Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in pp collisions at s√ = 13 TeV with the ATLAS detector

JHEP04(2021)174 Published for SISSA by Springer Received:December 8, 2020 Accepted:March 12, 2021 Published:April 19, 2021 Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: A search for new phenomena with top quark pairs in final states with one isolated electron or muon, multiple jets, and large missing transverse momentum is performed. Signal regions are designed to search for two-, three-, and four-body decays of the directly pair-produced supersymmetric partner of the top quark (stop). Additional signal regions are designed specifically to search for spin-0 mediators that are produced in association with a pair of top quarks and decay into a pair of dark-matter particles. The search is performed using the Large Hadron Collider proton-proton collision dataset at a centre-of-mass energy of √s = 13 TeV recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb −1 . No significant excess above the Standard Model background is observed, and limits at 95% confidence level are set in the stop-neutralino mass plane and as a function of the mediator mass or the dark-matter particle mass. Stops are excluded up to 1200GeV (710GeV) in the two-body (three-body) decay scenario. In the four-body scenario stops up to 640 GeV are excluded for a stop-neutralino mass difference of 60GeV. Scalar and pseudoscalar dark-matter mediators are excluded up to 200GeV when the coupling strengths of the mediator to Standard Model and dark-matter particles are both equal to one and when the mass of the dark-matter particle is 1 GeV. Keywords: Hadron-Hadron scattering (experiments), Supersymmetry ArXiv ePrint: 2012.03799 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP04(2021)174 JHEP04(2021)174 Contents 1 Introduction 1 2 Signal models and search strategy 2 3 ATLAS detector and data collection 4 4 Simulated event samples 5 5 Event reconstruction 7 6 Discriminating variables 9 6.1 Dileptonic t¯ treconstruction 10 6.2 Reconstruction of hadronic top decays 10 6.3 Backgrounds with mismeasured missing momentum 11 6.4 Variables for compressed ˜ t1→t+˜χ0 111 7 Signal regions 12 7.1 ˜ t1→t+˜χ0 113 7.2 Compressed ˜ t1→t+˜χ0 114 7.3 ˜ t1→bW ˜χ0 115 7.4 ˜ t1→bff0˜χ0 116 7.5 Dark matter 17 8 Backgrounds 18 8.1 Control and validation regions for ˜ t1→t+˜χ0 1and spin-0 mediator signals 19 8.2 Control and validation regions for compressed ˜ t1→t+˜χ0 125 8.3 Control and validation regions for ˜ t1→bW ˜χ0 127 8.4 Control and validation regions for ˜ t1→bff0˜χ0 127 9 Systematic uncertainties 30 10 Results 33 11 Interpretations 35 12 Conclusion 39 The ATLAS collaboration 48 – i – JHEP04(2021)174 1 Introduction This paper presents a search for new phenomena in events with top quark pairs, in a final state with exactly one isolated charged lepton (electron or muon, 1 henceforth referred to as ‘lepton’) from the decay of an onor off-shell W boson, jets, and a significant amount of missing transverse momentum ( ~p miss T ), the magnitude of which is denoted by Emiss T . This experimental signature may arise in Supersymmetry (SUSY) [ 1 – 7 ] or in models with a spin-0 mediator produced in association with top quarks [ 8 , 9 ] and subsequently decaying into a pair of dark matter (DM) particles. SUSY extends the Standard Model (SM) by introducing a supersymmetric partner for each SM particle, the two having identical quantum numbers except for a half-unit difference in spin. Searches for a light supersymmetric partner of the top quark, referred to as the top squark or ‘stop’, are of particular interest after the discovery of the Higgs boson [ 10 , 11 ] at the Large Hadron Collider (LHC). Stops may largely cancel out divergent loop corrections to the Higgs boson mass [ 12 – 19 ], and thus, supersymmetry may provide an elegant solution to the hierarchy problem [ 20 – 23 ]. The superpartners of the leftand right-handed top quarks, ˜ tL and ˜ tR , mix to form two mass eigenstates, ˜ t1 and ˜ t2 , where ˜ t1 is the lighter of the two. Significant mass splitting between the ˜ t1 and ˜ t2 particles is possible due to the large top quark Yukawa coupling. A generic R -parity-conserving 2 minimal supersymmetric extension of the SM (MSSM) [ 7 , 12 , 24 – 26 ] predicts pair production of SUSY particles and the existence of a stable lightest supersymmetric particle (LSP). The mass eigenstates from the linear superposition of charged or neutral SUSY partners of the Higgs and electroweak gauge bosons (higgsinos, winos and binos) are called charginos ˜χ± 1,2 and neutralinos ˜χ0 1,2,3,4 . The lightest neutralino ( ˜χ0 1 ), assumed to be the LSP, may provide a potential dark matter (DM) candidate because it is stable and only interacts weakly with ordinary matter [ 27 , 28 ]. This paper presents a search for direct pair production of ˜ t1 particles, with significant amount of Emiss T , from the two weakly interacting LSPs that escape detection. Scenarios with onand off-shell production of W bosons and top quarks in the stop decays are considered, leading to two-, threeand four-body decays of the stop. The search for a spin-0 mediator produced in association with top quarks and subsequently decaying into a pair of DM particles is motivated by SM extensions which respect the principle of minimal flavour violation resulting in the interaction strength between the spin-0 mediator and the SM quarks being proportional to the fermion masses via Yukawa-type couplings. Dedicated searches for direct ˜ t1 pair production were recently reported by the ATLAS [ 29 – 32 ] and CMS [ 33 – 40 ] Collaborations. Previous ATLAS and CMS searches extend the lower limit on ˜ t1 masses at 95% confidence level to 1.2 TeV in the two-body decay scenario and up to ∼ 450 GeV in the three-body decay scenario. Searches for spin-0 mediators produced in association with heavy-flavour quarks and decaying into a pair of DM particles have also been reported by the ATLAS [ 29 , 41 ] and CMS [ 42 ] Collaborations. 1Electrons and muons from τ-lepton decays are included. 2 A multiplicative quantum number, referred to as R -parity, is introduced in SUSY models to conserve baryon and lepton number where R-parity is 1(−1) for all SM (SUSY) particles. 1 JHEP04(2021)174 ˜ t ˜ t tW tW p p ˜χ0 1 bℓ ν ˜χ0 1 b q q (a) ˜ t ˜ t W W p p ˜χ0 1 bℓ ν ˜χ0 1 b q q (b) ˜ t ˜ t p p bℓ ν ˜χ0 1 b q q ˜χ0 1 (c) Figure 1. Diagrams illustrating the stop decay modes, which are referred to as (a) ˜ t1→t + ˜χ0 1 , (b) ˜ t1→bW ˜χ0 1 and (c) ˜ t1→bff0˜χ0 1 . In these diagrams, the charge-conjugate symbols are omitted for simplicity. All the processes considered involve the production of a squark-antisquark pair. 2 Signal models and search strategy Two classes of physics models are targeted by this search, the production of ˜ t1 pairs in simplified SUSY models [ 43 – 45 ] where the only light sparticles are ˜ t1 and ˜χ0 1 , and simplified benchmark models for DM production that assume the existence of a spin-0 mediator particle that can be produced in association with two top quarks [ 41 , 46 ] and decays into a pair of DM particles χ¯χ. The experimental signatures of stop pair production can vary dramatically, depending on the mass-splitting between ˜ t1 and ˜χ0 1 . Figure 1illustrates the two-, threeand fourbody stop decays considered in this paper. As flavour-changing neutral current processes are not considered, the dominant among the two-, threeor four-body stop decays is assumed to have a 100% branching ratio in a given ∆ m˜ t1,˜χ0 1 regime. In the regime where ∆ m˜ t1,˜χ0 1 = m ( ˜ t1 ) −m ( ˜χ0 1 )is larger than the top quark mass mtop , the two-body decay ˜ t1→t + ˜χ0 1 dominates. At smaller ∆ m˜ t1,˜χ0 1 , the three-body decay ˜ t1→bW ˜χ0 1 dominates as long as ∆ m˜ t1,˜χ0 1 is larger than the sum of the b -quark and W boson masses. At the smallest values of ∆ m˜ t1,˜χ0 1 the dominant decay channel is the four-body decay ˜ t1→bff0˜χ0 1 . The stop is always assumed to decay promptly. The searches for stops presented in this paper use several signal regions dedicated to each of the decay channels ˜ t1→t + ˜χ0 1 , ˜ t1→bW ˜χ0 1 and ˜ t1→bff0˜χ0 1 . For instance, specific signal regions target the so-called compressed region where the stop undergoes a ˜ t1→t + ˜χ0 1 decay but where ∆ m˜ t1,˜χ0 1≈mtop . The selections are optimised for given benchmark model points, and are binned in key variables to retain sensitivity to the widest possible range of ˜ t1and ˜χ0 1masses. The mediator-based DM scenarios consist of simplified models with a DM particle χ that is a SM singlet and a single spin-0 mediator that couples χ to SM fermions. Both the scenarios where the mediator is a scalar, φ , or a pseudoscalar, a , are considered, as illustrated in figure 2. These models have four parameters: the mass of the mediator, mmed, the DM mass, mDM , the DM-mediator coupling, gχ , and the coupling of the mediator to the SM fermions, gq . In the models considered, the interaction strength between the mediator and 2 JHEP04(2021)174 φ/a ¯ t t g g ¯χ χ Figure 2. A representative Feynman diagram for spin-0 mediator production. The φ / a is the scalar/pseudoscalar mediator, which decays into a pair of dark matter (χ) particles. SM particles is proportional to the fermion masses via Yukawa-type couplings, and therefore final states involving top quarks dominate over those involving other fermions. Due to the associated production of top quarks with undetected DM particles in the same event, the mediator-based DM model predicts an excess of t¯ t + Emiss T final-state events above the SM expectation. A dedicated signal region common to both the scalar and pseudoscalar models is developed. The signal region is binned in the azimuthal angle ∆ φ ( ~p miss T, ` )between the missing transverse momentum and the leading lepton, to retain maximum sensitivity to both the scalar and pseudoscalar models and to a large range of mediator and DM particle masses. The searches presented are based on eight dedicated analyses that target the various scenarios mentioned above. Each of these analyses corresponds to a set of event selection criteria, referred to as a signal region (SR), and is optimised to achieve three standard deviation expected sensitivity to the targeted benchmark model. Two techniques are employed to define the SRs: ‘cut-and-count’ and ‘shape-fit’ methods. The former is based on counting events in a single region of phase space, and is employed in the eight analyses. The latter is used in several SRs to improve the exclusion reach if no excess is observed in the cut-and-count signal regions, and employs SRs split into multiple bins in one or two key discriminating kinematic variables. The shape-fit method exploits the varying signal-tobackground ratios in different bins to provide sensitivity to a wider range of new-particle masses than can be achieved by a single cut-and-count SR. Including these background-rich regions in the single-bin discovery SRs would significantly reduce the sensitivity to the targeted signatures. The main background processes after the signal selections include t¯ t , t¯ t + Z ( →ν¯ν ), W +jets and the associated production of a single top quark and a W boson ( Wt ). Backgrounds from these SM processes are estimated by exploiting dedicated control regions (CRs) enriched in these processes. The backgrounds are normalised to data by applying a likelihood fit simultaneously to the SR and associated CRs, making the analysis more robust against potential mis-modelling in simulated events and reducing the uncertainties in the background normalisation. Before looking at the data in the signal regions, the background modelling and the normalisation procedure are tested in a series of validation regions (VRs) by applying the normalisation factors determined by a background-only fit in the CRs. A background-only fit to the CRs and SRs then provides a statistical test that 3 JHEP04(2021)174 Signal scenario Benchmark Signal Region Exclusion technique Section ˜ t1→t+˜χ0 1m(˜ t1,˜χ0 1) = (800,400) GeV tN_med shape-fit of Emiss Tand mT7.1 ˜ t1→t+˜χ0 1m(˜ t1,˜χ0 1) = (950,1) GeV tN_high –7.1 ˜ t1→t+˜χ0 1m(˜ t1,˜χ0 1) = (225,52) GeV tN_diag_low cut-and-count 7.2 ˜ t1→t+˜χ0 1m(˜ t1,˜χ0 1) = (500,327) GeV tN_diag_high cut-and-count 7.2 ˜ t1→bW ˜χ0 1m(˜ t1,˜χ0 1) = (500,380) GeV bWN shape-fit in RNN score 7.3 ˜ t1→bff0˜χ0 1m(˜ t1,˜χ0 1) = (450,400) GeV bffN_btag shape-fit in p` T/Emiss Tand ∆φ(~pb-jet T, ~p miss T)7.4 ˜ t1→bff0˜χ0 1m(˜ t1,˜χ0 1) = (450,430) GeV bffN_softb shape-fit in p` T/Emiss T7.4 Spin-0 mediator m(φ/a, χ) = (20,1) GeV DM shape-fit in ∆φ(~p miss T, `)7.5 Table 1. Signal scenarios, benchmark models and signal regions. For each SR, the table lists the analysis technique used for exclusion limits. The last column points to the section where the signal region is defined. For tN_high no exclusion technique is defined. The tN_med shape-fit also covers the tN_high-like phase space. quantifies the existence and extent of a potential excess of events in data in the SRs. In the absence of an excess, exclusion limits are set on the associated model parameters by using the theoretical cross-sections. An overview of the signal regions and the benchmark models for optimisation is presented in table 1. 3 ATLAS detector and data collection The ATLAS detector [ 47 ] at the LHC is a multipurpose particle detector with almost 4 π coverage in solid angle around the interaction point. 3 It consists of an inner tracking detector (ID) surrounded by a superconducting solenoid providing a 2T axial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer (MS), which is based on three large air-core toroidal superconducting magnets consisting of eight coils each. The ID provides charged-particle tracking in the range |η|< 2 . 5. During the LHC shutdown between Run 1 (2010–2012) and Run 2 (2015–2018), a new innermost layer of silicon pixels was added [ 48 – 50 ], which improves the track impact parameter resolution, vertex position resolution and b -tagging performance [ 51 ]. High-granularity electromagnetic and hadronic calorimeters provide energy measurements up to |η| = 4 . 9. The electromagnetic calorimeters, as well as the hadronic calorimeters in the endcap and forward regions, are sampling calorimeters with liquid argon as the active medium and lead, copper, or tungsten absorbers. The hadronic calorimeter in the central region of the detector is a sampling calorimeter with scintillator tiles as the active medium and steel absorbers. The MS surrounds the calorimeters and has three layers of precision tracking chambers with coverage up to |η| = 2 . 7and fast detectors for triggering in the region |η|< 2 . 4. A two-level trigger 3 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). The transverse momentum, pT, is defined in the x–yplane. 4 JHEP04(2021)174 Process ME event generator ME PDF PS and UE tune Cross-section hadronisation calculation t¯ tPowheg-Box v2 [55] NNPDF3.0 [56]Pythia 8 [57] A14 [58] NNLO+NNLL [59–64] Single-top t-channel Powheg-Box v1 NNPDF3.0 Pythia 8 A14 NNLO+NNLL [65] sand Wt-channel Powheg-Box v2 NNPDF3.0 Pythia 8 A14 NNLO+NNLL [66,67] V+jets (V=W/Z)Sherpa 2.2.1 [68] NNPDF3.0 Sherpa Default NNLO [69] Diboson Sherpa 2.2.1–2.2.2 NNPDF3.0 Sherpa Default NLO Multiboson Sherpa 2.2.1–2.2.2 NNPDF3.0 Sherpa Default NLO t¯ t+VMG5_aMC@NLO 2.3.3 [70] NNPDF3.0 Pythia 8 A14 NLO [70] SUSY signal MadGraph 2.6.2 [70] NNPDF2.3 [71]Pythia 8 A14 NNLO+NNLL [72,73] DM signal MadGraph 2.6.2 NNPDF3.0 Pythia 8 A14 NLO [74,75] Table 2. Overview of the nominal simulated samples. The cross-sections of top, single-top and SUSY samples were calculated at next-to-next-to-leading order (NNLO) with the resummation of soft gluon emission at next-to-next-to-leading-logarithm (NNLL) accuracy. The V +jets background samples were calculated at NNLO. The cross-sections of other background and DM samples were calculated at next-to-leading order (NLO). system [ 52 ] is used to select events. The first-level trigger is hardware-based, followed by a software-based trigger system. The results in this paper utilise the full Run 2 data sample collected from 2015 to 2018 at a centre-of-mass energy of √s = 13 TeV . The average number of simultaneous pp interactions per bunch crossing, referred to as ‘pile-up’, in the recorded data is approximately 34. After the application of beam, detector and data-quality requirements, the total integrated luminosity is 139fb−1 . The uncertainty in the combined 2015–2018 integrated luminosity is 1.7%. It is derived from the calibration of the luminosity scale using x – y beam-separation scans, following a methodology similar to that detailed in ref. [ 53 ], and using the LUCID-2 detector for the baseline luminosity measurements [54]. All events were recorded with triggers that accepted events with Emiss T above a given threshold. The Emiss T triggers relied on energy measurements in the calorimeter and on several algorithms based on cells, jets or topological clusters in addition to two methods for correcting for the effects of pile-up. The triggers were fully efficient for events passing an offline-reconstruction requirement of Emiss T>230 GeV. 4 Simulated event samples Samples of Monte Carlo (MC) simulated events are used for the description of the SM background processes and to model the signals. Details of the simulation samples used, including the matrix element (ME) event generator and parton distribution function (PDF) set, the parton shower (PS) and hadronisation model, the set of tuned parameters (tune) for the underlying event (UE) and the order of the cross-section calculation, are summarised in table 2. 5 JHEP04(2021)174 The samples produced with MadGraph5_aMC@NLO [ 70 ] and Powheg-Box [ 55 , 76 – 79 ] used EvtGen v1.6.0 [ 80 ] for the modelling of b -hadron decays. The signal samples were all processed with a fast simulation [ 81 ], whereas all background samples were processed with the full simulation of the ATLAS detector [ 81 ] based on Geant 4[ 82 ]. All samples were produced with varying numbers of minimum-bias interactions generated by Pythia 8 with the A3 tune [ 83 ] and overlaid on the hard-scattering event to simulate the effect of multiple pp interactions in the same or nearby bunch crossings. The number of interactions per bunch crossing was reweighted to match the distribution in data. The nominal t¯ t sample and single-top sample cross-sections were calculated at NNLO with the resummation of soft gluon emission at NNLL accuracy and were generated with Powheg-Box (at NLO accuracy) interfaced to Pythia 8 for parton showering and hadronisation. Additional t¯ t samples were generated with MadGraph5_aMC@NLO (at NLO accuracy)+Pythia 8 and Powheg-Box+Herwig 7 [ 84 , 85 ] for modelling comparisons and the evaluation of systematic uncertainties [ 86 ]. The t¯ t and Wt processes have identical WWbb final states and can interfere. Additional t¯ t , Wt and WWbb samples were generated as multi-leg processes at LO with MadGraph and used to estimate the systematic uncertainty from the interference modelling. The tN_med and tN_high regions receive significant contributions from both t¯ t and Wt in a phase space where the interference is significant. Techniques used to model the interference such as diagram subtraction (DS) and diagram removal (DR) [ 87 ] were shown to provide predictions bracketing the data [ 88 ], but can lead to large uncertainties. Both schemes are investigated in this paper, but the DR scheme is ultimately used for the nominal Wt sample. The W +jets and Z +jets samples were generated with Sherpa 2.2.1 [ 68 , 89 ] with up to two partons at NLO and up to four partons at leading order (LO). Diboson and multiboson [ 90 ] events were generated with Sherpa 2.2.1 and 2.2.2. For dibosons, the events include up to one parton at NLO and up to three partons at LO. For triboson processes, up to two extra partons were considered at LO. The Sherpa samples used matrix elements from Comix [ 91 ] and OpenLoops [ 92 ], which were merged with the Sherpa parton shower [ 93 ] using the ME+PS@NLO prescription [ 94 ]. The W +jets and Z +jets events were further normalised to the NNLO cross-sections [69]. The t¯ t + V samples were generated with MadGraph5_aMC@NLO (at NLO accuracy) interfaced to Pythia 8 for parton showering and hadronisation. The corresponding MC tune and generator comparisons can be found in ref. [95]. The SUSY samples were generated at LO with MadGraph 2.6.2 including up to two extra partons, and interfaced to Pythia 8 for parton showering and hadronisation. For the ˜ t1→t + ˜χ0 1 samples, the stop was decayed in Pythia 8 using only phase-space considerations and not the full ME. Since the decay products in the generated event samples did not preserve spin information, a polarisation reweighting was applied following refs. [ 96 , 97 ]. A value of cos θt = 0 . 553 was assumed, corresponding to a ˜ t1 composed mainly of ˜ tR ( ∼ 70%). For the ˜ t1→bW ˜χ0 1 and ˜ t1→bff0˜χ0 1 samples the stops were decayed with MadSpin [ 98 ], interfaced to Pythia 8 for the parton showering. MadSpin emulates kinematic distributions such as the mass of the bW(∗) system to a good approximation without calculating the full ME. 6 JHEP04(2021)174 The signal cross-sections for stop pair production were calculated to approximate next-tonext-to-leading order in the strong coupling constant, adding the resummation of soft gluon emission at next-to-next-to-leading-logarithm accuracy(approximate NNLO+NNLL) [ 73 , 99 – 101 ]. The nominal cross-section and its uncertainty were derived using the PDF4LHC15_mc PDF set, following the recommendations of ref. [ 102 ]. The stop pair production cross-section varies from approximately 200 fb at m˜ t1=600GeV to about 2fb at m˜ t1=1150GeV. Signal events for the spin-0 scalar and pseudoscalar mediator models were generated at LO with up to one additional parton with MadGraph 2.6.2 interfaced to Pythia 8 for parton showering and hadronisation. In the DM sample generation the couplings of the mediator to the DM and SM particles ( gχ and gq ) were set to one. When interpreting the experimental results, a single common coupling g = gχ = gq is always assumed. Coupling values of g = 1 as well as g < 1are considered. The kinematics of the mediator decay were found to not depend strongly on the values of the couplings; however, the particle kinematic distributions are sensitive to the scalar or pseudoscalar nature of the mediator and to the mediator and DM particle masses. The cross-sections were computed at NLO [ 74 , 75 ] and decrease significantly when the mediator is produced off-shell. The production cross-section varies from approximately 26 pb to 130fb over a scalar mediator mass range of 10 to 200 GeV and from approximately 600 fb to 120 fb over a pseudoscalar mediator mass range of 10 to 200GeV. 5 Event reconstruction Events selected in the analysis must satisfy a series of beam, detector and data-quality criteria. The primary vertex, defined as the reconstructed vertex with the highest Ptracks p2 T , must have at least two associated tracks with pT>500 MeV. Depending on the quality and kinematic requirements imposed, reconstructed physics objects are labelled as either baseline or signal, where the latter is a subset of the former, with tighter selection criteria applied. Baseline objects are used when classifying overlapping selected objects and to compute the missing transverse momentum. Background contributions from t¯ t and Wt production where both W bosons decay leptonically, referred to as dileptonic t¯ t or Wt events, are suppressed by vetoing events with more than one baseline lepton. Signal objects are used to construct kinematic and discriminating variables necessary for the event selection. Electrons are identified as energy clusters formed in the electromagnetic calorimeter matched to tracks in the ID. Baseline electrons are required to have pT> 4 . 5 GeV and |η|< 2 . 47, and to satisfy ‘LooseAndBLayer’ likelihood identification criteria that follow the methodology described in ref. [ 103 ]. Furthermore, their longitudinal impact parameter ( z0 ), defined as the distance along the beam direction between the primary vertex and the track’s point of closest approach to the beam axis, must satisfy |z0sin θ|< 0 . 5mm where θ is the polar angle of the track. Signal electrons must satisfy all the baseline requirements and have a transverse impact parameter ( d0 ) that satisfies |d0|/σd0< 5, where σd0 is the uncertainty in d0 . Furthermore, signal electrons are required to be isolated. The isolation is defined as the sum of the transverse energy or momentum reconstructed in a cone of 7 JHEP04(2021)174 Selection tN_med tN_high Preselection hard-lepton preselection Njet,Nb-jet ≥(4,1) ≥(4,1) Jet pT[GeV] >(100, 90, 70, 50) >(120, 50, 50, 25) Emiss T[GeV] >230 >520 Emiss T,⊥[GeV] >400 – Hmiss T,sig >16 >25 mT[GeV] >220 >380 Topness >9>8 mreclustered top [GeV] >150 ∆R(b, `)<2.8<2.6 Exclusion technique Based on shape-fit in Emiss Tand mTin tN_med Emiss T∈[230, 400], mT>220 Emiss T∈[400, 500], mT>220 Bin boundaries [GeV] Emiss T∈[500, 600], mT∈[220, 380] Emiss T∈[500, 600], mT>380 Emiss T>600, mT∈[220, 380] Emiss T>600, mT>380 Table 4. Event selections defining the signal regions tN_med and tN_high. The tN_med and tN_high definitions are given in table 4. A common exclusion region is defined by performing a two-variable shape-fit on the tN_med signal region, if no excess is observed in the single-bin discovery signal regions. The binning is designed to maximise the excluded parameter space in the m˜ t1 – m˜χ0 1 plane. The two variables chosen for the binning are the two discriminating variables that best distinguish between tN_med and tN_high , namely Emiss Tand mT. The resulting six bins are given in table 4. 7.2 Compressed ˜ t1→t+˜χ0 1 The kinematics of the decay ˜ t1→t + ˜χ0 1 in the region where ∆ m˜ t1,˜χ0 1≈mtop differ significantly from the two signal regions defined above, and the stop signal is kinematically very similar to the dominant t¯ t background. This region of parameter space is referred to as the diagonal region. Two dedicated signal regions, tN_diag_low and tN_diag_high , are designed to target scenarios on the diagonal for low-mass and high-mass stops respectively. The sensitivity of the tN_diag_low SR is such that it is expected to be able to exclude scenarios with ∆ m˜ t1,˜χ0 1 = mtop and m ( ˜ t1 )between 200 and 250 GeV . Both the tN_diag_low and tN_diag_high signal regions rely on the presence of a highpT ISR jet, which serves to boost the di-stop system. The signal region definitions are shown in table 5and are used both for exclusion and for discovery. 14 JHEP04(2021)174 Selection tN_diag_low tN_diag_high Preselection hard-lepton preselection without τ-lepton veto Njet,Nb-jet >(4, 1) Jet pT[GeV] >(400, 40, 40, 40) mT[GeV] >150 >110 Emiss T[GeV] – >400 mT2 [GeV] – <360 ∆mα T[GeV] >40 – ∆mdyn T[GeV] – >60 mlep ˜ t1[GeV] <600 – mdyn ˜χ0 1[GeV] >5[220, 595] x1–>−0.2 Exclusion technique cut-and-count Table 5. Event selections defining the signal regions tN_diag_low and tN_diag_high. 7.3 ˜ t1→bW ˜χ0 1 The signal region for the decay ˜ t1→bW ˜χ0 1 is labelled bWN and defined using an optimised two-step machine learning (ML) approach, applied to events preselected according to the hard-lepton preselection criteria and additionally satisfying mT> 110 GeV . The background mostly consists of t¯ t , which has strong similarities to the signal in this region of phase space. For this reason the ML technique is selected. The jet multiplicity in signal events varies significantly due to the potential presence of ISR jets and fluctuations in the number of low-energy jets reconstructed from the hadronically decaying W boson. To deal with the variable number of signal jets, the first step of the ML procedure is to use a recurrent neural network (RNN) that has the ability to extract information from sequences of variable length [ 132 ]. The RNN uses a long short-term memory (LSTM) algorithm [ 133 ] and takes the four-momentum vectors of the jets as inputs. The LSTM output becomes the input of the second step, made up of a shallow neutral network (NN) with a single hidden layer and an output corresponding to the signal probability. The RNN and NN are trained simultaneously in one step. The NN uses the following discriminating variables as input: output of the RNN, Emiss T , mT , the azimuthal φ angle of ~p miss T , the azimuthal angle ∆ φ ( ~p miss T, ` )between the lepton and ~p miss T , the invariant mass m`b of the lepton and the b -tagged jet, the transverse momentum of the b-tagged jet, the lepton four-momentum vector, Njet and Nb-jet. Before training, the hard-lepton preselection and the additional selection mT> 110 GeV are applied. The size of the training sample is a crucial aspect for the performance of any ML method. Generating fully simulated signal samples with adequate sample sizes after the hard-lepton preselection and mT> 110 GeV is computationally expensive. To overcome this difficulty, signal events without detector simulation were used for the training to enhance the number of signal events by two orders of magnitude. Fully simulated SM background 15 JHEP04(2021)174 Selection bWN bWN-TCR bWN-TVR Preselection hard-lepton preselection Njet,Nb-jet ≥(4,1) Jet pT[GeV] > (25, 25, 25, 25) mT[GeV] >110 >150 >150 NNbWN >0.9∈[0.4,0.6] ∈[0.60,0.65] Exclusion technique shape-fit in NNbWN Bin boundaries {0.65,0.7,0.75,0.8,0.82,0.84,0.86,0.88,0.90,0.92,1.0} and mT>150 GeV if NNbWN<0.8 Table 6. Event selections defining the signal region bWN, along with its CR and VR. events were available in sufficiently large numbers to be used directly for the training. For the signal, the generated events are ‘smeared’ using a dedicated procedure to emulate the effects of detector simulation and reconstruction. Parameterisations for reconstruction and identification efficiencies are obtained from dedicated ATLAS measurements and applied to jets, leptons and b -tagged jet identification. Particle-level electron, muon and jet fourmomentum vectors are smeared according to their respective pT , η and identification working point. The Emiss T is recomputed from all smeared objects. The kinematic distributions of all input variables after smearing are found to have fair agreement with distributions after full event reconstruction. The output score of the ML classifier, denoted NNbWN , shows good agreement between smeared samples and fully simulated samples after full event reconstruction. The classifier output also shows a good agreement between simulation and data. The smeared samples are used only for the training, while signal and background predictions are obtained with the samples described in section 4. The discovery signal region is defined by selecting events with NNbWN > 0 . 9. The exclusion limits are obtained by performing a shape-fit using ten bins in NNbWN , with bin boundaries { 0 . 65 , 0 . 7 , 0 . 75 , 0 . 8 , 0 . 82 , 0 . 84 , 0 . 86 , 0 . 88 , 0 . 90 , 0 . 92 , 1 . 0 } . The t¯ t background in the first three bins is reduced by applying an additional selection, namely mT> 150 GeV . The selections that define the bWN signal region are presented in table 6. 7.4 ˜ t1→bff0˜χ0 1 The four-body decay ˜ t1→bff0˜χ0 1 occurs when ∆ m˜ t1,˜χ0 1 is smaller than the W boson mass. In this scenario, the decay products have low momenta and often fall below the standard jet and lepton reconstruction pT thresholds. It is therefore necessary to apply a soft-lepton preselection and require the presence of a high-momentum ISR jet, with pT> 200 GeV , to boost the momenta of the final-state particles. A first four-body signal region, labelled as bffN_btag , is optimised by requiring the presence of at least one b -tagged jet. The background in the bffN_btag signal region mostly consists of t¯ t events. Because the b - tagged jets are required to have pT> 20 GeV , bffN_btag is not sensitive to ∆ m˜ t1,˜χ0 1 below ∼ 40 GeV . For this reason a second signal region, labelled as bffN_softb , is defined. This 16 JHEP04(2021)174 Selection bffN_softb bffN_btag Preselection soft-lepton preselection Njet ≥1≥2 Jet pT[GeV] >200 Nb-jet =0 ≥1 b-jet pT[GeV] – <50 NSV ≥1– mT[GeV] >90 Emiss T[GeV] >250 – ∆φ(~p miss T, `)[rad] <2.0– CT2 [GeV] – >400 ∆φ(pb-jet T, ~p miss T)[rad] – <1.5 p` T/Emiss T<0.04 <0.05 Exclusion technique shape-fit in p` T/Emiss Tshape-fit in p` T/Emiss T and ∆φ(pb-jet T, ~p miss T) Bin boundaries in p` T/Emiss T{0,0.015,0.025,0.04,0.06,0.08} {0,0.03,0.06,0.1} Bin boundaries in ∆φ(pb-jet T, ~p miss T)[rad] {0,0.8,1.5} Table 7. Event selections defining the signal regions bffN_softb and bffN_btag. region does not rely on b -tagged jets but instead requires a soft b -tag identified by the presence of a secondary vertex. The dominant background processes in this region are t¯ t and W +jets. The bffN_btag signal region also exploits the correlation between the ISR jet pT and Emiss T by cutting on the CT2 variable defined by CT2 = min ( Emiss T, pISR T− 25 GeV ). The key variable used at the last stage of the selection is the ratio of the lepton’s transverse momentum to the missing transverse momentum, p` T/Emiss T , which has small values for the ˜ t1→bff0˜χ0 1 signal and large values for the backgrounds. The exact definitions of the four-body signal regions are given in table 7. For exclusion limits, the last selection, namely on p` T/Emiss T , is replaced by a shape-fit. In the bffN_softb , the shape-fit is performed in five bins of the variable p` T/Emiss T with bin boundaries { 0 , 0 . 015 , 0 . 025 , 0 . 04 , 0 . 06 , 0 . 08 } . In the bffN_btag signal region the shape-fit is performed in two variables, namely three bins in p` T/Emiss T with bin boundaries { 0 , 0 . 03 , 0 . 06 , 0 . 1 } and two bins in ∆ φ ( pb-jet T, ~p miss T )with bin boundaries {0,0.8,1.5}. 7.5 Dark matter The dominant background to the search for spin-0 mediator models is the t¯ t + V process. The optimisation of this signal region favours a selection with at least two b -tagged jets and a leading b -tagged jet with pT> 80 GeV . The distribution of ∆ φ ( ~p miss T, ` )differentiates the scalar and pseudoscalar models from each other and also from the background. The resulting DM_scalar and DM_pseudoscalar signal region definitions are given in table 8. In addition to the selection criteria optimised for discovery described above, the exclusion sensitivity is maximised by relying on a shape-fit in the region DM_scalar with the binning in ∆φ(~p miss T, `)given in table 8. 17 JHEP04(2021)174 Selection DM_scalar DM_pseudoscalar Preselection hard-lepton preselection Njet,Nb-jet ≥(4,2) Jet pT[GeV] >(80,60,30,25) b-tagged jet pT[GeV] >(80,25) Emiss T[GeV] >230 Hmiss T,sig >15 mT[GeV] >180 Topness >8 mreclustered top [GeV] >150 ∆φ(jeti, ~p miss T),i∈[1,4] [rad] >0.9 ∆φ(~p miss T, `)[rad] >1.1>1.5 Exclusion technique Based on shape-fit in ∆φ(~p miss T, `) Bin boundaries in ∆φ(~p miss T, `){1.1,1.5,2.0,2.5, π} Table 8. Event selections defining the DM signal regions. 8 Backgrounds Data can be used to constrain the normalisation of the most significant background processes. To this end, control regions (CRs) are defined by minimally modifying the SR selections to suppress the signal while enhancing the fraction of the targeted background process. The CRs are then incorporated into a simultaneous likelihood fit to constrain the background process normalisations in the signal region. The ratio of the number of background events of a given process in the SR to those in a CR is estimated in MC background samples but is allowed to deviate from that ratio within dedicated MC modelling systematic uncertainties. Less significant background processes, such as diboson production and Z +jets, are estimated directly from MC simulation since they typically represent only a few percent of the signal region yields. CRs are defined to normalise t¯ t (TCR), W +jets (WCR), single-top (STCR) and t¯ t + Z (TZCR). Whether a control region is defined for a given background and signal region depends on the relative contribution of the process to the SR yield. To validate the background estimates from the CRs, validation regions (VRs) are introduced for t¯ t (TVR) and W +jets (WVR). The VRs are disjoint from both the SRs and CRs. The TZCR is designed to be as close as possible to the signal region in order to obtain the most precise estimate of the large t¯ t + Z background, and thus does not leave space between the SR and the CR to introduce a VR for this process. Background normalisations, referred to as normalisation factors (NF), determined in the CRs are applied to the VRs and compared with the data. The VRs are not included in the final simultaneous fit, but provide a statistically independent test of the background estimates in background-dominated regions. 18 JHEP04(2021)174 Signal Region Signal Scenario TCR WCR STCR TZCR TVR WVR tN_med ˜ t1→t+˜χ0 1X X X X X X tN_high ˜ t1→t+˜χ0 1X X X X X X tN_diag_low ˜ t1→t+˜χ0 1X– – – X– tN_diag_high ˜ t1→t+˜χ0 1X– – – X– bWN ˜ t1→bW ˜χ0 1X– – – X– bffN_btag ˜ t1→bff0˜χ0 1X X – – X X bffN_softb ˜ t1→bff0˜χ0 1X X – – X X DM spin-0 mediator X– – X X – Table 9. Summary of the control and validation regions used (X) for each signal region. The CRs and VRs are designed to minimise potential contamination from signal processes. The signal contamination is generally well below 10%, but in some TCRs and TVRs, for models close to the previously excluded region of parameter space, it can reach approximately 15%. The signal contributions to the CRs are not included in the backgroundonly fits but are taken into account in the exclusion fits described in section 11. The CRs and VRs used for each SR are summarised in table 9. If a process is not normalised via a control region then it is estimated directly from MC simulation and theoretical cross-sections. 8.1 Control and validation regions for ˜ t1→t+˜χ0 1and spin-0 mediator signals The dominant background process in the tN_med , tN_high and DM signal regions is t¯ t + Z , and therefore each of these SRs uses a dedicated TZCR. The TZCRs aim at capturing t¯ t + Z events where the Z boson decays into two electrons or muons, and thus is kinematically similar to the t¯ t + Z background in the signal regions where the Z boson decays into a pair of neutrinos. This CR is built by selecting events with three leptons (electrons or muons), one pair of which must be of opposite charge and same flavour with an invariant mass within 10 GeV of the Z boson mass. The exact definitions of the TZCRs follow the definitions of the tN_med , tN_high and DM SRs in terms of the number of jets, b -tagged jets and jet pT thresholds. A modified missing momentum variable, ˜ Emiss T , is defined, where the leptons associated with the Z boson decay are considered invisible. The ˜ Emiss T is the magnitude of the vector with components ~ ˜pmiss x,y derived from the x, y components ~pmiss x,y of ~p miss T introduced in section 5. The components ~ ˜pmiss x,y are obtained as follows: ~ ˜pmiss x,y = ~pmiss x,y + ~pl2 x,y + ~pl3 x,y , where ~pl2 x,y and ~pl3 x,y are the x, y components of the momenta of the leptons that make up the Z boson candidate. The TZCRs require ˜ Emiss T> 230 GeV . The remaining SR selections are not applied to the TZCRs, in order to retain a large enough event sample. The W +jets and dileptonic t¯ t processes are significant in tN_med and tN_high , and therefore dedicated CRs, WCR and TCR, are employed. The DM signal region also employs a TCR but does not require a WCR due to the smaller size of the W +jets background. These CRs have the same requirements on the number of jets, the number of b -tagged 19 JHEP04(2021)174 jets and the jet pT thresholds as listed in tables 4and 8for their respective signal regions. Table 10 presents the definitions of the TCRs, WCRs and VRs, by showing which selections differ from the tN_med and tN_high SRs definitions. Neither W +jets nor dileptonic t¯ t processes yield hadronic top decays, so a veto on the presence of a hadronic reclustered top candidate is used to ensure orthogonality with the signal regions. The number of events in TCR and WCR is increased by relaxing several selections compared with the SR selections. The Hmiss T,sig selection is lowered to 10 for both tN_med and tN_high , and to 13 for DM . In addition, Emiss T,⊥ is lowered to 300 GeV for tN_med , while Emiss T is lowered to 450 GeV for tN_high. In the DM signal region the requirement on ∆φ(jeti, ~p miss T)is lowered to 0.6. The topness and mT selections are used to differentiate between WCR and TCR. In the WCR, mT is required to be in the range 30–90 GeV , compatible with the presence of a semileptonic W decay, but incompatible with dileptonic t¯ t because of the topness selection. In the TCR, the topness selection of the SR is inverted, thus selecting events compatible with dileptonic t¯ t , while mT> 120 GeV is required, as larger values are favoured by the presence of two leptonically decaying W bosons. The TCR dedicated to the DM signal region has the same mT selection as its signal region, mT> 180 GeV . The purity of the WCR is further improved by using only positively charged leptons, exploiting the lepton charge asymmetry in W+jets events from pp collisions. To validate the dileptonic t¯ t background normalisation, a TVR dominated by t¯ t production is designed. The TVRs for tN_med and tN_high have the same selections as the corresponding TCR, except for the veto on the presence of a hadronic reclustered top quark candidate, which is replaced by a selection requiring the presence of such a hadronic top quark, with a mass mreclustered top >150 GeV. The validation of the W +jets background for tN_high is performed with a WVR with the same selection as tN_high but requiring the presence of a hadronic reclustered top candidate with mreclustered top > 150 GeV and Hmiss T,sig > 25, in order to be closer to the SR. The WVR for tN_med is defined starting from the WCR selections, but replacing several selections with those used in its SR: Emiss T> 400 GeV , Hmiss T,sig > 16 and the presence of a hadronic top quark, with a mass mreclustered top >150 GeV. The DM SR contains only a small fraction of W+jets events due to the requirement of two b -tagged jets, and therefore only a TVR is considered. It is constructed from the DM SR definition, but with the topness selection inverted, and to increase the number of events and limit signal contamination, the selection on ∆φ(jeti, ~p miss T)is relaxed to 0.6. The tN_med and tN_high definitions permit the construction of a STCR with enough data events for comparison with the DSand DR-based MC predictions. The STCR is defined with selections close to those of the WCR, but requires a second b -tagged jet, 30 < mT< 120 GeV and the distance ∆ R ( b1, b2 )between the two b -tagged jets to be larger than 1.4. To ensure orthogonality with the WCR, events with two b -tagged jets inside the WCR must have ∆ R ( b1, b2 ) < 1 . 4. It is found that the DS and DR scheme predictions bracket the observed number of events in the STCR data, with a large discrepancy between the two predictions. The largest discrepancy is observed in the STCR associated with the tN_med SR. The data-to-prediction ratio in the STCR is 0.1 +0.3 −0.1 with the DR scheme and 1.5±1.3with the DS scheme. 20 JHEP04(2021)174 Selection tN_med tN_med-TCR (-TVR) tN_med-WCR (-WVR) tN_med-STCR mreclustered top [GeV] >150 veto (>150) veto (>150) veto Hmiss T,sig >16 >10 >10 (>16)>10 Emiss T,⊥[GeV] >400 >300 >300 (>400) 350 mT[GeV] >220 >120 ∈[30,90] ∈[30,120] Topness >9<9>9>10 ∆R(b, `)<2.8– – – ∆R(b1, b2)– – <1.4>1.4 Lepton charge – – >0– Nb-jet ≥1≥1≥1≥2 Selection tN_high tN_high-TCR (-TVR) tN_high-WCR (-WVR) tN_high-STCR mreclustered top [GeV] >150 veto (>150) veto (>150) veto Hmiss T,sig >25 >10 >10 (>25)>10 Emiss T[GeV] >520 >450 >450 >450 mT[GeV] >380 >120 ∈[30,90] ∈[30,120] Topness >8<8>8>10 ∆R(b, `)<2.6– – – ∆R(b1, b2)– – <1.4>1.4 Lepton charge – – >0– Nb-jet ≥1≥1≥1≥2 Selection DM DM-TCR (-TVR) mreclustered top [GeV] >150 veto (>150) Hmiss T,sig >15 >13 (>15) Topness >8<8 ∆φ(jeti, ~p miss T)[rad] >0.9>0.6 Table 10. Event selections defining the CRs and VRs in tN_med , tN_high and DM relative to their respective signal regions. Only variables for which the selection criteria in the CRs or VRs differ from those in the SRs are listed. The availability of the STCR allows the normalisation of the single-top background to be constrained from data. The fit to the STCR is performed with both the DS and DR MC schemes, and the resulting two predictions for single-top in the STCR and in the SRs are compatible within uncertainties. Therefore, once the STCR is used to constrain the single-top normalisation, the choice of the DS or DR scheme is found to have a negligible impact on the single-top prediction in the SR. In accordance with ref. [ 88 ], the DR scheme is used for the default W t sample. Figures 3,4and 5compare data and prediction in CRs and VRs for several variables used in the ˜ t1→t + ˜χ0 1 and DM SRs. Good agreement is observed between data and prediction, within uncertainties. 21 JHEP04(2021)174 8−6−4−2−0 2 4 6 8 Topness 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 35 40 Events / 2 Data Total SM 2Ltt 1Ltt Others ATLAS -1 = 13 TeV, 139 fbs tN_med-TCR (a) 250 300 350 400 450 500 550 600 [GeV] miss T E ~ 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 35 Events / 50 GeV Data Total SM Multiboson Vtt Z+jets tWZ t+X ATLAS -1 = 13 TeV, 139 fbs tN_med-TZCR (b) 300 400 500 600 700 800 [GeV] miss T E 0.5 1 1.5 Data / SM 0 2 4 6 8 10 12 14 16 18 Events / 50 GeV Data Total SM 2Ltt 1Ltt W+jets Single top Multiboson Others ATLAS -1 = 13 TeV, 139 fbs tN_med-STCR (c) 300 350 400 450 500 550 600 [GeV] miss T, E 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 35 40 45 Events / 50 GeV Data Total SM 1Ltt W+jets Multiboson Others ATLAS -1 = 13 TeV, 139 fbs tN_med-WCR (d) Figure 3. Selected kinematic distributions in tN_med CRs: (a) topness in the TCR, (b) ˜ Emiss T in the TZCR, (c) Emiss T in the STCR, (d) Emiss T,⊥ in the WCR. The distributions shown are post-fit, i.e. each background is scaled by a normalisation factor obtained from a background-only likelihood fit to the CRs (see table 14). The hatched area around the total SM prediction and the hatched band in the Data/SM ratio include all statistical and systematic uncertainties. The last (first) bin contains overflows (underflows). 22 JHEP04(2021)174 10 11 12 13 14 15 16 Topness 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 Events Data Total SM 2Ltt 1Ltt W+jets Single top Others ATLAS -1 = 13 TeV, 139 fbs tN_high-STCR (a) 250 300 350 400 450 500 550 600 [GeV] miss T E ~ 0.5 1 1.5 Data / SM 0 10 20 30 40 50 60 70 80 Events / 50 GeV Data Total SM Multiboson Vtt Z+jets tWZ t+X ATLAS -1 = 13 TeV, 139 fbs tN_high-TZCR (b) 1 2 3 b-jet N 0.5 1 1.5 Data / SM 0 10 20 30 40 50 60 70 80 Events Data Total SM 2Ltt Others ATLAS -1 = 13 TeV, 139 fbs tN_high-TCR (c) 0 200 400 600 800 1000 [GeV] miss T, E 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 Events / 75 GeV Data Total SM 1Ltt W+jets Multiboson Others ATLAS -1 = 13 TeV, 139 fbs tN_high-WCR (d) Figure 4. Selected kinematic distributions in tN_high CRs: (a) topness in the STCR, (b) ˜ Emiss T in the TZCR, (c) Nb-jet in the TCR, (d) Emiss T,⊥ in the WCR. The distributions shown are post-fit, i.e. each background is scaled by a normalisation factor obtained from a background-only likelihood fit to the CRs (see table 14). The hatched area around the total SM prediction and the hatched band in the Data/SM ratio include all statistical and systematic uncertainties. The last (first) bin contains overflows (underflows). 23 JHEP04(2021)174 400 450 500 550 600 650 [GeV] T2 C 0.5 1 1.5 Data / SM 0 20 40 60 80 100 Events / 25 GeV Data Total SM 2Ltt 1Ltt W+jets Single top Others ATLAS -1 = 13 TeV, 139 fbs bffN_btag-TCR (a) 100 150 200 250 300 350 400 450 [GeV] T m 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 35 40 45 Events / 40 GeV Data Total SM 2Ltt 1Ltt W+jets Single top Others ATLAS -1 = 13 TeV, 139 fbs bffN_btag-TVR (b) 0.2 0.4 0.6 0.8 1 miss T / E T Lepton p 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 35 Events / 0.1 Data Total SM 2Ltt 1Ltt W+jets Single top Multiboson Others ATLAS -1 = 13 TeV, 139 fbs bffN_btag-WCR (c) 400 450 500 550 600 650 700 750 800 [GeV] miss T E 0.5 1 1.5 Data / SM 0 5 10 15 20 25 30 Events / 40 GeV Data Total SM 2Ltt 1Ltt W+jets Single top Multiboson Others ATLAS -1 = 13 TeV, 139 fbs bffN_btag-WVR (d) Figure 9. Selected kinematic distributions in bffN_btag CRs and VRs: (a) CT2 in the TCR, (b) mT in the TVR, (c) p` T/Emiss T in the WCR, (d) Emiss T in the WVR. The distributions shown are post-fit, i.e. each background is scaled by a normalisation factor obtained from a background-only likelihood fit to the CRs (see table 14). The hatched area around the total SM prediction and the hatched band in the Data/SM ratio include all statistical and systematic uncertainties. The last (first) bin contains overflows (underflows). 9 Systematic uncertainties The systematic uncertainties in the background estimates arise from multiple experimental and theoretical sources and can enter the SR background yield either via direct predictions from theoretical cross-sections or from uncertainties in the extrapolation from CRs to SRs. The sources of systematic uncertainties are grouped into categories whose labels are defined in parentheses in the paragraphs below. Their effect on the background predictions in the SRs is summarised in table 13. The systematic uncertainties are included as nuisance parameters constrained by Gaussian probability distributions and profiled in the likelihood fits. 30 JHEP04(2021)174 Experimental uncertainties arise from imperfect knowledge of the jet energy scale (JES), jet energy resolution (JER) [ 113 ], scale and resolution of the Emiss T soft term ( Emiss T experimental) [ 123 ], as well as the modelling of the b -tagging or soft b -tagging efficiencies and mis-tag rates [ 117 ] ( b -tagging experimental). Other experimental uncertainties arise from the modelling of the lepton energy scales, energy resolutions, reconstruction and identification efficiencies (Leptons experimental). There is also an experimental uncertainty arising from the reweighting of the simulation as a function of the number of interactions per bunch crossing in data and the additional cuts applied to jets to ensure they arise from the hard-scatter primary vertex (Pile-up). Backgrounds such as dibosons and Z +jets, derived directly from a MC prediction and a theoretical cross-section, have theoretical systematic uncertainties (Theory) arising from theoretical cross-section calculations, including those related to parton distribution functions and factorisation and normalisation scales. The systematic uncertainty on the integrated luminosity is also included in this category. As shown in table 9, the single-top, t¯ t + Z and W +jets backgrounds are also predicted directly from MC simulations for some SRs, in which case the theory uncertainties apply also to those processes. When the yield from a background such as t¯ t , single-top, t¯ t + V or W +jets is normalised using a CR, modelling uncertainties affect the extrapolation from the control to the signal region, but not the overall normalisation. In each of these cases, the background has a normalisation systematic uncertainty (Normalisation) from the fit, arising from the statistical power of the CR for the given background and a modelling uncertainty (Modelling) that affects the extrapolation factor from the CR to the SR. The uncertainties in the modelling of the t¯ t background include effects related to the MC event generator, the hadronisation modelling and the amount of initialand final-state radiation [ 86 ]. The MC generator uncertainty is estimated by taking the full difference in event yields between Powheg-Box v2+Pythia 8 and MadGraph5_aMC@NLO v2.6.0 +Pythia 8. Events generated with Powheg-Box v2 are showered and subsequently hadronised with either Pythia 8 or Herwig 7.0 in order to estimate the effect from modelling of the hadronisation. The systematic uncertainty from the amount of initialand final-state radiation is derived by comparing Powheg-Box results obtained with different shower radiation, NLO radiation and modified factorisation and renormalisation scales. The single-top Wt process modelling uncertainty is derived from the size of the interference between t¯ t and Wt using the t¯ t , Wt and WWbb samples generated with MadGraph. It is obtained by comparing Wt with the difference between WWbb and t¯ t . The Wt sample generated with MadGraph is found to be in good agreement with the nominal samples generated with Powheg-Box v2+Pythia 8. For the tN_med and tN_high SRs where STCR is used, the Wt modelling uncertainty enters via the ratio of the number of Wt events in the signal region to the number in the STCR. Given the potentially large modelling uncertainty in the interference between t¯ t and Wt , the modelling uncertainty is also evaluated for the DM SR by comparing the predicted single-top yield from Wt with the difference between WWbb and t¯ t. The modelling uncertainties considered for t¯ t + Z are the renormalisation and factorisation scales, and the amount of initialand final-state radiation, obtained by 31 JHEP04(2021)174 SR Uncertainty [%] tN_med tN_high tN_diag_low tN_diag_high bWN bffN_btag bffN_softb DM t¯ tnormalisation 4.4 2.7 12.3 15.8 7.8 6.6 3.7 3.0 t¯ t+Znormalisation 9.0 6.8 – – – – – 8.5 W+jets normalisation 3.0 4.8 – – – 5.5 11.1 – Wt normalisation 2.8 3.4 – – – – – – t¯ tmodelling 3.0 9.1 18.4 29.3 17.6 3.1 3.3 4.1 t¯ t+Zmodelling 7.7 7.1 – – – – – 7.4 W+jets modelling 2.3 3.8 – – – 4.3 9.7 – Wt modelling 0.5 0.8 – – – – – 6.4 JER 10.9 5.1 4.1 5.0 6.1 1.8 7.6 6.5 Emiss Texperimental 0.7 0.4 1.0 0.2 0.9 2.4 3.4 0.1 b-tagging experimental 1.7 3.6 1.3 0.9 1.6 1.9 3.2 3.1 JES 6.0 2.5 4.7 4.1 2.7 6.1 11.7 2.4 Leptons experimental 1.0 1.6 1.3 0.3 0.1 2.3 4.9 0.6 Pile-up 1.1 1.2 1.2 0.3 0.8 1.0 2.1 1.1 Theory 0.9 1.3 1.4 0.5 4.8 3.8 3.7 0.7 MC statistics 4.1 6.6 5.8 3.5 3.1 4.9 17.2 3.2 Total 19 17 24 33 20 12 27 15 Table 13. Summary of the dominant systematic uncertainties as a percentage of the total predicted background yields in the SRs, obtained from the background-only fits described in section 10. considering the variation of the same parameters used for the t¯ t initialand final-state radiation systematic uncertainties. The W +jets modelling uncertainties include generator modelling, derived by considering an alternative W +jets sample generated with MadGraph as well as modified factorisation, renormalisation, resummation and parton matching scales. Most of the SRs are binned in one or two variables in order to enhance sensitivity to a wider range of models for exclusion limits. In this situation the normalisation factors to go from the CR to the SR are rederived specifically for each bin of the SR. The modelling systematic uncertainties are also rederived following the scheme above but applied to the normalisation factor from the CR to each specific bin of the SR. The SUSY signal cross-section uncertainty is taken from an envelope of cross-section predictions using different PDF sets and factorisation and renormalisation scales as described in ref. [ 134 ]. The uncertainty in the DM production cross-section is derived from the scale variations and PDF choices. Dedicated uncertainties in the SUSY and DM signal acceptance due to the modelling of additional radiation, factorisation, renormalisation and parton matching scales are considered. The total systematic uncertainty for the SUSY models varies between 9% and 35%, increasing at higher stop mass and at lower values of ∆ m˜ t1,˜χ0 1 . For the spin-0 mediator signals the total systematic uncertainty is between 15% and 18%. 32 JHEP04(2021)174 tN_med tN_med tN_high tN_high tN_diag_low tN_diag_high bWN bffN_btag bffN_btag bffN_softb bffN_softb DM tN_med tN_high tN_diag_low tN_diag_high bWN bffN_btag bffN_softb DM VR Events 50 100 150 200 250 300 Data tt Vtt Single top Total SM W+jets Multiboson Other SR Events 10 20 30 40 50 60 Signal RegionsValidation Regions ATLAS -1 = 13 TeV, 139 fbs Significance -2 0 2 TVR tN_med WVR tN_med TVR tN_high WVR tN_high TVR tN_diag_low TVR tN_diag_high TVR bWN TVR bffN_btag WVR bffN_btag TVR bffN_softb WVR bffN_softb TVR DM tN_med tN_high tN_diag_low tN_diag_high bWN bffN_btag bffN_softb DM Figure 10. The upper panel shows the comparison between the observed data ( nobs ) and the predicted SM background ( nexp ) in all VRs and SRs. The background predictions are obtained using the background-only fit, and the hatched area around the SM prediction includes all uncertainties. The bottom panel shows the Z significance of the observed number of events given the SM expectation. 10 Results To determine the SM background yields in the SRs, a background-only likelihood fit is performed for each analysis. The fit does not use the signal region data, but only the dedicated CRs to normalise the backgrounds. The number of observed events and the predicted number of SM background events from the background-only fits in all VRs and SRs are shown in figure 10 together with the Z significance of the observation. The SRs are not mutually exclusive and are therefore not statistically independent. In all SRs, the distributions indicate good agreement between the data and the SM background estimate. The largest excess over the background-only hypothesis is 1.9σobserved in the tN_high SR. The number of observed events together with the predicted number of SM background events in all SRs are summarised in table 14, showing the breakdown of the various backgrounds that contribute to the SRs. The table also lists the results for the fit parameters that control the normalisation of the main backgrounds (normalisation factors, NFs), 6 together with the associated fit uncertainties including the theoretical modelling uncertainties. To quantify the level of agreement of the SM background-only hypothesis with the observations in the SRs, a profile-likelihood-ratio test [ 135 ] is performed. The resulting p -values ( p0 ) are also presented in the table together with the Z significances. For SRs with an observed number of events below the SM prediction, the p0 values are capped at 0.5. Model-independent upper limits on beyond-the-SM contributions are derived for 6 The t¯ t NFs in the tN_diag_low , bffN_btag , and bffN_softb SRs are applied to semileptonic and dileptonic t¯ tevents while all other SRs apply the t¯ tNFs to the dileptonic component only. 33 JHEP04(2021)174 tN_med tN_high tN_diag_low tN_diag_high bWN bffN_btag bffN_softb DM Observed 21 17 21 11 35 14 10 56 Total SM 21 ±4 9.5±1.6 15 ±4 10.1±3.4 44 ±9 11.3±1.4 8.7±2.3 56 ±8 t¯ t7.2±1.2 2.0±1.0 13.0±2.8 9.6±2.6 38 ±9 6.9±1.1 2.2±0.6 14 ±4 t¯ tV 8.3±2.5 3.5±1.0 0.55 ±0.17 0.12 ±0.04 1.1±1.1 0.21 ±0.12 0.06 ±0.04 29 ±6 Single top 0.4+0.6 −0.40.27+0.34 −0.27 1.24 ±0.27 0.26 ±0.06 1.7±1.7 0.8±0.5 0.22 ±0.08 7 ±4 W+jets 2.5±2.3 2.3±1.0 0.41 ±0.13 0.080 ±0.020 1.3±0.6 1.9±0.8 4.8±2.1 2.56 ±0.24 Multiboson 1.49 ±0.21 1.06 ±0.16 0.070 ±0.020 0.020 ±0.010 1.22 ±0.30 1.07 ±0.35 0.89 ±0.32 1.31 ±0.18 Other 0.78 ±0.06 0.320 ±0.024 – – – 0.40 ±0.13 0.52 ±0.19 2.15 ±0.16 t¯ tNF 0.98+0.14 −0.12 0.90 ±0.12 0.88+0.13 −0.12 0.73+0.14 −0.13 1.06 ±0.10 0.80+0.09 −0.08 0.68 ±0.10 1.12+0.15 −0.13 t¯ tV NF 0.95+0.22 −0.20 0.92 ±0.17 – – – – – 1.18+0.20 −0.18 Single top NF 0.11+0.26 −0.11 0.12+0.22 −0.12 – – – – – – W+jets NF 0.96+0.25 −0.23 0.86 ±0.17 – – – 0.83 ±0.28 1.04+0.22 −0.20 - p0(Z) 0.49 (0.03) 0.01 (2.20) 0.17 (0.95) 0.31 (0.50) 0.50 (0.00) 0.20 (0.84) 0.26 (0.64) 0.50 (0.00) Nlimit non-SM exp. 12.4+5.4 −2.69.8+2.8 −1.814.1+4.8 −3.39.7+3.7 −2.121.8+7.6 −7.98.6+3.6 −1.28.9+3.8 −2.027.4+7.6 −5.0 Nlimit non-SM obs. 12.9 16.2 17.7 10.9 15.3 11.7 10.5 28.2 Table 14. The number of observed events in the various SRs together with the expected numbers of background events and their uncertainties as predicted by the background-only fits, the normalisation factors for the background predictions obtained in the fit, the probabilities (represented by p0and Z values) that the observed numbers of events are compatible with the background-only hypothesis, and the expected ( Nlimit non-SM exp.) and observed ( Nlimit non-SM obs.) 95% CL upper limits on the number of beyond-SM events. each SR. A generic signal model is assumed that contributes only to the SR and for which neither experimental nor theoretical systematic uncertainties except for the luminosity uncertainty are considered. All limits are calculated using the CL s prescription [ 136 ]. The NFs are compatible with unity in most cases. One exception is for the single-top NFs in the tN_med and tN_high SRs. The single-top NFs are significantly below unity when using the DR scheme for the treatment of the interference between the Wt and t¯ t processes. When changing to the DS scheme, the NFs become larger than unity but the predicted number of single-top events in the signal regions after the fit does not change significantly. This is explained by the fact that the DS and DR schemes give the same SR to STCR event yield ratio, within uncertainties. The t¯ t NFs in tN_diag_high , bffN_btag and bffN_softb are below unity, which could potentially point to some mismodelling in this extreme region of phase-space. But good agreement is seen in the t¯ t VRs, giving confidence in the t¯ t background estimates. Figures 11 and 12 show comparisons between the observed data and the SM background prediction with all SR selections applied except the requirement on the plotted variable. The expected distributions from representative signal benchmark models are overlaid. 34 JHEP04(2021)174 0 5 10 15 Topness 0 5 10 15 20 25 30 35 40 Events / 2 Data Total SM 2Ltt W+jets Multiboson Vtt Others ) = 800, 400 GeV 0 1 χ ∼ , 1 t ~ m( ) = 950, 1 GeV 0 1 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs tN_med (a) 400 500 600 700 800 900 1000 [GeV] miss T E 0 2 4 6 8 10 12 14 16 18 Events / 40 GeV Data Total SM 2Ltt W+jets Multiboson Vtt Others ) = 800, 400 GeV 0 1 χ ∼ , 1 t ~ m( ) = 950, 1 GeV 0 1 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs tN_high (b) 50−0 50 100 [GeV] α T m∆ 1− 10 1 10 2 10 3 10 4 10 Events / 20 GeV Data Total SM 2Ltt 1Ltt Others ) = 225, 52 GeV 1 0 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs tN_diag_low (c) 0 100 200 [GeV] dyn T m∆ 0 1 2 3 4 5 6 7 8 9 Events / 20 GeV Data Total SM 2Ltt 1Ltt Others ) = 500, 327 GeV 1 0 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs tN_diag_high (d) Figure 11. Kinematic distributions in the (a) tN_med , (b) tN_high , (c) tN_diag_low and (d) tN_diag_high SRs. The full event selection in the corresponding signal region is applied, except for the requirement (indicated by an arrow) that is imposed on the variable being plotted. The distributions shown are post-fit, i.e. each background is scaled by a normalisation factor obtained from a background-only likelihood fit to the CRs (see table 14). In addition to the background prediction, a signal model is shown on each plot. The hatched area around the total SM prediction includes statistical and experimental uncertainties. The last (first) bin contains overflows (underflows). 11 Interpretations No significant excess is observed, and exclusion limits based on profile-likelihood fits are set for the stop pair production models and the spin-0 mediator models. Exclusion limits at 95% confidence level (CL) are obtained by selecting the signal region with the lowest expected CL s value for each signal model and the exclusion contours are derived by interpolating in the CL s value. The signal uncertainties and potential signal contributions to all regions are taken into account, and all uncertainties except those in the theoretical signal cross-section are included in the fit. In all exclusion plots, the ± 1 σexp uncertainty band indicates how much the expected limit is affected by the systematic and statistical uncertainties included in the fit. The ± 1 σSUSY theory uncertainty lines around the observed limit illustrate the change 35 JHEP04(2021)174 0 0.02 0.04 0.06 0.08 0.1 miss T / E T Lepton p 1 10 2 10 3 10 Events Data Total SM 2Ltt 1Ltt W+jets Single top Multiboson Others ) = 450, 400 GeV 1 0 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs bffN_btag (a) 0 1 2 SV multiplicity 1 10 2 10 3 10 4 10 5 10 Events Data Total SM 2Ltt 1Ltt W+jets Multiboson Z+jets Others ) = 450, 430 GeV 1 0 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs bffN_softb (b) 0.4 0.6 0.8 1 bWN NN 10 2 10 3 10 4 10 5 10 Events Data Total SM 2Ltt 1Ltt W+jets Single top Multiboson +Vtt ) = 500, 380 GeV 0 1 χ ∼ , 1 t ~ m( ATLAS -1 = 13 TeV, 139 fbs bWN (c) 0 1 2 3 ,l) [rad] miss T p (φ∆ 0 10 20 30 40 50 Events Data Total SM 2Ltt W+jets Single top Ztt Others )=20,1 GeVχ,φm( )=20,1 GeVχm(a, ATLAS -1 = 13 TeV, 139 fbs DM (d) Figure 12. Kinematic distributions in the (a) bffN_btag , (b) bffN_softb , (c) bWN and (d) DM SRs. The full event selection in the corresponding signal region is applied, except for the requirement (indicated by an arrow) that is imposed on the variable being plotted. In the DM SR, the signal is normalised under the assumption of the coupling strength g =1.0. The distributions shown are post-fit, i.e. each background is scaled by a normalisation factor obtained from a background-only likelihood fit to the CRs (see table 14). In addition to the background prediction, a signal model is shown on each plot. The hatched area around the total SM prediction includes statistical and experimental uncertainties. The last (first) bin contains overflows (underflows). in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical cross-section uncertainty. Figures 13 and 14 show the expected and observed exclusion contours as a function of the stop mass, the neutralino mass and the mass difference between the stop and the neutralino, for the ˜ t1→t + ˜χ0 1 , ˜ t1→bW ˜χ0 1 and ˜ t1→bff0˜χ0 1 scenarios. In models with a massless neutralino, stop masses up to 1200 GeV are excluded at 95% CL. In the diagonal region, where the mass difference between the stop and the neutralino coincides with the mass of the top quark, stop masses up to 600GeV are excluded, which covers the previously unexcluded diagonal region between 210GeV and 250 GeV in stop mass. In the three-body (four-body) region, stop masses up to 710 GeV (640 GeV ) are excluded for a neutralino mass 36 JHEP04(2021)174 200 400 600 800 1000 1200 1400 [GeV] 1 t ~ m 0 100 200 300 400 500 600 700 800 900 1000 [GeV] 0 1 χ ∼ m ATLAS -1 = 13 TeV, 139 fbs Limit at 95% CLLimit at 95% CL 0 1 χ ∼ t → 1 t ~ , 0 1 χ ∼ bW → 1 t ~ , 0 1 χ ∼ bff' → 1 t ~ production, 1 t ~ 1 t ~ ) th σ1 ±Observed limit ( ) exp σ1 ±Expected limit ( , -1 = 8 TeV 20 fbsATLAS -1 = 13 TeV 36 fbs ) < 0 0 1 χ ∼ , 1 t ~ m( ∆ W + m b ) < m 0 1 χ ∼ , 1 t ~ m( ∆ t ) < m 0 1 χ ∼ , 1 t ~ m( ∆ Figure 13. Expected and observed 95% CL excluded regions in the plane of m˜χ0 1 and m˜ t1 for direct stop pair production assuming either a ˜ t1→t + ˜χ0 1 , ˜ t1→bW ˜χ0 1 or ˜ t1→bff0˜χ0 1 decay with a branching ratio of 100%. The excluded regions from previous publications [ 29 – 31 , 137 ] are shown by the shaded area and include additional topologies. The diagonal dashed lines indicate the kinematical border of the stop decay modes. of approximately 580 GeV . The small excess observed in tN_high does not appear because the exclusion limits are obtained from the shape-fit in the tN_med signal region (table 4). The Emiss T,⊥ requirement applied in tN_med but not in tN_high removes most of the excess. The shape-fit is designed to have better expected sensitivity than the single-bin SRs over the whole ˜ t1→t+˜χ0 1parameter space. Figure 15 shows the upper limit on the ratio of the production cross-section for the spin0 mediator model to the theoretical cross-section. Limits are shown under the hypothesis of a scalar or pseudoscalar mediator for a fixed DM candidate mass. Scalar and pseudoscalar mediator masses up to approximately 200 GeV are excluded at 95% CL, assuming a 1 GeV dark-matter particle mass and a common coupling of g = 1 to SM and dark-matter particles. With the common coupling reduced to g = 0 . 8, mediator masses up to approximately 100 GeV are excluded. Models with a mediator mass of 10 GeV and a dark-matter particle mass of 1GeV are excluded down to a coupling of approximately g= 0.7. 37 JHEP04(2021)174 200 300 400 500 600 700 800 [GeV] 1 t ~ m 20 40 60 80 100 120 140 160 180 200 ) [GeV] 0 1 χ ∼ , 1 t ~ m(∆ ATLAS -1 = 13 TeV, 139 fbs Limit at 95% CL 0 1 χ ∼ t → 1 t ~ , 0 1 χ ∼ bW → 1 t ~ , 0 1 χ ∼ bff' → 1 t ~ production, 1 t ~ 1 t ~ ) th σ1 ±Observed limit ( ) exp σ1 ±Expected limit ( , -1 = 8 TeV 20 fbsATLAS -1 = 13 TeV 36 fbs 1 0 χ ∼ t 1 0 χ ∼ bW 1 0 χ ∼ bff' Figure 14. Expected and observed 95% CL excluded regions in the plane of ∆( m˜ t1, m˜χ0 1 )and m˜ t1 for direct stop pair production assuming either a ˜ t1→t + ˜χ0 1 , ˜ t1→bW ˜χ0 1 or ˜ t1→bff0˜χ0 1 decay with a branching ratio of 100%. The excluded regions from previous publications [ 29 – 31 , 137 ] are shown by the shaded area and include additional topologies. The horizontal dashed lines indicate the kinematical border of the stop decay modes. 10 2 10 [GeV] φ m 1− 10 1 10 2 10 (g=1.0) Th σ/ obs σ95% CL limit on ATLAS -1 = 13 TeV, 139 fbs Scalar χχ → φ, φ+t t = 1 GeV χ g = 1.0, m Observed 95% CL Expected 95% CL σ1 ±Expected σ2 ±Expected (g=1.0) Th σTheory unc. on cross-section (a) 10 2 10 [GeV] a m 1− 10 1 10 2 10 (g=1.0) Th σ/ obs σ95% CL limit on ATLAS -1 = 13 TeV, 139 fbs Pseudoscalar χχ →+a, at t = 1 GeV χ g = 1.0, m Observed 95% CL Expected 95% CL σ1 ±Expected σ2 ±Expected (g=1.0) Th σTheory unc. on cross-section (b) Figure 15. Upper limit on the ratio of the production cross-section for the spin-0 mediator model to the theoretical cross-section under the hypothesis of (a) a scalar or (b) a pseudoscalar mediator. The limit is shown as a function of the mediator mass for a fixed mass of the DM candidate of 1GeV. The coupling of the mediator to SM and DM particles is assumed to be g= 1. 38 JHEP04(2021)174 12 Conclusion This paper presents searches for direct stop pair production covering various regions of SUSY phase space and searches for a spin-0 mediator decaying into pair-produced dark-matter particles. The searches use the final state with one isolated lepton, jets, and Emiss T. The analysis uses 139fb−1 of pp collision data collected with the ATLAS detector at the LHC at a centre-of-mass energy of √s = 13 TeV . The largest excess over the backgroundonly hypothesis is 1 . 9 σ in the tN_high signal region. As no significant deviation from the Standard Model expectation is observed, exclusion limits at 95% confidence level are derived for the models considered. Stops are excluded up to 1200 GeV (710 GeV ) in the two-body (three-body) decay scenario, extended from about 1000 GeV (400–600 GeV ) in previous results. The introduction of ML techniques contributes to the significantly improved sensitivity in the challenging three-body decay scenario. In the four-body scenario, the exclusion of stops is extended from about 400 GeV in earlier results to up to 640GeV for a stop-neutralino mass difference of 60 GeV . The introduction of the soft b -tagging algorithm contributes to the significantly improved sensitivity at small mass differences between the stop and the lightest neutralino. Scalar and pseudoscalar dark-matter mediators are excluded up to 200 GeV for a common coupling of g = 1 to Standard Model and dark-matter particles, from 100GeV in earlier results. The introduction of a shape-fit in the DM signal region contributes to this improvement. Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. 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Angelidakis9, A. Angerami39, A.V. Anisenkov122b,122a, A. Annovi72a, C. Antel54, M.T. Anthony149, E. Antipov129, M. Antonelli51, D.J.A. Antrim171, F. Anulli73a, M. Aoki82, J.A. Aparisi Pozo174, M.A. Aparo156, L. Aperio Bella46, N. Aranzabal36, V. Araujo Ferraz81a, R. Araujo Pereira81b, C. Arcangeletti51, A.T.H. Arce49, F.A. Arduh89, J-F. Arguin110, S. Argyropoulos52, J.-H. Arling46, A.J. Armbruster36, A. Armstrong171, O. Arnaez167, H. Arnold120, Z.P. Arrubarrena Tame114, G. Artoni134, K. Asai126, S. Asai163, T. Asawatavonvanich165, N. Asbah59, E.M. Asimakopoulou172, L. Asquith156, J. Assahsah35e, K. Assamagan29, R. Astalos28a, R.J. Atkin33a, M. Atkinson173, N.B. Atlay19, H. Atmani65, K. Augsten141, V.A. Austrup182, G. Avolio36, M.K. Ayoub15a, G. Azuelos110,am, H. Bachacou144, K. Bachas162, M. Backes134, F. Backman45a,45b, P. Bagnaia73a,73b, M. Bahmani85, H. Bahrasemani152, A.J. Bailey174, V.R. Bailey173, J.T. Baines143, C. Bakalis10, O.K. Baker183, P.J. Bakker120, E. Bakos16, D. Bakshi Gupta8, S. Balaji157, E.M. Baldin122b,122a, P. Balek180, F. Balli 144 , W.K. Balunas 134 , J. Balz 100 , E. Banas 85 , M. Bandieramonte 138 , A. Bandyopadhyay 24 , Sw. Banerjee181,i, L. Barak161, W.M. Barbe38, E.L. Barberio105, D. Barberis55b,55a, M. Barbero102, G. Barbour95, T. Barillari115, M-S. Barisits36, J. Barkeloo131, T. Barklow153, R. Barnea160, B.M. Barnett143, R.M. Barnett18, Z. Barnovska-Blenessy60a, A. Baroncelli60a, G. Barone29, A.J. Barr134, L. Barranco Navarro45a,45b, F. Barreiro99, J. Barreiro Guimarães da Costa15a, U. Barron161, S. Barsov137, F. Bartels61a, R. Bartoldus153, G. Bartolini102, A.E. Barton90, P. Bartos28a, A. Basalaev46, A. Basan100, A. Bassalat65,aj, M.J. Basso167, R.L. Bates57, S. Batlamous35f, J.R. Batley32, B. Batool151, M. Battaglia145, M. Bauce73a,73b, F. Bauer144,*, K.T. Bauer171, H.S. Bawa31, J.B. Beacham49, T. Beau135, P.H. Beauchemin170, F. Becherer52, P. Bechtle24, H.C. Beck53, H.P. Beck20,p, K. Becker178, C. Becot46, A. Beddall12d, A.J. Beddall12a, V.A. Bednyakov80, M. Bedognetti120, C.P. Bee155, T.A. Beermann182, M. Begalli81b, M. Begel29, A. Behera155, J.K. Behr46, F. Beisiegel24, M. Belfkir5, A.S. Bell95, G. Bella161, L. Bellagamba23b, A. Bellerive34, P. Bellos9, K. Beloborodov122b,122a, K. Belotskiy112, N.L. Belyaev112, D. Benchekroun35a, N. Benekos10, Y. Benhammou161, D.P. Benjamin6, M. Benoit54, J.R. Bensinger26, S. Bentvelsen120, L. Beresford 134 , M. Beretta 51 , D. Berge 19 , E. Bergeaas Kuutmann 172 , N. Berger 5 , B. Bergmann 141 , L.J. Bergsten26, J. Beringer18, S. Berlendis7, G. Bernardi135, C. Bernius153, F.U. Bernlochner24, T. Berry94, P. Berta100, C. Bertella15a, A. Berthold48, I.A. Bertram90, O. Bessidskaia Bylund182, N. Besson144, A. Bethani101, S. Bethke115, A. Betti42, A.J. Bevan93, J. Beyer115, 48 JHEP04(2021)174 D.S. Bhattacharya177, P. Bhattarai26, V.S. Bhopatkar6, R. Bi138, R.M. Bianchi138, O. Biebel114, D. Biedermann19, R. Bielski36, K. Bierwagen100, N.V. Biesuz72a,72b, M. Biglietti75a, T.R.V. Billoud110, M. Bindi53, A. Bingul12d, C. Bini73a,73b, S. Biondi23b,23a, C.J. Birch-sykes101, M. Birman 180 , T. Bisanz 36 , J.P. Biswal 3 , D. Biswas 181,i , A. Bitadze 101 , C. Bittrich 48 , K. Bjørke 133 , T. Blazek28a, I. Bloch46, C. Blocker26, A. Blue57, U. Blumenschein93, G.J. Bobbink120, V.S. Bobrovnikov122b,122a, S.S. Bocchetta97, D. Bogavac14, A.G. Bogdanchikov122b,122a, C. Bohm45a, V. Boisvert94, P. Bokan53, T. Bold84a, A.E. Bolz61b, M. Bomben135, M. Bona93, J.S. Bonilla131, M. Boonekamp144, C.D. Booth94, H.M. Borecka-Bielska91, L.S. Borgna95, A. Borisov123, G. Borissov90, J. Bortfeldt36, D. Bortoletto134, D. Boscherini23b, M. Bosman14, J.D. Bossio Sola 104 , K. Bouaouda 35a , J. Boudreau 138 , E.V. Bouhova-Thacker 90 , D. Boumediene 38 , S.K. Boutle57, A. Boveia127, J. Boyd36, D. Boye33c, I.R. Boyko80, A.J. Bozson94, J. Bracinik21, N. Brahimi60d,60c, G. Brandt182, O. Brandt32, F. Braren46, B. Brau103, J.E. Brau131, W.D. Breaden Madden57, K. Brendlinger46, L. Brenner46, R. Brenner172, S. Bressler180, B. Brickwedde100, D.L. Briglin21, D. Britton57, D. Britzger115, I. Brock24, R. Brock107, G. Brooijmans39, W.K. Brooks146d, E. Brost29, P.A. Bruckman de Renstrom85, B. Brüers46, D. Bruncko28b, A. Bruni23b, G. Bruni23b, L.S. Bruni120, S. Bruno74a,74b, M. Bruschi23b, N. Bruscino73a,73b, L. Bryngemark153, T. Buanes17, Q. Buat36, P. Buchholz151, A.G. Buckley57, I.A. Budagov80, M.K. Bugge133, F. Bührer52, O. Bulekov112, B.A. Bullard59, T.J. Burch121, S. Burdin91, C.D. Burgard120, A.M. Burger129, B. Burghgrave8, J.T.P. Burr46, C.D. Burton11, J.C. Burzynski103, V. Büscher100, E. Buschmann53, P.J. Bussey57, J.M. Butler25, C.M. Buttar57, J.M. Butterworth95, P. Butti36, W. Buttinger36, C.J. Buxo Vazquez107, A. Buzatu158, A.R. Buzykaev122b,122a, G. Cabras23b,23a, S. Cabrera Urbán174, D. Caforio56, H. Cai138, V.M.M. Cairo153, O. Cakir4a, N. Calace36, P. Calafiura18, G. Calderini135, P. Calfayan66, G. Callea57, L.P. Caloba81b, A. Caltabiano74a,74b, S. Calvente Lopez99, D. Calvet38, S. Calvet38, T.P. Calvet102, M. Calvetti72a,72b, R. Camacho Toro135, S. Camarda36, D. Camarero Munoz99, P. Camarri74a,74b, M.T. Camerlingo75a,75b, D. Cameron133, C. Camincher36, S. Campana36, M. Campanelli 95 , A. Camplani 40 , V. Canale 70a,70b , A. Canesse 104 , M. Cano Bret 78 , J. Cantero 129 , T. Cao161, Y. Cao173, M.D.M. Capeans Garrido36, M. Capua41b,41a, R. Cardarelli74a, F. Cardillo149, G. Carducci41b,41a, I. Carli142, T. Carli36, G. Carlino70a, B.T. Carlson138, E.M. Carlson176,168a, L. Carminati69a,69b, R.M.D. Carney153, S. Caron119, E. Carquin146d, S. Carrá46, G. Carratta23b,23a, J.W.S. Carter167, T.M. Carter50, M.P. Casado14,f, A.F. Casha167, F.L. Castillo174, L. Castillo Garcia14, V. Castillo Gimenez174, N.F. Castro139a,139e, A. Catinaccio 36 , J.R. Catmore 133 , A. Cattai 36 , V. Cavaliere 29 , E. Cavallaro 14 , V. Cavasinni 72a,72b , E. Celebi12b, F. Celli134, K. Cerny130, A.S. Cerqueira81a, A. Cerri156, L. Cerrito74a,74b, F. Cerutti18, A. Cervelli23b,23a, S.A. Cetin12b, Z. Chadi35a, D. Chakraborty121, J. Chan181, W.S. Chan120, W.Y. Chan91, J.D. Chapman32, B. Chargeishvili159b, D.G. Charlton21, T.P. Charman93, C.C. Chau34, S. Che127, S. Chekanov6, S.V. Chekulaev168a, G.A. Chelkov80,ah, B. Chen79, C. Chen60a, C.H. Chen79, H. Chen29, J. Chen60a, J. Chen39, J. Chen26, S. Chen136, S.J. Chen15c, X. Chen15b, Y. Chen60a, Y-H. Chen46, H.C. Cheng63a, H.J. Cheng15a, A. Cheplakov80, E. Cheremushkina123, R. Cherkaoui El Moursli35f, E. Cheu7, K. Cheung64, T.J.A. Chevalérias144, L. Chevalier144, V. Chiarella51, G. Chiarelli72a, G. Chiodini68a, A.S. Chisholm21, A. Chitan27b, I. Chiu163, Y.H. Chiu176, M.V. Chizhov80, K. Choi11, A.R. Chomont73a,73b, S. Chouridou162, Y.S. Chow120, L.D. Christopher33e, M.C. Chu63a, X. Chu15a,15d, J. Chudoba140, J.J. Chwastowski85, L. Chytka130, D. Cieri115, K.M. Ciesla85, D. Cinca47, V. Cindro92, I.A. Cioară27b, A. Ciocio18, F. Cirotto70a,70b, Z.H. Citron180,j, M. Citterio69a, D.A. Ciubotaru27b, B.M. Ciungu167, A. Clark54, M.R. Clark39, P.J. Clark50, S.E. Clawson101, C. Clement45a,45b, Y. Coadou102, M. Cobal67a,67c, A. Coccaro55b, J. Cochran79, R. Coelho Lopes De Sa 103 , H. Cohen 161 , A.E.C. Coimbra 36 , B. Cole 39 , A.P. Colijn 120 , J. Collot 58 , 49 JHEP04(2021)174 P. Conde Muiño 139a,139h , S.H. Connell 33c , I.A. Connelly 57 , S. Constantinescu 27b , F. Conventi 70a,an , A.M. Cooper-Sarkar134, F. Cormier175, K.J.R. Cormier167, L.D. Corpe95, M. Corradi73a,73b, E.E. Corrigan97, F. Corriveau104,ab, M.J. Costa174, F. Costanza5, D. Costanzo149, G. Cowan94, J.W. Cowley 32 , J. Crane 101 , K. Cranmer 125 , R.A. Creager 136 , S. Crépé-Renaudin 58 , F. Crescioli 135 , M. Cristinziani24, V. Croft170, G. Crosetti41b,41a, A. Cueto5, T. Cuhadar Donszelmann171, H. Cui15a,15d, A.R. Cukierman153, W.R. Cunningham57, S. Czekierda85, P. Czodrowski36, M.M. Czurylo 61b , M.J. Da Cunha Sargedas De Sousa 60b , J.V. Da Fonseca Pinto 81b , C. Da Via 101 , W. Dabrowski84a, F. Dachs36, T. Dado28a, S. Dahbi33e, T. Dai106, C. Dallapiccola103, M. Dam40, G. D’amen29, V. D’Amico75a,75b, J. Damp100, J.R. Dandoy136, M.F. Daneri30, M. Danninger152, V. Dao36, G. Darbo55b, O. Dartsi5, A. Dattagupta131, T. Daubney46, S. D’Auria69a,69b, C. David168b, T. Davidek142, D.R. Davis49, I. Dawson149, K. De8, R. De Asmundis70a, M. De Beurs120, S. De Castro23b,23a, N. De Groot119, P. de Jong120, H. De la Torre107, A. De Maria15c, D. De Pedis73a, A. De Salvo73a, U. De Sanctis74a,74b, M. De Santis74a,74b, A. De Santo156, J.B. De Vivie De Regie65, C. Debenedetti145, D.V. Dedovich80, A.M. Deiana42, J. Del Peso99, Y. Delabat Diaz46, D. Delgove65, F. Deliot144, C.M. Delitzsch7, M. Della Pietra70a,70b, D. Della Volpe54, A. Dell’Acqua36, L. Dell’Asta74a,74b, M. Delmastro5, C. Delporte 65 , P.A. Delsart 58 , D.A. DeMarco 167 , S. Demers 183 , M. Demichev 80 , G. Demontigny 110 , S.P. Denisov123, L. D’Eramo121, D. Derendarz85, J.E. Derkaoui35e, F. Derue135, P. Dervan91, K. Desch24, K. Dette167, C. Deutsch24, M.R. Devesa30, P.O. Deviveiros36, F.A. Di Bello73a,73b, A. Di Ciaccio 74a,74b , L. Di Ciaccio 5 , W.K. Di Clemente 136 , C. Di Donato 70a,70b , A. Di Girolamo 36 , G. Di Gregorio72a,72b, B. Di Micco75a,75b, R. Di Nardo75a,75b, K.F. Di Petrillo59, R. Di Sipio167, C. Diaconu102, F.A. Dias40, T. Dias Do Vale139a, M.A. Diaz146a, F.G. Diaz Capriles24, J. Dickinson18, E.B. Diehl106, J. Dietrich19, S. Díez Cornell46, A. Dimitrievska18, W. Ding15b, J. Dingfelder24, S.J. Dittmeier61b, F. Dittus36, F. Djama102, T. Djobava159b, J.I. Djuvsland17, M.A.B. Do Vale147, M. Dobre27b, D. Dodsworth26, C. Doglioni97, J. Dolejsi142, Z. Dolezal142, M. Donadelli81c, B. Dong60c, J. Donini38, A. D’onofrio15c, M. D’Onofrio91, J. Dopke143, A. Doria70a, M.T. Dova89, A.T. Doyle57, E. Drechsler152, E. Dreyer152, T. Dreyer53, A.S. Drobac170, D. Du60b, T.A. du Pree120, Y. Duan60d, F. Dubinin111, M. Dubovsky28a, A. Dubreuil54, E. Duchovni180, G. Duckeck114, O.A. Ducu27b, D. Duda115, A. Dudarev36, A.C. Dudder100, E.M. Duffield18, M. D’uffizi101, L. Duflot65, M. Dührssen36, C. Dülsen182, M. Dumancic180, A.E. Dumitriu27b, A.K. Duncan57, M. Dunford61a, A. Duperrin102, H. Duran Yildiz4a, M. Düren56, A. Durglishvili159b, D. Duschinger48, B. Dutta46, D. Duvnjak1, G.I. Dyckes136, M. Dyndal36, S. Dysch101, B.S. Dziedzic85, M.G. Eggleston49, T. Eifert8, G. Eigen17, K. Einsweiler18, T. Ekelof172, H. El Jarrari35f, V. Ellajosyula172, M. Ellert172, F. Ellinghaus182, A.A. Elliot93, N. Ellis36, J. Elmsheuser29, M. Elsing36, D. Emeliyanov143, A. Emerman39, Y. Enari163, M.B. Epland49, J. Erdmann47, A. Ereditato20, P.A. Erland85, M. Errenst36, M. Escalier65, C. Escobar174, O. Estrada Pastor174, E. Etzion161, H. Evans66, M.O. Evans156, A. Ezhilov137, F. Fabbri57, L. Fabbri23b,23a, V. Fabiani119, G. Facini178, R.M. Faisca Rodrigues Pereira 139a , R.M. Fakhrutdinov 123 , S. Falciano 73a , P.J. Falke 24 , S. Falke 36 , J. Faltova142, Y. Fang15a, Y. Fang15a, G. Fanourakis44, M. Fanti69a,69b, M. Faraj67a,67c,q, A. Farbin8, A. Farilla75a, E.M. Farina71a,71b, T. Farooque107, S.M. Farrington50, P. Farthouat36, F. Fassi35f, P. Fassnacht36, D. Fassouliotis9, M. Faucci Giannelli50, W.J. Fawcett32, L. Fayard65, O.L. Fedin137,o, W. Fedorko175, A. Fehr20, M. Feickert173, L. Feligioni102, A. Fell149, C. Feng60b, M. Feng49, M.J. Fenton171, A.B. Fenyuk123, S.W. Ferguson43, J. Ferrando46, A. Ferrante173, A. Ferrari172, P. Ferrari120, R. Ferrari71a, D.E. Ferreira de Lima61b, A. Ferrer174, D. Ferrere54, C. Ferretti106, F. Fiedler100, A. Filipčič92, F. Filthaut119, K.D. Finelli25, M.C.N. Fiolhais139a,139c,a, L. Fiorini174, F. Fischer114, J. Fischer100, W.C. Fisher107, T. Fitschen21, I. Fleck151, P. Fleischmann106, T. Flick182, B.M. Flierl114, L. Flores136, 50 JHEP04(2021)174 L.R. Flores Castillo63a, F.M. Follega76a,76b, N. Fomin17, J.H. Foo167, G.T. Forcolin76a,76b, B.C. Forland66, A. Formica144, F.A. Förster14, A.C. Forti101, E. Fortin102, M.G. Foti134, D. Fournier65, H. Fox90, P. Francavilla72a,72b, S. Francescato73a,73b, M. Franchini23b,23a, S. Franchino61a, D. Francis36, L. Franco5, L. Franconi20, M. Franklin59, G. Frattari73a,73b, A.N. Fray93, P.M. Freeman21, B. Freund110, W.S. Freund81b, E.M. Freundlich47, D.C. Frizzell128, D. Froidevaux36, J.A. Frost134, M. Fujimoto126, C. Fukunaga164, E. Fullana Torregrosa174, T. Fusayasu116, J. Fuster174, A. Gabrielli23b,23a, A. Gabrielli36, S. Gadatsch54, P. Gadow115, G. Gagliardi55b,55a, L.G. Gagnon110, G.E. Gallardo134, E.J. Gallas134, B.J. Gallop143, G. Galster 40 , R. Gamboa Goni 93 , K.K. Gan 127 , S. Ganguly 180 , J. Gao 60a , Y. Gao 50 , Y.S. Gao 31,l , F.M. Garay Walls146a, C. García174, J.E. García Navarro174, J.A. García Pascual15a, C. Garcia-Argos52, M. Garcia-Sciveres18, R.W. Gardner37, N. Garelli153, S. Gargiulo52, C.A. Garner167, V. Garonne133, S.J. Gasiorowski148, P. Gaspar81b, A. Gaudiello55b,55a, G. Gaudio71a, I.L. Gavrilenko111, A. Gavrilyuk124, C. Gay175, G. Gaycken46, E.N. Gazis10, A.A. Geanta27b, C.M. Gee145, C.N.P. Gee143, J. Geisen97, M. Geisen100, C. Gemme55b, M.H. Genest58, C. Geng106, S. Gentile73a,73b, S. George94, T. Geralis44, L.O. Gerlach53, P. Gessinger-Befurt 100 , G. Gessner 47 , S. Ghasemi 151 , M. Ghasemi Bostanabad 176 , M. Ghneimat 151 , A. Ghosh 65 , A. Ghosh 78 , B. Giacobbe 23b , S. Giagu 73a,73b , N. Giangiacomi 23b,23a , P. Giannetti 72a , A. Giannini70a,70b, G. Giannini14, S.M. Gibson94, M. Gignac145, D.T. Gil84b, D. Gillberg34, G. Gilles182, D.M. Gingrich3,am, M.P. Giordani67a,67c, P.F. Giraud144, G. Giugliarelli67a,67c, D. Giugni69a, F. Giuli74a,74b, S. Gkaitatzis162, I. Gkialas9,g, E.L. Gkougkousis14, P. Gkountoumis10, L.K. Gladilin113, C. Glasman99, J. Glatzer14, P.C.F. Glaysher46, A. Glazov46, G.R. Gledhill131, I. Gnesi41b,b, M. Goblirsch-Kolb26, D. Godin110, S. Goldfarb105, T. Golling54, D. Golubkov123, A. Gomes139a,139b, R. Goncalves Gama53, R. Gonçalo139a,139c, G. Gonella131, L. Gonella21, A. Gongadze80, F. Gonnella21, J.L. Gonski39, S. González de la Hoz174, S. Gonzalez Fernandez14, C. Gonzalez Renteria18, R. Gonzalez Suarez172, S. Gonzalez-Sevilla54, G.R. Gonzalvo Rodriguez174, L. Goossens36, N.A. Gorasia21, P.A. Gorbounov124, H.A. Gordon29, B. Gorini36, E. Gorini68a,68b, A. Gorišek92, A.T. Goshaw49, M.I. Gostkin80, C.A. Gottardo119, M. Gouighri35b, A.G. Goussiou148, N. Govender33c, C. Goy5, I. Grabowska-Bold84a, E.C. Graham 91 , J. Gramling 171 , E. Gramstad 133 , S. Grancagnolo 19 , M. Grandi 156 , V. Gratchev 137 , P.M. Gravila27f, F.G. Gravili68a,68b, C. Gray57, H.M. Gray18, C. Grefe24, K. Gregersen97, I.M. Gregor46, P. Grenier153, K. Grevtsov46, C. Grieco14, N.A. Grieser128, A.A. Grillo145, K. Grimm31,k, S. Grinstein14,w, J.-F. Grivaz65, S. Groh100, E. Gross180, J. Grosse-Knetter53, Z.J. Grout95, C. Grud106, A. Grummer118, J.C. Grundy134, L. Guan106, W. Guan181, C. Gubbels175, J. Guenther36, A. Guerguichon65, J.G.R. Guerrero Rojas174, F. Guescini115, D. Guest171, R. Gugel100, T. Guillemin5, S. Guindon36, U. Gul57, J. Guo60c, W. Guo106, Y. Guo60a, Z. Guo102, R. Gupta46, S. Gurbuz12c, G. Gustavino128, M. Guth52, P. Gutierrez128, C. Gutschow95, C. Guyot144, C. Gwenlan134, C.B. Gwilliam91, E.S. Haaland133, A. Haas125, C. Haber18, H.K. Hadavand8, A. Hadef60a, M. Haleem177, J. Haley129, J.J. Hall149, G. Halladjian 107 , G.D. Hallewell 102 , K. Hamano 176 , H. Hamdaoui 35f , M. Hamer 24 , G.N. Hamity 50 , K. Han60a,v, L. Han60a, S. Han18, Y.F. Han167, K. Hanagaki82,t, M. Hance145, D.M. Handl114, M.D. Hank37, R. Hankache135, E. Hansen97, J.B. Hansen40, J.D. Hansen40, M.C. Hansen24, P.H. Hansen40, E.C. Hanson101, K. Hara169, T. Harenberg182, S. Harkusha108, P.F. Harrison178, N.M. Hartman153, N.M. Hartmann114, Y. Hasegawa150, A. Hasib50, S. Hassani144, S. Haug20, R. Hauser 107 , L.B. Havener 39 , M. Havranek 141 , C.M. Hawkes 21 , R.J. Hawkings 36 , S. Hayashida 117 , D. Hayden107, C. Hayes106, R.L. Hayes175, C.P. Hays134, J.M. Hays93, H.S. Hayward91, S.J. Haywood143, F. He60a, M.P. Heath50, V. Hedberg97, S. Heer24, A.L. Heggelund133, C. Heidegger52, K.K. Heidegger52, W.D. Heidorn79, J. Heilman34, S. Heim46, T. Heim18, B. Heinemann46,ak, J.G. Heinlein136, J.J. Heinrich131, L. Heinrich36, J. Hejbal140, L. Helary61b, 51 JHEP04(2021)174 A. Held 125 , S. Hellesund 133 , C.M. Helling 145 , S. Hellman 45a,45b , C. Helsens 36 , R.C.W. Henderson 90 , Y. Heng181, L. Henkelmann32, A.M. Henriques Correia36, H. Herde26, Y. Hernández Jiménez33e, H. Herr100, M.G. Herrmann114, T. Herrmann48, G. Herten52, R. Hertenberger114, L. Hervas36, T.C. Herwig136, G.G. Hesketh95, N.P. Hessey168a, H. Hibi83, A. Higashida163, S. Higashino82, E. Higón-Rodriguez174, K. Hildebrand37, J.C. Hill32, K.K. Hill29, K.H. Hiller46, S.J. Hillier21, M. Hils 48 , I. Hinchliffe 18 , F. Hinterkeuser 24 , M. Hirose 132 , S. Hirose 52 , D. Hirschbuehl 182 , B. Hiti 92 , O. Hladik140, D.R. Hlaluku33e, J. Hobbs155, N. Hod180, M.C. Hodgkinson149, A. Hoecker36, D. Hohn52, D. Hohov65, T. Holm24, T.R. Holmes37, M. Holzbock114, L.B.A.H. Hommels32, T.M. Hong138, J.C. Honig52, A. Hönle115, B.H. Hooberman173, W.H. Hopkins6, Y. Horii117, P. Horn 48 , L.A. Horyn 37 , S. Hou 158 , A. Hoummada 35a , J. Howarth 57 , J. Hoya 89 , M. Hrabovsky 130 , J. Hrdinka77, J. Hrivnac65, A. Hrynevich109, T. Hryn’ova5, P.J. Hsu64, S.-C. Hsu148, Q. Hu29, S. Hu60c, Y.F. Hu15a,15d,ao, D.P. Huang95, Y. Huang60a, Y. Huang15a, Z. Hubacek141, F. Hubaut102, M. Huebner24, F. Huegging24, T.B. Huffman134, M. Huhtinen36, R. Hulsken58, R.F.H. Hunter34, P. Huo155, N. Huseynov80,ac, J. Huston107, J. Huth59, R. Hyneman106, S. Hyrych 28a , G. Iacobucci 54 , G. Iakovidis 29 , I. Ibragimov 151 , L. Iconomidou-Fayard 65 , P. Iengo 36 , R. Ignazzi 40 , O. Igonkina 120,y,* , R. Iguchi 163 , T. Iizawa 54 , Y. Ikegami 82 , M. Ikeno 82 , D. Iliadis 162 , N. Ilic119,167,ab, F. Iltzsche48, H. Imam35a, G. Introzzi71a,71b, M. Iodice75a, K. Iordanidou168a, V. Ippolito 73a,73b , M.F. Isacson 172 , M. Ishino 163 , W. Islam 129 , C. Issever 19,46 , S. Istin 160 , F. Ito 169 , J.M. Iturbe Ponce63a, R. Iuppa76a,76b, A. Ivina180, H. Iwasaki82, J.M. Izen43, V. Izzo70a, P. Jacka140, P. Jackson1, R.M. Jacobs46, B.P. Jaeger152, V. Jain2, G. Jäkel182, K.B. Jakobi100, K. Jakobs52, T. Jakoubek180, J. Jamieson57, K.W. Janas84a, R. Jansky54, M. Janus53, P.A. Janus84a, G. Jarlskog97, A.E. Jaspan91, N. Javadov80,ac, T. Javůrek36, M. Javurkova103, F. Jeanneau144, L. Jeanty131, J. Jejelava159a, P. Jenni52,c, N. Jeong46, S. Jézéquel5, H. Ji181, J. Jia155, H. Jiang79, Y. Jiang60a, Z. Jiang153, S. Jiggins52, F.A. Jimenez Morales38, J. Jimenez Pena115, S. Jin15c, A. Jinaru27b, O. Jinnouchi165, H. Jivan33e, P. Johansson149, K.A. Johns7, C.A. Johnson66, R.W.L. Jones90, S.D. Jones156, T.J. Jones91, J. Jongmanns61a, J. Jovicevic 36 , X. Ju 18 , J.J. Junggeburth 115 , A. Juste Rozas 14,w , A. Kaczmarska 85 , M. Kado 73a,73b , H. Kagan127, M. Kagan153, A. Kahn39, C. Kahra100, T. Kaji179, E. Kajomovitz160, C.W. Kalderon29, A. Kaluza100, A. Kamenshchikov123, M. Kaneda163, N.J. Kang145, S. Kang79, Y. Kano117, J. Kanzaki82, L.S. Kaplan181, D. Kar33e, K. Karava134, M.J. Kareem168b, I. Karkanias162, S.N. Karpov80, Z.M. Karpova80, V. Kartvelishvili90, A.N. Karyukhin123, A. Kastanas45a,45b, C. Kato60d,60c, J. Katzy46, K. Kawade150, K. Kawagoe88, T. Kawaguchi117, T. Kawamoto 144 , G. Kawamura 53 , E.F. Kay 176 , S. Kazakos 14 , V.F. Kazanin 122b,122a , R. Keeler 176 , R. Kehoe42, J.S. Keller34, E. Kellermann97, D. Kelsey156, J.J. Kempster21, J. Kendrick21, K.E. Kennedy39, O. Kepka140, S. Kersten182, B.P. Kerševan92, S. Ketabchi Haghighat167, M. Khader173, F. Khalil-Zada13, M. Khandoga144, A. Khanov129, A.G. Kharlamov122b,122a, T. Kharlamova122b,122a, E.E. Khoda175, A. Khodinov166, T.J. Khoo54, G. Khoriauli177, E. Khramov80, J. Khubua159b, S. Kido83, M. Kiehn54, C.R. Kilby94, E. Kim165, Y.K. Kim37, N. Kimura95, B.T. King91,*, A. Kirchhoff53, D. Kirchmeier48, J. Kirk143, A.E. Kiryunin115, T. Kishimoto163, D.P. Kisliuk167, V. Kitali46, C. Kitsaki10, O. Kivernyk24, T. Klapdor-Kleingrothaus52, M. Klassen61a, C. Klein34, M.H. Klein106, M. Klein91, U. Klein91, K. Kleinknecht100, P. Klimek121, A. Klimentov29, T. Klingl24, T. Klioutchnikova36, F.F. Klitzner114, P. Kluit120, S. Kluth115, E. Kneringer77, E.B.F.G. Knoops102, A. Knue52, D. Kobayashi88, T. Kobayashi163, M. Kobel48, M. Kocian153, T. Kodama163, P. Kodys142, D.M. Koeck156, P.T. Koenig24, T. Koffas34, N.M. Köhler36, M. Kolb144, I. Koletsou5, T. Komarek130, T. Kondo82, K. Köneke52, A.X.Y. Kong1, A.C. König119, T. Kono126, V. Konstantinides95, N. Konstantinidis95, B. Konya97, R. Kopeliansky66, S. Koperny84a, K. Korcyl85, K. Kordas162, G. Koren161, A. Korn95, I. Korolkov14, E.V. Korolkova149, 52 JHEP04(2021)174 N. Korotkova113, O. Kortner115, S. Kortner115, V.V. Kostyukhin149,166, A. Kotsokechagia65, A. Kotwal 49 , A. Koulouris 10 , A. Kourkoumeli-Charalampidi 71a,71b , C. Kourkoumelis 9 , E. Kourlitis 6 , V. Kouskoura29, R. Kowalewski176, W. Kozanecki101, A.S. Kozhin123, V.A. Kramarenko113, G. Kramberger92, D. Krasnopevtsev60a, M.W. Krasny135, A. Krasznahorkay36, D. Krauss115, J.A. Kremer100, J. Kretzschmar91, P. Krieger167, F. Krieter114, A. Krishnan61b, K. Krizka18, K. Kroeninger47, H. Kroha115, J. Kroll140, J. Kroll136, K.S. Krowpman107, U. Kruchonak80, H. Krüger24, N. Krumnack79, M.C. Kruse49, J.A. Krzysiak85, O. Kuchinskaia166, S. Kuday4b, D. Kuechler46, J.T. Kuechler46, S. Kuehn36, A. Kugel61a, T. Kuhl46, V. Kukhtin80, Y. Kulchitsky108,af, S. Kuleshov146b, Y.P. Kulinich173, M. Kuna58, T. Kunigo86, A. Kupco140, T. Kupfer47, O. Kuprash52, H. Kurashige83, L.L. Kurchaninov168a, Y.A. Kurochkin108, A. Kurova112, M.G. Kurth15a,15d, E.S. Kuwertz36, M. Kuze165, A.K. Kvam148, J. Kvita130, T. Kwan104, F. La Ruffa41b,41a, C. Lacasta174, F. Lacava73a,73b, D.P.J. Lack101, H. Lacker19, D. Lacour 135 , E. Ladygin 80 , R. Lafaye 5 , B. Laforge 135 , T. Lagouri 146b , S. Lai 53 , I.K. Lakomiec 84a , J.E. Lambert128, S. Lammers66, W. Lampl7, C. Lampoudis162, E. Lançon29, U. Landgraf52, M.P.J. Landon93, M.C. Lanfermann54, V.S. Lang52, J.C. Lange53, R.J. Langenberg103, A.J. Lankford171, F. Lanni29, K. Lantzsch24, A. Lanza71a, A. Lapertosa55b,55a, S. Laplace135, J.F. Laporte144, T. Lari69a, F. Lasagni Manghi23b,23a, M. Lassnig36, T.S. Lau63a, A. Laudrain65, A. Laurier 34 , M. Lavorgna 70a,70b , S.D. Lawlor 94 , M. Lazzaroni 69a,69b , B. Le 101 , E. Le Guirriec 102 , A. Lebedev79, M. LeBlanc7, T. LeCompte6, F. Ledroit-Guillon58, A.C.A. Lee95, C.A. Lee29, G.R. Lee17, L. Lee59, S.C. Lee158, S. Lee79, B. Lefebvre168a, H.P. Lefebvre94, M. Lefebvre176, C. Leggett 18 , K. Lehmann 152 , N. Lehmann 20 , G. Lehmann Miotto 36 , W.A. Leight 46 , A. Leisos 162,u , M.A.L. Leite81c, C.E. Leitgeb114, R. Leitner142, D. Lellouch180,*, K.J.C. Leney42, T. Lenz24, S. Leone72a, C. Leonidopoulos50, A. Leopold135, C. Leroy110, R. Les167, C.G. Lester32, M. Levchenko137, J. Levêque5, D. Levin106, L.J. Levinson180, D.J. Lewis21, B. Li15b, B. Li106, C-Q. Li60a, F. Li60c, H. Li60a, H. Li60b, J. Li60c, K. Li148, L. Li60c, M. Li15a,15d, Q. Li15a,15d, Q.Y. Li 60a , S. Li 60d,60c , X. Li 46 , Y. Li 46 , Z. Li 60b , Z. Li 134 , Z. Li 104 , Z. Liang 15a , M. Liberatore 46 , B. Liberti74a, A. Liblong167, K. Lie63c, S. Lim29, C.Y. Lin32, K. Lin107, R.A. Linck66, R.E. Lindley7, J.H. Lindon21, A. Linss46, A.L. Lionti54, E. Lipeles136, A. Lipniacka17, T.M. Liss173,al, A. Lister175, J.D. Little8, B. Liu79, B.X. Liu6, H.B. Liu29, J.B. Liu60a, J.K.K. Liu 37 , K. Liu 60d,60c , M. Liu 60a , P. Liu 15a , Y. Liu 46 , Y. Liu 15a,15d , Y.L. Liu 106 , Y.W. Liu 60a , M. Livan 71a,71b , A. Lleres 58 , J. Llorente Merino 152 , S.L. Lloyd 93 , C.Y. Lo 63b , E.M. Lobodzinska 46 , P. Loch7, S. Loffredo74a,74b, T. Lohse19, K. Lohwasser149, M. Lokajicek140, J.D. Long173, R.E. Long90, L. Longo36, K.A. Looper127, I. Lopez Paz101, A. Lopez Solis149, J. Lorenz114, N. Lorenzo Martinez5, A.M. Lory114, P.J. Lösel114, A. Lösle52, X. Lou46, X. Lou15a, A. Lounis65, J. Love 6 , P.A. Love 90 , J.J. Lozano Bahilo 174 , M. Lu 60a , Y.J. Lu 64 , H.J. Lubatti 148 , C. Luci 73a,73b , F.L. Lucio Alves15c, A. Lucotte58, F. Luehring66, I. Luise135, L. Luminari73a, B. Lund-Jensen154, M.S. Lutz161, D. Lynn29, H. Lyons91, R. Lysak140, E. Lytken97, F. Lyu15a, V. Lyubushkin80, T. Lyubushkina80, H. Ma29, L.L. Ma60b, Y. Ma95, D.M. Mac Donell176, G. Maccarrone51, A. Macchiolo115, C.M. Macdonald149, J.C. MacDonald149, J. Machado Miguens136, D. Madaffari174, R. Madar38, W.F. Mader48, M. Madugoda Ralalage Don129, N. Madysa48, J. Maeda83, T. Maeno29, M. Maerker48, V. Magerl52, N. Magini79, J. Magro67a,67c,q, D.J. Mahon 39 , C. Maidantchik 81b , T. Maier 114 , A. Maio 139a,139b,139d , K. Maj 84a , O. Majersky 28a , S. Majewski131, Y. Makida82, N. Makovec65, B. Malaescu135, Pa. Malecki85, V.P. Maleev137, F. Malek58, U. Mallik78, D. Malon6, C. Malone32, S. Maltezos10, S. Malyukov80, J. Mamuzic174, G. Mancini70a,70b, I. Mandić92, L. Manhaes de Andrade Filho81a, I.M. Maniatis162, J. Manjarres Ramos48, K.H. Mankinen97, A. Mann114, A. Manousos77, B. Mansoulie144, I. Manthos162, S. Manzoni120, A. Marantis162, G. Marceca30, L. Marchese134, G. Marchiori135, M. Marcisovsky140, L. Marcoccia74a,74b, C. Marcon97, C.A. Marin Tobon36, M. Marjanovic128, 53 JHEP04(2021)174 Z. Marshall18, M.U.F. Martensson172, S. Marti-Garcia174, C.B. Martin127, T.A. Martin178, V.J. Martin 50 , B. Martin dit Latour 17 , L. Martinelli 75a,75b , M. Martinez 14,w , P. Martinez Agullo 174 , V.I. Martinez Outschoorn103, S. Martin-Haugh143, V.S. Martoiu27b, A.C. Martyniuk95, A. Marzin36, S.R. Maschek115, L. Masetti100, T. Mashimo163, R. Mashinistov111, J. Masik101, A.L. Maslennikov122b,122a, L. Massa23b,23a, P. Massarotti70a,70b, P. Mastrandrea72a,72b, A. Mastroberardino41b,41a, T. Masubuchi163, D. Matakias29, A. Matic114, N. Matsuzawa163, P. Mättig24, J. Maurer27b, B. Maček92, D.A. Maximov122b,122a, R. Mazini158, I. Maznas162, S.M. Mazza145, J.P. Mc Gowan104, S.P. Mc Kee106, T.G. McCarthy115, W.P. McCormack18, E.F. McDonald105, J.A. Mcfayden36, G. Mchedlidze159b, M.A. McKay42, K.D. McLean176, S.J. McMahon143, P.C. McNamara105, C.J. McNicol178, R.A. McPherson176,ab, J.E. Mdhluli33e, Z.A. Meadows103, S. Meehan36, T. Megy38, S. Mehlhase114, A. Mehta91, B. Meirose43, D. Melini160, B.R. Mellado Garcia33e, J.D. Mellenthin53, M. Melo28a, F. Meloni46, A. Melzer24, E.D. Mendes Gouveia139a,139e, L. Meng36, X.T. Meng106, S. Menke115, E. Meoni41b,41a, S. Mergelmeyer19, S.A.M. Merkt138, C. Merlassino134, P. Mermod54, L. Merola70a,70b, C. Meroni69a, G. Merz106, O. Meshkov113,111, J.K.R. Meshreki151, J. Metcalfe6, A.S. Mete6, C. Meyer66, J-P. Meyer144, F. Miano156, M. Michetti19, R.P. Middleton143, L. Mijović50, G. Mikenberg180, M. Mikestikova140, M. Mikuž92, H. Mildner149, M. Milesi105, A. Milic167, C.D. Milke 42 , D.W. Miller 37 , A. Milov 180 , D.A. Milstead 45a,45b , R.A. Mina 153 , A.A. Minaenko 123 , I.A. Minashvili159b, A.I. Mincer125, B. Mindur84a, M. Mineev80, Y. Minegishi163, L.M. Mir14, M. Mironova134, A. Mirto68a,68b, K.P. Mistry136, T. Mitani179, J. Mitrevski114, V.A. Mitsou174, M. Mittal60c, O. Miu167, A. Miucci20, P.S. Miyagawa93, A. Mizukami82, J.U. Mjörnmark97, T. Mkrtchyan61a, M. Mlynarikova142, T. Moa45a,45b, S. Mobius53, K. Mochizuki110, P. Mogg114, S. Mohapatra39, R. Moles-Valls24, K. Mönig46, E. Monnier102, A. Montalbano152, J. Montejo Berlingen36, M. Montella95, F. Monticelli89, S. Monzani69a, N. Morange65, D. Moreno22a, M. Moreno Llácer174, C. Moreno Martinez14, P. Morettini55b, M. Morgenstern160, S. Morgenstern48, D. Mori152, M. Morii59, M. Morinaga179, V. Morisbak133, A.K. Morley36, G. Mornacchi36, A.P. Morris95, L. Morvaj155, P. Moschovakos36, B. Moser120, M. Mosidze159b, T. Moskalets144, H.J. Moss149, J. Moss31,m, E.J.W. Moyse103, S. Muanza102, J. Mueller138, R.S.P. Mueller 114 , D. Muenstermann 90 , G.A. Mullier 97 , D.P. Mungo 69a,69b , J.L. Munoz Martinez 14 , F.J. Munoz Sanchez101, P. Murin28b, W.J. Murray178,143, A. Murrone69a,69b, J.M. Muse128, M. Muškinja18, C. Mwewa33a, A.G. Myagkov123,ah, A.A. Myers138, J. Myers131, M. Myska141, B.P. Nachman18, O. Nackenhorst47, A.Nag Nag48, K. Nagai134, K. Nagano82, Y. Nagasaka62, J.L. Nagle29, E. Nagy102, A.M. Nairz36, Y. Nakahama117, K. Nakamura82, T. Nakamura163, H. Nanjo132, F. Napolitano61a, R.F. Naranjo Garcia46, R. Narayan42, I. Naryshkin137, T. Naumann46, G. Navarro22a, P.Y. Nechaeva111, F. Nechansky46, T.J. Neep21, A. Negri71a,71b, M. Negrini23b, C. Nellist119, C. Nelson104, M.E. Nelson45a,45b, S. Nemecek140, M. Nessi36,e, M.S. Neubauer 173 , F. Neuhaus 100 , M. Neumann 182 , R. Newhouse 175 , P.R. Newman 21 , C.W. Ng 138 , Y.S. Ng19, Y.W.Y. Ng171, B. Ngair35f, H.D.N. Nguyen102, T. Nguyen Manh110, E. Nibigira38, R.B. Nickerson 134 , R. Nicolaidou 144 , D.S. Nielsen 40 , J. Nielsen 145 , M. Niemeyer 53 , N. Nikiforou 11 , V. Nikolaenko123,ah, I. Nikolic-Audit135, K. Nikolopoulos21, P. Nilsson29, H.R. Nindhito54, Y. Ninomiya82, A. Nisati73a, N. Nishu60c, R. Nisius115, I. Nitsche47, T. Nitta179, T. Nobe163, D.L. Noel 32 , Y. Noguchi 86 , I. Nomidis 135 , M.A. Nomura 29 , M. Nordberg 36 , J. Novak 92 , T. Novak 92 , O. Novgorodova48, R. Novotny141, L. Nozka130, K. Ntekas171, E. Nurse95, F.G. Oakham34,am, H. Oberlack115, J. Ocariz135, A. Ochi83, I. Ochoa39, J.P. Ochoa-Ricoux146a, K. O’Connor26, S. Oda88, S. Odaka82, S. Oerdek53, A. Ogrodnik84a, A. Oh101, S.H. Oh49, C.C. Ohm154, H. Oide165, M.L. Ojeda167, H. Okawa169, Y. Okazaki86, M.W. O’Keefe91, Y. Okumura163, T. Okuyama82, A. Olariu27b, L.F. Oleiro Seabra139a, S.A. Olivares Pino146a, D. Oliveira Damazio29, J.L. Oliver1, M.J.R. Olsson171, A. Olszewski85, J. Olszowska85, 54 JHEP04(2021)174 D.C. O’Neil152, A.P. O’neill134, A. Onofre139a,139e, P.U.E. Onyisi11, H. Oppen133, R.G. Oreamuno Madriz121, M.J. Oreglia37, G.E. Orellana89, D. Orestano75a,75b, N. Orlando14, R.S. Orr167, V. O’Shea57, R. Ospanov60a, G. Otero y Garzon30, H. Otono88, P.S. Ott61a, G.J. Ottino18, M. Ouchrif35e, J. Ouellette29, F. Ould-Saada133, A. Ouraou144,*, Q. Ouyang15a, M. Owen57, R.E. Owen21, V.E. Ozcan12c, N. Ozturk8, J. Pacalt130, H.A. Pacey32, K. Pachal49, A. Pacheco Pages14, C. Padilla Aranda14, S. Pagan Griso18, G. Palacino66, S. Palazzo50, S. Palestini36, M. Palka84b, D. Pallin38, P. Palni84a, C.E. Pandini54, J.G. Panduro Vazquez94, P. Pani46, G. Panizzo67a,67c, L. Paolozzi54, C. Papadatos110, K. Papageorgiou9,g, S. Parajuli42, A. Paramonov6, C. Paraskevopoulos10, D. Paredes Hernandez63b, S.R. Paredes Saenz134, B. Parida180, T.H. Park167, A.J. Parker31, M.A. Parker32, F. Parodi55b,55a, E.W. Parrish121, J.A. Parsons39, U. Parzefall52, L. Pascual Dominguez135, V.R. Pascuzzi18, J.M.P. Pasner145, F. Pasquali 120 , E. Pasqualucci 73a , S. Passaggio 55b , F. Pastore 94 , P. Pasuwan 45a,45b , S. Pataraia 100 , J.R. Pater101, A. Pathak181,i, J. Patton91, T. Pauly36, J. Pearkes153, B. Pearson115, M. Pedersen133, L. Pedraza Diaz119, R. Pedro139a, T. Peiffer53, S.V. Peleganchuk122b,122a, O. Penc140, H. Peng60a, B.S. Peralva81a, M.M. Perego65, A.P. Pereira Peixoto139a, L. Pereira Sanchez45a,45b, D.V. Perepelitsa29, E. Perez Codina168a, F. Peri19, L. Perini69a,69b, H. Pernegger36, S. Perrella139a, A. Perrevoort120, K. Peters46, R.F.Y. Peters101, B.A. Petersen36, T.C. Petersen40, E. Petit102, A. Petridis1, C. Petridou162, F. Petrucci75a,75b, M. Pettee183, N.E. Pettersson 103 , K. Petukhova 142 , A. Peyaud 144 , R. Pezoa 146d , L. Pezzotti 71a,71b , T. Pham 105 , F.H. Phillips 107 , P.W. Phillips 143 , M.W. Phipps 173 , G. Piacquadio 155 , E. Pianori 18 , A. Picazio 103 , R.H. Pickles101, R. Piegaia30, D. Pietreanu27b, J.E. Pilcher37, A.D. Pilkington101, M. Pinamonti67a,67c, J.L. Pinfold3, C. Pitman Donaldson95, M. Pitt161, L. Pizzimento74a,74b, M.-A. Pleier29, V. Pleskot142, E. Plotnikova80, P. Podberezko122b,122a, R. Poettgen97, R. Poggi54, L. Poggioli135, I. Pogrebnyak107, D. Pohl24, I. Pokharel53, G. Polesello71a, A. Poley152, A. Policicchio73a,73b, R. Polifka142, A. Polini23b, C.S. Pollard46, V. Polychronakos29, D. Ponomarenko112, L. Pontecorvo36, S. Popa27a, G.A. Popeneciu27d, L. Portales5, D.M. Portillo Quintero 58 , S. Pospisil 141 , K. Potamianos 46 , I.N. Potrap 80 , C.J. Potter 32 , H. Potti 11 , T. Poulsen97, J. Poveda174, T.D. Powell149, G. Pownall46, M.E. Pozo Astigarraga36, P. Pralavorio102, S. Prell79, D. Price101, M. Primavera68a, M.L. Proffitt148, N. Proklova112, K. Prokofiev63c, F. Prokoshin80, S. Protopopescu29, J. Proudfoot6, M. Przybycien84a, D. Pudzha137, A. Puri173, P. Puzo65, D. Pyatiizbyantseva112, J. Qian106, Y. Qin101, A. Quadt53, M. Queitsch-Maitland36, A. Qureshi1, M. Racko28a, F. Ragusa69a,69b, G. Rahal98, J.A. Raine54, S. Rajagopalan29, A. Ramirez Morales93, K. Ran15a,15d, T. Rashid65, D.M. Rauch46, F. Rauscher114, S. Rave100, B. Ravina149, I. Ravinovich180, J.H. Rawling101, M. Raymond36, A.L. Read 133 , N.P. Readioff 58 , M. Reale 68a,68b , D.M. Rebuzzi 71a,71b , G. Redlinger 29 , K. Reeves 43 , J. Reichert136, D. Reikher161, A. Reiss100, A. Rej151, C. Rembser36, A. Renardi46, M. Renda27b, M.B. Rendel115, S. Resconi69a, E.D. Resseguie18, S. Rettie95, B. Reynolds127, E. Reynolds21, O.L. Rezanova122b,122a, P. Reznicek142, E. Ricci76a,76b, R. Richter115, S. Richter46, E. Richter-Was84b, M. Ridel135, P. Rieck115, O. Rifki46, M. Rijssenbeek155, A. Rimoldi71a,71b, M. Rimoldi46, L. Rinaldi23b, T.T. Rinn173, G. Ripellino154, I. Riu14, P. Rivadeneira46, J.C. Rivera Vergara176, F. Rizatdinova129, E. Rizvi93, C. Rizzi36, S.H. Robertson104,ab, M. Robin46, D. Robinson32, C.M. Robles Gajardo146d, M. Robles Manzano100, A. Robson57, A. Rocchi74a,74b, E. Rocco100, C. Roda72a,72b, S. Rodriguez Bosca174, A.M. Rodríguez Vera168b, S. Roe 36 , J. Roggel 182 , O. Røhne 133 , R. Röhrig 115 , R.A. Rojas 146d , B. Roland 52 , C.P.A. Roland 66 , J. Roloff29, A. Romaniouk112, M. Romano23b,23a, N. Rompotis91, M. Ronzani125, L. Roos135, S. Rosati73a, G. Rosin103, B.J. Rosser136, E. Rossi46, E. Rossi75a,75b, E. Rossi70a,70b, L.P. Rossi55b, L. Rossini69a,69b, R. Rosten14, M. Rotaru27b, B. Rottler52, D. Rousseau65, G. Rovelli71a,71b, A. Roy11, D. Roy33e, A. Rozanov102, Y. Rozen160, X. Ruan33e, F. Rühr52, 55 JHEP04(2021)174 125 Department of Physics, New York University, New York NY; United States of America 126 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 127 Ohio State University, Columbus OH; United States of America 128 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 129 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 130 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 131 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 132 Graduate School of Science, Osaka University, Osaka; Japan 133 Department of Physics, University of Oslo, Oslo; Norway 134 Department of Physics, Oxford University, Oxford; United Kingdom 135 LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris; France 136 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 137 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg; Russia 138 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 139 (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)Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal 140 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 141 Czech Technical University in Prague, Prague; Czech Republic 142 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 143 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 144 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 145 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 146 (a) Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; (b) Universidad Andres Bello, Department of Physics, Santiago;(c)Instituto de Alta Investigación, Universidad de Tarapacá;(d)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile 147 Universidade Federal de São João del Rei (UFSJ), São João del Rei; Brazil 148 Department of Physics, University of Washington, Seattle WA; United States of America 149 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 150 Department of Physics, Shinshu University, Nagano; Japan 151 Department Physik, Universität Siegen, Siegen; Germany 152 Department of Physics, Simon Fraser University, Burnaby BC; Canada 153 SLAC National Accelerator Laboratory, Stanford CA; United States of America 154 Physics Department, Royal Institute of Technology, Stockholm; Sweden 155 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 156 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 157 School of Physics, University of Sydney, Sydney; Australia 158 Institute of Physics, Academia Sinica, Taipei; Taiwan 159 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;(b)High Energy Physics Institute, Tbilisi State University, Tbilisi; Georgia 160 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 161 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 162 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 62 JHEP04(2021)174 163 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan 164 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan 165 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 166 Tomsk State University, Tomsk; Russia 167 Department of Physics, University of Toronto, Toronto ON; Canada 168 (a) TRIUMF, Vancouver BC; (b) Department of Physics and Astronomy, York University, Toronto ON; Canada 169 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 170 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 171 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 172 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 173 Department of Physics, University of Illinois, Urbana IL; United States of America 174 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain 175 Department of Physics, University of British Columbia, Vancouver BC; Canada 176 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 177 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 178 Department of Physics, University of Warwick, Coventry; United Kingdom 179 Waseda University, Tokyo; Japan 180 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel 181 Department of Physics, University of Wisconsin, Madison WI; United States of America 182 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 183 Department of Physics, Yale University, New Haven CT; United States of America aAlso at Borough of Manhattan Community College, City University of New York, New York NY; United States of America bAlso at Centro Studi e Ricerche Enrico Fermi; Italy 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 Financial and Management Engineering, University of the Aegean, Chios; Greece h Also at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America iAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America jAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel kAlso at Department of Physics, California State University, East Bay; United States of America lAlso at Department of Physics, California State University, Fresno; United States of America mAlso at Department of Physics, California State University, Sacramento; United States of America nAlso at Department of Physics, King’s College London, London; United Kingdom o Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia pAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland qAlso at Dipartimento di Matematica, Informatica e Fisica, Università di Udine, Udine; Italy rAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russia sAlso at Giresun University, Faculty of Engineering, Giresun; Turkey tAlso at Graduate School of Science, Osaka University, Osaka; Japan 63 JHEP04(2021)174 uAlso at Hellenic Open University, Patras; Greece vAlso at IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France wAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain xAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany yAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands zAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia; Bulgaria aa Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary ab Also at Institute of Particle Physics (IPP); Canada ac Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan ad Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain ae Also at Istanbul University, Dept. of Physics, Istanbul; Turkey af Also at Joint Institute for Nuclear Research, Dubna; Russia ag Also at Louisiana Tech University, Ruston LA; United States of America ah Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia ai Also at National Research Nuclear University MEPhI, Moscow; Russia aj Also at Physics Department, An-Najah National University, Nablus; Palestine ak Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany al Also at The City College of New York, New York NY; United States of America am Also at TRIUMF, Vancouver BC; Canada an Also at Università di Napoli Parthenope, Napoli; Italy ao Also at University of Chinese Academy of Sciences (UCAS), Beijing; China ∗Deceased 64