-sections throughout this measurement. 3.2 Background processes simulation samples The QCD production of V + jets events was simulated with the Sherpa 2.2.1 [ 104 ] generator using NLO matrix elements for up to two partons, and LO matrix elements for up to four partons, calculated with the Comix [ 105 ] and OpenLoops [ 106 – 108 ] libraries. They were matched with the Sherpa parton shower [ 109 ] using the MEPS@NLO prescription [ 110 – 113 ] using the set of tuned parameters developed by the Sherpa authors. The NNPDF3.0nnlo set of PDFs [62] was used and the samples are normalised to a NNLO prediction [114]. – 6 – JHEP08(2022)175 Electroweak production of ``jj , `νjj and ννjj final states was generated with Sherpa 2.2.1, using LO matrix elements with up to two additional parton emissions. The matrix elements were merged with the Sherpa parton shower following the MEPS@LO prescription and using the set of tuned parameters developed by the Sherpa authors. Similarly to the QCD V + jets processes, the NNPDF3.0nnlo set of PDFs was employed. The samples were produced using the VBF approximation, which avoids an overlap with semileptonic diboson topologies by requiring a t-channel colour-singlet exchange. They are normalised using the Sherpa cross-section predictions. QCD and electroweak predictions for V + jets events are grouped in the analysis and collectively referred to as V+jets in the rest of the paper. The production of t¯ t events was modelled by the Powheg Box v2 generator at NLO with the NNPDF3.0nlo PDF set and the hdamp parameter 7 set to 1.5 mtop [ 115 ]. The events were interfaced to Pythia 8.230 to model the parton shower, hadronisation, and underlying event, with parameters set according to the A14 tune and using the NNPDF2.3lo set of PDFs. The decays of bottom and charm hadrons were performed by EvtGen as for the t¯ tH sample. The t¯ t sample is normalised to the cross-section prediction at NNLO in QCD including the resummation of NNLL soft-gluon terms calculated using Top++ 2.0 [ 116 – 122 ]. Single-top s-channel (t-channel) production was modelled using the Powheg Box v2 [ 55 – 58 ] generator at NLO in QCD in the five-flavour (four-flavour) scheme with the NNPDF3.0nlo set of PDFs [ 62 ]. The events were interfaced with Pythia 8.230 [ 64 ] using the A14 tune [ 66 ] and the NNPDF2.3lo PDF set. The sample is normalised to the theory prediction calculated at NLO in QCD with Hathor 2.1 [123,124]. Diboson production was simulated with the Sherpa 2.2.1 or 2.2.2 generator depending on the process. Fully leptonic final states and semileptonic final states, where one boson decays leptonically and the other hadronically, were generated using matrix elements at NLO accuracy in QCD for up to one additional parton and at LO accuracy for up to three additional parton emissions. Samples for the loop-induced processes gg →V V were generated using LO-accurate matrix elements for up to one additional parton emission for both the fully leptonic and semileptonic final states. The matrix element calculations were matched and merged with the Sherpa parton shower based on Catani-Seymour dipole factorisation [ 105 , 109 ] using the MEPS@NLO prescription. The virtual QCD corrections were provided by the OpenLoops library. The NNPDF3.0nnlo set of PDFs was used [ 62 ], along with the dedicated set of tuned parton-shower parameters developed by the Sherpa authors. The samples are normalised to a NLO prediction [125]. The background originating from H→WW∗ decays was modelled using the same simulation strategy as the H→ττ signal. 7 The hdamp parameter is a resummation damping factor and one of the parameters that controls the matching of Powheg matrix elements to the parton shower and thus effectively regulates the highpT radiation against which the t¯ tsystem recoils. – 7 – JHEP08(2022)175 4 Object and event selection The topology of H→ττ events requires the reconstruction of electrons, muons, visible products of hadronically decaying τ -leptons ( τhad-vis ), jets (along with their b -tagging properties) and missing transverse momentum. The numbers of reconstructed electrons, muons and τhad-vis in each event are used to define the different channels of the analysis. Requirements on the number of additional jets in the event are used in the signal region categorisation and to suppress backgrounds. 4.1 Object reconstruction Tracks measured in the ID are used to reconstruct interaction vertices [ 126 ], of which the one with the highest sum of squared transverse momenta of the associated tracks is selected as the primary vertex of the hard interaction. Electrons are reconstructed from topological clusters of energy deposits in the electromagnetic calorimeter which are matched to a track reconstructed in the ID [ 127 ]. They are required to satisfy the ‘Loose’ identification criteria, to have pT>15 GeV , and to be in the fiducial volume of the ID and the high-granularity electromagnetic calorimeters, |ηcluster|< 2 . 47. The transition region between the barrel and endcap calorimeters (1 . 37 <|ηcluster|< 1 . 52) is excluded except for the Z→`` control region where it is kept to facilitate the embedding procedure (see section 5.1). In the τeτµ and τeτhad channels, the selected electron is further required to satisfy the ‘Medium’ identification, which has an associated efficiency of 80% to 90% , and the ‘Loose’ isolation criterion [ 127 ] in the signal regions and most control regions, which has an efficiency of 90% for 15GeV candidates, increasing to more than 98% for 30GeV candidates. In the τeτhad channel, the requirement on the electron transverse momentum is further tightened by 1GeV above the nominal trigger pT threshold for electrons matched to the single-electron trigger to ensure operation at the trigger’s plateau efficiency. Similarly, in the τeτµ channel, the requirement is tightened if the event is accepted by the single-electron trigger or the electron-muon trigger. Table 2 summarises the exact requirements used depending on the data-taking period. Muons are reconstructed from signals in the MS matched with tracks inside the ID. They are required to satisfy the ‘Loose’ identification criteria [ 132 ], corresponding to an efficiency above 97% for all muon candidates considered in this analysis, and to have pT>10 GeV and |η|< 2 . 5. In the τeτµ and τµτhad channels, the selected muon in the signal regions is further required to satisfy a ‘Tight’ isolation criterion [ 132 ] based on track information. This requirement has an efficiency increasing from 85% to 99% for muons with transverse momentum increasing from 10GeV to 50GeV and above. In the τµτhad channel, the requirement on the muon transverse momentum is further tightened to select events in which the single-muon trigger operates with very high efficiency. Similarly, in the τeτµ channel, the requirement is further tightened if the event is accepted by the single-muon trigger or the electron-muon trigger. Table 2summarises the requirements used depending on the data-taking period. Jets are reconstructed using a particle-flow algorithm [ 133 ] from noise-suppressed positive-energy topological clusters in the calorimeter using the antikt algorithm with a – 8 – JHEP08(2022)175 Variable VBF V(had)H ttH vs t¯ tttH vs Z→ττ Jet properties Invariant mass of the two leading jets • • pT(jj)• • Product of ηof the two leading jets • Sub-leading jet pT• Leading jet η• Sub-leading jet η• Scalar sum of all jets pT• • Scalar sum of all b-tagged jets pT• Best W-candidate dijet invariant mass • • Best top-quark-candidate three-jet invariant mass • • Angular distances ∆φbetween the two leading jets • ∆ηbetween the two leading jets • • ∆Rbetween the two leading jets • ∆R(ττ, jj)• ∆R(τ, τ)• • Smallest ∆R(any two jets) • |∆η(τ, τ)| • • τprop. pT(ττ)• Sub-leading τ pT• Sub-leading τ η • H cand. pT(Hjj)• • pT(H)/pT(jj)• ~ Emiss T Missing transverse momentum Emiss T• • • Smallest ∆φ(τ, ~ Emiss T)• Table 5. Variables used in the four multivariate taggers employed in the analysis. For each tagger, the presence or absence of a • indicates whether the variable is used or not. The symbol τ stands for any reconstructed τ -lepton candidate (electron, muon or τhad-vis ) as appropriate in each channel. The symbols ττ and jj indicate the vectorial sums of the momenta of two visible τ -lepton candidates and of the two leading jets, respectively. The Higgs boson candidate H is formed by the vector sum of the two τ -lepton candidates’ momenta and ~ Emiss T . The W candidate is built as the pair of nonb -tagged jets in the event with invariant mass closest to mW . The top-quark candidate is built as the system of the W candidate and a b -tagged jet in the event with invariant mass closest to mtop . – 15 – JHEP08(2022)175 The signal-enhancing separation in this category uses two BDTs: one BDT is optimised to enhance t¯ tH signal events over Z→ττ background events, while the second BDT is optimised to enhance t¯ tH signal events over t¯ t background events. A variety of twodimensional combinations of requirements on the two BDT scores were studied, using the expected counting-experiment statistical significance, 10 including an estimate of the systematic uncertainties in the background normalisations, as an estimator for their performance; none was found to outperform a simple rectangular requirement in the plane formed by the two BDT scores, and this was the requirement ultimately selected. Of all Higgs boson events selected in the ttH_0 (ttH_1) categories 74% ( 92% ) are due to the t¯ tH process. All other event categories in the τhadτhad channel require that no b -tagged jets with pT>20 GeV and |η|<2.5are present. VBF categorisation. The VBF categories are designed to select Higgs bosons produced from the fusion of two vector bosons emitted by two quarks of the colliding protons. The scattered quarks give rise to two highpT jets with a large rapidity gap and therefore large invariant mass mjj . This signature allows VBF events to be experimentally distinguished from the other Higgs production modes and Z→ττ events. To match the STXS qq →H particle-level pjet T requirement and mjj binning, events selected in the VBF categories must have mjj >350 GeV and pT of the sub-leading jet greater than 30 GeV . Additional selection criteria are applied to enhance the VBF Higgs production mode relative to the Z→ττ background. The product of the pseudorapidities of the two leading jets ( η ( j0 ) ×η ( j1 )) is required to be negative (i.e. jets must be in opposite hemispheres of the detector). The absolute difference in pseudorapidity ( | ∆ ηjj| ) is required to be greater than 3. Finally, the visible decay products of the τ -leptons are required to be reconstructed in the rapidity gap between the VBF jets. The VBF tagger is optimised by treating both the ggF H→ττ and Z→ττ events as backgrounds and relies solely on observables based on the kinematics of the two leading jets (see table 5). While the expected contribution from ggF H→ττ events is small, the considerably larger theoretical uncertainty associated with its cross-section prediction in this kinematic phase space can significantly enlarge the systematic uncertainty of the VBF production cross-section measurement. The BDT score requirement used to define the categories was optimised to give the smallest uncertainty in the VBF cross-section, and provides a selection where the fraction of VBF events among all Higgs boson events is about 94% ( 63% ) in the VBF_1 (VBF_0) region. V(had)H categorisation. To match the STXS qq →V ( →qq ) H particle-level pjet T requirement and mjj binning, events selected in the V(had) categories must satisfy 60 GeV < mjj <120 GeV and pTof the sub-leading jet greater than 30 GeV. The V(had)H tagger was trained by treating all Higgs events produced by processes other than V H as background. The BDT score requirement used to define the two categories was optimised to give the smallest uncertainty for the V(had)H cross-section, and provides 10The “Poisson-Binomial model” in ref. [148]. – 16 – JHEP08(2022)175 Njets(pT>30 GeV)pT(H)bins in GeV [100, 120] [120, 200] [200, 300] [300,∞[ Exactly 1 boost_0_1J boost_1_1J boost_2 boost_3 At least 2 boost_0_ge2J boost_1_ge2J Table 6. Definition of the six categories in the boosted phase space. a selection where the expected fraction of V(had)H among all Higgs boson events is 66% (24%) in the VH_1 (VH_0) category. Boost categorisation. Events failing to meet the criteria of the VBF, V(had)H and ttH categories but having highpT Higgs candidates are considered for the ‘boost’ categories targeting ggF events with large Higgs boson transverse momentum. The reconstructed Higgs boson transverse momentum, pT ( H ), is determined from the Higgs boson candidate defined by the vectorial sum of the momenta of the visible decay products of the τ -leptons and ~ Emiss T . Events in the boost category must satisfy pT ( H ) >100 GeV . To match the STXS gg →H particle-level requirements, events are further categorised by pT ( H )value and by the total number of jets with pT greater than 30GeV ( Njets ( pT> 30 GeV )). Events with pT ( H ) <200 GeV are separated into 1-jet and ≥ 2-jet categories, while for pT ( H ) >200 GeV events with at least one jet are considered without further jet-multiplicity separation of the events. Table 6describes the boost phase-space categorisation. The three analysis channels are therefore split into six kinematic categories in the boost phase space for a total of eighteen categories in the fit performed for the cross-section measurement. Summary. Nine bins of the STXS framework are targeted in the measurement presented in this paper and are illustrated in figure 1. The expected signal yields for each of these bins is presented in figure 2(a), while figure 2(b) illustrates the relative population of these nine bins in each reconstruction category described in this section. Events selected in each reconstruction category are used to build templates of the mMMC ττ variable for each of the nine bins. As illustrated in figure 2, ggF events produced with pT ( H )< 200GeV and two additional jets forming a system with mjj > 350GeV are mainly reconstructed in the VBF_0 category ( 61% ) and the boost_1_ge2J category ( 36% ). It is difficult to select these events in only a single category but through the simultaneous usage of all the categories, their production rate can be measured. In contrast, the reconstructed ggF event candidates satisfying 60GeV < pT ( H )< 120GeV are further separated into those produced with a single jet (boost_0_1J) and those produced with two jets forming a system with mjj < 350GeV (boost_0_ge2J). However, the categorisation does not provide enough sensitivity to measure these two contributions individually and they are therefore combined. – 17 – JHEP08(2022)175 100<pT(H)<120 g2 jets ttH VH pT(H)>200 ggF ggF pT(H)>300 ggF 200<pT(H)<300 ggF g2 jets mjj>350 pT(H)<200 pT(H)>300 200<pT(H)<300 g2 jets =1 jet mjj>350 120<pT(H)<200 60<pT(H)<120 g2 jets =0 b-tags VBF_1 VBF_0 VBF tagger V(had)H tagger 60<mjj<120 |·jj|>3 ·j1×·j2<0 mjj>350 VH_1 VH_0 ttH taggers ttH_1 ttH_0 g5 jets and g2 b-tags or g6 jets and g1 b-tags boost_3 pT(H)>200 pT(H)>300 boost_2 200<pT(H)<300 boost_1_ge2J boost_0_ge2J pT(H)<200 120<pT(H)<200 120<pT(H)<200 100<pT(H)<120 boost_0_1J boost_1_1J g2 jets =1 jet ATLAS Event categories H³𝜏𝜏 Cross-sections in STXS stage 1.2 framework mjj<350 ggF =1 jet 120<pT(H)<200 120<pT(H)<200 ggF g2 jets mjj<350 60<pT(H)<120 ggF =1 jet 60<pT(H)<120 60<pT(H)<120 ggF g2 jets mjj<350 120<pT(H)<200 g1 jets =0 b-tags ttH EW qqH mjj>350 EW qqH EW qqH 60<mjj<120 Production modes mjj>350 60<mjj<120 V³qq' 𝜏lep𝜏had 𝜏had𝜏had 𝜏e𝜏¿ VBF Figure 1. Sketch of the event categorisation and the targeted cross-sections in the STXS stage 1.2 framework (bins). The relative contributions to each event category from the two most dominant STXS bins are indicated by the two colours used along the width of the category box. The requirements on pT(H)and mjj are given in units of GeV. – 18 – JHEP08(2022)175 0 20 40 60 80 100 120 140 160 180 200 220 Expected Signal Yields STXS Binning boost_0_1J boost_0_ge2J boost_1_1J boost_1_ge2J boost_2 boost_3 VH_0 VH_1 VBF_0 VBF_1 ttH_0 ttH_1 Reconstructed Category 104.2 39.0 4.7 0.0 0.0 2.0 0.9 4.7 0.0 54.5 3.3 21.1 0.1 0.0 7.9 1.1 5.2 0.5 38.0 17.2 20.1 0.0 4.4 1.9 12.6 0.0 30.9 24.0 28.7 0.0 36.0 3.9 25.9 1.9 0.1 14.7 14.6 14.5 4.5 2.4 25.5 1.3 0.0 0.0 0.0 9.8 75.8 0.0 1.1 12.0 0.7 63.9 9.8 92.4 51.3 12.5 1.6 74.5 2.1 1.1 1.9 0.5 7.1 5.9 2.6 0.1 35.9 0.2 0.4 9.8 5.7 4.0 23.8 8.0 61.4 1.5 0.4 0.2 0.2 0.0 3.1 1.5 4.0 0.1 0.0 0.2 0.0 0.5 0.8 0.4 0.5 0.3 0.3 8.5 0.0 0.0 0.0 0.2 0.2 0.1 0.0 0.1 6.7 223.4 188.5 200.3 191.0 131.2 SimulationATLAS ττ →, H -1 = 13 TeV, 139 fbs N(jets): (H) [GeV]: T p [GeV]: jj m 1≥1 2≥ 0≥ 0≥ 2≥ 2≥ 2≥ [60, 120] [120, 200] [200, 300] [∞[300, [0, 200] ♠ [0, 350] [0, 350] [∞[350, [60, 120] [∞[350, qq)H→ Z(→gluon fusion + gg qq)H→VBF + V( ttH (a) 0 10 20 30 40 50 60 70 80 90 100 Expected Signal Purity [%] STXS Binning boost_0_1J boost_0_ge2J boost_1_1J boost_1_ge2J boost_2 boost_3 VH_0 VH_1 VBF_0 VBF_1 ttH_0 ttH_1 Reconstructed Category 25.1 3.0 0.0 0.0 1.3 0.6 3.0 0.0 3.5 22.6 0.1 0.0 8.4 1.1 5.5 0.5 12.0 5.4 6.3 0.0 1.4 0.6 4.0 0.0 9.1 7.1 8.4 0.0 10.6 1.2 7.6 0.6 0.0 5.3 5.3 5.2 1.6 0.9 9.2 0.5 0.0 0.0 0.0 9.9 0.0 1.1 12.0 0.7 20.7 3.2 29.9 16.6 4.1 0.5 24.1 0.7 0.3 3.4 0.9 13.0 10.8 4.8 0.1 0.4 0.8 3.2 1.9 1.3 7.8 2.6 20.1 0.5 0.1 0.1 0.2 0.0 2.2 1.0 2.9 0.0 0.0 1.3 0.1 4.7 7.4 3.8 4.0 2.5 2.4 0.0 0.0 0.3 2.7 3.0 0.7 0.4 1.0 67.0 58.3 70.4 55.5 72.1 76.3 65.8 62.5 93.5 73.6 91.9 SimulationATLAS ττ →, H -1 = 13 TeV, 139 fbs N(jets): (H) [GeV]: T p [GeV]: jj m 1≥1 2≥ 0≥ 0≥ 2≥ 2≥ 2≥ [60, 120] [120, 200] [200, 300] [∞[300, [0, 200] ♠ [0, 350] [0, 350] [∞[350, [60, 120] [∞[350, qq)H→ Z(→gluon fusion + gg qq)H→VBF + V( ttH (b) Figure 2. (a) Expected H→ττ signal yield in each of the reconstructed-event categories of the analysis ( y -axis) for each of the nine measured STXS bins ( x -axis). (b) Relative contribution of each of the nine measured STXS bins to the total H→ττ signal expectation in each reconstructed-event category. The spades symbol ( ♠ ) indicates that the criteria for mjj only apply to events with at least two reconstructed jets. Yields are summed over the three ττ decay channels ( τeτµ , τlepτhad , τhadτhad ). – 19 – JHEP08(2022)175 5 Background modelling The expectations from SM processes other than the H→ττ signal in the phase space of the analysis are evaluated using a mixture of simulations and data-driven techniques. Processes with τhad-vis , prompt light leptons or light leptons from τ -lepton decays are estimated through simulations. Among these, Z ( →ττ ) + jets and top processes are dominant, and dedicated control regions are employed to validate the simulations of both processes and to constrain their normalisation in the signal regions. For the Z ( →ττ ) + jets background, a control region enriched in Z ( →`` ) + jets events is defined as described in section 5.1. In the τeτµ and τlepτhad channels, control regions enriched in top-induced processes are defined by replacing the b -jet veto from the event selection (see table 4) with a requirement of at least one b-tagged jet. Using these control regions, the templates of the mMMC ττ observable from the simulations are checked in each event category (see section 4.3). Very good agreement with the data is observed. Smaller background contributions are due to diboson, Z ( →`` ) + jets and H→WW∗ processes. They are normalised to their theoretical expectations. Contributions from lightand heavy-flavour jets misidentified as electrons, muons or τhad-vis , as well as non-prompt electrons or muons, collectively referred to as misidentified τ background, are estimated using data-driven techniques. Their estimation is detailed in section 5.2. Figure 3illustrates the measured composition of the selected events in each category of the analysis. – 20 – JHEP08(2022)175 0 10 20 30 40 50 60 70 80 90 100 Relative Contribution [%] boost_0_1J boost_0_ge2J boost_1_1J boost_1_ge2J boost_2 boost_3 VH_0 VH_1 VBF_0 VBF_1 ttH_0 ttH_1 Category ATLAS -1 = 13 TeV, 139 fbs signal regions had τ had τ →H Other backgr. ll→Z τMisidentified ττ →Z Top ττ →H 0 10 20 30 40 50 60 70 80 90 100 Relative Contribution [%] boost_0_1J boost_0_ge2J boost_1_1J boost_1_ge2J boost_2 boost_3 VH_0 VH_1 VBF_0 VBF_1 Category ATLAS -1 = 13 TeV, 139 fbs signal regions had τ lep τ →H Other backgr. ll→Z τMisidentified ττ →Z Top ττ →H (a) τhadτhad (b) τlepτhad 0 10 20 30 40 50 60 70 80 90 100 Relative Contribution [%] boost_0_1J boost_0_ge2J boost_1_1J boost_1_ge2J boost_2 boost_3 VH_0 VH_1 VBF_0 VBF_1 Category ATLAS -1 = 13 TeV, 139 fbs signal regions µ τ e τ →H Other backgr. ll→Z τMisidentified ττ →Z Top ττ →H (c) τeτµ Figure 3. Relative contribution of each process to the total measured yields in each category of the analysis for the (a) τhadτhad , (b) τlepτhad and (c) τeτµ channels, within 100 GeV < mMMC ττ < 150 GeV . ‘Other backgr.’ includes diboson and H→WW∗processes. – 21 – JHEP08(2022)175 5.1 Z→τ τ background modelling using Z→`` events Events from the Z ( →ττ ) + jets process form the dominant source of background in this measurement. They account for 79% of the background across all signal regions, and up to 90% of the background in the most boosted regime investigated in the analysis. They are estimated using MC simulations validated with data. The predictions from these MC simulations are corrected using dedicated control regions based on the Z ( →`` ) + jets process with kinematic properties of the events similar to those of the corresponding signal regions as explained in the following. In order to mimic as well as possible the boson kinematics and the associated production of jets in Z ( →ττ ) + jets events selected in the signal regions, the selected Z ( →`` ) + jets events are modified through a simplified implementation of the embedding procedure. The kinematic properties of the boson are reconstructed with a much better resolution in the Z→`` decay channel than in the Z→ττ one due to the absence of neutrinos and the excellent momentum resolution of the ATLAS detector for electrons and muons. While the original method presented in refs. [ 36 , 37 ] relied on substituting the detector signatures of the objects before re-reconstructing the event, the simplified embedding consists of a rescaling of the transverse momentum of each reconstructed lepton through parameterisations, followed by a recomputation of all the relevant kinematic quantities in the analysis. The method used entails a significant reduction of complexity. Embedding techniques are of particular interest in this analysis, where no statistically significant study of the Z ( →ττ ) + jets background can be performed in data without looking at the signal regions. In this context, the simplified embedding can be applied to data events passing the Z ( →`` ) + jets selection, thus obtaining a Z→ττ control region that is orthogonal to the signal region. This control region can also be used to measure the Z→ττ normalisation in a phase space relevant to this measurement. The Z ( →`` ) + jets events are selected using the single-lepton triggers and are required to have exactly two electrons or two muons with opposite charge. The selected electrons and muons must satisfy the identification and isolation criteria defined in table 4. Additionally, the invariant mass of the dilepton system must be above 80GeV . The selected sample contains about 9 . 3 · 10 6 data events and 99% of them are expected to come from Z ( →`` )+ jets processes. A small contribution from diboson and top processes with two electrons or two muons in the final state is also expected and the embedding procedure is also applied to them. Contributions from processes with jets misidentified as leptons were found to be negligible. Selected events in data and simulation are then randomly separated into three subsets to provide a statistically independent control region for each of the τeτµ , τlepτhad and τhadτhad signal regions. Weights derived in simulations are applied to each event to remove the kinematic biases and normalisation effects introduced by the electron and muon trigger, reconstruction, identification, and isolation algorithms. The four-vectors of the reconstructed electrons and muons are used to pair each lepton in the Z ( →`` ) + jets event with a scaling term, which parameterises the effects of τ -lepton decay kinematics and of the energy calibration algorithms for τ -leptons with similar four-vectors. The scaling term is derived as a function – 22 – JHEP08(2022)175 of the transverse momentum and the pseudorapidity of the τ -lepton before it decays. The original four-vectors of the electrons and muons are scaled using this term so that they match those of the visible reconstructed decay products of either leptonically or hadronically decaying τ -leptons. The Z ( →`` ) + jets event yields are then reweighted to account for the expected efficiencies of the reconstruction, identification and calibration steps for the visible τdecay products. The per-lepton weights assume collinearity of the τ -lepton and its visible decay products and cannot take into account any correlation between the boson decay products. All event variables used in the signal region definitions are recalculated using the kinematics of the new final-state physics objects, and a weight is applied to each event to account for the expected trigger efficiency associated with these objects. The implementation of the new embedding procedure is validated by comparing Z→`` simulated events, after applying this procedure, with Z→ττ simulations, where both the kinematic and spin-correlation effects are modelled correctly. Figure 4shows good agreement between the distributions of the two samples for two illustrative cases and indicates that the assumptions made in calculating the weights have negligible impact on the relevant observables. All uncertainties affecting the reconstructed physics objects used in embedding are propagated through the full procedure, including those associated with the parameterisations. Dedicated uncertainties affecting each control region are assigned to account for the differences in modelling observed between the Z→ττ and embedded Z→`` MC predictions, which are expected to come from approximations associated with the simplified embedding procedure. These uncertainties are derived by studying the change in the data-to-simulation normalisation factors as events are moved between different control regions to cover the observed acceptance mismodeling. They are found to be at the 1% level and cover for the residual non-closure observed in figure 4. Distributions for this control region, and a comparison with the embedding of all the simulated background processes, are shown in figure 5. The observed discrepancies are consistent with the results reported in dedicated measurements of the Z +jets processes [ 149 , 150 ]. The impact of this mismodelling on the analysis is alleviated by the use of control regions mimicking the event selection criteria after the embedding procedure is applied to data and simulated events. – 23 – JHEP08(2022)175 50 100 150 200 ) [GeV] had τ ( T p 0.9 0.95 1 1.05 1.1 Closure ττ → ll / Z →Embedded Z Uncertainty ττ → ll / Z →Embedded Z Uncertainty 0 5 10 15 20 3 10× GeV 10 / Entries ll→Z ll→Embedded Z ττ →Z SimulationATLAS -1 = 13 TeV, 139 fbs had τ lep τ Boost + VBF + V(had)H selection ll→Z ll→Embedded Z ττ →Z 0 50 100 150 200 [GeV] miss T E 0.9 0.95 1 1.05 1.1 Closure ττ → ll / Z →Embedded Z Uncertainty ττ → ll / Z →Embedded Z Uncertainty 0 5 10 15 3 10× GeV 10 / Entries ll→Z ll→Embedded Z ττ →Z SimulationATLAS -1 = 13 TeV, 139 fbs had τ lep τ Boost + VBF + V(had)H selection ll→Z ll→Embedded Z ττ →Z (a) pT(τhad) (b) Emiss T Figure 4. Comparison of kinematic quantities for Z→`` simulated events in the τlepτhad channel before (light purple histogram) and after (dark green histogram) the embedding procedure, in the boost, VBF and V(had)H phase spaces combined. The distribution for Z→ττ simulated events (dashed blue line) is also shown. (a) pT distribution of the simulated τhad in the event. For the Z→`` events, the reconstructed lepton with the highest pT in the event is shown. For the Z→`` events after the embedding procedure, a scaling term is applied to the pT of the lepton chosen to mimic the τhad as described in the text. (b) Emiss T distribution. The bottom panels display the ratio of embedded Z→`` events to Z→ττ events. The error bars display the statistical uncertainties in the ratio and the dashed blue band illustrates the statistical uncertainty in the Z→ττ simulation. – 24 – JHEP08(2022)175 For Z +jets, uncertainties were considered for renormalisation ( µr ), factorisation ( µf ) and resummation scale ( µq ) variations, for the jet-to-parton matching scheme (CKKW), for variations in the choice of αs value, and for the choice of PDFs. Uncertainties from missing higher orders were evaluated [ 156 ] using six variations of the QCD µr and µf scales in the matrix elements by factors of 0 . 5and 2, avoiding the extreme variations in opposite directions. Uncertainties in the nominal PDF set were evaluated using 100 replica variations; an uncertainty is derived in each bin of the mMMC ττ templates by evaluating the ± 1 σ spread of the 100 replica variations. The effect of the uncertainty in the strong coupling constant αs was assessed by variations of ± 0 . 001. The resummation scale uncertainties were estimated using generator-level parameterisations derived from samples with µq varied by factors of 2 and 0.5 from its nominal value. Similarly, the jet-to-parton matching uncertainties were estimated using generator-level parameterisations derived from samples with the CKKW parameter set to 15GeV and 30GeV, compared to the nominal value of 20 GeV. For t¯ t , uncertainties were considered for the choice of matrix element and parton shower generators, the choice of model for initialand final-state radiation (ISR and FSR respectively), and the choice of PDFs. The uncertainty due to ISR was estimated by simultaneously varying the hdamp parameter and the µr and µf scales, and propagating the αs uncertainties through the Var3c parameter of the A14 tune as described in ref. [ 157 ]. The impact of FSR was evaluated by varying the µr scale for emissions from the parton shower by factors of 2 and 0.5. The impact of using a different matrix element was evaluated by comparing the nominal t¯ t sample with an event sample produced using MadGraph5_aMC@NLO 2.6.0 instead of Powheg Box v2 but keeping the same parton shower model. The impact of using a different parton shower and hadronisation model was evaluated by comparing the nominal t¯ t sample with an event sample which was interfaced with Herwig 7.04 [ 158 , 159 ] instead of Pythia 8 and used the H7UE set of tuned parameters [159] and the MMHT2014lo PDF set [160]. The NNPDF3.0lo replicas were used to evaluate the PDF uncertainties for the nominal PDF. For both Z +jets and t¯ t , the central value of the PDF was additionally compared with the central values of the CT14nnlo [161] and MMHT2014nnlo [160] PDF sets. Theory uncertainties in Z +jets and t¯ t predictions represent a sub-leading contribution, compared to signal theoretical uncertainties and experimental uncertainties (see table 7). For renormalisation and factorisation scale variations and PDF uncertainties, their impact on the extrapolation factor between each SR and its corresponding Z→`` control region, and on the shape of the mMMC ττ distribution, is treated as uncorrelated across the different categories. This choice is driven by the structure of the statistical analysis, which employs a dedicated control region to constrain the Z +jets prediction in each signal region. 6.3 Signal theoretical uncertainties Signal theoretical uncertainties are the dominant source of uncertainty for this analysis. For each signal process, several sources of uncertainty are considered, including the uncertainty in the total inclusive cross-section (evaluated only for the pp →H→ττ cross-section measurement), the parton-shower and hadronisation model effect and the migration uncertainties among the STXS bins. The migration uncertainties stem from the – 31 – JHEP08(2022)175 determination of the kinematic quantities used in the STXS framework as well the expected relative contribution of each process in the signal regions. These uncertainties can affect signal acceptance in the various SRs as well as the mMMC ττ shape. For all production modes, uncertainties are estimated for the PDF and αs , the parton shower and hadronisation model, and missing higher orders in the matrix element calculation. PDF and αs uncertainties were estimated using the PDF4LHC15nlo set of eigenvectors. The impact of using a different parton shower and hadronisation model is evaluated by comparing the nominal sample with an event sample which was interfaced with Herwig 7 instead of Pythia 8. The effects on the signal expectations are treated as uncorrelated between the production modes, and the comparison leads to the largest uncertainty in the pp →H→ττ cross-section measurement. Uncertainties from missing higher orders are calculated following the methodology outlined in refs. [27,162] and are determined as follows. For the ggF process, 15 main sources of uncertainty were considered. Four of these are jet-multiplicity-related uncertainties due to missing high-order corrections, and are estimated using the approach described in refs. [ 27 , 163 ]. Three uncertainties parameterise the uncertainties in modelling the Higgs boson pT and the 0-jet bin, one of which encapsulates the treatment of the top-quark mass in the loop corrections. Three uncertainties take into account dijet mass migrations across the STXS bin boundaries. Finally, three uncertainties are considered for the modelling of the ggF process in the VBF phase space. Two of them are derived using the method described in ref. [ 164 ], from the study of the selection of exactly two or at least three jets. The third one is derived from the comparison of the Powheg prediction with MadGraph5_aMC@NLO samples using the FxFx prescriptions [ 165 ] to merge the jet multiplicities and it also applies to the V H phase space. As the ggF process in the VBF phase space is difficult to model, the impact of increasing its contribution in the VBF_1 category was estimated. Doubling its contribution induced a 7% shift in the apparent VBF production cross-section. For the VBF and V H processes, ten uncertainties related to the STXS categorisation were considered: one related to the inclusive cross-section of the process, one related to the two-jet requirement, one related to the Higgs boson pT selection at 200GeV , one related to the pT balance between the Higgs boson and the dijet system in events with two or three jets, and six uncertainties taking into account dijet mass migrations across the STXS bin boundaries. For the t¯ tH process, six other uncertainties are included: one related to the inclusive cross-section of the process, and five migration uncertainties related to Higgs boson pT boundaries in the STXS scheme. 7 Statistical analysis A statistical analysis of the collected data is performed to measure the pp →H→ττ crosssections. The procedure relies on a likelihood function constructed as the product of Poisson probability terms over the bins of the input distributions. The uncertainties affecting the model (see section 6) are included in the likelihood function through nuisance parameters that are constrained by Gaussian probability terms that multiply the Poisson probability – 32 – JHEP08(2022)175 terms. The parameters of interest (POIs) of the model are estimated by maximising the likelihood. The likelihood function comprises 32 signal regions and 36 control regions. In each signal region, Poisson terms describe the expected event counts in each bin of the mMMC ττ distribution, while in each control region a single Poisson term describes the total expected event yield in that region. Figure 7illustrates the usage of the signal and control regions in the construction of the likelihood function. The test statistic is constructed from the profile likelihood ratio and the confidence intervals on the parameters of interest are derived unsing the asymptotic approximation [166]. The normalisation of the Z→ττ background is left as a freely floating parameter in the fit in several regions. Each signal region in the boost, VBF and V(had)H categories is paired with an associated embedded Z→`` control region and both share a common Z→ττ normalisation factor. Additionally, a common Z→ττ normalisation factor is shared between the ttH_0 and ttH_1 signal regions. In total, 31 floating normalisation factors are defined in order to constrain the yields of the Z→ττ background in the signal regions. The normalisation of the top processes is also allowed to float freely with six normalisation factors defined for boost, VBF, and V(had)H signal regions in the τeτµ and τlepτhad channels separately and one for the ttH categories in the τhadτhad channel. The other backgrounds are normalised to their expected cross-section and the integrated luminosity of the recorded data. In the signal regions, a smoothing procedure is applied to remove potentially large local fluctuations in the mMMC ττ templates caused by the limited size of the MC samples used to build the templates. The mMMC ττ template of uncertainties that are subject to large statistical fluctuations is smoothed, and uncertainties that have a negligible impact on the final results are pruned away sample-by-sample and region-by-region. The mMMC ττ discriminant distributions in each SR are binned in a way that maximises the significance of each targeted signal production mode, taking into account the full uncertainties. Effectively, this leads to a fine binning near the resonant Z→ττ peak with coarser binning further away from it. Three different measurements are performed. They include the branching ratio of H→ττ and are performed with true Higgs boson rapidity |yH|< 2 . 5. They differ in the definition of the POIs (see also figure 1): 1. pp →H→ττ cross-section: a single POI, corresponding to the pp →H→ττ cross-section, is estimated by the fit. In the likelihood function, the signal yields in each category are parameterised as the product of the pp →H→ττ cross-section, the integrated luminosity and the efficiency (including the acceptance of the ATLAS detector) of the selection for a SM Higgs boson with a mass of 125.09GeV . In this measurement, the relative contributions to the pp →H→ττ cross-section from the various production modes are fixed to the SM predictions. 2. Cross-sections per production mode: four POIs, corresponding to the cross-sections of the four dominant production modes (ggF, VBF, V H , t¯ tH ) of the Higgs boson, are estimated by the fit. In this configuration, the event yields in the likelihood – 33 – JHEP08(2022)175 function are the sum of those from each individual production mode, parameterised as a function of the POI similarly to the way for the first measurement. 3. Reduced Simplified Template Cross-Sections: nine POIs, corresponding to the crosssections of merged bins of the STXS stage 1.2 framework shown in figure 2, to which this analysis is sensitive, are determined by the fit. The cross-sections for t¯ tH production and for VBF + qq →V ( →qq ) H production are measured. The latter is measured for events with particle-level dijet mass between 60GeV and 120GeV or above 350GeV . In addition, the cross-section of ggF production is measured in six bins of the phase space. One of them is a combination of two bins in the stage 1.2 prescription: events with one jet and intermediate pT ( H )( 60 to 120GeV ) are measured together with events with two or more jets, low mjj ( <350 GeV ) and the same intermediate pT(H). – 34 – JHEP08(2022)175 Signal Regions VBF 0 VBF 1 Signal Regions boost 0 1J boost 0 ge2J boost 1 1J boost 1 ge2J boost 2 boost 3 Signal Regions VH 0 VH 1 Embed. Z →`` CRs VBF 0 VBF 1 Embed. Z →`` CRs boost 0 1J boost 0 ge2J boost 1 1J boost 1 ge2J boost 2 boost 3 Embed. Z →`` CRs VH 0 VH 1 tt(0`)H →τhadτhad SRs ttH 0 ttH 1 Top CR τeτµ,τlepτhad only Top CR τeτµ,τlepτhad only Top CR τeτµ,τlepτhad only VBF topology Boost topology V(had)H topology ttH topology 7 Top NFs 31 Z →τ τ NFs Figure 7. Graphical representation of the regions considered in the likelihood function and the normalisation factors (NFs) defined in the analysis. The four unfilled black boxes represent the four main topologies targeted in this measurement. Within each unfilled black box, the dark filled coloured boxes represent from left to right, the Top control regions, the signal regions and the Z→`` control regions. When applicable the subcategories are represented by a light filled colour. Each blue solid arrowed-line represents a normalisation factor that applies to the Z ( →ττ ) + jets process in the signal regions and to the Z ( →`` ) + jets process in the Z→`` control regions. Each orange dashed arrowed-line represents a normalisation factor that applies to the top processes in the signal regions and to the top processes in the Top control regions. The arrow ends of each line indicate which regions are connected by each normalisation factor. In the likelihood function, there are signal regions and Z→`` control region for each final state in the VBF, boost and V(had)H topologies. Therefore, the ten signal regions and Z→`` control regions are repeated three times. The Top control regions are only used in the τeτµ and τlepτhad final states. Additionally, only one Top control region is considered by each topology. – 35 – JHEP08(2022)175 8 Results The results of the statistical analysis (see section 7) performed for the pp →H→ττ crosssection measurement are presented in figures 8,9,10 and 11. Additional figures displaying the results of the total cross-section measurement with the binning used in the statistical analysis are available in the appendix. The observed event yields and predictions as computed by the fit in the signal regions of the analysis are reported in tables 8,9,10,11,12 and 13. Excellent agreement is observed between the data and the expectations. All measurements include the branching ratio of H→ττ and are performed with true Higgs boson rapidity |yH|<2.5. The pp →H→ττ cross-section is measured to be 2.94 ±0.21(stat)+ 0.37 −0.32(syst)pb , in agreement with the SM predictions (3.17 ±0.09 pb) with a p-value of 0.58. The measurement is also performed in the τhadτhad , τlepτhad and τeτµ final states separately and in the boost, VBF, V(had)H and tt(0 ` ) H→τhadτhad categories. The results are illustrated in figure 12. The p -values for the compatibility of the measurements are 0 . 30 across τ-lepton decay modes and 0.72 across kinematic categories. The same dataset is subsequently used to measure the production cross-section for the Higgs boson in the four dominant production mechanisms. The results are illustrated in figure 13(a) and reported in table 14 with a breakdown of the uncertainties. They are all consistent with the SM predictions, with a p -value of 0 . 98. The measurement establishes the observation of the VBF production of the Higgs boson in the ττ decay channel with an observed (expected) significance of 5.3σ(6.2σ). The VBF production cross-section measurement is the most precise of the four dominant production mechanisms. The theoretical uncertainties in VBF production are smaller than in the other channels, and the VBF_1 categories represent the best combination of high signal yields and purity in this measurement. The measured VBF cross-section is 0.197 ±0.028(stat)+ 0.032 −0.026(syst)pb . The second most precisely measured cross-section is that of ggF, 2.7±0.4(stat)+ 0.9 −0.6(syst)pb , corresponding to an observed (expected) significance of 3 . 9 σ (4 . 6 σ ). The V H and t¯ tH production modes are determined with lower precision. The measured V H cross-section is 0.12 ±0.06(stat) ±0.04(syst)pb , while the t¯ tH cross-section is 0.033+ 0.033 −0.029(stat)+ 0.022 −0.017(syst)pb . Figure 13(b) illustrates the observed correlation between the measured cross-section parameters in the fit. The ggF cross-section exhibits an anticorrelation of 24% and 29% with the VBF and V H cross-sections respectively. This is caused by a significant contribution of ggF events to the VBF_0, VH_0 and VH_1 categories as illustrated by figure 2. The simultaneous measurement of the cross-sections of the four dominant production modes is compatible with the SM expectations, with a p -value of 0 . 88. Finally, the pp →H→ττ cross-sections are measured as a function of pT ( H ), Njets ( pT> 30 GeV )and mjj in a reduced set of the bins of the stage 1.2 of the STXS framework. The results, illustrated in figure 14(a), are reported in table 15. They are in very good agreement with the SM expectations. The gluon-gluon fusion + gg →Z ( →qq ) H production mode is measured in four pT ( H )intervals starting at 60GeV . For pT ( H )values between 120GeV and 200GeV , the measurements are further separated depending on the number of jets in the event. The best precision is obtained in the pT ( H )interval between 200GeV and 300GeV and in the pT ( H )regime above 300GeV . The cross-sections are determined with an uncertainty of 37% and 42% respectively. – 36 – JHEP08(2022)175 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 Events / 10 GeV ×103 ATLAS : = 13TeV,139fb21 All ë ë SRs Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 20.25 0.00 0.25 Data 2 Bkg 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 Events / 10 GeV ×103 ATLAS : = 13TeV,139fb21 All ë ë SRs Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 20.25 0.00 0.25 Data 2 Bkg 0 1000 2000 3000 4000 5000 6000 7000 Events / 10 GeV ATLAS : = 13TeV,139fb21 All ë ëã SRs Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 2100 0 100 Data 2 Bkg (a) τhadτhad (b) τlepτhad (c) τeτµ Figure 8. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the (a) τhadτhad , (b) τlepτhad and (c) τeτµ signal regions. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. The EW production mode includes the VBF and qq →V ( →qq ) H processes and is measured in mjj intervals. In the interval with mjj between 60GeV and 120GeV , the measurement has an uncertainty of 63% . The EW production mode for events with mjj greater than 120GeV is measured with an uncertainty of 26% and is the most precise crosssection determined within the simplified template cross-section framework in this paper. It exhibits an anti-correlation of approximately 40%with the cross-section for gluon-gluon fusion events produced in the same interval ( mjj >350 GeV ) as illustrated on figure 14(b). – 37 – JHEP08(2022)175 0 5 10 15 20 25 30 35 40 Events / 10 GeV ×103 ATLAS : = 13TeV,139fb21 All Boost SRs Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 20.5 0.0 0.5 Data 2 Bkg 0 20 40 60 80 100 120 140 160 180 Events / 10 GeV ATLAS : = 13TeV,139fb21 All VBF_1 SRs Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 100 200 300 400 500 600 Events / 10 GeV ATLAS : = 13TeV,139fb21 All VH_1 SRs Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 225 0 25 Data 2 Bkg (a) boost (b) VBF_1 (c) VH_1 Figure 9. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the (a) boost, (b) VBF_1 and (c) VH_1 signal regions. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. 0 10 20 30 40 50 60 70 Events / 10 GeV ATLAS : = 13TeV,139fb21 ³ë ë VBF_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 220 0 20 Data 2 Bkg 0 20 40 60 80 100 Events / 10 GeV ATLAS : = 13TeV,139fb21 ³ë ë VBF_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 5 10 15 20 25 30 35 Events / 10 GeV ATLAS : = 13TeV,139fb21 ³ë ëã VBF_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 210 0 10 Data 2 Bkg (a) τhadτhad (b) τlepτhad (c) τeτµ Figure 10. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the VBF_1 categories of (a) τhadτhad , (b) τlepτhad and (c) τeτµ signal regions. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 38 – JHEP08(2022)175 0 25 50 75 100 125 150 175 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë ttH_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Top Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 220 0 20 Data 2 Bkg 0 2 4 6 8 10 12 14 16 18 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë ttH_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Top Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 25 0 5 Data 2 Bkg (a) ttH_0 (b) ttH_1 Figure 11. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the (a) ttH_0 and (b) ttH_1 categories of the τhadτhad channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 39 – JHEP08(2022)175 VBF τhadτhad V(had)H τhadτhad tt(0`)H→τhadτhad VBF_0 VBF_1 VH_0 VH_1 ttH_0 ttH_1 Z→ττ 2051 ±50 115 ±11 4636 ±84 539 ±24 265 ±24 20 ±4 Fake 1027 ±68 39.6 ±5.3 1627 ±110 112 ±10 182 ±17 6.5 ±1.7 Top 239 ±26 15.2 ±3.4 Other backgrounds 57.1 ±5.2 1.7 ±0.6 209 ±12 43.1 ±2.9 15.7 ±2.1 5.0 ±0.8 ggF, H→ττ 38.5 ±9.5 3.2 ±1.8 72 ±14 6.8 ±1.7 1.96 ±0.41 0.42 ±0.08 VBF, H→ττ 72 ±10 40 ±5 8.1 ±1.3 0.5 ±0.1 0.23 ±0.03 <0.01 WH,H→ττ 1.00 ±0.14 <0.01 15.2 ±2.5 9.8 ±1.5 0.24 ±0.03 0.034 ±0.005 ZH,H→ττ 0.8 ±0.1 <0.01 12.4 ±2.3 5.4 ±1.1 0.69 ±0.15 0.15 ±0.02 ttH,H→ττ 0.19 ±0.03 <0.01 0.52 ±0.07 0.22 ±0.03 7.5 ±1.6 5.4 ±1.3 tH,H→ττ 0.41 ±0.06 <0.01 0.25 ±0.03 0.07 ±0.01 0.92 ±0.13 0.41 ±0.06 bbH,H→ττ 0.10 ±0.02 <0.01 0.14 ±0.02 0.015±0.002 0.27 ±0.04 0.09 ±0.02 Total background 3135 ±84 156 ±12 6472 ±136 694 ±26 703 ±33 46.6 ±5.3 Total signal 113 ±15 43.6 ±5.2 109 ±16 23.0 ±3.2 12 ±2 6.6 ±1.4 Total 3248 ±84 200 ±12 6581 ±135 717 ±26 715 ±33 53.3 ±5.5 Data 3318 197 6532 720 727 49 Table 8. Observed event yields and predictions as computed by the fit in the VBF, V(had)H and tt(0 ` ) H→τhadτhad signal regions of the τhadτhad channel. In the VBF and V(had)H categories, the top processes are estimated with the other backgrounds (diboson, H→WW ∗ ) by the fit. Uncertainties include statistical and systematic components. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. VBF τlepτhad V(had)H τlepτhad VBF_0 VBF_1 VH_0 VH_1 Z→ττ 2362 ±59 162 ±12 6724 ±112 535 ±23 Fake 611 ±49 30 ±3 1315 ±126 80 ±8 Top 107 ±12 5.3 ±1.5 243 ±25 27 ±5 Other backgrounds 139 ±17 5.8 ±2.4 396 ±39 50 ±4 ggF, H→ττ 71 ±28 3.5 ±1.2 87.3 ±20.3 5.1 ±2.2 VBF, H→ττ 84.4 ±11.2 52 ±6 9.3 ±1.6 0.5 ±0.2 WH,H→ττ 0.83 ±0.11 0.011 ±0.002 17.4 ±2.6 8.1 ±1.2 ZH,H→ττ 0.86 ±0.12 <0.01 13.3 ±2.5 5.0 ±0.9 ttH,H→ττ 0.10 ±0.01 <0.01 0.35±0.05 0.13 ±0.02 tH,H→ττ 0.26 ±0.04 0.023 ±0.003 0.17±0.03 0.030±0.004 bbH,H→ττ 0.09 ±0.01 0.16±0.02 Total background 3219 ±75 203 ±13 8678 ±143 692 ±24 Total signal 158 ±30 56 ±7 128 ±23 19 ±3 Total 3377 ±76 259 ±13 8806 ±143 711 ±24 Data 3402 267 8780 743 Table 9. Observed event yields and predictions as computed by the fit in the VBF and V(had)H signal regions of the τlepτhad channel. Uncertainties include statistical and systematic components. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 40 – JHEP08(2022)175 STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [168]. – 47 – JHEP08(2022)175 0 200 400 600 800 1000 1200 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã VH_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 20 40 60 80 100 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã VH_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 220 0 20 Data 2 Bkg 0 50 100 150 200 250 300 350 400 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã VBF_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 10 20 30 40 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã VBF_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 220 0 20 Data 2 Bkg Figure 15. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the V(had)H and VBF categories of the τeτµ channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. A Distributions of mMMC τ τ and fit projection in each signal region Figures 15,16,17,18,19 and 20 show all distributions that enter the likelihood fit with the best-fit parameters derived from the fit with a single parameter of interest (inclusive cross-section measurement). – 48 – JHEP08(2022)175 0 200 400 600 800 1000 1200 1400 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã boost_0_1J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 100 200 300 400 500 600 700 800 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã boost_0_ge2J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 250 500 750 1000 1250 1500 1750 2000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã boost_1_1J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 60 80 100 120 140 160 180 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 200 400 600 800 1000 1200 1400 1600 1800 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã boost_1_ge2J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 100 200 300 400 500 600 700 800 900 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã boost_2 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 60 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 50 100 150 200 250 300 350 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ëã boost_3 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg Figure 16. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the boost categories of the τeτµ channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 49 – JHEP08(2022)175 0 500 1000 1500 2000 2500 3000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VH_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 50 100 150 200 250 300 350 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VH_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 60 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 200 400 600 800 1000 1200 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VBF_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 20 40 60 80 100 120 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VBF_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg Figure 17. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the V(had)H and VBF categories of the τlepτhad channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 50 – JHEP08(2022)175 0 500 1000 1500 2000 2500 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_0_1J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 60 80 100 120 140 160 180 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 200 400 600 800 1000 1200 1400 1600 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_0_ge2J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 1000 2000 3000 4000 5000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_1_1J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 1000 2000 3000 4000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_1_ge2J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 500 1000 1500 2000 2500 3000 3500 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_2 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 200 400 600 800 1000 1200 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_3 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 60 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg Figure 18. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the boost categories of the τlepτhad channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 51 – JHEP08(2022)175 0 50 100 150 200 250 300 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë ttH_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Top Other backgrounds Misidentified ë 75 100 125 150 175 200 ëë [GeV] 220 0 20 Data 2 Bkg 0 2 4 6 8 10 12 14 16 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë ttH_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Top Other backgrounds Misidentified ë 100 120 140 160 180 200 ëë [GeV] 210 0 10 Data 2 Bkg 0 500 1000 1500 2000 2500 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VH_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 50 100 150 200 250 300 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VH_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 75 100 125 150 175 200 ëë [GeV] 225 0 25 Data 2 Bkg 0 200 400 600 800 1000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VBF_0 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 20 40 60 80 100 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë VBF_1 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg Figure 19. Distribution of the reconstructed ττ invariant mass (mMMC ττ ) for all events in the ttH, V(had)H and VBF categories of the τhadτhad channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 52 – JHEP08(2022)175 0 500 1000 1500 2000 2500 3000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_0_1J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 200 400 600 800 1000 1200 1400 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_0_ge2J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 1000 2000 3000 4000 5000 6000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_1_1J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 2100 0 100 Data 2 Bkg 0 1000 2000 3000 4000 5000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_1_ge2J SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 50 75 100 125 150 175 200 ëë [GeV] 2100 0 100 Data 2 Bkg 0 500 1000 1500 2000 2500 3000 3500 4000 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_2 SR Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 60 80 100 120 140 160 180 200 ëë [GeV] 250 0 50 Data 2 Bkg 0 50 100 150 200 250 300 350 400 Events / 10 GeV ATLAS : = 13TeV, 139fb21 ³ë ë boost_3 Data Uncertainty ³ëë (0.93 × SM) ³ëë Other backgrounds Misidentified ë 80 100 120 140 160 180 200 ëë [GeV] 225 0 25 Data 2 Bkg Figure 20. Distribution of the reconstructed ττ invariant mass ( mMMC ττ ) for all events in the boost categories of the τhadτhad channel. The bottom panel shows the differences between the numbers of observed data events and expected background events (black points). The observed Higgs boson signal, corresponding to ( σ×B ) / ( σ×B ) SM = 0 . 93, is shown with a filled red histogram. Entries with values above the x -axis range are shown in the last bin of each distributions. The dashed band indicates the total uncertainty on the total predicted yields. The prediction for each sample is determined from the likelihood fit performed to measure the pp →H→ττ cross-section. – 53 – JHEP08(2022)175 Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. 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Burdin88, C.D. Burgard44,A.M. Burger125,B. Burghgrave7,J.T.P. Burr44,C.D. Burton10, J.C. Burzynski148,E.L. Busch37,V. Büscher96,P.J. Bussey55,J.M. Butler23,C.M. Buttar55, J.M. Butterworth92,W. Buttinger139, C.J. Buxo Vazquez103,A.R. Buzykaev117b,117a, G. Cabras21b,S. Cabrera Urbán169,D. Caforio54,H. Cai134,V.M.M. Cairo149,O. Cakir3a, N. Calace34,P. Calafiura16,G. Calderini131,P. Calfayan63,G. Callea55, L.P. Caloba78b, D. Calvet36,S. Calvet36,T.P. Calvet98,M. Calvetti69a,69b,R. Camacho Toro131,S. Camarda34, D. Camarero Munoz95,P. Camarri71a,71b,M.T. Camerlingo72a,72b,D. Cameron129, C. Camincher171,M. Campanelli92,A. Camplani38,V. Canale67a,67b,A. Canesse100, M. Cano Bret75,J. Cantero125,Y. Cao168,F. Capocasa24,M. Capua39b,39a,A. Carbone66a,66b, R. Cardarelli71a,J.C.J. Cardenas7,F. Cardillo169,G. Carducci39b,39a,T. Carli34,G. Carlino67a, B.T. Carlson134,E.M. Carlson171,163a,L. Carminati66a,66b,M. Carnesale70a,70b, R.M.D. Carney149,S. Caron114,E. Carquin142e,S. Carrá44,G. Carratta21b,21a,J.W.S. Carter162, T.M. Carter 48 ,D. Casadei 31c ,M.P. Casado 12,g , A.F. Casha 162 ,E.G. Castiglia 178 ,F.L. Castillo 59a , L. Castillo Garcia12,V. Castillo Gimenez169,N.F. Castro135a,135e,A. Catinaccio34, J.R. Catmore129, A. Cattai34,V. Cavaliere27,N. Cavalli21b,21a,V. Cavasinni69a,69b,E. Celebi11b, F. Celli130,M.S. Centonze65a,65b,K. Cerny126,A.S. Cerqueira78a,A. Cerri152,L. Cerrito71a,71b, F. Cerutti16,A. Cervelli21b,S.A. Cetin11b,Z. Chadi33a,D. Chakraborty116,M. Chala135f, J. Chan 176 ,W.S. Chan 115 ,W.Y. Chan 88 ,J.D. Chapman 30 ,B. Chargeishvili 155b ,D.G. Charlton 19 , T.P. Charman90,M. Chatterjee18,S. Chekanov5,S.V. Chekulaev163a,G.A. Chelkov77,ae, A. Chen 102 ,B. Chen 157 ,B. Chen 171 , C. Chen 58a ,C.H. Chen 76 ,H. Chen 13c ,H. Chen 27 ,J. Chen 58c , J. Chen24,S. Chen132,S.J. Chen13c,X. Chen58c,X. Chen13b,Y. Chen58a,Y-H. Chen44, C.L. Cheng176,H.C. Cheng60a,A. Cheplakov77,E. Cheremushkina44,E. Cherepanova77, R. Cherkaoui El Moursli33e,E. Cheu6,K. Cheung61,L. Chevalier140,V. Chiarella49, G. Chiarelli69a,G. Chiodini65a,A.S. Chisholm19,A. Chitan25b,Y.H. Chiu171,M.V. Chizhov77,s, K. Choi 10 ,A.R. Chomont 70a,70b ,Y. Chou 99 , Y.S. Chow 115 ,T. Chowdhury 31f ,L.D. Christopher 31f , M.C. Chu60a,X. Chu13a,13d,J. Chudoba136,J.J. Chwastowski82,D. Cieri111,K.M. Ciesla82, V. Cindro89,I.A. Cioară25b,A. Ciocio16,F. Cirotto67a,67b,Z.H. Citron175,k,M. Citterio66a, D.A. Ciubotaru25b,B.M. Ciungu162,A. Clark52,P.J. Clark48,J.M. Clavijo Columbie44, S.E. Clawson 97 ,C. Clement 43a,43b ,L. Clissa 21b,21a ,Y. Coadou 98 ,M. Cobal 64a,64c ,A. Coccaro 53b , J. Cochran76,R.F. Coelho Barrue135a,R. Coelho Lopes De Sa99,S. Coelli66a,H. Cohen157, A.E.C. Coimbra34,B. Cole37,J. Collot56,P. Conde Muiño135a,135g,S.H. Connell31c, – 65 – JHEP08(2022)175 I.A. Connelly55,E.I. Conroy130,F. Conventi67a,ak,H.G. Cooke19,A.M. Cooper-Sarkar130, F. Cormier 170 ,L.D. Corpe 34 ,M. Corradi 70a,70b ,E.E. Corrigan 94 ,F. Corriveau 100,y ,M.J. Costa 169 , F. Costanza4,D. Costanzo145,B.M. Cote123,G. Cowan91,J.W. Cowley30,K. Cranmer121, S. Crépé-Renaudin56,F. Crescioli131,M. Cristinziani147,M. Cristoforetti73a,73b,b,V. Croft165, G. Crosetti39b,39a,A. Cueto34,T. Cuhadar Donszelmann166,H. Cui13a,13d,A.R. Cukierman149, W.R. Cunningham55,F. Curcio39b,39a,P. Czodrowski34,M.M. Czurylo59b, M.J. Da Cunha Sargedas De Sousa58a,J.V. Da Fonseca Pinto78b,C. Da Via97,W. Dabrowski81a, T. Dado 45 ,S. Dahbi 31f ,T. Dai 102 ,C. Dallapiccola 99 ,M. Dam 38 ,G. D’amen 27 ,V. D’Amico 72a,72b , J. Damp96,J.R. Dandoy132,M.F. Daneri28,M. Danninger148,V. Dao34,G. Darbo53b, S. Darmora5,A. Dattagupta127,S. D’Auria66a,66b,C. David163b,T. Davidek138,D.R. Davis47, B. Davis-Purcell 32 ,I. Dawson 90 ,K. De 7 ,R. De Asmundis 67a ,M. De Beurs 115 ,S. De Castro 21b,21a , N. De Groot 114 ,P. de Jong 115 ,H. De la Torre 103 ,A. De Maria 13c ,D. De Pedis 70a ,A. De Salvo 70a , U. De Sanctis71a,71b,M. De Santis71a,71b,A. De Santo152,J.B. De Vivie De Regie56, D.V. Dedovich77,J. Degens115,A.M. Deiana40,J. Del Peso95,Y. Delabat Diaz44,F. Deliot140, C.M. Delitzsch6,M. Della Pietra67a,67b,D. Della Volpe52,A. Dell’Acqua34,L. Dell’Asta66a,66b, M. Delmastro4,P.A. Delsart56,S. Demers178,M. Demichev77,S.P. Denisov118,L. D’Eramo116, D. Derendarz82,J.E. Derkaoui33d,F. Derue131,P. Dervan88,K. Desch22,K. Dette162, C. Deutsch22,P.O. Deviveiros34,F.A. Di Bello70a,70b,A. Di Ciaccio71a,71b,L. Di Ciaccio4, A. Di Domenico70a,70b,C. Di Donato67a,67b,A. Di Girolamo34,G. Di Gregorio69a,69b, A. Di Luca73a,73b,B. Di Micco72a,72b,R. Di Nardo72a,72b,C. Diaconu98,F.A. Dias115, T. Dias Do Vale135a,M.A. Diaz142a,F.G. Diaz Capriles22,J. Dickinson16,M. Didenko169, E.B. Diehl 102 ,J. Dietrich 17 ,S. Díez Cornell 44 ,C. Diez Pardos 147 ,A. Dimitrievska 16 ,W. Ding 13b , J. Dingfelder22,I-M. Dinu25b,S.J. Dittmeier59b,F. Dittus34,F. Djama98,T. Djobava155b, J.I. Djuvsland15,M.A.B. Do Vale143,D. Dodsworth24,C. Doglioni94,J. Dolejsi138,Z. Dolezal138, M. Donadelli78c,B. Dong58c,J. Donini36,A. D’onofrio13c,M. D’Onofrio88,J. Dopke139, A. Doria67a,M.T. Dova86,A.T. Doyle55,E. Drechsler148,E. Dreyer148,T. Dreyer51, A.S. Drobac165,D. Du58a,T.A. du Pree115,F. Dubinin107,M. Dubovsky26a,A. Dubreuil52, E. Duchovni175,G. Duckeck110,O.A. Ducu34,25b,D. Duda111,A. Dudarev34,M. D’uffizi97, L. Duflot62,M. Dührssen34,C. Dülsen177,A.E. Dumitriu25b,M. Dunford59a,S. Dungs45, K. Dunne43a,43b,A. Duperrin98,H. Duran Yildiz3a,M. Düren54,A. Durglishvili155b,B. Dutta44, B.L. Dwyer116,G.I. Dyckes16,M. Dyndal81a,S. Dysch97,B.S. Dziedzic82,B. Eckerova26a, M.G. Eggleston47,E. Egidio Purcino De Souza78b,L.F. Ehrke52,T. Eifert7,G. Eigen15, K. Einsweiler16,T. Ekelof167,Y. El Ghazali33b,H. El Jarrari33e,A. El Moussaouy33a, V. Ellajosyula167,M. Ellert167,F. Ellinghaus177,A.A. Elliot90,N. Ellis34,J. Elmsheuser27, M. Elsing34,D. Emeliyanov139,A. Emerman37,Y. Enari159,J. Erdmann45,A. Ereditato18, P.A. Erland82,M. Errenst177,M. Escalier62,C. Escobar169,O. Estrada Pastor169,E. Etzion157, G. Evans135a,H. Evans63,M.O. Evans152,A. Ezhilov133,F. Fabbri55,L. 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, M. Feickert 168 ,L. Feligioni 98 ,A. Fell 145 ,C. Feng 58b ,M. Feng 13b ,M.J. Fenton 166 , A.B. Fenyuk 118 , S.W. Ferguson41,J. Ferrando44,A. Ferrari167,P. Ferrari115,R. Ferrari68a,D. Ferrere52, C. Ferretti102,F. Fiedler96,A. Filipčič89,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. Flierl 110 ,L. Flores 132 ,M. Flores 31d ,L.R. Flores Castillo 60a ,F.M. Follega 73a,73b ,N. Fomin 15 , J.H. Foo 162 , B.C. Forland 63 ,A. Formica 140 ,F.A. Förster 12 ,A.C. Forti 97 , E. Fortin 98 ,M.G. Foti 130 , L. Fountas8,D. Fournier62,H. Fox87,P. Francavilla69a,69b,S. Francescato57,M. Franchini21b,21a, – 66 – JHEP08(2022)175 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. Frost 130 ,Y. Fu 58a ,M. Fujimoto 122 ,E. Fullana Torregrosa 169 ,J. Fuster 169 ,A. Gabrielli 21b,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. Gao 29,m ,F.M. Garay Walls 142a ,C. García 169 ,J.E. García Navarro 169 ,J.A. García Pascual 13a , 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. Gentile 70a,70b ,S. George 91 ,W.F. George 19 ,T. Geralis 42 , L.O. Gerlach 51 ,P. Gessinger-Befurt 34 , M. Ghasemi Bostanabad171,A. Ghosh166,A. Ghosh75,B. Giacobbe21b,S. Giagu70a,70b, N. Giangiacomi 162 ,P. Giannetti 69a ,A. Giannini 67a,67b ,S.M. Gibson 91 ,M. Gignac 141 ,D.T. Gil 81b , 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,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. Gonçalo135a,135c,G. Gonella127, L. Gonella19,A. Gongadze77,F. Gonnella19,J.L. Gonski37,S. González de la Hoz169, S. Gonzalez Fernandez12,R. Gonzalez Lopez88,C. Gonzalez Renteria16,R. Gonzalez Suarez167, S. Gonzalez-Sevilla52,G.R. Gonzalvo Rodriguez169,R.Y. González Andana142a,L. Goossens34, N.A. Gorasia19,P.A. Gorbounov119,H.A. Gordon27,B. Gorini34,E. Gorini65a,65b,A. Gorišek89, A.T. Goshaw47,M.I. Gostkin77,C.A. Gottardo114,M. Gouighri33b,V. Goumarre44, A.G. Goussiou 144 ,N. Govender 31c ,C. Goy 4 ,I. Grabowska-Bold 81a ,K. Graham 32 ,E. Gramstad 129 , 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. Guan 176 ,C. Gubbels 170 ,J. Guenther 34 ,J.G.R. Guerrero Rojas 169 ,F. Guescini 111 ,D. Guest 17 , R. Gugel 96 ,A. Guida 44 ,T. Guillemin 4 ,S. Guindon 34 ,J. Guo 58c ,L. Guo 62 ,Y. Guo 102 ,R. Gupta 44 , 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. Heath 48 ,V. Hedberg 94 ,A.L. Heggelund 129 ,N.D. Hehir 90 ,C. Heidegger 50 ,K.K. Heidegger 50 , 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. Hellman 43a,43b ,C. Helsens 34 , R.C.W. Henderson 87 ,L. Henkelmann 30 , A.M. Henriques Correia 34 , H. Herde149,Y. Hernández Jiménez151, H. Herr96,M.G. Herrmann110,T. Herrmann46, G. Herten50,R. Hertenberger110,L. Hervas34,N.P. Hessey163a,H. Hibi80,S. Higashino79, E. Higón-Rodriguez169, K.H. Hiller44,S.J. Hillier19,M. Hils46,I. Hinchliffe16,F. Hinterkeuser22, – 67 – JHEP08(2022)175 M. Hirose 128 ,S. Hirose 164 ,D. Hirschbuehl 177 ,B. Hiti 89 , O. Hladik 136 ,J. Hobbs 151 ,R. Hobincu 25e , 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. Hönle111,B.H. Hooberman168,W.H. Hopkins5, Y. Horii 112 ,L.A. Horyn 35 ,S. Hou 154 ,J. Howarth 55 ,J. Hoya 86 ,M. Hrabovsky 126 ,A. Hrynevich 105 , T. Hryn’ova4,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. Huegging 22 ,T.B. Huffman 130 ,M. Huhtinen 34 ,S.K. Huiberts 15 ,R. Hulsken 56 ,N. Huseynov 77,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. Jäkel177,K. Jakobs50,T. Jakoubek175,J. Jamieson55, K.W. Janas81a,G. Jarlskog94,A.E. Jaspan88, N. Javadov77,z,T. Javůrek34,M. Javurkova99, F. Jeanneau140,L. Jeanty127,J. Jejelava155a,aa,P. Jenni50,e,S. Jézéquel4,J. Jia151,Z. Jia13c, Y. Jiang 58a ,S. Jiggins 48 ,J. Jimenez Pena 111 ,S. Jin 13c ,A. Jinaru 25b ,O. Jinnouchi 160 ,H. Jivan 31f , 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. Kahra 96 ,T. Kaji 174 ,E. Kajomovitz 156 ,C.W. Kalderon 27 ,A. Kamenshchikov 118 ,M. Kaneda 159 , N.J. Kang 141 ,S. Kang 76 ,Y. Kano 112 ,D. Kar 31f ,K. Karava 130 ,M.J. Kareem 163b ,I. Karkanias 158 , S.N. Karpov 77 ,Z.M. Karpova 77 ,V. Kartvelishvili 87 ,A.N. Karyukhin 118 ,E. Kasimi 158 ,C. Kato 58d , 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. Kerševan89,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. Kodys 138 ,D.M. Koeck 152 ,P.T. Koenig 22 ,T. Koffas 32 ,N.M. Köhler 34 ,M. Kolb 140 ,I. Koletsou 4 , T. Komarek 126 ,K. Köneke 50 ,A.X.Y. Kong 1 ,T. Kono 122 , V. Konstantinides 92 ,N. Konstantinidis 92 , B. Konya 94 ,R. Kopeliansky 63 ,S. Koperny 81a ,K. Korcyl 82 ,K. Kordas 158 , G. Koren 157 ,A. Korn 92 , 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. Krishnan 59b ,M. Krivos 138 ,K. Krizka 16 ,K. Kroeninger 45 ,H. Kroha 111 ,J. Kroll 136 ,J. Kroll 132 , K.S. Krowpman 103 ,U. Kruchonak 77 ,H. Krüger 22 , N. Krumnack 76 ,M.C. Kruse 47 ,J.A. Krzysiak 82 , A. 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, – 68 – JHEP08(2022)175 M. Kuna56,A. Kupco136, T. Kupfer45,O. Kuprash50,H. Kurashige80,L.L. Kurchaninov163a, Y.A. Kurochkin 104 ,A. Kurova 108 , M.G. Kurth 13a,13d ,E.S. Kuwertz 34 ,M. Kuze 160 ,A.K. Kvam 144 , 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. Lançon27,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. Lenz 22 ,S. Leone 69a ,C. Leonidopoulos 48 ,A. Leopold 150 ,C. Leroy 106 ,R. Les 103 ,C.G. Lester 30 , M. Levchenko133,J. Levêque4,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. Liang 13a ,M. Liberatore 44 ,B. Liberti 71a ,K. Lie 60c ,J. Lieber Marin 78b ,K. Lin 103 ,R.A. Linck 63 , R.E. Lindley 6 ,J.H. Lindon 2 ,A. Linss 44 ,E. Lipeles 132 ,A. Lipniacka 15 ,T.M. Liss 168,ai ,A. Lister 170 , 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,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. Loffredo 71a,71b ,T. Lohse 17 ,K. Lohwasser 145 ,M. Lokajicek 136 ,J.D. Long 168 ,I. Longarini 70a,70b , L. Longo34,R. Longo168,I. Lopez Paz12,A. Lopez Solis44,J. Lorenz110,N. Lorenzo Martinez4, A.M. Lory110,A. Lösle50,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. Maio 135a,135b,135d ,K. Maj 81a ,O. Majersky 26a ,S. Majewski 127 ,N. Makovec 62 , V. Maksimovic 14 , 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. Mandić89,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. Maček89,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, W.P. McCormack 16 ,E.F. McDonald 101 ,A.E. McDougall 115 ,J.A. Mcfayden 152 ,G. Mchedlidze 155b , M.A. McKay40,K.D. McLean171,S.J. McMahon139,P.C. McNamara101,R.A. McPherson171,y, – 69 – JHEP08(2022)175 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. Mijović48,G. Mikenberg175, M. Mikestikova136,M. Mikuž89,H. Mildner145,A. Milic162,C.D. Milke40,D.W. Miller35, L.S. Miller 32 ,A. Milov 175 , D.A. Milstead 43a,43b , T. Min 13c ,A.A. Minaenko 118 ,I.A. Minashvili 155b , 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. Mjörnmark94,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. Mönig44,E. Monnier98, L. Monsonis Romero169,A. Montalbano148,J. Montejo Berlingen34, M. Montella123,F. Monticelli86,N. Morange62,A.L. Moreira De Carvalho135a, M. Moreno Llácer169,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. Muškinja16,C. Mwewa27, A.G. Myagkov118,ae,A.J. Myers7, A.A. Myers134,G. Myers63,M. Myska137,B.P. Nachman16, O. Nackenhorst 45 ,A.Nag Nag 46 ,K. Nagai 130 ,K. Nagano 79 ,J.L. Nagle 27 ,E. Nagy 98 ,A.M. Nairz 34 , 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. Nicolaidou 140 ,D.S. Nielsen 38 ,J. Nielsen 141 ,M. Niemeyer 51 ,N. Nikiforou 10 ,V. Nikolaenko 118,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. Novotny 113 ,L. Nozka 126 ,K. Ntekas 166 , E. Nurse 92 ,F.G. Oakham 32,aj ,J. Ocariz 131 ,A. Ochi 80 , 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’Keefe88, Y. Okumura159, A. Olariu25b,L.F. Oleiro Seabra135a,S.A. Olivares Pino142d, D. Oliveira Damazio 27 ,D. Oliveira Goncalves 78a ,J.L. Oliver 166 ,M.J.R. Olsson 166 ,A. Olszewski 82 , J. Olszowska 82 ,Ö.O. Öncel 22 ,D.C. O’Neil 148 ,A.P. O’neill 130 ,A. Onofre 135a,135e ,P.U.E. Onyisi 10 , R.G. Oreamuno Madriz116,M.J. Oreglia35,G.E. Orellana86,D. Orestano72a,72b,N. Orlando12, R.S. Orr162,V. O’Shea55,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, L. Paolozzi52,C. Papadatos106,S. Parajuli40,A. Paramonov5,C. Paraskevopoulos9, D. Paredes Hernandez60b,S.R. Paredes Saenz130,B. Parida175,T.H. Park162,A.J. Parker29, – 70 – JHEP08(2022)175 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. Petroff 62 ,F. Petrucci 72a,72b ,A. Petrukhin 147 ,M. Pettee 178 ,N.E. Pettersson 34 ,K. Petukhova 138 , 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. Plesanovs 50 ,V. Pleskot 138 , E. Plotnikova 77 ,P. Podberezko 117b,117a ,R. Poettgen 94 ,R. Poggi 52 , 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-Maitland 34 ,G. Rabanal Bolanos 57 ,F. Ragusa 66a,66b ,J.A. Raine 52 ,S. Rajagopalan 27 , K. Ran13a,13d,D.F. Rassloff59a,D.M. Rauch44,S. Rave96,B. Ravina55,I. Ravinovich175, M. Raymond 34 ,A.L. Read 129 ,N.P. Readioff 145 ,D.M. Rebuzzi 68a,68b ,G. Redlinger 27 ,K. Reeves 41 , 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. Rodríguez Vera163b, S. Roe34,A.R. Roepe124,J. Roggel177,O. Røhne129,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. Rühr50, A. Ruiz-Martinez169,A. Rummler34,Z. Rurikova50,N.A. Rusakovich77,H.L. Russell34, L. Rustige36,J.P. Rutherfoord6,E.M. Rüttinger145,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, A. Salnikov 149 ,J. Salt 169 ,A. Salvador Salas 12 ,D. Salvatore 39b,39a ,F. Salvatore 152 ,A. Salzburger 34 , D. Sammel50,D. Sampsonidis158,D. Sampsonidou58d,58c,J. Sánchez169,A. Sanchez Pineda4, – 71 – JHEP08(2022)175 V. Sanchez Sebastian169,H. Sandaker129,C.O. Sander44,I.G. Sanderswood87,J.A. Sandesara99, M. Sandhoff 177 ,C. Sandoval 20b ,D.P.C. Sankey 139 ,M. Sannino 53b,53a ,A. Sansoni 49 ,C. Santoni 36 , 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. Schäfer96,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. Singh 162 ,S. Sinha 44 ,S. Sinha 31f ,M. Sioli 21b,21a ,I. Siral 127 ,S.Yu. Sivoklokov 109 ,J. Sjölin 43a,43b , A. Skaf 51 ,E. Skorda 94 ,P. Skubic 124 ,M. Slawinska 82 ,K. Sliwa 165 , V. Smakhtin 175 ,B.H. Smart 139 , J. Smiesko 138 ,S.Yu. Smirnov 108 ,Y. Smirnov 108 ,L.N. Smirnova 109,r ,O. Smirnova 94 ,E.A. Smith 35 , H.A. Smith 130 ,M. Smizanska 87 ,K. Smolek 137 ,A. Smykiewicz 82 ,A.A. Snesarev 107 ,H.L. Snoek 115 , 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. Solovyev 133 ,P. Sommer 145 ,H. Son 165 ,A. Sonay 12 ,W.Y. Song 163b ,A. Sopczak 137 , A.L. Sopio 92 , F. Sopkova 26b ,S. Sottocornola 68a,68b ,R. Soualah 64a,64c ,A.M. Soukharev 117b,117a ,Z. Soumaimi 33e , D. South44,S. Spagnolo65a,65b,M. Spalla111,M. Spangenberg173,F. Spanò91,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. Starko 163b ,P. Staroba 136 ,P. Starovoitov 59a ,S. Stärz 100 ,R. Staszewski 82 ,G. Stavropoulos 42 , P. Steinberg27,A.L. Steinhebel127,B. Stelzer148,163a,H.J. Stelzer134,O. Stelzer-Chilton163a, H. Stenzel 54 ,T.J. Stevenson 152 ,G.A. Stewart 34 ,M.C. Stockton 34 ,G. Stoicea 25b ,M. Stolarski 135a , S. Stonjek 111 ,A. Straessner 46 ,J. Strandberg 150 ,S. Strandberg 43a,43b ,M. Strauss 124 ,T. Strebler 98 , P. Strizenec 26b ,R. Ströhmer 172 ,D.M. Strom 127 ,L.R. Strom 44 ,R. Stroynowski 40 ,A. Strubig 43a,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, M. 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, – 72 – JHEP08(2022)175 164 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 165 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 166 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 167 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 168 Department of Physics, University of Illinois, Urbana IL; United States of America 169 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 170 Department of Physics, University of British Columbia, Vancouver BC; Canada 171 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 172 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 173 Department of Physics, University of Warwick, Coventry; United Kingdom 174 Waseda University, Tokyo; Japan 175 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel 176 Department of Physics, University of Wisconsin, Madison WI; United States of America 177 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 178 Department of Physics, Yale University, New Haven CT; United States of America aAlso at Borough of Manhattan Community College, City University of New York, New York NY; United States of America bAlso at Bruno Kessler Foundation, Trento; Italy cAlso at Center for High Energy Physics, Peking University; China dAlso at Centro Studi e Ricerche Enrico Fermi; Italy eAlso at CERN, Geneva; Switzerland fAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland gAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain hAlso at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece i Also at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America jAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America kAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel lAlso at Department of Physics, California State University, East Bay; United States of America mAlso at Department of Physics, California State University, Fresno; United States of America nAlso at Department of Physics, California State University, Sacramento; United States of America oAlso at Department of Physics, King’s College London, London; United Kingdom p Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia qAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland rAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russia sAlso at Faculty of Physics, Sofia University, ‘St. Kliment Ohridski’, Sofia; Bulgaria tAlso at Graduate School of Science, Osaka University, Osaka; Japan uAlso at Hellenic Open University, Patras; Greece vAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain wAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany xAlso at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary yAlso at Institute of Particle Physics (IPP); Canada zAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan – 79 – JHEP08(2022)175 aa Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia ab Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain ac Also at Istanbul University, Dept. of Physics, Istanbul; Turkey ad Also at Joint Institute for Nuclear Research, Dubna; Russia ae Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia af Also at National Research Nuclear University MEPhI, Moscow; Russia ag Also at Physics Department, An-Najah National University, Nablus; Palestine ah Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany ai Also at The City College of New York, New York NY; United States of America aj Also at TRIUMF, Vancouver BC; Canada ak Also at Università di Napoli Parthenope, Napoli; Italy al Also at University of Chinese Academy of Sciences (UCAS), Beijing; China am Also at Yeditepe University, Physics Department, Istanbul; Turkey ∗Deceased – 80 –