Measurement of the top-quark mass using a leptonic invariant mass in pp collisions at √s = 13 TeV with the ATLAS detector

JHEP06(2023)019 Published for SISSA by Springer Received:September 2, 2022 Accepted:March 19, 2023 Published:June 5, 2023 Measurement of the top-quark mass using a leptonic invariant mass in pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: A measurement of the top-quark mass ( mt ) in the t¯ t→lepton + jets channel is presented, with an experimental technique which exploits semileptonic decays of b -hadrons produced in the top-quark decay chain. The distribution of the invariant mass m`µ of the lepton, ` (with ` = e, µ ), from the W -boson decay and the muon, µ , originating from the b -hadron decay is reconstructed, and a binned-template profile likelihood fit is performed to extract mt . The measurement is based on data corresponding to an integrated luminosity of 36.1 fb −1 of √s = 13 TeV pp collisions provided by the Large Hadron Collider and recorded by the ATLAS detector. The measured value of the top-quark mass is mt = 174 . 41 ± 0 . 39 ( stat. ) ± 0 . 66 ( syst. ) ± 0 . 25 ( recoil ) GeV , where the third uncertainty arises from changing the Pythia8 parton shower gluon-recoil scheme, used in top-quark decays, to a recently developed setup. Keywords: Hadron-Hadron Scattering , Top Physics ArXiv ePrint: 2209.00583 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP06(2023)019 JHEP06(2023)019 Contents 1 Introduction 1 2 ATLAS experiment 2 3 Data and simulation 3 3.1 Data sample and object definition 3 3.2 Object and event selections 3 3.3 Signal and background simulations 5 3.4 Modelling of heavy-quark fragmentation, hadron production and decays 7 4 Analysis 12 4.1 Event yields and sample composition 12 4.2 Extraction of the top-quark mass 16 5 Measurement uncertainties 18 5.1 Statistical and datasets 18 5.2 Modelling of the signal process 22 5.3 Modelling of background processes 25 5.4 Detector response 26 6 Conclusions 29 The ATLAS collaboration 37 1 Introduction The large mass of the top-quark plays a role in much of the dynamics of elementary particles via loop diagrams. In the Standard Model (SM), the large top-quark mass significantly affects the radiative corrections to both the Higgs boson and W -boson masses, providing a relationship that can be used for precision tests of the consistency of the SM [ 1 ]. Furthermore, a precise measurement of the top-quark mass is required to predict the evolution of the Higgs quartic coupling at high scales [ 2 , 3 ]. If performed with a precision of the order of a few hundred MeV , the direct determination of the top-quark mass from its decay products, and the indirect measurements from top-quark production cross-sections or kinematic distributions, are important not only for the constraints mentioned above, but also for the challenge of the interpretation of such measurements in the context of a strongly interacting particle theory [4,5]. A direct measurement of the top-quark mass ( mt ) is presented that uses a partial, leptonic-only, invariant mass reconstruction of the top-quark decay products. The analysis 1 JHEP06(2023)019 is performed from a sample of reconstructed t¯ t events in the ` +jets channel, where one of the two W -bosons from the topand antitop-quarks decays leptonically. In the top-quark decay t→Wb , the invariant mass m`µ of the lepton ` (with ` = e, µ ) from the W -boson decay and the muon µ from a semileptonic decay of a b -hadron is constructed as the observable sensitive to the parent mt value. The advantages of a strategy based on the invariant mass of visible leptonic decay products for the measurement of the top-quark mass rest mainly on the smaller sensitivity to the jet energy calibration and energy resolution, compared to the standard direct reconstruction methods, and on less sensitivity to top-quark production modelling (owing to the boost-invariant construction) than in methods based on the W -decay lepton alone [ 6 ]. Moreover, methods with different types of systematic uncertainties are important when combining measurements, and for testing the consistency of the theoretical interpretation of the top-quark mass. In this analysis the m`µ distribution from models with different top-quark mass hypotheses is compared with data, and the optimal value of mt is determined from a binned-template profile likelihood fit. A similar technique was first employed by the CDF Collaboration at the Tevatron collider [ 7 ], and a closely related analysis with J/ψ decays has been presented by the CMS Collaboration [ 8 ]; however, both these analyses yielded uncertainties in mt of several GeV . Until now, the most precise measurement of the top-quark mass in the t¯ t→` +jets channel by the ATLAS Collaboration was mt = 172 . 08 ± 0 . 39 (stat.) ± 0 . 82 (syst.) GeV , whereas combining multiple ATLAS measurements gave mt = 172 . 69 ± 0 . 48 GeV [ 9 ]. The CMS Collaboration reports its most precise combination as mt = 172 . 44 ± 0 . 49 GeV [ 10 ], and the Tevatron experiments report a combined value of mt = 174 . 30 ± 0 . 65 GeV [ 11 ]. Finally, the mass of the top-quark is indirectly determined from global electroweak fits as mt= 176.4±2.1GeV [12]. 2 ATLAS experiment The ATLAS experiment [ 13 ] at the LHC is a multipurpose particle detector with a forwardbackward symmetric cylindrical geometry and a near 4 π coverage in solid angle. 1 It consists of an inner tracking detector surrounded by a thin superconducting solenoid providing a 2 T axial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer. The inner tracking detector covers the pseudorapidity range |η|< 2 . 5and consists of silicon pixel, silicon microstrip, and transition radiation tracking detectors. The innermost layer, known as the insertable B-layer [ 14 , 15 ], was added in 2014 and provides high-resolution hits at small radius to improve the tracking performance. Lead/liquid-argon (LAr) sampling calorimeters provide electromagnetic (EM) energy measurements with high granularity. A steel/scintillator-tile hadronic calorimeter covers the central pseudorapidity range ( |η|< 1 . 7). The endcap and forward regions are instrumented with LAr calorimeters for both the EM 1 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 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. 2 JHEP06(2023)019 and hadronic energy measurements up to |η| = 4 . 9. The muon spectrometer surrounds the calorimeters and is based on three large air-core toroid superconducting magnets with eight coils each and a bending power of 2.0 to 7.5 Tm. It includes a system of precision tracking chambers covering the region |η|< 2 . 7and fast detectors for triggering in the range |η|< 2 . 4. A two-level trigger system was used to select events [ 16 ]. The first-level trigger is implemented in hardware and uses a subset of the detector information to reduce the accepted rate to at most 100 kHz. This is followed by the software-based high-level trigger, which reduces the event rate to around 1 kHz. An extensive software suite [ 17 ] is used in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 3 Data and simulation 3.1 Data sample and object definition The analysis is performed with the 2015 and 2016 proton-proton collision data sample produced by the LHC at a centre-of-mass energy of √s = 13 TeV and collected by the ATLAS experiment, corresponding to an integrated luminosity of 36.1fb −1 [ 18 ]. The data sample was recorded during stable beam conditions, and all relevant ATLAS detector subsystems were required to be operational. The average number of pp collisions in the same bunch crossing (referred to as pile-up) in the data sample is 24.1. Electron candidates are reconstructed from energy deposits (clusters) in the electromagnetic calorimeter matched to reconstructed tracks in the inner detector. Candidates in the transition region 1 . 37 <|ηcluster|< 1 . 52 between the calorimeter barrel and endcaps are excluded. Muon candidates are reconstructed from track segments in the layers of the muon spectrometer, and matched to tracks found in the inner detector. The final muon candidates are re-fitted using the complete track information from both detector systems. Jet candidates are reconstructed from three-dimensional topological EM-scale energy clusters [ 19 ] in the calorimeter using the antikt jet algorithm [ 20 , 21 ] with a radius parameter R = 0 . 4. The reconstructed jets are calibrated to the level of stable-particle jets by the application of a jet energy scale (JES) correction derived from simulation and in situ corrections based on 13 TeV data [ 22 ]. There is no dedicated energy scale correction for jets with semileptonic heavy-flavour hadron decays. The missing transverse momentum, Emiss T , is defined as the magnitude of the negative vector sum of the transverse momentum, pT , of all reconstructed and calibrated physics objects in the event, with an extra term added to account for soft energy in the event that is not associated with any of the reconstructed objects [ 23 ]. This soft term is calculated from inner-detector tracks matched to the primary vertex in order to make it more resilient to pile-up contamination. 3.2 Object and event selections The event selection is designed to collect a sample of t¯ tcandidate events in the final state `νbjj0¯ b , where ` = e or µ and the jj0 are the jets produced in the decay of the W -boson into quarks, and at least one of the b -initiated jets is associated with a muon from the 3 JHEP06(2023)019 semileptonic decay of a b -hadron. The goal is to select events where the lepton ` from the W -boson and the b -initiated jet with the muon from semileptonic decay come from the same top-quark. Events are required to pass either a single-electron or single-muon trigger. Multiple trigger types were used: the lowest-threshold triggers include isolation requirements to reduce the trigger rate and had pT thresholds of 20 GeV for muons and 24 GeV for electrons in 2015 data, and 26 GeV for both lepton types in 2016 data [ 16 , 24 , 25 ]. These triggers were complemented by others with higher pT thresholds and no isolation requirements to increase event acceptance. Events must have at least one reconstructed vertex, i.e. at least two tracks with pT> 0 . 4 GeV consistent with the beam-collision region in the x – y plane. If multiple vertices are reconstructed, then the primary vertex is taken to be the one with the largest sum, over the tracks assigned to it, of the transverse momentum squared of each track. Events are further selected based on the presence of an electron or muon candidate from the decay of a W -boson, called ‘primary’ leptons. Primary-lepton electron candidates must satisfy a ‘tight’ likelihood-based identification criterion [ 26 ], be matched to the corresponding trigger, and have pT> 27 GeV , |η|< 2 . 47 with the exclusion of 1 . 37 < |η|< 1 . 52, longitudinal impact parameter |z0sin θ|< 0 . 5mm and transverse impact parameter significance |d0/σ ( d0 ) |< 5, where σ ( d0 )is the uncertainty in the transverse impact parameter. Background from photon conversions, hadrons, and electrons produced away from the primary vertex (‘non-prompt’ electrons) is reduced by requiring the primary electron candidates to pass an isolation requirement based on the surrounding tracks and topological clusters in the calorimeter [ 26 ]. Primary-lepton muon candidates must satisfy a ‘medium’ quality identification criterion [ 27 ], be matched to the corresponding trigger, and have pT> 27 GeV , |η|< 2 . 5, longitudinal impact parameter |z0sin θ|< 0 . 5mm and transverse impact parameter significance |d0/σ ( d0 ) |< 3. Background from hadrons and from muons produced away from the primary vertex (‘non-prompt’ muons) is reduced by requiring primary muon candidates to pass an isolation requirement based on the surrounding tracks and topological clusters in the calorimeter, and be separated by ∆ R > 0 . 4from the nearest selected jet. If the nearest selected jet is ∆ R≤ 0 . 4from the muon and has less than three associated tracks (including the muon track), the muon is kept and the jet is removed from the jet list, to ensure high efficiency for muons undergoing significant energy loss in the calorimeter. Events with more than one candidate primary lepton with pT> 25 GeV are vetoed, in order to reject events from the t¯ tdileptonic decay channel. Jet candidates are required to have pT> 25 GeV and |η|< 2 . 5, and a multivariate jetvertex-tagger (JVT) is applied to suppress jets from pile-up [ 28 ]. During jet reconstruction, no distinction is made between identified electrons and jet energy deposits. Therefore, if any of the jets lie within ∆ R = 0 . 2of a selected electron, the single closest jet is discarded in order to avoid double-counting electrons as jets. After this, electrons that are within ∆ R = 0 . 4of a remaining jet are removed. Jets are identified as originating from a b -quark ( b -tagged) using two techniques, one based on the reconstruction of a displaced jet (DJ tagging) and the other based on the semileptonic decay of a b -hadron into a so-called ‘soft muon’ (SMT tagging). For DJ tagging, multivariate techniques are used to combine 4 JHEP06(2023)019 information about the impact parameters of displaced tracks and the topological properties of secondary and tertiary decay vertices reconstructed within the jet [ 29 ]. The algorithm is trained on simulated t¯ t events to discriminate b -jets from a background consisting of light-flavour jets and c -jets. A selection corresponding to an efficiency of 77% for b -jets in t¯ t events is employed, with a rejection rate of a factor of 7 (100) for c -jets (light jets). The SMT tagging is performed by requiring the presence of a muon candidate satisfying the ‘tight’ quality identification criterion [ 27 ], pT> 8 GeV and |η|< 2 . 5, with loose requirements on the impact parameters ( |d0|< 3mm, |z0sin θ|< 3mm) and with a distance ∆ R < 0 . 4 from a selected jet candidate. The definition of the muon object for the SMT tagging was optimised by maximising the efficiency for muons originating from the semileptonic decays of b - and c -hadrons (selecting approximately 50% of b -jets containing a muon, which are in turn 20% of all b -jets produced in t¯ t events), minimising the misidentification rate (about 10 −3 per light-flavour jet, mostly due to the decays of pions and kaons), and minimising the uncertainty in the measured top-quark mass. If more than one muon satisfying the criteria above is found within a given jet, the muon with the highest pTis chosen. Events must have at least one SMT-tagged jet and at least one DJ-tagged jet (which could be the same jet), among a total of at least four jet candidates with pT> 30 GeV (with the exception of the SMT-tagged jet which may have a pT as low as 25 GeV ). If more than one SMT-tagged jet is found in the event, only the one with the highestpT muon is considered. The SMT muon and the primary lepton must be separated by ∆ R`,µ < 2. The presence of at least one neutrino in the final state is inferred from the requirements that Emiss T> 30 GeV and Emiss T + mT ( W ) > 60 GeV . 2 The requirement that the SMT muon and the primary lepton must be separated by ∆ R`,µ < 2enhances the fraction of events where both leptons come from the same top-quark, in contrast to events where the two leptons originate from different top-quarks. The selected events are categorised as same-sign (SS) events or opposite-sign (OS) events according to the charge signs of the primary lepton and the soft muon. When both leptons come from the same top-quark, opposite-sign events are enriched in direct b→µX decays, while same-sign events have a large contribution from sequential b→cX0→µX00 decays; both samples carry information about the mass of the parent top-quark though. Finally, the invariant mass of the primary lepton and the soft muon ( m`µ ) is required to be between 15 and 80 GeV , as this is the region most sensitive to the top-quark mass. This requirement also suppresses the Z -boson, J/ψ and Υresonances. 3.3 Signal and background simulations A number of Monte Carlo (MC) simulation samples are used to model the expected signal of top-quark pairs and the background. The MC samples were processed either through the full ATLAS detector simulation [ 30 ] based on Geant4 [ 31 ] or through a faster simulation making use of parameterised showers in the calorimeters [ 32 ]. Additional simulated pp collisions generated by Pythia 8.186 [ 33 ] with the MSTW2008 [ 34 , 35 ] set of leadingorder (LO) parton distribution functions (PDFs) and a set of tuned parameters called the 2 The transverse mass is given by mT ( W ) = p2p` TEmiss T(1 −cos ∆φ) , where p` T is the transverse momentum of the muon (electron) and ∆ φ is the azimuthal angle separation between the lepton and the direction of the missing transverse momentum. 5 JHEP06(2023)019 A2 tune [36] were used to model the effects of both in-time and out-of-time pile-up. They were superimposed on the MC events, matching the luminosity profile of the recorded data. All simulated samples were processed through the same reconstruction algorithms and analysis chain as the data. Simulated MC events were corrected so that the object identification efficiencies and energy and momentum scales and resolutions matched those determined from data control samples [ 22 , 27 , 28 , 37 ]. The modelling of SMT muons and their misidentification due to light-hadron decays and detector background (‘SMT fake’) was studied using control samples as well. The efficiency of muon identification in jets was calibrated using muons from J/ψ and Z decays, and checked as a function of nearby track and calorimeter activity, and of the muon’s transverse impact parameter d0 . The calibration of the misidentification rate was performed, using the sample of W +jets events described in the following of the section, with the same approach used in ref. [ 38 ]. A data-driven technique, described in detail in ref. [ 39 ], is used to measure in data the normalisation and flavour composition of such events and to derive corrections that are applied to simulated samples for the calibration. A light-jets dominated sample of W + 1 jet events is then defined by selecting events where the jet is SMT-tagged but is not DJ-tagged, and using the SS category. A data-to-simulation scale factor (SF) of 1 . 10 ± 0 . 14 is measured. A slight miscalibration of the pT of jets that contain a soft muon was observed, and the pT of these jets was corrected in the simulation with a factor of 0 . 967 ± 0 . 024. It has been measured by studying the distribution of the ratio of the pT values of the SMT-tagged jet and the average non-SMT-tagged jet in t¯ t data and simulation. The t¯ t sample was generated using the hvq program [ 40 ] in the Powheg-Box v2 generator [ 41 , 42 ] with the NNPDF3.0nlo set of PDFs [ 43 ] and the top-quark mass set to 172.5 GeV . Additional samples with different top-quark mass hypotheses were produced in the range of mt between 165 and 180 GeV , with steps of 0 . 5 GeV between 170 and 175 GeV . The samples have been produced using the appropriate top-quark decay width values predicted at next-to-next-to-leading order (NNLO) as a function of mt [ 44 ]. The hvq program uses on-shell matrix elements for production of t¯ t pairs at next-to-leading order (NLO) in quantum chromodynamics (QCD). Off-shell effects and top-quark decays, including spin correlations, were approximated using Madspin [ 45 ]. Parton showers and hadronisation were modelled by Pythia 8.2 [ 33 ] using a dedicated ‘A14rb ’ setting of the ATLAS A14 [ 46 ] tune, as detailed in section 3.4. The A14 tune is based on LEP and Tevatron collider data and also uses a combination of ATLAS √s =7 TeV measurements of the underlying event, jet production, Z -boson production and top-quark production in order to constrain the parameters for the showers, multiple parton interactions and colour reconnection effects. Radiation in top-quark decays was handled entirely by the parton-shower generator, which implements matrix-element corrections with an accuracy equivalent to a calculation at the NLO level. The hdamp parameter, which controls the pT of the first additional emission beyond the Born configuration, was set to 1.5 times the top-quark mass of each sample. The main effect of the hdamp setting is to regulate the highpT emission against which the t¯ t system recoils. The EvtGen v1.2.0 [ 47 ] program was used to simulate bottom and charm hadron mixing and decays. The production 6 JHEP06(2023)019 fractions and the branching ratios (BR) of the decay of b -hadrons and c -hadrons into muons were rescaled to the latest values from the Particle Data Group (PDG) [ 1 ], as detailed in section 3.4. The simulated t¯ t event sample was normalised to the Top++2.0 [ 48 ] theoretical cross-section of 832 +46 −51 pb, calculated at NNLO in QCD and including resummation of next-to-next-to-leading logarithmic (NNLL) soft gluon terms [49–53]. The main backgrounds to candidate signal events come from the production of a single top-quark, and from a Wor Z-boson in association with jets. A small background contribution arises from diboson ( WW , WZ , ZZ ) production. Events not containing prompt leptons also contribute to the selected sample via the misidentification of a jet or a photon as an electron, or the presence of non-prompt electrons or muons passing the prompt isolated lepton selection. This contribution is referred to as ‘multijet’ background, and was estimated using data by following the matrix method described in ref. [54]. The production of V +jets was simulated with the Sherpa 2.2.1 [ 55 ] generator using next-to-leading-order (NLO) matrix elements (ME) for up to two partons, and leadingorder (LO) matrix elements for up to four partons calculated with the Comix [ 56 ] and OpenLoops [ 57 – 59 ] libraries. They were matched with the Sherpa parton shower [ 60 ] using the MEPS@NLO prescription [ 61 – 64 ] with the set of tuned parameters developed by the Sherpa authors. The NNPDF3.0nnlo set of PDFs [ 43 ] was used and the samples were normalised to a next-to-next-to-leading-order (NNLO) prediction [ 65 ]. The normalisation of the W +jet background and the fractions of W -bosons produced in association with heavy-flavour quarks are extracted from data, taking advantage of the intrinsic W -boson charge asymmetry in this process [ 66 ]. The Z +jets contribution is estimated from MC simulation and checked in a data control sample. Diboson processes were simulated with the Sherpa 2.1.1 event generator. They were calculated using Comix and OpenLoops, and merged with the Sherpa parton shower according to the MEPS@NLO prescription. The CT10nlo PDF set [ 67 ] was used in conjunction with dedicated parton shower tuning developed by the Sherpa authors. Samples of t− , Wt - and s -channel single top-quark background events were generated with Powheg-Box v1, using the 4-flavour scheme for the NLO matrix element calculations together with the fixed four-flavour PDF set CT10f4. Overlaps between the t¯ t and Wt final states were removed with the ‘diagram removal’ prescription [ 68 ]. For all top processes, top-quark spin correlations were preserved (for t-channel, top quarks were decayed using Madspin). All single top-quark samples were interfaced to Pythia 6.428 [ 69 ] with the CTEQ6L1 PDF set [ 70 ] and the corresponding Perugia 2012 [ 71 ] set of tuned parameters. The EvtGen v1.2.0 program was used to model properties of the bottom and charm hadron decays. The single top-quark t - and s -channel samples were normalised to the approximate NNLO theoretical cross-sections [72–74]. 3.4 Modelling of heavy-quark fragmentation, hadron production and decays The modelling of the momentum transfer between the b -quark and the b -hadron is an important aspect of this analysis. The Monte Carlo event generators, such as the Pythia, Herwig [ 75 , 76 ] and Sherpa programs, describe this transfer according to phenomenological models, namely the string and cluster models containing parameters which are tuned to data. 7 JHEP06(2023)019 The Pythia8 program uses parametric functions to describe the b -quark fragmentation function, while Herwig7 and Sherpa use a non-parametric model which handles the complete parton-shower evolution. The free parameters in those models are typically fit to measurements from e+e− colliders, and this analysis assumes that the b -quark fragmentation function is the same in e+e−and pp collisions, as supported by dedicated studies. The Lund-Bowler parameterisation [77,78] in Pythia8 was used. It is given by f(z) = 1 z1+brbm2 b (1 −z)aexp(−bm2 T/z), where a , b and rb are the function parameters, mb is the b -quark mass, mT = qm2 B+p2 T the b -hadron transverse mass ( mB being the b -hadron mass), and z is the fraction of the longitudinal energy carried by the b -hadron with respect to the b -quark, in the light-cone reference frame. The fragmentation function is defined at the hadronisation scale and it is evolved by the parton shower to the process scale through DGLAP evolution equations. In Pythia8, the values of a and b were fit to data sensitive to light-quark fragmentation [ 79 ], such as charged-particle multiplicities, event shapes and scaled momentum distributions. They are assumed to be universal for lightand heavy-quarks, while the rb parameter is specific to b-quark fragmentation. The description of the b -quark fragmentation in the ATLAS A14 tune is improved by fitting for the StringZ:rFactB Pythia8 parameter (corresponding to rb ) following the approach given in refs. [ 80 – 83 ]. The A14 tune sets the parton shower αs to 0.127, whereas the value of 0.1365 is used in Monash [ 79 ]. However, both Monash and A14 set rb = 0 . 855. Since the b -quark fragmentation is controlled both by αs and rb , the procedure described in the following is used to determine a value of rb more appropriate for a value of αs = 0 . 127. The fit uses the A14 tune with e+e− collision data from the ALEPH, DELPHI and OPAL experiments at the LEP collider, and from the SLD experiment at the SLC collider [ 84 – 87 ]. The distribution of xB = 2 pB·pZ/m2 Z from semileptonically decaying b -hadrons in e+e−→Z→b¯ b events is used, where pB and pZ are the four-momenta of the b -hadron and the Z -boson, respectively. In the Z rest frame, mZ is twice the beam energy and therefore xB = 2 EB/mZ , where EB is the energy of the b -hadron. The fit is performed using Rivet v3.1.0 [ 88 ] to implement the measurements. The effect of the matrix-element corrections for e+e−→Z→b¯ bg is taken into account. Eighty simulated samples of 1M e+e−→Z→b¯ b events were produced using Pythia8 with different values of the rb parameter in the interval [0.8–1.4] and compared to the experimental data in HEPDATA format. The extraction of the best rb value is performed through a standard binned χ2 test on the experimental xB distribution where statistical and systematic uncertainties are taken into account for each of the four experiments. In addition, for the results of ALEPH, DELPHI and OPAL, bin-by-bin correlations are taken into account in the fit procedure. The SLD experiment did not provide the full covariance matrix for the total uncertainties and therefore the χ2 fit for this experiment is performed ignoring bin-by-bin correlations. For each experiment, the χ2 minimisation is performed and the best rb value and its uncertainty are found. The results are summarised in table 1. The values of χ2/ndf for DELPHI and SLD experiments show a poor modelling of the data by the simulated templates. Therefore, 8 JHEP06(2023)019 0 10 20 30 40 50 60 70 80 90 100 ) [GeV] SMT µ ( T p 0.7 0.85 1 1.15 Data / Pred. prob = 0.27 2 χ/ndf = 11.1 / 9 2 χ 0 1000 2000 3000 4000 5000 Events / 1 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Pre-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (a) 3−2−1−0 1 2 3 ) SMT µ ( η 0.7 0.85 1 1.15 Data / Pred. prob = 0.54 2 χ/ndf = 24.7 / 26 2 χ 0 1000 2000 3000 4000 5000 6000 7000 Events / 0.2 ATLAS -1 = 13 TeV, 36.1 fbs SS selection Pre-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (b) 0 20 40 60 80 100 120 140 ) [GeV] primary l( T p 0.7 0.85 1 1.15 Data / Pred. prob = 0.18 2 χ/ndf = 14.0 / 10 2 χ 0 1000 2000 3000 4000 5000 6000 7000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Pre-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (c) 0 20 40 60 80 100 120 140 160 180 200 ) [GeV]W( T m 0.7 0.85 1 1.15 Data / Pred. prob = 0.29 2 χ/ndf = 28.3 / 25 2 χ 0 500 1000 1500 2000 2500 3000 3500 4000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Pre-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (d) Figure 4. Comparison of data and prediction before the fit described in section 4.2 in the SS sample, i.e. for events with primary lepton and the soft muon with same charges, for the (a) soft muon pT , (b) soft muon η , (c) primary lepton pT and (d) W -boson transverse mass. The prediction reports the expected event contribution from the signal and backgrounds. The uncertainty band includes statistical and systematic uncertainties, but does not include the recoil uncertainty. 15 JHEP06(2023)019 20 30 40 50 60 70 80 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 Fraction of events = 170.5 GeV t m = 172.5 GeV t m = 174.5 GeV t m ATLAS Simulation = 13 TeVs OS selection 20 30 40 50 60 70 80 [GeV] µ l m 0.94 0.96 0.98 1 1.02 1.04 1.06 = 172.5 GeV t mRatio to (a) 20 30 40 50 60 70 80 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Fraction of events = 170.5 GeV t m = 172.5 GeV t m = 174.5 GeV t m ATLAS Simulation = 13 TeVs SS selection 20 30 40 50 60 70 80 [GeV] µ l m 0.94 0.96 0.98 1 1.02 1.04 1.06 = 172.5 GeV t mRatio to (b) Figure 5. Sensitivity of the m`µ distribution to different input top-quark masses from simulated events, separately for the OS and SS samples. 4.2 Extraction of the top-quark mass The distribution of the invariant mass of the primary lepton and the soft muon, m`µ is used to determine the mass of the parent top-quark. A binned-template profile likelihood fit is performed, with a Poisson likelihood model and systematic uncertainties included as Gaussian-constrained nuisance parameters [ 106 ]. Only the range of m`µ between 15 and 80 GeV is considered in the fit, since the tail of the m`µ distribution is more sensitive to t¯ t modelling uncertainties and higher-order corrections, and to the Z +jets background. The fit is performed simultaneously for the OS and SS charge-combination samples, and figure 5 shows the sensitivity of each of these distributions to variations of the top-quark mass, as well as the binning used by the templates. The SS sample has less sensitivity than the OS sample due to the larger incidence of sequential b→c→µ decays, where the SMT muon carries a smaller fraction of the parent b -quark momentum, and due to the larger fraction of events in which the leptons originate from different top-quarks. The fit uses template histograms simulated as for the nominal t¯ t sample but with different values for the input top-quark mass. The templates from the different mass samples are interpolated with piece-wise linear functions built bin by bin. To improve the stability of the method, the templates are smoothed assuming a linear dependence on mt for the fraction of the total number of events in each bin. A maximum-likelihood fit is performed with three free parameters: mt , which controls the shape of the m`µ distribution for t¯ t events, and the normalisation factors for t¯ t events in the OS and SS samples. The normalisation factors ensure that the total of the t¯ t signal and background events is always equal to the total number of selected data events, and no mt information is extracted from the number of events. The uncertainty due to the limited number of simulated events, and due to statistical fluctuations in the background estimates based on control samples, is evaluated by defining 16 JHEP06(2023)019 a new source of systematic uncertainty for each bin of the prediction, which modifies the bin content by its statistical uncertainty. Since a very large number of systematic uncertainties are considered a priori, a pruning procedure is applied to reduce the number of statistically insignificant systematic uncertainties affecting the prediction of each of the signal and background processes. A systematic variation of the m`µ templates is excluded if the total predicted change is smaller than 0.05% of the nominal bin content for all bins. The impact on the total estimated uncertainty is smaller than 0.03 GeV. The top-quark mass determination from the fit is found to be linear and unbiased with respect to the input top-quark mass hypothesis by means of pseudo-experiments, and its uncertainty from the likelihood ratio is also checked to ensure it reports the correct statistical coverage. The fit method and the event selection were optimised to minimise the total uncertainty in mt in a ‘blinded’ approach, i.e. using pseudo-data and data without knowledge of the best-fit top-quark mass. The fit yields: mt= 174.41 ±0.39 (stat.)±0.66 (syst.)±0.25 (recoil)GeV, where the statistical, systematic and recoil uncertainties are described in detail in section 5. Figure 6shows the post-fit m`µ distributions in the OS and SS samples; a goodness-of-fit test is performed using the saturated model technique [ 1 , 107 ] and returns a probability of 56%. Figures 7and 8display the corresponding post-fit plots for the kinematic variables of figures 3and 4. The data distributions are well described by the prediction, with the primary lepton pT exhibiting a slight trend which is traced to the boost of the t¯ t system, but which has no appreciable impact on the determined top-quark mass. This was confirmed by detailed checks, performed by testing the impact of NNLO corrections on the top quark kinematics, and by performing a test fit including the lepton pT as a second fit variable. In all cases the impact on the measurement was shown to be well within the quoted modelling uncertainties associated with ISR effects and ME generator choice. The post-fit uncertainties shown in figures 7and 8are significantly reduced with respect to the pre-fit ones shown in figures 3and 4due to the t¯ t normalisation being treated as a free parameter in the fit procedure; the normalisation uncertainty considered at pre-fit level is thus removed after the fit procedure. The likelihood scan with the best-fit top-quark mass value is shown in figure 9. Checks were performed by fitting the OS and SS regions separately, giving mt ( OS ) = 174 . 63 ± 0 . 47 ( stat. ) ± 0 . 75 ( syst. ) GeV and mt ( SS ) = 173 . 88 ± 0 . 74 ( stat. ) ± 1 . 01 ( syst. ) GeV . Checks were performed by separately fitting the electron and muon channels, different W -decay lepton charges and different configurations of b -tagging and event selection, and were found to all give consistent results. Checks also included the extraction of the topquark mass with alternative statistical methods, namely using analytic functions for m`µ with a parametric dependence on the top-quark mass, only using the mean value of the m`µ distribution, and using a binned-template likelihood fit without including systematic uncertainties as nuisance parameters. In particular, the inclusion of systematic uncertainties as nuisance parameters in the fit reduces the total uncertainty by 2.6%, in line with reasonable constraints from the fit. 17 JHEP06(2023)019 20 30 40 50 60 70 80 [GeV] µ l m 0.95 0.975 1 1.025 Data / Pred. 0 2000 4000 6000 8000 10000 12000 14000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs OS selection Post-Fit Data )c/b from SMT (tt )W from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (a) 20 30 40 50 60 70 80 [GeV] µ l m 0.95 0.975 1 1.025 Data / Pred. 0 2000 4000 6000 8000 10000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Post-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (b) Figure 6. Post-fit m`µ distributions in the (a) OS sample and in the (b) SS sample. The prediction reports the event contribution for the signal and backgrounds. The uncertainty band includes statistical and systematic uncertainties, but does not include the recoil uncertainty. The SS (same sign) or OS (opposite sign) refers to the charge signs of the primary lepton and the soft muon. 5 Measurement uncertainties The individual sources of uncertainty and the evaluation of their effect on mt are described in the following. Many sources of systematic uncertainty are considered, corresponding to a total of 151 individual variations. Table 6summarises the impact on mt of the main sources of systematic uncertainty, and each systematic uncertainty is accompanied by an estimate of its statistical precision performed using the bootstrap method [108]. 5.1 Statistical and datasets The uncertainty related to the size of the data sample (data statistical uncertainty) is obtained by performing the fit while keeping constant all of the nuisance parameters associated with the systematic uncertainties. The data statistical uncertainty obtained by using both the OS and SS selections is ± 0.39GeV, while the OS sample alone yields ± 0.47GeV, highlighting the contribution to the sensitivity from the SS sample. The uncertainty due to the limited size of the simulated signal and background samples includes both the impact of the signal samples size on mt -dependent templates used for the top mass interpolation, and the uncertainty of the backgrounds due to the limited size of the corresponding MC samples. This includes the multijet background, which is estimated with a data control sample. The uncertainty in the combined 2015–2016 integrated luminosity is 2.1% [ 18 ], obtained using the LUCID-2 detector [ 109 ] for the primary luminosity measurements. The distribution of the average number of interactions per bunch crossing (pile-up activity) in each MC sample 18 JHEP06(2023)019 0 10 20 30 40 50 60 70 80 90 100 ) [GeV] SMT µ ( T p 0.7 0.85 1 1.15 Data / Pred. prob = 0.97 2 χ/ndf = 2.8 / 9 2 χ 0 1000 2000 3000 4000 5000 6000 7000 Events / 1 GeV ATLAS -1 = 13 TeV, 36.1 fbs OS selection Post-Fit Data )c/b from SMT (tt )W from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (a) 3−2−1−0 1 2 3 ) SMT µ ( η 0.7 0.85 1 1.15 Data / Pred. prob = 0.34 2 χ/ndf = 28.3 / 26 2 χ 0 2000 4000 6000 8000 10000 Events / 0.2 ATLAS -1 = 13 TeV, 36.1 fbs OS selection Post-Fit Data )c/b from SMT (tt )W from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (b) 0 20 40 60 80 100 120 140 ) [GeV] primary l( T p 0.7 0.85 1 1.15 Data / Pred. prob = 0.24 2 χ/ndf = 12.7 / 10 2 χ 0 2000 4000 6000 8000 10000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs OS selection Post-Fit Data )c/b from SMT (tt )W from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (c) 0 20 40 60 80 100 120 140 160 180 200 ) [GeV]W( T m 0.7 0.85 1 1.15 Data / Pred. prob = 0.72 2 χ/ndf = 20.5 / 25 2 χ 0 1000 2000 3000 4000 5000 6000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs OS selection Post-Fit Data )c/b from SMT (tt )W from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (d) Figure 7. Post-fit comparison of data and prediction in the OS sample, i.e. for events with primary lepton and the soft muon with opposite charges, for the (a) soft muon pT , (b) soft muon η , (c) primary lepton pT and (d) W -boson transverse mass. The prediction reports the event contribution for the signal and backgrounds. The uncertainty band includes statistical and systematic uncertainties, but does not include the recoil uncertainty. 19 JHEP06(2023)019 0 10 20 30 40 50 60 70 80 90 100 ) [GeV] SMT µ ( T p 0.7 0.85 1 1.15 Data / Pred. prob = 0.96 2 χ/ndf = 3.1 / 9 2 χ 0 1000 2000 3000 4000 5000 6000 Events / 1 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Post-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (a) 3−2−1−0 1 2 3 ) SMT µ ( η 0.7 0.85 1 1.15 Data / Pred. prob = 0.52 2 χ/ndf = 25.0 / 26 2 χ 0 1000 2000 3000 4000 5000 6000 7000 Events / 0.2 ATLAS -1 = 13 TeV, 36.1 fbs SS selection Post-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (b) 0 20 40 60 80 100 120 140 ) [GeV] primary l( T p 0.7 0.85 1 1.15 Data / Pred. prob = 0.18 2 χ/ndf = 13.8 / 10 2 χ 0 1000 2000 3000 4000 5000 6000 7000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Post-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (c) 0 20 40 60 80 100 120 140 160 180 200 ) [GeV]W( T m 0.7 0.85 1 1.15 Data / Pred. prob = 0.40 2 χ/ndf = 26.2 / 25 2 χ 0 500 1000 1500 2000 2500 3000 3500 4000 Events / 5 GeV ATLAS -1 = 13 TeV, 36.1 fbs SS selection Post-Fit Data )c/b from SMT (tt fake)SMT (tt Single top Other backgrounds Uncertainty (d) Figure 8. Post-fit comparison of data and prediction in the SS sample, i.e. for events with primary lepton and the soft muon with same charges, for the (a) soft muon pT , (b) soft muon η , (c) primary lepton pT and (d) W -boson transverse mass. The prediction reports the event contribution for the signal and backgrounds. The uncertainty band includes statistical and systematic uncertainties, but does not include the recoil uncertainty. 20 JHEP06(2023)019 Source Unc. on mt[GeV] Stat. precision [GeV] Statistical and datasets Data statistics 0.39 Signal and background model statistics 0.17 Luminosity <0.01 ±0.01 Pile-up 0.07 ±0.03 Modelling of signal processes Monte Carlo event generator 0.04 ±0.06 b, c-hadron production fractions 0.11 ±0.01 b, c-hadron decay BRs 0.40 ±0.01 b-quark fragmentation rb0.19 ±0.06 Parton shower αFSR S0.07 ±0.04 Parton shower and hadronisation model 0.06 ±0.07 Initial-state QCD radiation 0.23 ±0.08 Colour reconnection <0.01 ±0.02 Choice of PDFs 0.07 ±0.01 Modelling of background processes Soft muon fake 0.16 ±0.03 Multijet 0.07 ±0.02 Single top 0.01 ±0.01 W/Z+jets 0.17 ±0.01 Detector response Leptons 0.12 ±0.01 Jet energy scale 0.13 ±0.02 Soft muon jet pTcalibration <0.01 ±0.01 Jet energy resolution 0.08 ±0.07 b-tagging 0.10 ±0.01 Missing transverse momentum 0.15 ±0.01 Total stat. and syst. uncertainties (excluding recoil) 0.77 ±0.03 Recoil uncertainty 0.25 Total uncertainty 0.81 Table 6. Impact of main sources of uncertainty on mt . Each row of the table corresponds to a group of individual systematic variations. For each uncertainty source the fit is repeated with the corresponding group of nuisance parameters fixed to their best-fit values. The contribution from each source is then evaluated by subtracting in quadrature the uncertainty obtained in this fit from that of the full fit. The total systematic uncertainty is different from the sum in quadrature of the different groups due to correlations among nuisance parameters in the fit. The last column shows the statistical uncertainty on each of the top-quark mass uncertainties as estimated with the bootstrap method [108]. 21 JHEP06(2023)019 171 172 173 174 175 176 177 [GeV] t m 0 1 2 3 4 5 6 7 8 9 10 )L ln(∆ 2 − ATLAS -1 = 13 TeV, 36.1 fbs stat. stat.+syst. Figure 9. Likelihood scan, showing the best-fit value and the statistical and total uncertainty profiles (excluding the recoil uncertainty). is reweighted to match the conditions in data, and a corresponding uncertainty is evaluated according to the uncertainty in the average number of interactions per bunch crossing. 5.2 Modelling of the signal process Uncertainties in the t¯ t signal modelling include all sources that affect the kinematics of the lepton from the W -boson decay and the kinematics of the b -hadron giving rise to the soft muon, and also the fraction of events from different soft-muon flavour components (from b -hadrons, c -hadrons, light jets and W -bosons). The t¯ t inclusive cross-section uncertainty does not affect the measurement, since the fit is based only on the shape of the distribution from t¯ tevents after background subtraction. Uncertainties that depend on the choice of NLO matching scheme in the t¯ t MC generator are estimated by comparing a sample generated with Powheg+Pythia8 with a sample generated with MadGraph5_aMC@NLO+Pythia8 [ 110 ] (referred to hereafter as aMC@NLO+Pythia8). The aMC@NLO matching requires specific settings of the Pythia8 shower to retain the NLO accuracy. The matrix-element corrections are switched off for both initial-state radiation and the global-recoil settings that are used for final-state radiation emissions. These settings are different from the nominal Powheg+Pythia8. In order to have a coherent comparison, an alternative Powheg+Pythia8 sample was generated with the same Pythia8 configuration as that used to shower aMC@NLO events. Additionally, since aMC@NLO+Pythia8 is known to describe poorly the distribution of the boost of the t¯ t system ( pt¯ t T ) [ 111 ], the pt¯ t T in aMC@NLO+Pythia8 is reweighted to that of the Powheg+Pythia8 sample. The full difference between the top-quark masses 22 JHEP06(2023)019 obtained with the two samples is considered as the positive and negative uncertainty due to the MC generator NLO matching. Uncertainties in the b -hadron production fractions and the BRs of the inclusive decays of b - and c -hadrons into muons are derived from the uncertainties in the rescaling procedure, described in section 3.4 and shown in tables 2and 3. These uncertainties are propagated through the analysis. In addition, a check was performed to verify that the impact on m`µ due to the different admixture of D -mesons involved in b→cµ + X transitions was within the uncertainty assigned to b→µ inclusive BRs. For this purpose, the exclusive decays B0→D−µν , B0→D∗ (2010) −µν , B+→D0µν , B+→D∗ (2007) 0µν and their charge conjugates were considered. For each of these decays, a kinematic selection similar to that of the main selection was applied to the events and the BR of each decay was varied within the uncertainty quoted by the PDG. The impact on m`µ was found to be significantly smaller than the effect of varying only the BR of the inclusive b→µ decays. The impact on m`µ from the uncertainties in the B0 (s) mixing parameters is much smaller than the impact from imperfect knowledge of the b -hadron production fractions and from the heavy-quark hadrons BRs. Similarly, the impact of the modelling of the soft-muon kinematics in the exclusive semileptonic decays of b - and c -hadrons in EvtGen v1.2.0 was tested by varying the various BRs within their uncertainties. The total impact was found to be negligible with respect to the impact of the inclusive b -hadron production fractions and heavy-quark hadrons BRs uncertainties. Uncertainties in the modelling of the parton shower and hadronisation processes include the estimation of several components. An alternative simulation of the t¯ t sample is considered whereby the Powheg-Box generator is matched to the Herwig 7.1.3 generator for the modelling of the parton shower and hadronisation. The Herwig 7.1.3 generator release includes several improvements in the shower description for heavy-quark fragmentation, together with a new tune to e+e− data. The angle-ordered shower algorithm is preferred to the dipole shower for this sample, as it better describes both the shower evolution in the 7 TeV ATLAS measurement of jet shapes in t¯ t events [ 112 ], and the xB distribution of LEP data, although it does not describe the xB spectrum of LEP data as well as Pythia8. The sample based on Herwig 7.1.3, when compared with the nominal t¯ t simulation used in the fit, allows the effects of changes in the shower algorithm, and therefore in initialand final-state emissions, to be assessed using alternative but coherent models of b -quark fragmentation and hadronisation, the underlying event and colour reconnection. The full difference between the top-quark masses obtained with the two samples of Powheg events showered with Pythia8 A14rb and Herwig 7.1.3 is considered as the positive and negative uncertainty from variations of the parton shower and hadronisation modelling. To evaluate the uncertainty on the modelling of the b -quark fragmentation, additional samples were produced with the value of rb in the fragmentation function varied by its uncertainty of ± 0 . 02. Additionally, the uncertainty on the choice of the renormalisation and factorisation scales in the final-state radiation (FSR) description of Pythia8 is evaluated using alternative simulated t¯ t samples generated with the scale parameters explicitly 3 varied 3 With explicit scale variations, dedicated alternative MC samples are generated with the µR,F scales 23 JHEP06(2023)019 up and down by factors of √2 [ 113 ]. For these alternative settings, the appropriate rb values are determined following the same fit procedure described in section 3.4, in order to still correctly model the xB distribution for LEP/SLD data. It must be noted that the xB distribution will however be different for the t¯ t samples with varied scales with respect to the nominal one, due to process-dependent effects. This uncertainty is labelled αFSR S in table 6. As a check of the separate rb and αFSR S uncertainties reported in table 6, a sample of Powheg events showered with Pythia using the Monash tune [ 79 ] (which has different values of αS and rb than the A14rb tune), is used to obtain an equivalent systematic uncertainty. The change in the measured top-quark mass obtained with this sample is 0 . 30 ± 0 . 06 GeV , consistent with the uncertainties associated with αFSR S , rb and the hadronisation model, listed in table 6. In the modelling of the parton shower of the b -quark from t→Wb with Pythia 8.2, there is the possibility to change the default gluon recoil scheme from recoiling against the b -quark (the nominal setting, referred to here as RTB), to recoiling against the W -boson (recoilToColoured=off, referred to as RTW) [ 114 ]. Before Pythia version 8.160, the RTW was the only possibility, but it could give unphysical radiation patterns and it is now kept as an option to understand the effect this setting has in view of previous measurements. This setting changes the modelling of second and subsequent gluon emission from quarks produced by coloured resonance decays, such as the b -quark in a t→Wb process, but it has no impact for example on Z→b¯ b decays. A third recoil scheme has been recently made available via the UserHook functionality of Pythia 8.2 with the top-quark itself serving as recoiler for second and subsequent gluon emission of the b -quark (referred to as RTT). 4 The RTW and RTT setups give wider-angle gluon radiation, resulting in energy deposits that do not get clustered into the b -jet, and lower gluon-energy emission, altering the modelling of the b -quark fragmentation and hardening the b -hadron momenta. They also mildly change the W -boson p T and the angle between the W -boson and the b -hadron resulting from the top-quark decay. The recently-developed RTT option has been considered as an additional uncertainty in the measurement, even though the implementation could only be performed based on particle-level simulation and without a dedicated tune that would normally accompany a change of setup of this nature. The change of the recoil model modifies the distribution of the momentum fraction of the b-hadron xB=1 1−m2 W/m2 t+m2 b/m2 t 2pB·pt m2 t , where mw is the W -boson mass and pt is the top four-momentum. However, the Mellin moments of this distribution derived with the RTB setup agree well with those predicted by the NLO+NLL resummation convoluted with the Kartvelishvili model tuned on ALEPH, OPAL and SLD data [ 80 ]. 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Cieri 111, K.M. Ciesla 86, D. Cinca 49, V. Cindro 93, I.A. Cioară 27b, A. Ciocio 18a, F. Cirotto 72a,72b, Z.H. Citron 172,k, M. Citterio 71a, D.A. Ciubotaru27b, B.M. Ciungu 159, A. Clark 56, M.R. Clark 41, P.J. Clark 52, C. Clement 47a,47b, Y. Coadou 103, M. Cobal 69a,69c, A. Coccaro 57b, J. Cochran81, R. Coelho Lopes De Sa 104, H. Cohen 154, A.E.C. Coimbra 36, B. Cole 41, A.P. Colijn116, J. Collot 60, P. Conde Muiño 132a,132h, S.H. Connell 33b, I.A. Connelly 59, S. Constantinescu27b, F. Conventi 72a,ap, A.M. Cooper-Sarkar 128, G. Corcella53, F. Cormier 167, K.J.R. Cormier159, L.D. Corpe 96, M. Corradi 75a,75b, E.E. Corrigan 98, F. Corriveau 105,ac, M.J. Costa 166, F. Costanza 5, D. Costanzo 142, G. Cowan 95, J.W. Cowley 32, J. Crane 102, K. Cranmer 118, S.J. Crawley59, R.A. Creager 130, S. Crépé-Renaudin 60, F. Crescioli 129, M. Cristinziani 24, V. Croft 162, G. Crosetti 43b,43a, A. Cueto 5, T. Cuhadar Donszelmann 142, A.R. Cukierman 146, W.R. Cunningham 59, S. Czekierda 86, P. Czodrowski 36, M.J. Da Cunha Sargedas De Sousa 62b, J.V. Da Fonseca Pinto 82b, C. Da Via 102, W. Dabrowski 85a , F. Dachs 36 , T. Dado 28a , S. Dahbi 33d , T. Dai 107 , C. Dallapiccola 104 , M. Dam 42, G. D’amen 29, V. D’Amico 77a,77b, J. Damp 101, J.R. Dandoy 130, M.F. Daneri 30, N.S. Dann 102, M. Danninger 145, V. Dao 36, G. Darbo 57b, O. Dartsi5, A. Dattagupta 125, T. Daubney48, S. D’Auria 71a,71b, C. David 160b, T. Davidek 135, D.R. Davis 51, I. Dawson 142, K. De 8, R. De Asmundis 72a, M. De Beurs 116, S. De Castro 23b,23a , S. De Cecco 75a,75b , N. De Groot 115 , P. de Jong 116 , H. De la Torre 108 , A. De Maria 15c, D. De Pedis 75a, A. De Salvo 75a, U. De Sanctis 76a,76b, M. De Santis 76a,76b , A. De Santo 149 , K. De Vasconcelos Corga 103 , J.B. De Vivie De Regie 67 , C. Debenedetti 138, D.V. Dedovich38, A.M. Deiana 44, J. Del Peso 100, Y. Delabat Diaz 48, D. Delgove 67, F. Deliot 137,q, C.M. Delitzsch 7, M. Della Pietra 72a,72b, D. Della Volpe 56, A. Dell’Acqua 36, L. Dell’Asta 76a,76b, M. Delmastro 5, C. Delporte67, P.A. Delsart 60, D.A. DeMarco 159, S. Demers 175, M. Demichev 38, G. Demontigny109, S.P. Denisov 37, L. D’Eramo 129, D. Derendarz 86, J.E. Derkaoui 35d, F. Derue 129, P. Dervan 92, K. Desch 24 , C. Deterre 48 , K. Dette 159 , C. Deutsch 24 , M.R. Devesa 30 , P.O. Deviveiros 36 , F.A. Di Bello 56, A. Di Ciaccio 76a,76b, L. Di Ciaccio 5, W.K. Di Clemente 130, C. Di Donato 72a,72b, A. Di Girolamo 36, G. Di Gregorio 74a,74b, B. Di Micco 77a,77b, 39 JHEP06(2023)019 R. Di Nardo 77a,77b, K.F. Di Petrillo 61, R. Di Sipio 159, C. Diaconu 103, F.A. Dias 42, T. Dias Do Vale 132a, M.A. Diaz 139a, J. Dickinson 18a, E.B. Diehl 107, J. Dietrich 19, S. Díez Cornell 48, A. Dimitrievska 18a, W. Ding 15b, J. Dingfelder 24, F. Dittus 36, F. Djama 103, T. Djobava 152b, J.I. Djuvsland 17, M.A.B. Do Vale 140, M. Dobre 27b, D. Dodsworth 26, C. Doglioni 98, J. Dolejsi 135, Z. Dolezal 135, M. Donadelli 82c, B. Dong 62c, J. Donini 40, A. D’Onofrio 15c, M. D’Onofrio 92, J. Dopke 136, A. Doria 72a, M.T. Dova 90, A.T. Doyle 59, E. Drechsler 145, E. Dreyer 145, T. Dreyer 55, A.S. Drobac 162 , D. Du 62b , Y. Duan 62b , F. Dubinin 37 , M. Dubovsky 28a , A. Dubreuil 56 , E. Duchovni 172, G. Duckeck 110, A. Ducourthial 129, O.A. Ducu 109, D. Duda 111, A. Dudarev 36, A.C. Dudder 101, E.M. Duffield18a, L. Duflot 67, M. Dührssen 36, C. Dülsen 174, M. Dumancic 172, A.E. Dumitriu 27b, A.K. Duncan 59, M. Dunford 63a, A. Duperrin 103, H. Duran Yildiz 4a, M. Düren 58, A. Durglishvili 152b, D. Duschinger50, B. Dutta 48 , B.L. Dwyer 117 , G.I. Dyckes 130 , M. Dyndal 36 , S. Dysch 102 , B.S. Dziedzic 86 , K.M. Ecker111, M.G. Eggleston51, T. Eifert 8, G. Eigen 17, K. Einsweiler 18a, T. Ekelof 164, H. El Jarrari 35e, R. El Kosseifi103, V. Ellajosyula 164, M. Ellert 164, F. Ellinghaus 174, A.A. Elliot 94, N. Ellis 36, J. Elmsheuser 29, M. Elsing 36, D. Emeliyanov 136, A. Emerman 41, Y. Enari 156, M.B. Epland 51, J. Erdmann 49, A. Ereditato 20, P.A. Erland 86, M. Errenst 36, M. Escalier 67, C. Escobar 166, O. Estrada Pastor 166, E. Etzion 154, H. Evans 68, A. Ezhilov 37, F. Fabbri 59, L. Fabbri 23b,23a, V. Fabiani 115, G. Facini 170, R.M. Faisca Rodrigues Pereira 132a, R.M. Fakhrutdinov 37, S. Falciano 75a, P.J. Falke 5, S. Falke 5, J. Faltova 135, Y. Fang 15a, Y. Fang 15a,15d, G. Fanourakis 46, M. Fanti 71a,71b, M. Faraj 69a,69c,s, A. Farbin 8, A. Farilla 77a, E.M. Farina 73a,73b, T. Farooque 108, S.M. Farrington 52, P. Farthouat 36, F. Fassi 35e, P. Fassnacht 36, D. Fassouliotis 9, M. Faucci Giannelli 52, W.J. Fawcett 32, L. Fayard 67, O.L. Fedin 37,a, W. Fedorko 167, M. Feickert 165, L. Feligioni 103, A. Fell 142, C. Feng 62b, M. Feng 51, M.J. Fenton 163, A.B. Fenyuk37, S.W. Ferguson 45, J. Ferrando 48, A. Ferrante165, A. Ferrari 164, P. Ferrari 116, R. Ferrari 73a, D.E. Ferreira de Lima 63b, A. Ferrer 166, D. Ferrere 56 , C. Ferretti 107 , F. Fiedler 101 , A. Filipčič 93 , F. Filthaut 115 , K.D. Finelli 25 , M.C.N. Fiolhais 132a,132c,b, L. Fiorini 166, F. Fischer 110, W.C. Fisher 108, I. Fleck 144, P. Fleischmann 107, T. Flick 174, B.M. Flierl 110, L. Flores 130, L.R. Flores Castillo 65a, F.M. Follega 78a,78b, N. Fomin 17, J.H. Foo 159, G.T. Forcolin 78a,78b, A. Formica 137, F.A. Förster 14, A.C. Forti 102, A.G. Foster 21, M.G. Foti 128, D. Fournier 67, H. Fox 91, P. Francavilla 74a,74b, S. Francescato 75a,75b, M. Franchini 23b,23a, S. Franchino 63a, D. Francis36, L. Franconi 20, M. Franklin 61, A.N. Fray 94, P.M. Freeman21, B. Freund 109, W.S. Freund 82b, E.M. Freundlich 49, D.C. Frizzell 122, D. Froidevaux 36, J.A. Frost 128, C. Fukunaga 157, E. Fullana Torregrosa 166,∗, T. Fusayasu112, J. Fuster 166, A. Gabrielli 23b,23a, A. Gabrielli 18a, S. Gadatsch 56, P. Gadow 111, G. Gagliardi 57b,57a, L.G. Gagnon 109, B. Galhardo 132a, G.E. Gallardo 128, E.J. Gallas 128, B.J. Gallop 136, G. Galster42, R. Gamboa Goni 94, K.K. Gan 120, S. Ganguly 172, J. Gao 62a, Y. Gao 52, Y.S. Gao 31,m, C. García 166, J.E. García Navarro 166, J.A. García Pascual 15a, C. Garcia-Argos 54 , M. Garcia-Sciveres 18a , R.W. Gardner 39 , S. Gargiulo 54 , C.A. Garner 159 , V. Garonne 127, S.J. Gasiorowski 141, P. Gaspar 82b, A. Gaudiello 57b,57a, G. Gaudio 73a, I.L. Gavrilenko 37, A. Gavrilyuk 37, C. Gay 167, G. Gaycken 48, E.N. Gazis 10, 40 JHEP06(2023)019 M. Sahinsoy 63a , A. Sahu 174 , M. Saimpert 48 , M. Saito 156 , T. Saito 156 , H. Sakamoto 156 , A. Sakharov 118,ai, D. Salamani 56, G. Salamanna 77a,77b, J.E. Salazar Loyola139c, A. Salnikov 146, J. Salt 166, D. Salvatore 43b,43a, F. Salvatore 149, A. Salvucci 65a,65b,65c, A. Salzburger 36, J. Samarati36, D. Sammel 54, D. Sampsonidis 155, D. Sampsonidou 155, J. Sánchez 166, A. Sanchez Pineda 69a,36,69c, H. Sandaker 127, C.O. Sander 48, I.G. Sanderswood 91, M. Sandhoff 174, C. Sandoval 22, D.P.C. Sankey 136, M. Sannino 57b,57a, Y. Sano 113, A. Sansoni 53, C. Santoni 40, H. Santos 132a,132b, S.N. Santpur 18a, A. Santra 166, J.G. Saraiva 132a,132d, J. Sardain 129, O. Sasaki 83, K. Sato 161 , F. Sauerburger 54 , E. Sauvan 5 , P. Savard 159,ao , R. Sawada 156 , C. Sawyer 136 , L. Sawyer 97,ah, C. Sbarra 23b, A. Sbrizzi 23a, T. Scanlon 96, J. Schaarschmidt 141, P. Schacht 111, B.M. Schachtner 110, D. Schaefer 39, L. Schaefer 130, J. Schaeffer 101, S. Schaepe 36, U. Schäfer 101, A.C. Schaffer 67, D. Schaile 110, R.D. Schamberger 148, N. Scharmberg 102, V.A. Schegelsky 37, D. Scheirich 135, F. Schenck 19, M. Schernau 163, C. Schiavi 57b,57a , L.K. Schildgen 24 , Z.M. Schillaci 26 , E.J. Schioppa 36 , M. Schioppa 43b,43a , K.E. Schleicher 54, S. Schlenker 36, K.R. Schmidt-Sommerfeld 111, K. Schmieden 36, C. Schmitt 101, S. Schmitt 48, S. Schmitz101, J.C. Schmoeckel 48, L. Schoeffel 137, A. Schoening 63b, P.G. Scholer 54, E. Schopf 128, M. Schott 101, J.F.P. Schouwenberg 115, J. Schovancova 36 , S. Schramm 56 , F. Schroeder 174 , A. Schulte 101 , H-C. Schultz-Coulon 63a , M. Schumacher 54 , B.A. Schumm 138 , Ph. Schune 137 , A. Schwartzman 146 , T.A. Schwarz 107 , Ph. Schwemling 137, R. Schwienhorst 108, A. Sciandra 138, G. Sciolla 26, M. Scodeggio48, M. Scornajenghi 43b,43a, F. Scuri 74a, F. Scutti106, L.M. Scyboz 111, C.D. Sebastiani 75a,75b, P. Seema 19, S.C. Seidel 114, A. Seiden 138, B.D. Seidlitz 29, T. Seiss 39, J.M. Seixas 82b, G. Sekhniaidze 72a, S.J. Sekula 44, N. Semprini-Cesari 23b,23a, S. Sen 51, C. Serfon 79, L. Serin 67, L. Serkin 69a,69b, M. Sessa 62a, H. Severini 122, S. Sevova 146, T. Šfiligoj93, F. Sforza 57b,57a, A. Sfyrla 56, E. Shabalina 55, J.D. Shahinian 138, N.W. Shaikh 47a,47b, D. Shaked Renous 172, L.Y. Shan 15a, J.T. Shank 25, M. Shapiro 18a, A. Sharma 128, A.S. Sharma 1, P.B. Shatalov 37, K. Shaw 149, S.M. Shaw 102, M. Shehade172, Y. Shen122, A.D. Sherman25, P. Sherwood 96, L. Shi 151, S. Shimizu 83, C.O. Shimmin 175, Y. Shimogama 171 , M. Shimojima 112 , I.P.J. Shipsey 128 , S. Shirabe 158 , M. Shiyakova 38,aa , J. Shlomi 172, A. Shmeleva37, M.J. Shochet 39, J. Shojaii 106, D.R. Shope 122, S. Shrestha 120, E.M. Shrif 33d, E. Shulga 172, P. Sicho 133, A.M. Sickles 165, P.E. Sidebo 147, E. Sideras Haddad 33d, O. Sidiropoulou 36, A. Sidoti 23b, F. Siegert 50, Dj. Sijacki 16, M.Jr. Silva 173, M.V. Silva Oliveira 82a, S.B. Silverstein 47a, S. Simion67, R. Simoniello 101, S. Simsek 12b, P. Sinervo 159, V. Sinetckii 37, N.B. Sinev 125, S. Singh 145, M. Sioli 23b,23a, I. Siral 125, S.Yu. Sivoklokov 37,∗, J. Sjölin 47a,47b, E. Skorda 98, P. Skubic 122, M. Slawinska 86, K. Sliwa 162, R. Slovak 135, V. Smakhtin172, B.H. Smart 136, J. Smiesko 28b, N. Smirnov 37, S.Yu. Smirnov 37, Y. Smirnov 37, L.N. Smirnova 37,a, O. Smirnova 98, J.W. Smith 55, M. Smizanska 91, K. Smolek 134, A. Smykiewicz 86, A.A. Snesarev 37, H.L. Snoek 116, I.M. Snyder 125, S. Snyder 29, R. Sobie 168,ac, A. Soffer 154, A. Søgaard 52, F. Sohns 55, C.A. Solans Sanchez 36, E.Yu. Soldatov 37 , U. Soldevila 166 , A.A. Solodkov 37 , A. Soloshenko 38 , O.V. Solovyanov 37 , V. Solovyev 37, P. Sommer 142, H. Son 162, W. Song 136, W.Y. Song 160b, A. Sopczak 134, A.L. Sopio 96, F. Sopkova 28b, C.L. Sotiropoulou 74a,74b, S. Sottocornola 73a,73b, 47 JHEP06(2023)019 R. Soualah 69a,69c,g, D. South 48, S. Spagnolo 70a,70b, M. Spalla 111, M. Spangenberg 170, F. Spanò 95, D. Sperlich 54, T.M. Spieker 63a, G. Spigo 36, M. Spina 149, D.P. Spiteri 59, M. Spousta 135, A. Stabile 71a,71b, R. Stamen 63a, M. Stamenkovic 116, E. Stanecka 86, B. Stanislaus 128, M.M. Stanitzki 48, M. Stankaityte 128, B. Stapf 116, E.A. Starchenko 37, G.H. Stark 138, J. Stark 60, P. Staroba 133, P. Starovoitov 63a, S. Stärz 105, R. Staszewski 86, G. Stavropoulos 46, M. Stegler48, P. Steinberg 29, A.L. Steinhebel 125, B. Stelzer 145, H.J. Stelzer 131, O. Stelzer-Chilton 160a, H. Stenzel 58, T.J. Stevenson 149, G.A. Stewart 36, M.C. Stockton 36, G. Stoicea 27b, M. Stolarski 132a, S. Stonjek 111, A. Straessner 50, J. Strandberg 147, S. Strandberg 47a,47b, M. Strauss 122, P. Strizenec 28b, R. Ströhmer 169, D.M. Strom 125, R. Stroynowski 44, A. Strubig 52, S.A. Stucci 29, B. Stugu 17, J. Stupak 122, N.A. Styles 48, D. Su 146, W. Su 62c, S. Suchek 63a, V.V. Sulin 37 , M.J. Sullivan 92 , D.M.S. Sultan 56 , S. Sultansoy 4c , T. Sumida 87 , S. Sun 107 , X. Sun 102 , K. Suruliz 149 , C.J.E. Suster 150 , M.R. Sutton 149 , S. Suzuki 83 , M. Svatos 133 , M. Swiatlowski 39 , S.P. Swift 2 , T. Swirski 169 , A. Sydorenko 101 , I. Sykora 28a , M. Sykora 135 , T. Sykora 135, D. Ta 101, K. Tackmann 48,y, J. Taenzer154, A. Taffard 163, R. Tafirout 160a, H. Takai 29, R. Takashima 88, K. Takeda 84, T. Takeshita 143, E.P. Takeva 52, Y. Takubo 83, M. Talby 103, A.A. Talyshev 37, N.M. Tamir154, J. Tanaka 156, M. Tanaka158, R. Tanaka 67, S. Tapia Araya 165, S. Tapprogge 101, A. Tarek Abouelfadl Mohamed 129, S. Tarem 153 , K. Tariq 62b , G. Tarna 27b,d , G.F. Tartarelli 71a , P. Tas 135 , M. Tasevsky 133 , T. Tashiro87, E. Tassi 43b,43a, A. Tavares Delgado132a, Y. Tayalati 35e, A.J. Taylor 52, G.N. Taylor 106, W. Taylor 160b, A.S. Tee 91, R. Teixeira De Lima 146, P. Teixeira-Dias 95, H. Ten Kate36, J.J. Teoh 116, S. Terada83, K. Terashi 156, J. Terron 100, S. Terzo 14, M. Testa 53, R.J. Teuscher 159,ac, S.J. Thais 175, T. Theveneaux-Pelzer 48, F. Thiele 42, D.W. Thomas95, J.O. Thomas44, J.P. Thomas 21, P.D. Thompson 21, L.A. Thomsen 175, E. Thomson 130, E.J. Thorpe 94, R.E. Ticse Torres 55, V. Tikhomirov 37,a, Yu.A. Tikhonov 37, S. Timoshenko37, P. Tipton 175, S. Tisserant 103, K. Todome 23b,23a, S. Todorova-Nova 135, S. Todt50, J. Tojo 89, S. Tokár 28a, K. Tokushuku 83, E. Tolley 120, K.G. Tomiwa 33d, M. Tomoto 113, L. Tompkins 146,p, B. Tong 61, P. Tornambe 104, E. Torrence 125, H. Torres 50, E. Torró Pastor 141, C. Tosciri 128, J. Toth 103,ab, D.R. Tovey 142, A. Traeet17, C.J. Treado 118, T. Trefzger 169, F. Tresoldi 149, A. Tricoli 29, I.M. Trigger 160a , S. Trincaz-Duvoid 129 , D.A. Trischuk 167 , B. Trocmé 60 , A. Trofymov 137 , C. Troncon 71a , F. Trovato 149 , L. Truong 33b , M. Trzebinski 86 , A. Trzupek 86 , F. Tsai 48 , J.C-L. Tseng 128, P.V. Tsiareshka37,a, A. Tsirigotis 155,u, V. Tsiskaridze 148, E.G. Tskhadadze152a, M. Tsopoulou 155, I.I. Tsukerman 37, V. Tsulaia 18a, S. Tsuno 83, D. Tsybychev 148, Y. Tu 65b, A. Tudorache 27b, V. Tudorache 27b, T.T. Tulbure27a, A.N. Tuna 61, S. Turchikhin 38, D. Turgeman 172, I. Turk Cakir 4b,t, R.J. Turner21, R. Turra 71a, P.M. Tuts 41, S. Tzamarias 155, E. Tzovara 101, G. Ucchielli 49, K. Uchida156, F. Ukegawa 161, G. Unal 36, A. Undrus 29, G. Unel 163, F.C. Ungaro 106, Y. Unno 83, K. Uno 156, J. Urban 28b, P. Urquijo 106, G. Usai 8, Z. Uysal 12d, V. Vacek 134, B. Vachon 105 , K.O.H. Vadla 127 , A. Vaidya 96 , C. Valderanis 110 , E. Valdes Santurio 47a,47b , M. Valente 56, S. Valentinetti 23b,23a, A. Valero 166, L. Valéry 48, R.A. Vallance 21, A. Vallier 36, J.A. Valls Ferrer 166, T.R. Van Daalen 14, P. Van Gemmeren 6, I. Van Vulpen 116, M. Vanadia 76a,76b, W. Vandelli 36, M. Vandenbroucke 137, 48 JHEP06(2023)019 E.R. Vandewall 123, A. Vaniachine 37, D. Vannicola 75a,75b, R. Vari 75a, E.W. Varnes 7, C. Varni 57b,57a, T. Varol 151, D. Varouchas 67, K.E. Varvell 150, M.E. Vasile 27b, G.A. Vasquez 168, F. Vazeille 40, D. Vazquez Furelos 14, T. Vazquez Schroeder 36, J. Veatch 55, V. Vecchio 77a,77b, M.J. Veen 116, L.M. Veloce 159, F. Veloso 132a,132c, S. Veneziano 75a, A. Ventura 70a,70b, N. Venturi36, A. Verbytskyi 111, V. Vercesi 73a, M. Verducci 74a,74b , C.M. Vergel Infante 81 , C. Vergis 24 , W. Verkerke 116 , A.T. Vermeulen 116 , J.C. Vermeulen 116, M.C. Vetterli 145,ao, N. Viaux Maira 139c, M. Vicente Barreto Pinto 56, T. Vickey 142 , O.E. Vickey Boeriu 142 , G.H.A. Viehhauser 128 , L. Vigani 63b , M. Villa 23b,23a , M. Villaplana Perez 3, E. Vilucchi 53, M.G. Vincter 34, G.S. Virdee 21, A. Vishwakarma 48, C. Vittori 23b,23a, I. Vivarelli 149, M. Vogel 174, P. Vokac 134, S.E. von Buddenbrock 33d, E. Von Toerne 24, V. Vorobel 135, K. Vorobev 37, M. Vos 166, J.H. Vossebeld 92, M. Vozak 102, N. Vranjes 16, M. Vranjes Milosavljevic 16, V. Vrba134,∗, M. Vreeswijk 116, R. Vuillermet 36, I. Vukotic 39, P. Wagner 24, W. Wagner 174, J. Wagner-Kuhr 110, S. Wahdan 174, H. Wahlberg 90, V.M. Walbrecht 111, J. Walder 91, R. Walker 110, S.D. Walker95, W. Walkowiak 144, V. Wallangen47a,47b, A.M. Wang 61, A.Z. Wang 173, C. Wang 62c , F. Wang 173 , H. Wang 18a , H. Wang 3 , J. Wang 65a , J. Wang 63b , P. Wang 44 , Q. Wang122, R.-J. Wang 101, R. Wang 62a, R. Wang 6, S.M. Wang 151, W.T. Wang 62a, W.X. Wang 62a, Y. Wang 62a, Z. Wang 62c, C. Wanotayaroj 48, A. Warburton 105, C.P. Ward 32, D.R. Wardrope 96, N. Warrack 59, A. Washbrook52, A.T. Watson 21, M.F. Watson 21 , G. Watts 141 , B.M. Waugh 96 , A.F. Webb 11 , S. Webb 101 , C. Weber 175 , M.S. Weber 20, S.A. Weber 34, S.M. Weber 63a, A.R. Weidberg 128, J. Weingarten 49, M. Weirich 101, C. Weiser 54, P.S. Wells 36, T. Wenaus 29, T. Wengler 36, S. Wenig 36, N. Wermes 24, M.D. Werner 81, M. Wessels 63a, T.D. Weston20, K. Whalen 125, N.L. Whallon141, A.M. Wharton 91, A.S. White 107, A. White 8, M.J. White 1, D. Whiteson 163, B.W. Whitmore 91, W. Wiedenmann 173, C. Wiel 50, M. Wielers 136, N. Wieseotte 101 , C. Wiglesworth 42 , L.A.M. Wiik-Fuchs 54 , H.G. Wilkens 36 , L.J. Wilkins 95 , H.H. Williams130, S. Williams 32, C. Willis108, S. Willocq 104, I. Wingerter-Seez 5, E. Winkels 149, F. Winklmeier 125, O.J. Winston 149, B.T. Winter 54, M. Wittgen146, M. Wobisch 97, A. Wolf 101, T.M.H. Wolf 116, R. Wolff 103, R. Wölker 128, J. Wollrath54, M.W. Wolter 86, H. Wolters 132a,132c, V.W.S. Wong 167, N.L. Woods 138, S.D. Worm 21, B.K. Wosiek 86, K.W. Woźniak 86, K. Wraight 59, S.L. Wu 173, X. Wu 56, Y. Wu 62a, T.R. Wyatt 102 , B.M. Wynne 52 , S. Xella 42 , Z. Xi 107 , X. Xiao 107 , I. Xiotidis 149 , D. Xu 15a , H. Xu62a, H. Xu 62a, L. Xu 29, T. Xu 137, W. Xu 107, Z. Xu 62b, Z. Xu 146, B. Yabsley 150, S. Yacoob 33a, K. Yajima126, D.P. Yallup 96, N. Yamaguchi 89, Y. Yamaguchi 158, A. Yamamoto 83, M. Yamatani156, T. Yamazaki 156, Y. Yamazaki 84, Z. Yan 25, H.J. Yang 62c,62d, H.T. Yang 18a, S. Yang 62a, T. Yang 65c, X. Yang 62b,60, Y. Yang 156, W-M. Yao 18a, Y.C. Yap 48, Y. Yasu 83, E. Yatsenko 62c,62d, H. Ye 15c, J. Ye 44, S. Ye 29, I. Yeletskikh 38, M.R. Yexley 91, E. Yigitbasi 25, K. Yorita 171, K. Yoshihara 130, C.J.S. Young 36, C. Young 146, J. Yu 81, R. Yuan 62b,i, X. Yue 63a, M. Zaazoua 35e, B. Zabinski 86, G. Zacharis 10, E. Zaffaroni 56, T. Zakareishvili 152b, N. Zakharchuk 34, S. Zambito 61, D. Zanzi 36, D.R. Zaripovas 59, S.V. Zeißner 49, C. Zeitnitz 174, G. Zemaityte 128, J.C. Zeng 165, O. Zenin 37, T. Ženiš 28a, D. Zerwas 67, M. Zgubič 128, B. Zhang 15c, D.F. Zhang 15b, G. Zhang 15b, H. Zhang15c, J. Zhang 6, 49 JHEP06(2023)019 L. Zhang 15c, L. Zhang 62a, M. Zhang 165, R. Zhang 173, S. Zhang 107, X. Zhang 62b, Y. Zhang 15a,15d, Z. Zhang65a, Z. Zhang 67, P. Zhao 51, Z. Zhao 62a, A. Zhemchugov 38, Z. Zheng 107, D. Zhong 165, B. Zhou107, C. Zhou 173, M.S. Zhou 15a,15d, M. Zhou 148, N. Zhou 62c, Y. Zhou7, C.G. Zhu 62b, C. Zhu 15a,15d, H.L. Zhu 62a, H. Zhu 15a, J. Zhu 107, Y. Zhu 62a , X. Zhuang 15a , K. Zhukov 37 , V. Zhulanov 37 , D. Zieminska 68 , N.I. Zimine 38 , S. Zimmermann 54,∗, Z. Zinonos111, M. Ziolkowski 144, L. Živković 16, G. Zobernig 173, A. Zoccoli 23b,23a, K. Zoch 55, T.G. Zorbas 142, R. Zou 39, L. Zwalinski 36 1Department of Physics, University of Adelaide, Adelaide; Australia 2Physics Department, SUNY Albany, Albany NY; United States of America 3Department of Physics, University of Alberta, Edmonton AB; Canada 4 (a)Department of Physics, Ankara University, Ankara;(b)Istanbul Aydin University, Application and Research Center for Advanced Studies, Istanbul; (c) Division of Physics, TOBB University of Economics and Technology, Ankara; Türkiye 5LAPP, Univ. Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7Department of Physics, University of Arizona, Tucson AZ; United States of America 8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 (a)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul;(b)Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul;(c)Department of Physics, Bogazici University, Istanbul;(d)Department of Physics Engineering, Gaziantep University, Gaziantep; Türkiye 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b)Physics Department, Tsinghua University, Beijing;(c)Department of Physics, Nanjing University, Nanjing;(d)University of Chinese Academy of Science (UCAS), Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway 18 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA;(b)University of California, Berkeley CA; United States of America 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern; Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom 22 Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá; Colombia 23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna;(b)INFN Sezione di Bologna; Italy 24 Physikalisches Institut, Universität Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 (a) Transilvania University of Brasov, Brasov; (b) Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest;(c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; (d) National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca;(e)University Politehnica Bucharest, Bucharest;(f)West University in Timisoara, Timisoara; Romania 28 (a) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; (b) Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 50 JHEP06(2023)019 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, y CONICET, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires; Argentina 31 California State University, CA; United States of America 32 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 33 (a)Department of Physics, University of Cape Town, Cape Town;(b)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg;(c)University of South Africa, Department of Physics, Pretoria;(d)School of Physics, University of the Witwatersrand, Johannesburg; South Africa 34 Department of Physics, Carleton University, Ottawa ON; Canada 35 (a) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca;(b)Faculté des Sciences, Université Ibn-Tofail, Kénitra;(c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech;(d)LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda;(e)Faculté des sciences, Université Mohammed V, Rabat; Morocco 36 CERN, Geneva; Switzerland 37 Affiliated with an institute covered by a cooperation agreement with CERN 38 Affiliated with an international laboratory covered by a cooperation agreement with CERN 39 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 41 Nevis Laboratory, Columbia University, Irvington NY; United States of America 42 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 43 (a)Dipartimento di Fisica, Università della Calabria, Rende;(b)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy 44 Physics Department, Southern Methodist University, Dallas TX; United States of America 45 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 46 National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece 47 (a)Department of Physics, Stockholm University;(b)Oskar Klein Centre, Stockholm; Sweden 48 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 49 Fakultät Physik, Technische Universität Dortmund, Dortmund; Germany 50 Institut für Kernund Teilchenphysik, Technische Universität Dresden, Dresden; Germany 51 Department of Physics, Duke University, Durham NC; United States of America 52 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 53 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany 55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany 56 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland 57 (a)Dipartimento di Fisica, Università di Genova, Genova;(b)INFN Sezione di Genova; Italy 58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany 59 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom 60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America 62 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei;(b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao;(c)School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC, Shanghai; (d) Tsung-Dao Lee Institute, Shanghai; China 63 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg;(b)Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany 64 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 51 JHEP06(2023)019 65 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;(b)Department of Physics, University of Hong Kong, Hong Kong;(c)Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 66 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 67 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France 68 Department of Physics, Indiana University, Bloomington IN; United States of America 69 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b)ICTP, Trieste;(c)Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy 70 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy 71 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Università di Milano, Milano; Italy 72 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy 73 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy 74 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy 75 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy 76 (a)INFN Sezione di Roma Tor Vergata;(b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy 77 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 78 (a)INFN-TIFPA;(b)Università degli Studi di Trento, Trento; Italy 79 Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria 80 University of Iowa, Iowa City IA; United States of America 81 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 82 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora;(b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro;(c)Instituto de Física, Universidade de São Paulo, São Paulo; Brazil 83 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 84 Graduate School of Science, Kobe University, Kobe; Japan 85 (a)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow;(b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland 86 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 87 Faculty of Science, Kyoto University, Kyoto; Japan 88 Kyoto University of Education, Kyoto; Japan 89 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 90 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 91 Physics Department, Lancaster University, Lancaster; United Kingdom 92 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 93 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 94 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 95 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 96 Department of Physics and Astronomy, University College London, London; United Kingdom 97 Louisiana Tech University, Ruston LA; United States of America 98 Fysiska institutionen, Lunds universitet, Lund; Sweden 99 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne; France 100 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 101 Institut für Physik, Universität Mainz, Mainz; Germany 102 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 103 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 104 Department of Physics, University of Massachusetts, Amherst MA; United States of America 105 Department of Physics, McGill University, Montreal QC; Canada 52 JHEP06(2023)019 106 School of Physics, University of Melbourne, Victoria; Australia 107 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 108 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 109 Group of Particle Physics, University of Montreal, Montreal QC; Canada 110 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 111 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 112 Nagasaki Institute of Applied Science, Nagasaki; Japan 113 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 114 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 115 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands 116 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 117 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 118 Department of Physics, New York University, New York NY; United States of America 119 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 120 Ohio State University, Columbus OH; United States of America 121 Faculty of Science, Okayama University, Okayama; Japan 122 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 123 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 124 Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic 125 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 126 Graduate School of Science, Osaka University, Osaka; Japan 127 Department of Physics, University of Oslo, Oslo; Norway 128 Department of Physics, Oxford University, Oxford; United Kingdom 129 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France 130 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 131 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 132 (a) Laboratório de Instrumentação e Física Experimental de Partículas — LIP, Lisboa; (b) Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa;(c)Departamento de Física, Universidade de Coimbra, Coimbra;(d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa; (e) Departamento de Física, Universidade do Minho, Braga; (f) Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada (Spain);(g)Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica;(h)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal 133 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 134 Czech Technical University in Prague, Prague; Czech Republic 135 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 136 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 137 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 138 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 139 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago;(b)Universidad Andres Bello, Department of Physics, Santiago; (c) Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile 140 Universidade Federal de São João del Rei (UFSJ), São João del Rei; Brazil 141 Department of Physics, University of Washington, Seattle WA; United States of America 142 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 143 Department of Physics, Shinshu University, Nagano; Japan 53 JHEP06(2023)019 144 Department Physik, Universität Siegen, Siegen; Germany 145 Department of Physics, Simon Fraser University, Burnaby BC; Canada 146 SLAC National Accelerator Laboratory, Stanford CA; United States of America 147 Department of Physics, Royal Institute of Technology, Stockholm; Sweden 148 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 149 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 150 School of Physics, University of Sydney, Sydney; Australia 151 Institute of Physics, Academia Sinica, Taipei; Taiwan 152 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;(b)High Energy Physics Institute, Tbilisi State University, Tbilisi; Georgia 153 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 154 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 155 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 156 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan 157 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan 158 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 159 Department of Physics, University of Toronto, Toronto ON; Canada 160 (a)TRIUMF, Vancouver BC;(b)Department of Physics and Astronomy, York University, Toronto ON; Canada 161 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 162 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 163 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 164 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 165 Department of Physics, University of Illinois, Urbana IL; United States of America 166 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 167 Department of Physics, University of British Columbia, Vancouver BC; Canada 168 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 169 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 170 Department of Physics, University of Warwick, Coventry; United Kingdom 171 Waseda University, Tokyo; Japan 172 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel 173 Department of Physics, University of Wisconsin, Madison WI; United States of America 174 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 175 Department of Physics, Yale University, New Haven CT; United States of America aAlso Affiliated with an institute covered by a cooperation agreement with CERN b Also at Borough of Manhattan Community College, City University of New York, New York NY; United States of America cAlso at CERN, Geneva; Switzerland dAlso at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France eAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland fAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain gAlso at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah; United Arab Emirates h Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece iAlso at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 54 JHEP06(2023)019 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 pAlso at Department of Physics, Stanford University, Stanford CA; United States of America qAlso at Department of Physics, University of Adelaide, Adelaide; Australia rAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland sAlso at Dipartimento di Matematica, Informatica e Fisica, Università di Udine, Udine; Italy tAlso at Giresun University, Faculty of Engineering, Giresun; Türkiye uAlso at Hellenic Open University, Patras; Greece vAlso at IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France wAssociated at INFN e Laboratori Nazionali di Frascati, Frascati; Italy xAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain yAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany zAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands aa Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia; Bulgaria ab Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary ac Also at Institute of Particle Physics (IPP); Canada ad Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan ae Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia af Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain ag Also at Istanbul University, Dept. of Physics, Istanbul; Türkiye ah Also at Louisiana Tech University, Ruston LA; United States of America ai Also at Manhattan College, New York NY; United States of America aj Also at Physics Department, An-Najah National University, Nablus; Palestine ak Also at Physics Dept, University of South Africa, Pretoria; South Africa al Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany am Also at The City College of New York, New York NY; United States of America an Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China ao Also at TRIUMF, Vancouver BC; Canada ap Also at Università di Napoli Parthenope, Napoli; Italy ∗Deceased 55