Search for new phenomena in events with two opposite-charge leptons, jets and missing transverse momentum in pp collisions at s√ = 13 TeV with the ATLAS detector

JHEP04(2021)165 Published for SISSA by Springer Received:February 3, 2021 Accepted:March 3, 2021 Published:April 16, 2021 Search for new phenomena in events with two opposite-charge leptons, jets and missing transverse momentum in pp collisions at √s=13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: The results of a search for direct pair production of top squarks and for dark matter in events with two opposite-charge leptons (electrons or muons), jets and missing transverse momentum are reported, using 139fb−1of integrated luminosity from protonproton collisions at √s= 13 TeV, collected by the ATLAS detector at the Large Hadron Collider during Run 2 (2015–2018). This search considers the pair production of top squarks and is sensitive across a wide range of mass differences between the top squark and the lightest neutralino. Additionally, spin-0 mediator dark-matter models are considered, in which the mediator is produced in association with a pair of top quarks. The mediator subsequently decays to a pair of dark-matter particles. No significant excess of events is observed above the Standard Model background, and limits are set at 95% confidence level. The results exclude top squark masses up to about 1TeV, and masses of the lightest neutralino up to about 500 GeV. Limits on dark-matter production are set for scalar (pseudoscalar) mediator masses up to about 250 (300) GeV. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 2102.01444 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP04(2021)165 JHEP04(2021)165 Contents 1 Introduction 1 2 ATLAS detector 4 3 Data and simulated event samples 5 4 Object identification 6 5 Event selection 9 5.1 Discriminators and kinematic variables 9 5.2 Two-body event selection 11 5.3 Three-body event selection 11 5.4 Four-body event selection 12 6 Background estimation 13 6.1 Estimation of the backgrounds in the two-body selection 15 6.2 Estimation of the backgrounds in the three-body selection 15 6.3 Estimation of the backgrounds in the four-body selection 19 7 Systematic uncertainties 20 8 Results 26 8.1 Two-body selection results 26 8.2 Three-body selection results 28 8.3 Four-body selection results 28 9 Interpretation 29 10 Conclusion 32 The ATLAS collaboration 45 1 Introduction The Standard Model (SM) of particle physics is extremely successful in describing the phenomena of elementary particles and their interactions. Its predictive power has been proven with high precision by a wide range of experiments. However, despite its success, several important questions remain unanswered within the SM. One particularly striking omission is that it does not provide any explanation for dark matter (DM) [1,2]. This is a non-baryonic, non-luminous matter component of the universe, for which there is strong – 1 – JHEP04(2021)165 evidence from a range of astrophysical observations. A weakly interacting dark-matter candidate particle can be produced at the Large Hadron Collider (LHC) [3] in a variety of ways, as described, for example, by supersymmetry (SUSY) [4–9] or DM models. At the LHC, one of the most promising modes is the production of DM particle pairs in association with onor off-shell top quarks. Previous searches for DM candidates in association with a top quark pair have been performed by the ATLAS [10–16] and CMS [17–26] collaborations. However, those previous searches were statistically limited, or sensitive only up to limited particle masses. They also suffered from significant regions in which no limit could be placed because the kinematics of the decays made the signal events particularly difficult to identify. This paper aims to extend the sensitivity beyond that of the previous searches to higher masses, and to cover the regions in which the previous ATLAS results had no sensitivity [27,28]. It achieves this in part by exploiting a larger dataset, corresponding to 139 fb−1of proton-proton collision data collected by the ATLAS experiment during Run 2 of the LHC (2015–2018) at a centre-of-mass energy √s= 13 TeV. Further improvements in sensitivity are obtained by using a new discriminating variable, the ‘object-based Emiss T significance’ [29], lowering the lepton pTthresholds, and optimising a dedicated selection to target signal models in the most difficult kinematic regions. Signal models and kinematic regions. For DM production, the simplified benchmark models [30–32] assume the existence of a mediator particle which couples both to the SM and to the dark sector [33–35]. The couplings of the mediator to the SM fermions are then severely restricted by precision flavour measurements. An ansatz that automatically relaxes these constraints is Minimal Flavour Violation [36]. This assumption implies that the interaction between any new neutral spin-0 state and SM matter is proportional to the fermion masses via Yukawa-type couplings.1It follows that colour-neutral mediators would be produced mainly through loop-induced gluon fusion or in association with heavy-flavour quarks. Here, the DM particles χare assumed to be pair produced through the exchange of a spin-0 mediator, which can be a colour-neutral scalar or pseudoscalar particle (denoted by φor a, respectively), in association with a top quark pair: pp →χ¯χt¯ t(figure 1a). Alternatively, dark-matter particles are also predicted in supersymmetry, a space-time symmetry that for each SM particle postulates the existence of a partner particle whose spin differs by one-half unit. To avoid violation of baryon number (B) and lepton number (L) conservation, a multiplicative quantum number R-parity [37], defined as R= (−1)3(B−L)+2S, is assumed to be conserved. SUSY particles are then produced in pairs, and the lightest supersymmetric particle (LSP) is stable and, if only weakly interacting, a candidate for dark matter [38,39]. In the framework of a generic R-parity-conserving Minimal Supersymmetric Standard Model (MSSM) [40,41], the supersymmetric scalar partners of right-handed and left-handed quarks (squarks), ˜qRand ˜qL, can mix to form two mass eigenstates, ˜q1and ˜q2, with ˜q1defined to be the lighter one. In the case of the supersymmetric partner of the top quark, ˜ t, large mixing effects can lead to one of the 1Following ref. [34], couplings to Wand Zbosons, as well as explicit dimension-4 φ–hor a–hcouplings, are set to zero in this simplified model. In addition, the coupling of the mediator to the dark sector is not taken to be proportional to the mass of the DM candidates. – 2 – JHEP04(2021)165 t t W W φ/a b νℓ χ χ ℓ ν b (a) ˜ t ˜ t W W p p ˜χ0 1 bℓ ν ˜χ0 1 bℓ ν (b) ˜ t ˜ t p p bℓ ν ˜χ0 1 bℓ ν ˜χ0 1 (c) ˜ t ˜ t tW tW p p ˜χ0 1 bℓ ν ˜χ0 1 bℓ ν (d) Figure 1. Diagrams representing the signal models targeted by the searches: (a) the spin-0 mediator models, where the mediator decays into a pair of dark-matter particles and is produced in association with a pair of top quarks (pp →χ¯χt¯ t), (b) the three-body ˜ t1decay mode into an on-shell Wboson, a b-quark and the lightest neutralino (˜ t1→bW ˜χ0 1), (c) the four-body ˜ t1decay mode (˜ t1→b¯ `ν ˜χ0 1) where ¯ `and νare a anti-lepton with its neutrino and (d) the two-body ˜ t1decay into an on-shell top quark and the lightest neutralino (˜ t1→t˜χ0 1). For all the diagrams (a-d) the distinction between particle and anti-particle is omitted. top squark mass eigenstates, ˜ t1, being significantly lighter than the other squarks. The charginos and neutralinos are mixtures of the bino, winos and Higgsinos that are superpartners of the U(1) and SU(2) gauge bosons and the Higgs bosons, respectively. Their mass eigenstates are referred to as ˜χ± i(i= 1,2) and ˜χ0 j(j= 1,2,3,4) in order of increasing mass. In a large variety of models, the LSP, which is the DM candidate, is the lightest neutralino ˜χ0 1. Searches for direct pair production of the top squark and DM particles can be performed in final states with two leptons (electrons or muons) of opposite electric charge, jets and missing transverse momentum (figures 1b–1d). Depending on the mass difference between the top squark and the lighter SUSY particles, different decay modes are relevant. For m(W) + m(b)< m(˜ t1)−m(˜χ0 1)< m(t), the three-body decay ˜ t1→bW ˜χ0 1occurs through an off-shell top quark (figure 1b). For smaller mass differences, i.e. m(˜ t1)−m(˜χ0 1)< m(W) + m(b), the four-body decay channel ˜ t→bff0˜χ0 1, where f and f0are two fermions from the off-shell (W∗)decay, is assumed to occur (figure 1c). In this search, fand f0are a charged lepton and its associated anti-neutrino (or vice versa). For each of these two decay modes a dedicated event selection is performed to maximise the sensitivity. These selections are referred to as three-body and four-body selections in this paper. Direct pair production of top squarks which decay into an on-shell top quark and the lightest neutralino ˜ t1→t˜χ0 1, will occur when m(˜ t1)−m(˜χ0 1)> m(t)(figure 1d). The signature of the tt+DM process is similar to that of the simplified model shown in figure 1a, so the same selection is also used to constrain the ˜ t1→t˜χ0 1model and it is referred to as the two-body selection. The paper proceeds as follows; after a description of the ATLAS detector in section 2, the data and simulated Monte Carlo (MC) samples used in the analysis are detailed in section 3and the object identification is documented in section 4. The search strategy, the SM background estimations, and the systematic uncertainties are discussed in sections 5,6 and 7. The results and their statistical interpretations are presented in sections 8and 9. Finally, section 10 presents the conclusions. – 3 – JHEP04(2021)165 2 ATLAS detector The ATLAS detector [42] at the LHC covers nearly the entire solid angle around the collision point.2It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer with three large superconducting toroidal magnets. The inner-detector system (ID) is immersed in a 2T axial magnetic field and provides charged-particle tracking in the range |η|<2.5. The high-granularity silicon pixel detector covers the vertex region and typically provides four measurements per track, the first hit normally being in the insertable B-layer installed before Run 2 [43,44]. It is followed by the silicon microstrip tracker, which usually provides eight measurements per track. These silicon detectors are complemented by the transition radiation tracker (TRT), which enables radially extended track reconstruction up to |η|= 2.0. The TRT also provides electron identification information based on the fraction of hits (typically 30 in total) above a higher energy-deposit threshold corresponding to transition radiation. The calorimeter system covers the pseudorapidity range |η|<4.9. Within the region |η|<3.2, electromagnetic calorimetry is provided by barrel and endcap high-granularity lead/liquid-argon (LAr) calorimeters, with an additional thin LAr presampler covering |η|<1.8to correct for energy loss in material upstream of the calorimeters. Hadronic calorimetry is provided by the steel/scintillating-tile calorimeter, segmented into three barrel structures within |η|<1.7, and two copper/LAr hadronic endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements respectively. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by the superconducting air-core toroids. The field integral of the toroids ranges between 2.0 and 6.0 Tm across most of the detector. A set of precision chambers covers the region |η|<2.7with three layers of monitored drift tubes, complemented by cathode-strip chambers in the forward region, where the background is highest. The muon trigger system covers the range |η|<2.4with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. Interesting events are selected to be recorded by the first-level trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the high-level trigger [45]. The first-level trigger accepts events from the 40MHz bunch crossings at a rate below 100 kHz, which the high-level trigger reduces in order to record events to disk at about 1 kHz. 2ATLAS 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, φbeing the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θ as η=−ln tan(θ/2), and the rapidity in terms of energy Eand momentum pas y= 0.5[(E+pz)/(E−pz)]. Angular distance is measured in units of ∆R≡p(∆y)2+ (∆φ)2or ∆Rη≡p(∆η)2+ (∆φ)2. A vector energy ~ Eis defined by combining the energy deposited in the calorimeter with its deposit direction. – 4 – JHEP04(2021)165 3 Data and simulated event samples The data used in this analysis were collected by the ATLAS detector during pp collisions at a centre-of-mass energy of √s= 13 TeV from 2015 to 2018. The average number hµiof pp interactions per bunch crossing (pile-up) varies from 14 during 2015 to 38 during 2017– 2018. Only events taken in stable beam conditions, and for which all relevant detector systems were operational, are considered in this analysis. After data-quality requirements the data sample amounts to a total integrated luminosity of 139fb−1. The uncertainty in the combined 2015–2018 integrated luminosity is 1.7% [46], obtained using the LUCID-2 detector [47]. The two-body and three-body selections use events accepted by a trigger that requires a minimum of two electrons, two muons, or an electron and a muon [45]. Different triggerlevel thresholds for the transverse momentum of the leptons were used in different datataking periods, ranging between 8 and 22 GeV. Tighter thresholds are applied in the lepton offline selection, to ensure that the trigger efficiency is ‘on plateau’ in all of the relevant kinematic region. Missing transverse momentum triggers [48] are used in the four-body selection to increase the acceptance of low-pTleptons. The missing transverse momentum trigger threshold varied depending on data-taking conditions in the four years: 70GeV for data collected during 2015; in the range 90–110 GeV for data collected during 2016, and 110GeV for data collected during 2017 and 2018. Tighter offline requirements on the missing transverse momentum are defined accordingly to ensure event selection on the plateau region of the trigger efficiency curve. Simulated event samples are used for SM background estimations and to model the signal samples. Standard Model MC samples were processed through a full Geant4[49] simulation of the ATLAS detector, while a fast simulation based on parameterisation of the calorimeter response and Geant4simulation for all the other detector components [50] is used for the SUSY and DM signal samples. MC events are reconstructed using the same algorithms used for the data. To compensate for small residual differences between data and simulation in the lepton reconstruction efficiency, energy scale, energy resolution, trigger modelling, and b-tagging efficiency, the simulated events are reweighted using correction factors derived from data [51–53]. The events targeted by this analysis are characterised by two leptons with opposite electric charge, jets and missing transverse momentum. The main SM background contributions are expected to come from top quark pair production (tt), associated production of a Zboson and a top quark pair (ttZ), single-top decay in the Wt production channel (Wt), Z/γ∗+jets production and diboson processes (V V with V=W, Z). Matrix element and showering generators used for the SM backgrounds and signals are listed in table 1along with the relevant parton distribution function (PDF) sets, the configuration of underlying-event and hadronisation parameters (tunes), and the crosssection order in αsused to normalise the event yields. Additional MC samples are used to estimate systematic uncertainties, as detailed in section 7. The SUSY top squark pair signal samples were generated from leading-order (LO) matrix elements with up to two extra partons using MadGraph5_aMC@NLO 2.6.2 [54]. – 5 – JHEP04(2021)165 Physics process Generator Parton shower Normalisation PDF (generator) PDF (PS) SUSY Signals MadGraph5_aMC@NLO [54]. Pythia 8.212 +MadSpin [55,56] NNLO+NNLL [57–64] NNPDF2.3LO [68] NNPDF2.3LO [68] (three-body, four-body) SUSY Signals (two-body) MadGraph5_aMC@NLO Pythia 8.212 NNLO+NNLL [57–64] NNPDF2.3LO NNPDF2.3LO DM Signals (two-body) MadGraph5_aMC@NLO Pythia 8.212 NLO [69,70] NNPDF2.3LO NNPDF2.3LO t¯ tPowheg-Box v2 [74–76]Pythia 8.230 NNLO+NNLL [77] NNPDF3.0NLO [78] NNPDF2.3LO t¯ t+V(V=W, Z)MadGraph5_aMC@NLO Pythia 8.210 NLO [54,79] NNPDF3.0NLO NNPDF2.3LO Single top Powheg-Box v2 [74–76,80,81]Pythia 8.230 NLO+NNLL [82–86] NNPDF3.0NLO NNPDF2.3LO Z/γ∗(→``)+jets Sherpa 2.2.1 [87,88]Sherpa 2.2.1 NNLO [89] NNPDF3.0NNLO [78] NNPDF3.0NNLO [78] Diboson V V (V=W, Z)Sherpa 2.2.1 or 2.2.2 [87]Sherpa 2.2.1 or 2.2.2 NLO [90] NNPDF3.0NNLO NNPDF3.0NNLO Triboson V V V (V=W, Z)Sherpa 2.2.2 Sherpa 2.2.2 NLO [87,90] NNPDF3.0NNLO NNPDF3.0NNLO tt HPowheg-Box v2 [74,75,91]Pythia 8.230 NLO [54,79] NNPDF3.0NLO NNPDF2.3LO t¯ tWW MadGraph5_aMC@NLO Pythia 8.186 [71] NLO [54] NNPDF2.3LO NNPDF2.3LO t¯ tWZ MadGraph5_aMC@NLO Pythia 8.212 NLO [54] NNPDF3.0NLO NNPDF2.3LO tZ, t¯ tt¯ t, t¯ tt MadGraph5_aMC@NLO Pythia 8.230 NLO [54] NNPDF3.0NLO NNPDF2.3LO Table 1. Simulated signal and background event samples with the corresponding matrix element and parton shower (PS) generators, cross-section order in αsused to normalise the event yield, and the generator and PS PDF sets used. MadGraph5_aMC@NLO was interfaced to Pythia 8.212 + MadSpin [55,56] for the signal samples used in the three-body and four-body selections, while it was interfaced to Pythia 8.212 for the SUSY signal samples used for the interpretation of the twobody selection results. Signal cross-sections were calculated to next-to-next-to-leading order (NNLO) in αs, adding the resummation of soft gluon emission at next-to-next-toleading-logarithm accuracy (NNLO+NNLL) [57–64]. The nominal cross section and the uncertainty are derived using the PDF4LHC15 PDF set, following the recommendations presented in ref. [65]. Jet–parton matching was performed following the CKKW-L prescription [66]. The A14 tune [67] was used for the modelling of parton showering, hadronisation and the underlying event. Parton luminosities were provided by the NNPDF2.3LO PDF set [68]. The dark-matter signal samples were also generated from leading-order matrix elements, with up to one extra parton, using MadGraph5_aMC@NLO 2.6.2 interfaced to Pythia 8.212. In the DM samples generation the couplings of the scalar and pseudoscalar mediators to the SM and DM particles (gqand gχ) are set to one. The kinematics of the mediator decay are not strongly dependent on the values of the couplings; however, the particle kinematic distributions are sensitive to the nature of the mediator and to the mediator and DM particle masses. The cross-sections were computed at NLO [69,70]. Inelastic pp interactions were generated and overlaid onto the hard-scattering process to simulate the effect of multiple proton-proton interactions occurring during the same (in-time) or a nearby (out-of-time) bunch crossing. These were produced using Pythia 8.186 [71] and EvtGen [72] with the NNPDF2.3LO set of PDFs [68] and the A3 tune [73]. The MC samples were reweighted so that the distribution of the average number of interactions per bunch crossing reproduces the observed distribution in the data. 4 Object identification Candidate events are required to have a reconstructed vertex with at least two associated tracks, each with pT>500 MeV and originating from the beam collision region in the x–y – 6 – JHEP04(2021)165 plane. The primary vertex in the event is the vertex with the highest scalar sum of the squared transverse momenta of associated tracks. The leptons selected for analysis are classified as baseline or signal leptons depending on an increasingly stringent set of reconstruction quality criteria and kinematic selections, so that signal leptons are a subset of the baseline leptons. Baseline leptons are used in the calculation of missing transverse momentum (pmiss T), to resolve ambiguities between the analysis objects in the event, as described later, and for the fake/non-prompt (FNP) lepton background estimation described in section 6. Signal leptons are used for the final event selection. Baseline electron candidates are reconstructed from three-dimensional clusters of energy deposition in the electromagnetic calorimeter matched to ID tracks. These electron candidates are required to have pseudorapidity |η|<2.47,ET>4.5 GeV, and to pass a Loose likelihood-based identification requirement [51] with an additional condition on the number of hits in the B-layer. The tracks associated with electron candidates are required to have a longitudinal impact parameter3relative to the primary vertex |z0sin θ|<0.5mm, where θis the track’s polar angle. Baseline muon candidates are reconstructed by matching ID tracks, in the pseudorapidity region |η|<2.4for the two-body and three-body selections and |η|<2.7for the fourbody selection, with MS tracks or energy deposits in the calorimeter compatible with a minimum-ionising particle (calo-tagged muon). The resulting tracks are required to have apT>4 GeV and a |z0sin θ|<0.5mm from the primary vertex. Muon candidates are required to satisfy the Medium identification requirement, defined in ref. [52], based on the numbers of hits in the different ID and MS subsystems, and on the significance of the charge-to-momentum ratio q/p. Additional tighter selections are applied to the baseline lepton candidates to select the signal electrons or muons. Signal electrons are required to satisfy a Medium likelihoodbased identification requirement [51] and the track associated with a signal electron is required to have a significance |d0|/σ(d0)<5, where d0is the transverse impact parameter relative to the reconstructed primary vertex and σ(d0)is its uncertainty. Isolation criteria are applied to electrons by placing an upper limit on the sum of the transverse energy of the calorimeter energy clusters in a cone of size ∆Rη=q(∆η)2+ (∆φ)2= 0.2around the electron (excluding the deposit from the electron itself) and the scalar sum of the pT of tracks within a cone of ∆Rη= 0.2around the electron (excluding its own track). The isolation criteria are optimised such that the isolation selection efficiency is uniform across η. This varies from 90% for pT= 25 GeV to 99% for pT= 60 GeV in events with a Z boson decaying into pair of electrons [51]. For signal muons a significance in the transverse impact parameter |d0|/σ(d0)<3is required. Isolation criteria applied to muons require the scalar sum of the pTof tracks inside a cone of ∆Rη= 0.3around the muon (excluding its own track) to be less than 15% 3The transverse impact parameter is defined as the distance of closest approach in the transverse plane between a track and the beam-line. The longitudinal impact parameter corresponds to the z-coordinate distance between the point along the track at which the transverse impact parameter is defined and the primary vertex. – 7 – JHEP04(2021)165 of the muon pT. In addition, the sum of the transverse energy of the calorimeter energy clusters in a cone of ∆Rη= 0.2around the muon (excluding the energy from the lepton itself) must be less than 30% of the muon pT[52]. Jets are reconstructed from three-dimensional clusters of energy in the calorimeter [92] using the anti-ktjet clustering algorithm [93] as implemented in the FastJet package [94], with a radius parameter R= 0.4. The reconstructed jets are then calibrated by the application of a jet energy scale derived from 13TeV data and simulation [95]. Only jet candidates with pT>20 GeV and |η|<2.8are considered.4 To reduce the effects of pile-up, for jets with |η| ≤ 2.5and pT<120 GeV a significant fraction of the tracks associated with each jet are required to have an origin compatible with the primary vertex, as defined by the jet vertex tagger (JVT) [96]. This requirement reduces the fraction of jets from pile-up to 1%, with an efficiency for pure hard-scatter jets of about 90%. Finally, in order to remove events impacted by detector noise and non-collision backgrounds, specific jet-quality requirements [97,98] are applied, designed to provide an efficiency of selecting jets from proton-proton collisions above 99.5% (99.9%) for pT>20 (100) GeV. The MV2C10 boosted decision tree algorithm [53] identifies jets containing b-hadrons (‘b-jets’) by using quantities such as the impact parameters of associated tracks, and wellreconstructed secondary vertices. A selection that provides 77% efficiency for tagging b-jets in simulated t¯ tevents is used. The corresponding rejection factors against jets originating from c-quarks, from τ-leptons, and from light quarks and gluons in the same sample at this working point are 4.9, 15 and 110, respectively. To avoid reconstruction ambiguities and double counting of analysis objects, an overlap removal procedure is applied to the baseline leptons and jets in the order which follows. First, the calo-tagged muons are removed if sharing the track with electrons and, next, all electrons sharing an ID track with a muon are removed. Jets which are not b-tagged (with the tagging parameters corresponding to an efficiency of 85%) and which lie within a cone of ∆R=q(∆y)2+ (∆φ)2= 0.2around an electron candidate are removed. All jets lying within ∆R= 0.2of an electron are removed if the electron has pT>100 GeV. Finally, any lepton candidate is removed in favour of a jet candidate if it lies a distance ∆R < min(0.4,0.04 + 10/pT(`)) from the jet, where pT(`)is the pTof the lepton. The missing transverse momentum (pmiss T), with magnitude Emiss T, is defined as the negative vector sum of the transverse momenta for all baseline electrons, photons, muons and jets. Low-momentum tracks from the primary vertex that are not associated with reconstructed analysis objects are also included in the calculation. The Emiss Tvalue is adjusted for the calibration of the selected physics objects [99]. Linked to the Emiss Tvalue is the ‘object-based Emiss Tsignificance’, called simply ‘Emiss Tsignificance’ in this paper. This quantity measures the significance of Emiss Tbased upon the transverse momentum resolution of all objects used in the calculation of the pmiss T. It is defined as Emiss Tsignificance =|pmiss T| qσ2 L(1 −ρ2 LT) 4Hadronic τ-lepton decay products are treated as jets. – 8 – JHEP04(2021)165 6.1 Estimation of the backgrounds in the two-body selection The main background sources for the two-body selection are tt and ttZwith invisible decay of the Zboson. These processes are normalised to data in dedicated CRs: CR2-body tt and CRtt Z. The tt normalisation factor is extracted from different-flavour dilepton events. In order to test the reliability of the tt background prediction, two validation regions VR2-body tt ,DF and VR2-body tt ,SF are defined. The ttZproduction events with invisible decay of the Zboson are expected to dominate the tail of the m`` T2 distribution in the SRs and are normalised in the dedicated control region CRtt Z. Given the difficulty in achieving sufficient purity for this SM process because of the high contamination from tt events, a strategy based on a three-lepton final state is adopted. Events are selected if characterised by three charged leptons including at least one pair of SFOS leptons having invariant mass consistent with that of the Zboson (|m`` −mZ|<20 GeV). If more than one pair is identified, the one with m`` closest to the Zboson mass is chosen. Events are further required to have a jet multiplicity, njets, greater than or equal to three with at least two b-tagged jets. These selections target ttZproduction with the Zboson decaying into two leptons and tt decaying in the semileptonic channel. In order to select ttZevents whose kinematics, regardless of subsequent tt and Zdecays, emulate the kinematics of this background in the SRs, the momenta of the two leptons of the SFOS pair (p(`Z 1),p(`Z 2)) are vectorially added to the pmiss T, effectively treating them like the neutrino pair from the Zboson decay. A variable called Emiss T,corr =  pmiss T+p(`Z 1) + p(`Z 2)T  is constructed. Events characterised by high m`` T2 in the SRs are emulated by requiring high Emiss T,corr values in CRtt Z. In order to check the ttZbackground estimation, the validation region VR2-body tt Zwas defined. For this region, events with four leptons are selected and required to have at least one pair of SFOS leptons compatible with the Zboson decay. A variant of the mT2 variable called m4` T2 is defined from the pmiss T,corr =pmiss T+p(`Z 1) + p(`Z 2)Tand the momenta of the remaining two leptons. The definition of the control and validation regions used in the two-body selection is summarised in table 6. The expected signal contamination in the CRs is generally below ∼1%. The signal contamination in the VRs is less than 15% (7%) for a DM signal model with scalar (pseudoscalar) mediator mass of 100 GeV and DM mass of 1GeV. Figure 2illustrates the modelling of the shape of two important variables after the background fit: (a) shows the ∆φboost distribution with the CR2-body tt selection, and (b) shows the m`` distribution of the SFOS leptons in the CRtt Zselection. Good agreement is found between the data and the background model for all of the selection variables. The results of the fit are reported in table 7for the two-body CRs and VRs. The normalisations for fitted backgrounds are found to be consistent with the theoretical predictions when uncertainties are considered: the normalisation factors obtained from the fit for tt and ttZare 0.88 ±0.08 and 1.07 ±0.14 respectively. Good agreement, within one standard deviation of the SM background prediction, is observed in the VRs (see figure 3). 6.2 Estimation of the backgrounds in the three-body selection The dominant SM backgrounds in the three-body signal regions are diboson, tt and ttZ production. Dedicated CRs were defined, labelled as CR3-body V V and CR3-body tt , which are – 15 – JHEP04(2021)165 CR2-body tt CRtt ZVR2-body tt ,DF VR2-body tt ,SF VR2-body tt Z Lepton multiplicity 2 3 24 Lepton flavour DF at least one SFOS pair DF SF at least one SFOS pair pT(`1)[GeV] >25 >25 >25 >25 pT(`2)[GeV] >20 >20 >20 >20 pT(`3)[GeV] — >20 —>20 pT(`4)[GeV] — — — >20 m`` >20 —>20 — |m`` −mZ|[GeV] — <20 for at least one SFOS pair — >20 <20 for the SFOS pair nb-jets ≥1≥2with njets ≥3≥1>0 ∆φboost [rad] ≥1.5—<1.5— Emiss Tsignificance >8—>12 — Emiss T,corr [GeV] — >140 — — m`` T2 [GeV] [100, 120] — [100, 110] — m4` T2 [GeV] — — — >110 Table 6. Two-body selection. Control and validation regions definition. The common selection defined in section 5also applies to all regions. CR2-body tt CRtt ZVR2-body tt ,DF VR2-body tt ,SF VR2-body tt Z Observed events 230 247 45 38 26 Total (post-fit) SM events 230 ±15 246 ±16 50 ±15 42 ±11 25.7±3.4 Post-fit, tt 196 ±17 —44 ±15 36 ±11 — Post-fit, ttZ0.49 ±0.23 170 ±22 1.7±0.6 1.9±0.6 14.0±2.1 Wt 31 ±7—2.7±1.2 2.6±1.2— Diboson 1.0±0.6 17 ±4 0.50 ±0.25 0.59 ±0.32 8.7±3.0 Others 1.1±0.5 44 ±12 1.0±0.6 0.8±0.5 3.01 ±0.87 Fake and non-prompt 0.0+0.5 −0.016 ±8 0.0+0.5 −0.00.0+0.5 −0.00.0+0.5 −0.0 Table 7. Two-body selection. Background fit results for CR2-body tt , CRtt Z, VR2-body tt ,DF , VR2-body tt ,SF and VR2-body tt Z. “Others” includes contributions from VVV,ttt,tttt,ttW,ttWW ,ttWZ,ttH, and tZ processes. Combined statistical and systematic uncertainties are given. Entries marked ‘–’ indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty. kinematically close to the SRs and which have good purity in diboson and tt events respectively. The orthogonality between CRs and SRs is mainly ensured by the inversion of the ∆φR βcut. The normalisation of the ttZbackground is extracted using the same control region CRtt Zdefined for the two-body selection in section 6.1. Dedicated validation regions were defined to test the modelling of these processes: VR3-body V V for the diboson background, and VR(1)3-body tt and VR(2)3-body tt for the validation of the tt background, where VR(1)3-body tt is characterised by a b-jet veto while at least one b-jet is required in VR(2)3-body tt . The definition of the control and validation regions is summarised in table 8. – 16 – JHEP04(2021)165 20 40 60 80 100 120 Events / 0.1 Data Standard Model tt Wt Ztt FNP Diboson Others ATLAS -1 = 13 TeV, 139 fbs 2-body selection t t 2-body CR 0 0.5 1 1.5 2 2.5 3 [rad] boost φ∆ 0 1 2 Data / SM (a) 0 50 100 150 200 250 Events / 5 GeV Data Standard Model Ztt FNP Diboson Others ATLAS -1 = 13 TeV, 139 fbs 2-body selection Zt t CR 40 60 80 100 120 140 [GeV] SFOS ll m 0 1 2 Data / SM (b) Figure 2. Two-body selection. Distributions of (a) ∆φboost in CR2-body tt and (b) m`` of the two same-flavour and opposite-charge leptons candidate in CRtt Z, each after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes the contributions from V V V ,ttt,tttt,ttW,ttWW,ttWZ,ttH, and tZ. The hatched bands represent the total statistical and detector-related systematic uncertainty. The rightmost bin of (b) includes overflow events. In the upper panels, red arrows indicate the control region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. 50 100 150 200 250 300 Events Data Standard Model tt Wt Ztt FNP +jetsγZ/ Diboson Others ATLAS -1 = 13 TeV, 139 fbs 2-body selection ,SFt t 2-body VR 0 2 4 6 8 10 12 14 16 18 20 significance miss T E 0 1 2 Data / SM (a) 1 10 2 10 3 10 4 10 Events / 10 GeV Data Standard Model Ztt FNP Diboson Others ATLAS -1 = 13 TeV, 139 fbs 2-body selection Zt t 2-body VR 0 20 40 60 80 100 120 140 160 180 200 [GeV] 4l T2 m 0 1 2 Data / SM (b) Figure 3. Two-body selection. Distributions of the Emiss Tsignificance in (a) VR2-body tt ,SF and (b) m4` T2 in VR2-body tt Z, each after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from V V V ,ttt,tttt,ttW,ttWW ,ttWZ,ttH, and tZ processes. The hatched bands represent the total statistical and detector-related systematic uncertainty. The rightmost bin of each plot includes overflow events. In the upper panels, red arrows indicate the validation region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. – 17 – JHEP04(2021)165 CR3-body tt CR3-body V V VR(1)3-body tt VR(2)3-body tt VR3-body V V Lepton flavour DF DF+SF DF DF DF+SF pT(`1)[GeV] >25 >25 >25 >25 >25 pT(`2)[GeV] >20 >20 >20 >20 >20 m`` [GeV] >20 >20 >20 >20 >20 |m`` −mZ|[GeV] — >20 (SF only) — — >20 (SF only) nb-jets ≥2 = 0 = 0 ≥1 = 0 MR ∆[GeV] >80 >100 [80,105] [80,120] >100 RpT—>0.3>0.7>0.7>0.7 1/γR+1 > 0.7 >0.7>0.7>0.7 [0.45,0.7] Emiss Tsignificance >10 >10 >12 >12 >12 ∆φR β[rad] <2.3<2.3>2.3>2.3>2.3 Table 8. Three-body selection. Control and validation regions definitions. The common selection defined in section 5also applies to all regions. A further control region CRtt Zwas defined previously in table 7. CR3-body tt CR3-body V V CRtt ZVR(1)3-body tt VR(2)3-body tt VR3-body V V Observed events 192 169 247 41 137 84 Total (post-fit) SM events 192 ±14 169 ±13 247 ±16 38.3±5.9 142 ±25 97 ±15 Post-fit, tt 180 ±14 65 ±14 −25 ±5 130 ±24 44 ±11 Post-fit, ttZ1.57 ±0.33 1.36 ±0.31 172 ±23 0.07+0.12 −0.07 1.6±0.7 1.0±0.4 Post-fit, diboson 0.063 ±0.035 74 ±21 16 ±7 11 ±4 0.9±0.5 41 ±14 Wt 9.0±1.4 7.6±2.3−1.9±0.6 8.1±2.0 8.1±1.1 Z/γ∗+jets −13 ±5− − − 0.04+0.05 −0.04 Others 1.39 ±0.21 3.57 ±0.24 43 ±12 0.27 ±0.06 1.11 ±0.18 1.15 ±0.11 Fake and non-prompt 0.00+0.22 −0.00 5.0±1.9 16 ±8 0.00+0.27 −0.00 0.00+0.27 −0.00 1.8±1.5 Table 9. Three-body selection. Background fit results for CR3-body V V , CR3-body tt , CRttZ, VR3-body V V , VR(1)3-body tt and VR(2)3-body tt . “Others” includes contributions from VVV,ttt,tttt,ttW,ttWW, ttWZ,ttH, and tZ processes. Combined statistical and systematic uncertainties are given. Entries marked ‘–’ indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty. The expected signal contamination is below 2% in the CRs and reaches a maximum of 10% in the VRs for a top squark mass of ∼430 GeV. Table 9shows the expected and observed numbers of events in each of the control and validation regions after the background fit. The normalisation factors extracted from the fit of the backgrounds for the diboson, tt and ttZproduction processes are 0.92 ±0.28, 0.96±0.09 and 1.06±0.15 respectively. The total number of fitted background events in the validation regions is in agreement with the observed number of data events. Figure 4shows the distributions of ∆φR βfor the CR3-body V V and CR3-body tt selections after the background fit, – 18 – JHEP04(2021)165 0 5 10 15 20 25 30 35 40 Events / 0.1 rad Data Standard Model Diboson tt Wt Ztt FNP +jetsγZ/ Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection VV 3-body CR 0 0.5 1 1.5 2 2.5 3 [rad] R β φ∆ 0 1 2 Data / SM (a) 1− 10 1 10 2 10 3 10 4 10 Events / 0.1 rad Data Standard Model tt Wt Ztt FNP Diboson Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection t t 3-body CR 0 0.5 1 1.5 2 2.5 3 [rad] R β φ∆ 0 1 2 Data / SM (b) Figure 4. Three-body selection. Distributions of (a) ∆φR βin the CR3-body V V selection, and (b) in the CR3-body ttselection, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from V V V ,ttt,tttt,ttW,ttWW , ttWZ,ttH, and tZ processes. The hatched bands represent the total statistical and detectorrelated systematic uncertainty. In the upper panels, red arrows indicate the control region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. illustrating the MC modelling of the shape for this variable. Figure 5shows distributions of RpTin VR(1)3-body tt and VR(2)3-body tt , and of ∆φR βin VR3-body V V , after the background fit. Good agreement, within one standard deviation of the SM background prediction, is observed in the validation regions. 6.3 Estimation of the backgrounds in the four-body selection The dominant irreducible SM background sources for the four-body selection are tt and diboson: these backgrounds are normalised in two dedicated background-enriched control regions labelled as CR4-body tt and CR4-body V V . Some of the requirements defining the kinematics of the SRs are relaxed in order to allow the selection of tt events in CR4-body tt , while the R2`selection is adjusted to maintain complete orthogonality with the SRs. The diboson contribution in CR4-body V V is enhanced by limiting the number of jets in the event and the sub-leading jet pT, and by the additional veto on b-jets. The background predictions are tested in validation regions: VR4-body tt for tt validation and VR4-body V V and VR4-body V V,3`for diboson validation, with the latter two selecting, respectively, events with two and three leptons in the final state. For VR4-body V V,3`a new set of variables is defined in order to mimic the dibosons’ kinematics in the signal regions. The two SFOS leptons with an invariant mass closest to mZare considered as the two leptons coming from the decay of the Zboson. The momentum of the lepton (p(`Z paired)) of the selected pair having the same electric charge as the non-paired lepton is added to the pmiss Tin order to define Emiss T,1`,corr =  pmiss T+p(`Z paired)T   – 19 – JHEP04(2021)165 10 20 30 40 50 60 70 80 90 Events / 0.05 rad Data Standard Model tt Wt Ztt FNP Diboson Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection t t 3-body VR2 0.4 0.5 0.6 0.7 0.8 0.9 1 PT R 0 1 2 Data / SM (a) 1 10 2 10 Events / 0.1 rad Data Standard Model Diboson tt Wt Ztt FNP +jetsγZ/ Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection VV 3-body VR 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 [rad] R β φ∆ 0 1 2 Data / SM (b) Figure 5. Three-body selection. Distributions of (a) RpTin the validation region VR(2)3-body tt and (b) ∆φR βin the validation region VR3-body V V , after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from VVV,ttt,tttt, ttW,ttWW,ttWZ,ttH, and tZ processes. The hatched bands represent the total statistical and detector-related systematic uncertainty. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction. and R`,corr is defined as the ratio of Emiss T,1`,corr to the sum of the transverse momenta of two remaining OS leptons. The invariant mass of the remaining two leptons, called m``,corr, is also used. The definition of the control and validation regions used in the four-body selection is summarised in table 10. In the tt control region the signal contamination is ∼1% or less. In CR4-body V V , the typical signal contamination is about ∼1–2%, but reaches a maximum value of ∼5% for a top squark mass of ∼400 GeV and lightest-neutralino mass of ∼310 GeV at the boundary of the region excluded by the previous analysis. Signal contamination in the validation regions is below 10%. Table 11 shows the expected and observed numbers of events in each of the control and validation regions after the background fit. The normalisation factors extracted by the fit for the diboson and tt production processes are 1.00 ±0.25 and 0.90 ±0.12 respectively. The distributions of Emiss Tin CR4-body tt and R2`in CR4-body V V , after the background fit, are shown in figure 6. The distributions of pT(`2)in VR4-body tt ,njets in VR4-body V V and Emiss T,1`,corr in VR4-body V V,3`, after the background fit, are shown in figure 7. Good agreement between data and the SM predictions is observed. 7 Systematic uncertainties Systematic uncertainties are evaluated for the signal and for the background predictions. The main experimental uncertainties in the yields of the reconstructed objects, the theoretical uncertainties in the processes’ yields, and the uncertainties related to the MC modelling – 20 – JHEP04(2021)165 CR4-body tt CR4-body V V VR4-body tt VR4-body V V VR4-body V V,3` Lepton multiplicity 2 2 2 2 3 Lepton flavour DF+SF DF+SF DF+SF DF+SF at least one SFOS pair pT(`1)[GeV] <100 <100 <100 <100 <100 pT(`2)[GeV] <50 <50 <50 <50 <100 pT(`3)[GeV] — — — — <100 m`` [GeV] >10 >45 >10 >45 >10 |m`` −mZ|[GeV] — >10 for SF only — >10 for SF only — Emiss T[GeV] >350 >250 >250 >250 >250 pT(j1)[GeV] >150 >150 >150 >150 >150 min ∆R`2,ji>1>1>1>1>1 njets —≤2—≤4<5 nb-jets ≥2 = 0 ≥1 = 0 = 0 b-tagged j1— — True — — pT(j2)[GeV] — <40 if j2exists — — — Emiss Tsignificance >10 >10 >10 >10 >5 p`` T,boost [GeV] >280 >280 >280 >280 — R2`<5<4>5 [4,5] — R2`4j— — [0.3,0.38] — — Emiss T,1`,corr [GeV] — — — — >300 R2`,corr — — — — >5 m``,corr [GeV] — — — — >10 Table 10. Four-body selection. Control and validation regions definition. The common selection defined in section 5also applies to all regions. CR4-body tt CR4-body V V VR4-body tt VR4-body V V VR4-body V V,3` Observed events 149 163 86 168 25 Total (post-fit) SM events 149 ±12 162 ±13 86 ±20 173 ±14 27 ±5 Post-fit, tt 115 ±13 39 ±13 41 ±19 57 ±14 — Post-fit, diboson 0.7±0.5 89 ±18 1.5±0.6 75 ±18 19 ±6 Wt 27 ±4 11.9±1.8 18 ±5 10.3±0.8— Z/γ∗+jets 0.18 ±0.07 2.1±1.1 2.1±0.5 0.81 ±0.35 — ttZ1.32 ±0.34 0.18 ±0.09 0.52 ±0.17 0.41 ±0.16 0.120 ±0.029 Others 2.41 ±0.17 0.30 ±0.26 1.34 ±0.20 1.2±0.2 0.095 ±0.028 Fake and non-prompt 2.3±2.1 20 ±4 20.7±3.4 28 ±5 7.9±1.1 Table 11. Four-body selection. Background fit results for CR4-body tt , CR4-body V V , VR4-body tt , VR4-body V V and VR4-body V V,3`. The ‘Others’ category contains the contributions from V V V ,ttt,tttt,ttW,ttW W , ttWZ,ttH, and tZ. Combined statistical and systematic uncertainties are given. Entries marked ‘–’ indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty. – 21 – JHEP04(2021)165 1 10 2 10 3 10 4 10 5 10 6 10 Events / 25 GeV ATLAS 4-body selection 4-body t t CR -1 =13 TeV, 139 fbs Data Standard Model FNP tt Diboson Wt *+jetsγZ/ Ztt Others 250 300 350 400 450 500 550 [GeV] miss t E 0 1 2 Data / SM (a) 20 40 60 80 100 120 Events / 0.5 ATLAS 4-body selection 4-body VV CR -1 =13 TeV, 139 fbs Data Standard Model FNP tt Diboson Wt *+jetsγZ/ Ztt Others 1 2 3 4 5 6 7 8 9 2l R 0 1 2 Data / SM (b) Figure 6. Four-body selection. Distributions of (a) Emiss Tin CR4-body tt and (b) R2`in CR4-body V V after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from V V V ,ttt,tttt,ttW,ttW W ,ttW Z,ttH, and tZ processes. The hatched bands represent the total statistical and detector-related systematic uncertainty. The rightmost bin of each plot includes overflow events. In the upper panels, red arrows indicate the control region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction. of the SM backgrounds are described in this section. The statistical uncertainties in the simulated event samples are also taken into account. The main sources of experimental uncertainty are related to the jet energy scale (JES) and the jet energy resolution (JER). The JES and JER uncertainties are derived as a function of the pTand ηof the jet, as well as of the pile-up conditions and the jetflavour composition of the selected jet sample [112]. Uncertainties associated with the modelling of the b-tagging efficiencies for b-jets, c-jets and light-flavour jets [113,114] are also considered. The systematic uncertainties related to the modelling of Emiss Tin the simulation are estimated by propagating the uncertainties in the energy and momentum scales of electrons, muons and jets, as well as the uncertainties in the resolution and scale of the soft term [115]. Other detector-related systematic uncertainties, including those arising from lepton reconstruction efficiency, energy scale, energy resolution and in the modelling of the trigger efficiency [45,51,52,116,117], or the ones due to the pile-up reweighting and JVT are found to have a small impact on the results. Systematic uncertainties in the theoretical modelling of the observed final states can be broadly divided into uncertainties in the description of the parton-level final states (uncertainties in the proton PDF, cross-section, and strong coupling constant) and further uncertainties arising from the parton showering and hadronisation processes that convert partons into the hadronic final states. The uncertainties in the modelling of the tt background are estimated by varying the renormalisation and factorisation scales, as well as the amount of initialand final-state radiation produced when generating the samples [118,119]. Com- – 22 – JHEP04(2021)165 20 40 60 80 100 120 Events / 9 GeV ATLAS 4-body selection 4-body t t VR -1 =13 TeV, 139 fbs Data Standard Model FNP tt Diboson Wt *+jetsγZ/ Ztt Others 10 20 30 40 50 60 ) [GeV] 2 (l t p 0 1 2 Data / SM (a) 20 40 60 80 100 120 Events ATLAS 4-body selection 4-body VV VR -1 =13 TeV, 139 fbs Data Standard Model FNP tt Diboson Wt *+jetsγZ/ Ztt Others jets N 0 1 2 Data / SM 0 1 2 3 4 5 6 7 8 (b) 5 10 15 20 25 30 Events / 50 GeV ATLAS 4-body selection 4-body VV,3l VR -1 =13 TeV, 139 fbs Data Standard Model FNP Diboson Ztt Others 250 300 350 400 450 500 [GeV] miss T,1l,corr E 0 1 2 Data / SM (c) Figure 7. Four-body selection. Distributions of (a) pT(`2)in VR4-body tt , (b) njets in VR4-body V V and (c) Emiss T,1`,corr in VR4-body V V,3`after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from VVV,ttt,tttt,ttW,ttW W , ttWZ,ttH, and tZ processes. The hatched bands represent the total statistical and detector-related systematic uncertainty. The rightmost bin of each plot includes overflow events. In the upper panels, red arrows indicate the validation region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction. parison between the yields obtained with Powheg and MadGraph5_aMC@NLO [118] is used to estimate uncertainties from the event generator choice. For ttZproduction, in the two-body and three-body selections, the effects of QCD scale uncertainties are evaluated using seven-point variations of the factorisation and renormalisation scales [120]. Uncertainties for additional radiation contributions (ISR, FSR) are evaluated by comparing the nominal sample with one obtained with a Pythia tune enhancing the radiation [55]. In the four-body selection, since the ttZbackground contribution is minor, a total theoretical error of 14%, coming from the cross-section uncertainty [121], is applied instead. For tt and ttZproduction, the parton showering and hadronisation uncertainties are covered by the – 23 – JHEP04(2021)165 difference between samples obtained using the two different showering models implemented in Pythia and in Herwig. Single top quark production via the Wt-channel is a minor background in all the selections. An uncertainty in the acceptance due to the interference between tt and Wt production is assigned by comparing dedicated samples produced with Powheg and Pythia using the diagram removal (DR) and the diagram subtraction (DS) approaches [122]. The modelling uncertainties for the diboson background are estimated using the seven-point variations of the renormalisation and factorisation scales. Additional uncertainties in the resummation (QSF) and matching (CKKM) scales between the matrix element generator and parton shower are computed by varying the scale parameters in Sherpa [90]. For the other background processes which make minor contributions a conservative uncertainty is applied. These minor backgrounds are mainly ttWZ and ttW processes. A 30% uncertainty, driven by the DR versus DS difference for the ttWZ [123] process, is applied in the two-body and three-body selections. For the four-body selection a 22% uncertainty is applied for the uncertainty in the ttWcross-section [121]. For all the processes mentioned above the PDF uncertainties [65] were evaluated and found to be negligible. Systematic uncertainties in the data-driven FNP background estimate are expected due to potential differences in the FNP composition (heavy flavour, light flavour or photon conversions) between the regions defined in section 6and the CRFNP used to extract the fake factor. A FNP systematic error is evaluated in each of the regions by varying the FNP composition in the CRFNP to match that of the considered analysis region. The statistical error is also included by propagating the statistical uncertainty in the ratio used to compute the fake factor. For the four-body selection, where the FNP lepton background is dominant, a FNP closure uncertainty is also evaluated from the full difference between the data and the FNP predictions as observed in a validation region with two same-charge leptons with kinematics similar to the four-body selection. The closure uncertainty ranges between 13% and 33% in the regions where the FNP background is important. A 1.7% uncertainty in the luminosity measurement is considered for all signal and background estimates that are derived directly from MC simulations [46]. Tables 12,13 and 14 summarise the contributions from the different sources of systematic uncertainty in the total SM background predictions for the two-body, three-body and four-body signal regions. The total systematic uncertainty ranges between 14% and 26%, with the dominant sources being the MC statistical error, the JES and JER, the uncertainty in the background normalisation and the theoretical uncertainties. The SUSY signal cross-section uncertainty is evaluated from an envelope of the crosssection predictions using different PDF sets and factorisation and renormalisation scales as described in ref. [124]. The uncertainty in the DM production cross-section is derived from the scale variations and the PDF choices. The SUSY and DM theory signal uncertainties are computed from the variation of the radiation, renormalisation, factorisation and merging scales. These uncertainties are most relevant for the four-body selection, where the largest theory uncertainties are those resulting from radiation and are in the range 10% to 24% depending on the mass difference m(˜ t1)−m(˜χ0 1). For the DM signals the total systematic uncertainty is between 5% and 20%. – 24 – JHEP04(2021)165 Selection Signal Region σvis [fb] S95 obs S95 exp p(s= 0) Two-body SR2-body [110,∞)0.21 29.3 31+11 −80.5 SR2-body [120,∞)0.15 21.4 21+8 −60.4 SR2-body [140,∞)0.10 13.2 14+5 −40.5 SR2-body [160,∞)0.06 8.2 11+5 −3.00.5 SR2-body [180,∞)0.06 7.9 9.6+3.8 −2.80.5 SR2-body [200,∞)0.06 7.6 8.4+3.6 −2.30.5 SR2-body [220,∞)0.05 7.6 7.5+3.1 −2.00.5 Three-body SR-DF3-body W0.023 3.2 5.7+2.3 −1.50.5 SR-SF3-body W0.05 7.0 5.6+2.3 −1.50.27 SR-DF3-body t0.04 5.5 6.9+2.9 −1.90.5 SR-SF3-body t0.04 6.3 6.1+2.6 −1.60.5 Four-body SR4-body Small ∆m0.06 8.2 9.6+3.8 −2.50.5 SR4-body Large ∆m0.08 11.1 11.1+4.5 −3.00.5 Table 19. Model-independent 95% CL upper limits on the visible cross-section (σvis) of new physics, on the visible number of signal events (S95 obs), on the visible number of signal events (S95 exp) given the expected number of background events (and ±1σexcursions of the expected number), and the discovery p-value (p(s= 0)), all calculated with pseudo-experiments, are shown for each of the SRs. The p-value is reported as 0.5 if the observed yield is smaller than that predicted. its associated uncertainties in the CRs and SRs. All limits are quoted at 95% CL with the CLsmethod. When setting limits, the two-body selection binned SRs SR-DF2-body [x,y) and SR-SF2-body [x,y)regions are combined. Similarly, the SR-DF3-body W, SR-SF3-body W, SRDF3-body t, and SR-SF3-body tsignal regions are combined for the three-body selection, and so are SR4-body Small ∆mand SR4-body Large ∆mfor the four-body selection. Limits for simplified models in which pair-produced ˜ t1decay with 100% branching ratio into a top quark and ˜χ0 1are shown in the ˜ t1–˜χ0 1mass plane in figure 14a and in the m(˜ t1)–∆m(˜ t1,˜χ0 1)plane in figure 14b. The exclusion contour is the envelope of the exclusion regions obtained separately for the three selections. Top squark masses up to 1TeV are excluded for a massless lightest neutralino. Neutralino masses up to 500 GeV are excluded for m(˜ t1)above the top quark production kinematic limit. In the three-body decay region, top squark masses are excluded up to 600 GeV for ∆m(˜ t1,˜χ0 1) = 120 GeV, up to 550 GeV for ∆m(˜ t1,˜χ0 1)close to the top quark mass and up to 430 GeV for ∆m(˜ t1,˜χ0 1) close to the Wboson mass. In the four-body decay region, top squark masses are excluded up to 540GeV for ∆m(˜ t1,˜χ0 1) = 40 GeV. Top squark decay around the Wboson production kinematic limit is not fully excluded for m(˜ t1)above 400 GeV because there the four-body and three-body decay exclusion regions do not overlap. The four-body selection loses sensitivity for ∆m(˜ t, ˜χ0 1)&m(W)due to the upper bound of the sub-leading lepton pT while, for the three body selection, the MR ∆requirement suppresses the sensitivity for – 31 – JHEP04(2021)165 1 10 2 10 Events / Bin Data Standard Model tt Wt Ztt FNP Diboson Others )= (150,1) GeVχ,φ: m(φ+tt )= (150,1) GeVχ+a: m(a,tt ATLAS -1 = 13 TeV, 139 fbs 2-body selection DF 2-body SR 100 120 140 160 180 200 220 240 260 280 [GeV] T2 m 0 1 2 Data / SM (a) 1 10 2 10 Events / Bin Data Standard Model tt Wt Ztt FNP +jetsγZ/ Diboson Others )= (150,1) GeVχ,φ: m(φ+tt )= (150,1) GeVχ+a: m(a,tt ATLAS -1 = 13 TeV, 139 fbs 2-body selection SF 2-body SR 100 120 140 160 180 200 220 240 260 280 [GeV] T2 m 0 1 2 Data / SM (b) Figure 11. Two-body selection. Distributions of m`` T2 in SR2-body 110,∞for (a) different-flavour and (b) same-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from VVV,ttt,tttt,ttW,ttW W ,ttW Z, ttH, and tZ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference dark-matter signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction. ∆m(˜ t, ˜χ0 1).m(W)because of the smaller mass splitting. The three-body and two-body overlap in the sensitivity provides exclusion coverage around the top quark production kinematic limit up to m(˜ t1)of 540GeV. For the DM mediator models, figure 15 shows upper limits at 95% CL on the observed signal cross-section scaled to the theoretical signal cross-section for a coupling g=gq= gχ= 1, denoted by σobs/σTh(g= 1.0). These limits are obtained as a function of the mediator mass, assuming a specific DM particle mass of 1GeV. Both the scalar and pseudoscalar mediator cases are considered. The sensitivity is approximately constant for mediator masses below 100 GeV and the models are excluded for scalar (pseudoscalar) mediator masses up to 250 (300) GeV when assuming g= 1. 10 Conclusion This paper reports the results of a search for direct top squark pair production and for dark matter in a final state containing two leptons with opposite electric charge, jets and missing transverse momentum. The search uses an integrated luminosity of 139 fb−1of proton-proton collisions at √s= 13 TeV, as collected by the ATLAS experiment at the Large Hadron Collider during Run 2 (2015–2018). Compared to previous searches a significant improvement in sensitivity is obtained by using additional integrated luminosity and a new discriminating variable, the object- – 32 – JHEP04(2021)165 1− 10 1 10 2 10 3 10 4 10 Events / 5 GeV ) = (475,385) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ ) = (475,310) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ Data Standard Model Diboson tt Wt Ztt FNP +jetsγZ/ Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection 3-body W SR-DF 80 90 100 110 120 130 140 [GeV] R ∆ M 0 1 2 Data / SM (a) 1− 10 1 10 2 10 3 10 4 10 Events / 5 GeV ) = (475,385) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ ) = (475,310) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ Data Standard Model Diboson tt Wt Ztt FNP +jetsγZ/ Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection 3-body W SR-SF 80 90 100 110 120 130 140 [GeV] R ∆ M 0 1 2 Data / SM (b) 1 10 2 10 3 10 4 10 Events / 5 GeV ) = (475,385) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ ) = (475,310) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ Data Standard Model tt Wt Ztt FNP Diboson Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection 3-body t SR-DF 80 90 100 110 120 130 140 [GeV] R ∆ M 0 1 2 Data / SM (c) 1 10 2 10 3 10 4 10 Events / 5 GeV ) = (475,385) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ ) = (475,310) GeV 1 0 χ ∼ , 1 t ~ , m( 1 t ~ 1 t ~ Data Standard Model tt Wt Ztt FNP +jetsγZ/ Diboson Others ATLAS -1 = 13 TeV, 139 fbs 3-body selection 3-body t SR-SF 80 90 100 110 120 130 140 [GeV] R ∆ M 0 1 2 Data / SM (d) Figure 12. Three-body selection. Distributions of MR ∆in (a, b) SR3-body Wand (c, d) SR3-body tfor (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from V V V ,ttt, tttt,ttW,ttW W ,ttWZ,ttH, and tZ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. based Emiss Tsignificance. Moreover, in the small-∆m(˜ t1,˜χ0 1)region, an important gain in sensitivity is also achieved by lowering the pTthreshold for lepton selection. The data are found to be consistent with the Standard Model predictions. Assuming direct ˜ t1pair production with both top squarks decaying in either the two-body channel ˜ t1→t˜χ0 1, the three-body channel ˜ t1→bW ˜χ0 1, or the four-body channel ˜ t1→b`ν ˜χ0 1, constraints at 95% confidence level are placed on the minimum ˜ t1and ˜χ0 1masses up to – 33 – JHEP04(2021)165 1− 10 1 10 2 10 3 10 4 10 Events / 150 GeV ATLAS 4-body selection m∆Small 4-body SR -1 =13 TeV, 139 fbs Data Standard Model FNP tt Diboson Wt *+jetsγZ/ Ztt Others )=(400,380) GeV 1 0 χ ∼ , 1 t ~ ,m( 1 t ~ 1 t ~ )=(460,415) GeV 1 0 χ ∼ , 1 t ~ ,m( 1 t ~ 1 t ~ 300 400 500 600 700 800 900 1000 [GeV] miss T E 0 1 2 Data / SM (a) 2 4 6 8 10 12 14 16 18 Events / 0.04 ATLAS 4-body selection m∆Large 4-body SR -1 =13 TeV, 139 fbs Data Standard Model FNP tt Diboson Wt *+jetsγZ/ Ztt Others )=(400,380) GeV 1 0 χ ∼ , 1 t ~ ,m( 1 t ~ 1 t ~ )=(460,415) GeV 1 0 χ ∼ , 1 t ~ ,m( 1 t ~ 1 t ~ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 2l4j R 0 1 2 Data / SM (b) Figure 13. Four-body selection. (a) distributions of Emiss Tin SR4-body Small ∆mand (b) distribution of R2`4jin SR4-body Large ∆mfor events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributions from V V V ,ttt,tttt,ttW,ttW W , ttWZ,ttH, and tZ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. about 1 TeV and 500 GeV respectively. The results improve on the previous ATLAS limits obtained in a two-lepton final state and provide unique sensitivity among the ATLAS searches in the mass region where the decay ˜ t1→t˜χ0 1becomes kinematically allowed. For the dark-matter model, assuming spin-0 mediator production in association with a pair of top quarks and decay with 100% branching ratio into a pair of dark-matter particles, scalar (pseudoscalar) mediator masses up to about 250 (300) GeV are excluded at 95% confidence level for mediator couplings gq=gχ= 1 to Standard Model and dark-matter particles. 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, Ja- – 34 – JHEP04(2021)165 300 400 500 600 700 800 900 1000 1100 ) [GeV] 1 t ~ m( 100 200 300 400 500 600 700 ) [GeV] 1 0 χ ∼ m( ) = 0 1 0 χ ∼ , 1 t ~ m( ∆ W + m b ) = m 1 0 χ ∼ , 1 t ~ m( ∆ t ) = m 1 0 χ ∼ , 1 t ~ m( ∆ ) exp σ1 ±Expected Limit ( ) SUSY theory σ1 ±Observed Limit ( , arXiv: 1710.11412 -1 ATLAS 36.1 fb 1 0 χ ∼ / bff' 1 0 χ ∼ / bW 1 0 χ ∼ t→ 1 t ~ production; 1 t ~ 1 t ~ ATLAS -1 =13 TeV, 139 fbs All limits at 95% CL (a) 300 400 500 600 700 800 900 1000 1100 ) [GeV] 1 t ~ m( 0 50 100 150 200 250 ) [GeV] 1 0 χ ∼ , 1 t ~ m(∆ W + m b ) = m 1 0 χ ∼ , 1 t ~ m(∆ t ) = m 1 0 χ ∼ , 1 t ~ m(∆ ) exp σ1 ±Expected Limit ( ) SUSY theory σ1 ±Observed Limit ( , arXiv: 1710.11412 -1 ATLAS 36.1 fb 1 0 χ ∼ / bff' 1 0 χ ∼ / bW 1 0 χ ∼ t→ 1 t ~ production; 1 t ~ 1 t ~ ATLAS -1 =13 TeV, 139 fbs All limits at 95% CL (b) Figure 14. Exclusion limit contour (95% CL) for a simplified model assuming ˜ t1pair production, decaying via ˜ t1→t(∗)˜χ0 1with 100% branching ratio, in the (a) m(˜ t1)–m(˜χ0 1)and (b) m(˜ t1)– ∆m(˜ t1,˜χ0 1)planes. The dashed lines and the shaded bands are the expected limits and their ±1σ uncertainties. The thick solid lines are the observed limits for the central value of the signal crosssection. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by ±1σof the theoretical uncertainty. – 35 – JHEP04(2021)165 10 20 30 40 50 100 200 300 ) [GeV]φm( 1− 10 1 10 2 10 (g=1) Th σ/ obs σ95% CL limit on ATLAS -1 = 13 TeV , 139 fbs 2-body selection Scalar χχ → φ, φ + t t ) = 1 GeVχ= 1.0, m( χ = g q g= g Observed 95% CL Expected 95% CL σ1 ±Expected σ2 ±Expected (g=1.0)σTheory unc. on (a) 10 20 30 40 50 100 200 300 m(a) [GeV] 1− 10 1 10 2 10 (g=1) Th σ/ obs σ95% CL limit on ATLAS -1 = 13 TeV , 139 fbs 2-body selection Pseudoscalar χχ → + a, a t t ) = 1 GeVχ= 1.0, m( χ = g q g= g Observed 95% CL Expected 95% CL σ1 ±Expected σ2 ±Expected (g=1.0)σTheory unc. on (b) Figure 15. Exclusion limits for (a) tt +φscalar and (b) tt +apseudoscalar models as a function of the mediator mass for a DM particle mass of m(χ)=1GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of g=gq=gχ= 1. The solid (dashed) lines shows the observed (expected) exclusion limits. pan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Federation; 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, U.K.; DOE and NSF, U.S.A.. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada, CRC and IVADO, Canada; Beijing Municipal Science & Technology Commission, China; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, U.K. . The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [127]. Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. – 36 – JHEP04(2021)165 References [1] F. Zwicky, Die Rotverschiebung von extragalaktischen Nebeln,Helv. Phys. Acta 6(1933) 110, republished in [Gen. Rel. Grav. 41 (2009) 207] [INSPIRE]. [2] G. Bertone, D. Hooper and J. Silk, Particle dark matter: Evidence, candidates and constraints,Phys. 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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, E. Kasimi162, A. Kastanas45a,45b, C. Kato60d, J. Katzy46, K. Kawade150, K. Kawagoe88, T. Kawaguchi117, T. Kawamoto144, G. Kawamura53, E.F. Kay176, F.I. Kaya170, S. Kazakos14, V.F. Kazanin122b,122a, J.M. Keaveney33a, R. Keeler176, 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, F. Khalil-Zada13, M. Khandoga144, A. Khanov129, A.G. Kharlamov122b,122a, T. Kharlamova122b,122a, E.E. Khoda175, T.J. Khoo77, G. Khoriauli177, E. Khramov80, J. Khubua159b, S. Kido83, M. Kiehn36, E. Kim165, Y.K. Kim37, N. Kimura95, 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. Klimek36, A. Klimentov29, F. Klimpel36, T. Klingl24, T. Klioutchnikova36, F.F. Klitzner114, P. Kluit120, S. Kluth115, E. Kneringer77, E.B.F.G. Knoops102, A. Knue52, D. Kobayashi88, 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, – 49 – JHEP04(2021)165 I. Korolkov14, E.V. Korolkova149, N. Korotkova113, O. Kortner115, S. Kortner115, V.V. Kostyukhin149,166, A. Kotsokechagia65, A. Kotwal49, A. Koulouris10, A. Kourkoumeli-Charalampidi71a,71b, C. Kourkoumelis9, E. Kourlitis6, 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, K. Kreul19, P. Krieger167, F. Krieter114, S. Krishnamurthy103, A. Krishnan61b, M. Krivos142, 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, A. Kubota165, O. Kuchinskaia166, S. Kuday4b, D. Kuechler46, J.T. Kuechler46, S. Kuehn36, T. Kuhl46, V. Kukhtin80, Y. Kulchitsky108,ae, S. Kuleshov146b, Y.P. Kulinich173, M. Kuna58, 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, C. Lacasta174, F. Lacava73a,73b, D.P.J. Lack101, H. Lacker19, D. Lacour135, E. Ladygin80, R. Lafaye5, B. Laforge135, T. Lagouri146c, S. Lai53, I.K. Lakomiec84a, J.E. Lambert128, S. Lammers66, W. Lampl7, C. Lampoudis162, E. Lançon29, U. Landgraf52, M.P.J. Landon93, V.S. Lang52, J.C. Lange53, R.J. Langenberg103, A.J. Lankford171, F. Lanni29, K. Lantzsch24, A. Lanza71a, A. Lapertosa55b,55a, J.F. Laporte144, T. Lari69a, F. Lasagni Manghi23b,23a, M. Lassnig36, V. Latonova140, T.S. Lau63a, A. Laudrain100, A. Laurier34, M. Lavorgna70a,70b, S.D. Lawlor94, M. Lazzaroni69a,69b, B. Le101, E. Le Guirriec102, 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. Leggett18, K. Lehmann152, N. Lehmann20, G. Lehmann Miotto36, W.A. Leight46, A. Leisos162,v, M.A.L. Leite81c, C.E. Leitgeb114, R. Leitner142, K.J.C. Leney42, T. Lenz24, S. Leone72a, C. Leonidopoulos50, A. Leopold135, C. Leroy110, R. Les107, C.G. Lester32, M. Levchenko137, J. Levêque5, D. Levin106, L.J. Levinson180, D.J. Lewis21, B. Li15b, B. Li106, C-Q. Li60c,60d, F. Li60c, H. Li60a, H. Li60b, J. Li60c, K. Li148, L. Li60c, M. Li15a,15d, Q.Y. Li60a, S. Li60d,60c,b, X. Li46, Y. Li46, Z. Li60b, Z. Li134, Z. Li104, Z. Li91, Z. Liang15a, M. Liberatore46, B. Liberti74a, 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,aj, A. Lister175, J.D. Little8, B. Liu79, B.X. Liu152, H.B. Liu29, J.B. Liu60a, J.K.K. Liu37, K. Liu60d,60c, M. Liu60a, M.Y. Liu60a, P. Liu15a, X. Liu60a, Y. Liu46, Y. Liu15a,15d, Y.L. Liu106, Y.W. Liu60a, M. Livan71a,71b, A. Lleres58, J. Llorente Merino152, S.L. Lloyd93, C.Y. Lo63b, E.M. Lobodzinska46, P. Loch7, S. Loffredo74a,74b, T. Lohse19, K. Lohwasser149, M. Lokajicek140, J.D. Long173, R.E. Long90, I. Longarini73a,73b, L. Longo36, I. Lopez Paz101, A. Lopez Solis149, J. Lorenz114, N. Lorenzo Martinez5, A.M. Lory114, A. Lösle52, X. Lou45a,45b, X. Lou15a, A. Lounis65, J. Love6, P.A. Love90, J.J. Lozano Bahilo174, M. Lu60a, Y.J. Lu64, H.J. Lubatti148, C. Luci73a,73b, F.L. Lucio Alves15c, A. Lucotte58, F. Luehring66, I. Luise155, L. Luminari73a, B. Lund-Jensen154, N.A. Luongo131, 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, C.M. Macdonald149, J.C. MacDonald149, J. Machado Miguens136, R. Madar38, W.F. Mader48, M. Madugoda Ralalage Don129, N. Madysa48, J. Maeda83, T. Maeno29, M. Maerker48, V. Magerl52, N. Magini79, J. Magro67a,67c,r, D.J. Mahon39, C. Maidantchik81b, A. Maio139a,139b,139d, K. Maj84a, O. Majersky28a, S. Majewski131, Y. Makida82, N. Makovec65, B. Malaescu135, Pa. Malecki85, V.P. Maleev137, F. Malek58, D. Malito41b,41a, U. Mallik78, C. Malone32, S. Maltezos10, S. Malyukov80, J. Mamuzic174, G. Mancini51, J.P. Mandalia93, 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, – 50 – JHEP04(2021)165 G. Marchiori135, M. Marcisovsky140, L. Marcoccia74a,74b, C. Marcon97, M. Marjanovic128, Z. Marshall18, M.U.F. Martensson172, S. Marti-Garcia174, C.B. Martin127, T.A. Martin178, V.J. Martin50, B. Martin dit Latour17, L. Martinelli75a,75b, M. Martinez14,w, P. Martinez Agullo174, 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, A.E. McDougall120, J.A. Mcfayden18, G. Mchedlidze159b, M.A. McKay42, K.D. McLean176, S.J. McMahon143, P.C. McNamara105, C.J. McNicol178, R.A. McPherson176,aa, 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, A.M. Mendes Jacques Da Costa21, H.Y. Meng167, 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, M. Michetti19, R.P. Middleton143, L. Mijović50, G. Mikenberg180, M. Mikestikova140, M. Mikuž92, H. Mildner149, A. Milic167, C.D. Milke42, D.W. Miller37, L.S. Miller34, A. Milov180, D.A. Milstead45a,45b, A.A. Minaenko123, I.A. Minashvili159b, L. Mince57, A.I. Mincer125, B. Mindur84a, M. Mineev80, Y. Minegishi163, Y. Mino86, L.M. Mir14, M. Mironova134, T. Mitani179, J. Mitrevski114, V.A. Mitsou174, M. Mittal60c, O. Miu167, A. Miucci20, P.S. Miyagawa93, A. Mizukami82, J.U. Mjörnmark97, T. Mkrtchyan61a, M. Mlynarikova121, T. Moa45a,45b, S. Mobius53, K. Mochizuki110, P. Moder46, P. Mogg114, S. Mohapatra39, R. Moles-Valls24, K. Mönig46, E. Monnier102, A. Montalbano152, J. Montejo Berlingen36, M. Montella95, F. Monticelli89, S. Monzani69a, N. Morange65, A.L. Moreira De Carvalho139a, 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. Morvaj36, P. Moschovakos36, B. Moser120, M. Mosidze159b, T. Moskalets144, P. Moskvitina119, J. Moss31,n, E.J.W. Moyse103, S. Muanza102, J. Mueller138, R.S.P. Mueller114, D. Muenstermann90, G.A. Mullier97, J.J. Mullin136, D.P. Mungo69a,69b, J.L. Munoz Martinez14, F.J. Munoz Sanchez101, P. Murin28b, W.J. Murray178,143, A. Murrone69a,69b, J.M. Muse128, M. Muškinja18, C. Mwewa33a, A.G. Myagkov123,af, A.A. Myers138, G. Myers66, 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, M. Naseri34, 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,f, M.S. Neubauer173, F. Neuhaus100, M. Neumann182, R. Newhouse175, P.R. Newman21, C.W. Ng138, Y.S. Ng19, Y.W.Y. Ng171, B. Ngair35f, H.D.N. Nguyen102, T. Nguyen Manh110, E. Nibigira38, R.B. Nickerson134, R. Nicolaidou144, D.S. Nielsen40, J. Nielsen145, M. Niemeyer53, N. Nikiforou11, V. Nikolaenko123,af, I. Nikolic-Audit135, K. Nikolopoulos21, P. Nilsson29, H.R. Nindhito54, A. Nisati73a, N. Nishu60c, R. Nisius115, I. Nitsche47, T. Nitta179, T. Nobe163, D.L. Noel32, Y. Noguchi86, I. Nomidis135, M.A. Nomura29, M. Nordberg36, J. Novak92, T. Novak92, O. Novgorodova48, R. Novotny118, L. Nozka130, K. Ntekas171, E. Nurse95, F.G. Oakham34,ak, J. Ocariz135, A. Ochi83, I. Ochoa139a, J.P. Ochoa-Ricoux146a, K. O’Connor26, S. Oda88, S. Odaka82, S. Oerdek53, A. Ogrodnik84a, A. Oh101, C.C. Ohm154, H. Oide165, – 51 – JHEP04(2021)165 R. Oishi163, M.L. Ojeda167, H. Okawa169, Y. Okazaki86, M.W. O’Keefe91, Y. Okumura163, A. Olariu27b, L.F. Oleiro Seabra139a, S.A. Olivares Pino146a, D. Oliveira Damazio29, J.L. Oliver1, M.J.R. Olsson171, A. Olszewski85, J. Olszowska85, Ö.O. Öncel24, 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. Owen143, 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, P. Palni84a, C.E. Pandini54, J.G. Panduro Vazquez94, P. Pani46, G. Panizzo67a,67c, L. Paolozzi54, C. Papadatos110, K. Papageorgiou9,h, 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. Pasquali120, E. Pasqualucci73a, S. Passaggio55b, F. Pastore94, P. Pasuwan45a,45b, S. Pataraia100, J.R. Pater101, A. Pathak181,j, J. Patton91, T. Pauly36, J. Pearkes153, M. Pedersen133, L. Pedraza Diaz119, R. Pedro139a, T. Peiffer53, S.V. Peleganchuk122b,122a, O. Penc140, C. Peng63b, H. Peng60a, B.S. Peralva81a, M.M. Perego65, A.P. Pereira Peixoto139a, L. Pereira Sanchez45a,45b, D.V. Perepelitsa29, E. Perez Codina168a, L. Perini69a,69b, H. Pernegger36, S. Perrella36, A. Perrevoort120, K. Peters46, R.F.Y. Peters101, B.A. Petersen36, T.C. Petersen40, E. Petit102, V. Petousis141, C. Petridou162, F. Petrucci75a,75b, M. Pettee183, N.E. Pettersson103, K. Petukhova142, A. Peyaud144, R. Pezoa146d, L. Pezzotti71a,71b, T. Pham105, P.W. Phillips143, M.W. Phipps173, G. Piacquadio155, E. Pianori18, A. Picazio103, 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, A. Pizzini120, M.-A. Pleier29, V. Plesanovs52, V. Pleskot142, E. Plotnikova80, P. Podberezko122b,122a, R. Poettgen97, R. Poggi54, L. Poggioli135, I. Pogrebnyak107, D. Pohl24, I. Pokharel53, G. Polesello71a, A. Poley152,168a, 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 Quintero58, S. Pospisil141, K. Potamianos46, I.N. Potrap80, C.J. Potter32, H. Potti11, T. Poulsen97, J. Poveda174, T.D. Powell149, G. Pownall46, M.E. Pozo Astigarraga36, A. Prades Ibanez174, P. Pralavorio102, M.M. Prapa44, 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, G. Rabanal Bolanos59, M. Racko28a, F. Ragusa69a,69b, G. Rahal98, J.A. Raine54, S. Rajagopalan29, A. Ramirez Morales93, K. Ran15a,15d, D.F. Rassloff61a, D.M. Rauch46, F. Rauscher114, S. Rave100, B. Ravina57, I. Ravinovich180, M. Raymond36, A.L. Read133, N.P. Readioff149, M. Reale68a,68b, D.M. Rebuzzi71a,71b, G. Redlinger29, K. Reeves43, D. Reikher161, A. Reiss100, A. Rej151, C. Rembser36, A. Renardi46, M. Renda27b, M.B. Rendel115, A.G. Rennie57, 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,aa, M. Robin46, D. Robinson32, C.M. Robles Gajardo146d, M. Robles Manzano100, A. Robson57, A. Rocchi74a,74b, C. Roda72a,72b, S. Rodriguez Bosca174, A. Rodriguez Rodriguez52, A.M. Rodríguez Vera168b, S. Roe36, J. Roggel182, O. Røhne133, R. Röhrig115, R.A. Rojas146d, B. Roland52, C.P.A. Roland66, J. Roloff29, A. Romaniouk112, M. Romano23b,23a, N. Rompotis91, – 52 – JHEP04(2021)165 M. Ronzani125, L. Roos135, S. Rosati73a, G. Rosin103, B.J. Rosser136, E. Rossi46, E. Rossi75a,75b, E. Rossi70a,70b, L.P. Rossi55b, L. Rossini46, R. Rosten14, M. Rotaru27b, B. Rottler52, D. Rousseau65, G. Rovelli71a,71b, A. Roy11, D. Roy33e, A. Rozanov102, Y. Rozen160, X. Ruan33e, T.A. Ruggeri1, F. Rühr52, A. Ruiz-Martinez174, A. Rummler36, Z. Rurikova52, N.A. Rusakovich80, H.L. Russell104, L. Rustige38,47, J.P. Rutherfoord7, E.M. Rüttinger149, M. Rybar142, G. Rybkin65, E.B. Rye133, A. Ryzhov123, J.A. Sabater Iglesias46, P. Sabatini174, L. Sabetta73a,73b, S. Sacerdoti65, H.F-W. Sadrozinski145, R. Sadykov80, F. Safai Tehrani73a, B. Safarzadeh Samani156, M. Safdari153, P. Saha121, S. Saha104, M. Sahinsoy115, A. Sahu182, M. Saimpert36, M. Saito163, T. Saito163, H. Sakamoto163, D. Salamani54, G. Salamanna75a,75b, A. Salnikov153, J. Salt174, A. Salvador Salas14, D. Salvatore41b,41a, F. Salvatore156, A. Salvucci63a, A. Salzburger36, J. Samarati36, D. Sammel52, D. Sampsonidis162, D. Sampsonidou60d,60c, J. Sánchez174, A. Sanchez Pineda67a,36,67c, H. Sandaker133, C.O. Sander46, I.G. Sanderswood90, M. Sandhoff182, C. Sandoval22b, D.P.C. Sankey143, M. Sannino55b,55a, Y. Sano117, A. Sansoni51, C. Santoni38, H. Santos139a,139b, S.N. Santpur18, A. Santra174, K.A. Saoucha149, A. Sapronov80, J.G. Saraiva139a,139d, O. Sasaki82, K. Sato169, F. Sauerburger52, E. Sauvan5, P. Savard167,ak, R. Sawada163, C. Sawyer143, L. Sawyer96, I. Sayago Galvan174, C. Sbarra23b, A. Sbrizzi67a,67c, T. Scanlon95, J. Schaarschmidt148, P. Schacht115, D. Schaefer37, L. Schaefer136, U. Schäfer100, A.C. Schaffer65, D. Schaile114, R.D. Schamberger155, E. Schanet114, C. Scharf19, N. Scharmberg101, V.A. Schegelsky137, D. Scheirich142, F. Schenck19, M. Schernau171, C. Schiavi55b,55a, L.K. Schildgen24, Z.M. Schillaci26, E.J. Schioppa68a,68b, M. Schioppa41b,41a, K.E. Schleicher52, S. Schlenker36, K.R. Schmidt-Sommerfeld115, K. Schmieden100, C. Schmitt100, S. Schmitt46, L. Schoeffel144, A. Schoening61b, P.G. Scholer52, E. Schopf134, M. Schott100, J.F.P. Schouwenberg119, J. Schovancova36, S. Schramm54, F. Schroeder182, A. Schulte100, H-C. Schultz-Coulon61a, M. Schumacher52, B.A. Schumm145, Ph. Schune144, A. Schwartzman153, T.A. Schwarz106, Ph. Schwemling144, R. Schwienhorst107, A. Sciandra145, G. Sciolla26, F. Scuri72a, F. Scutti105, L.M. Scyboz115, C.D. Sebastiani91, K. Sedlaczek47, P. Seema19, S.C. Seidel118, A. Seiden145, B.D. Seidlitz29, T. Seiss37, C. Seitz46, J.M. Seixas81b, G. Sekhniaidze70a, S.J. Sekula42, N. Semprini-Cesari23b,23a, S. Sen49, C. Serfon29, L. Serin65, L. Serkin67a,67b, M. Sessa60a, H. Severini128, S. Sevova153, F. Sforza55b,55a, A. Sfyrla54, E. Shabalina53, J.D. Shahinian136, N.W. Shaikh45a,45b, D. Shaked Renous180, L.Y. Shan15a, M. Shapiro18, A. Sharma36, A.S. Sharma1, P.B. Shatalov124, K. Shaw156, S.M. Shaw101, M. Shehade180, Y. Shen128, A.D. Sherman25, P. Sherwood95, L. Shi95, C.O. Shimmin183, Y. Shimogama179, M. Shimojima116, J.D. Shinner94, I.P.J. Shipsey134, S. Shirabe165, M. Shiyakova80,y, J. Shlomi180, A. Shmeleva111, M.J. Shochet37, J. Shojaii105, D.R. Shope154, S. Shrestha127, E.M. Shrif33e, M.J. Shroff176, E. Shulga180, P. Sicho140, A.M. Sickles173, E. Sideras Haddad33e, O. Sidiropoulou36, A. Sidoti23b,23a, F. Siegert48, Dj. Sijacki16, M.Jr. Silva181, M.V. Silva Oliveira36, S.B. Silverstein45a, S. Simion65, R. Simoniello100, C.J. Simpson-allsop21, S. Simsek12b, P. Sinervo167, V. Sinetckii113, S. Singh152, S. Sinha33e, M. Sioli23b,23a, I. Siral131, S.Yu. Sivoklokov113, J. Sjölin45a,45b, A. Skaf53, E. Skorda97, P. Skubic128, M. Slawinska85, K. Sliwa170, V. Smakhtin180, B.H. Smart143, J. Smiesko28b, N. Smirnov112, S.Yu. Smirnov112, Y. Smirnov112, L.N. Smirnova113,s, O. Smirnova97, E.A. Smith37, H.A. Smith134, M. Smizanska90, K. Smolek141, A. Smykiewicz85, A.A. Snesarev111, H.L. Snoek120, I.M. Snyder131, S. Snyder29, R. Sobie176,aa, A. Soffer161, A. Søgaard50, F. Sohns53, C.A. Solans Sanchez36, E.Yu. Soldatov112, U. Soldevila174, A.A. Solodkov123, A. Soloshenko80, O.V. Solovyanov123, V. Solovyev137, P. Sommer149, H. Son170, A. Sonay14, W. Song143, W.Y. Song168b, A. Sopczak141, A.L. Sopio95, F. Sopkova28b, S. Sottocornola71a,71b, R. Soualah67a,67c, A.M. Soukharev122b,122a, D. South46, S. Spagnolo68a,68b, M. Spalla115, M. Spangenberg178, F. Spanò94, D. Sperlich52, T.M. Spieker61a, G. Spigo36, M. Spina156, D.P. Spiteri57, M. Spousta142, A. Stabile69a,69b, B.L. Stamas121, – 53 – JHEP04(2021)165 R. Stamen61a, M. Stamenkovic120, A. Stampekis21, E. Stanecka85, B. Stanislaus134, M.M. Stanitzki46, M. Stankaityte134, B. Stapf120, E.A. Starchenko123, G.H. Stark145, J. Stark58, P. Staroba140, P. Starovoitov61a, S. Stärz104, R. Staszewski85, G. Stavropoulos44, M. Stegler46, P. Steinberg29, A.L. Steinhebel131, B. Stelzer152,168a, H.J. Stelzer138, O. Stelzer-Chilton168a, H. Stenzel56, T.J. Stevenson156, G.A. Stewart36, M.C. Stockton36, G. Stoicea27b, M. Stolarski139a, S. Stonjek115, A. Straessner48, J. Strandberg154, S. Strandberg45a,45b, M. Strauss128, T. Strebler102, P. Strizenec28b, R. Ströhmer177, D.M. Strom131, R. Stroynowski42, A. Strubig45a,45b, S.A. Stucci29, B. Stugu17, J. Stupak128, N.A. Styles46, D. Su153, W. Su60d,148,60c, X. Su60a, N.B. Suarez138, V.V. Sulin111, M.J. Sullivan91, D.M.S. Sultan54, S. Sultansoy4c, T. Sumida86, S. Sun106, X. Sun101, C.J.E. Suster157, M.R. Sutton156, S. Suzuki82, M. Svatos140, M. Swiatlowski168a, S.P. Swift2, T. Swirski177, A. Sydorenko100, I. Sykora28a, M. Sykora142, T. Sykora142, D. Ta100, K. Tackmann46,x, J. Taenzer161, A. Taffard171, R. Tafirout168a, E. Tagiev123, R.H.M. Taibah135, R. Takashima87, K. Takeda83, T. Takeshita150, E.P. Takeva50, Y. Takubo82, M. Talby102, A.A. Talyshev122b,122a, K.C. Tam63b, N.M. Tamir161, J. Tanaka163, R. Tanaka65, S. Tapia Araya173, S. Tapprogge100, A. Tarek Abouelfadl Mohamed107, S. Tarem160, K. Tariq60b, G. Tarna27b,e, G.F. Tartarelli69a, P. Tas142, M. Tasevsky140, E. Tassi41b,41a, G. Tateno163, A. Tavares Delgado139a, Y. Tayalati35f, A.J. Taylor50, G.N. Taylor105, W. Taylor168b, H. Teagle91, A.S. Tee90, R. Teixeira De Lima153, P. Teixeira-Dias94, H. Ten Kate36, J.J. Teoh120, K. Terashi163, J. Terron99, S. Terzo14, M. Testa51, R.J. Teuscher167,aa, N. Themistokleous50, T. Theveneaux-Pelzer19, D.W. Thomas94, J.P. Thomas21, E.A. Thompson46, P.D. Thompson21, E. Thomson136, E.J. Thorpe93, V.O. Tikhomirov111,ag, Yu.A. Tikhonov122b,122a, S. Timoshenko112, P. Tipton183, S. Tisserant102, K. Todome23b,23a, S. Todorova-Nova142, S. Todt48, J. Tojo88, S. Tokár28a, K. Tokushuku82, E. Tolley127, R. Tombs32, K.G. Tomiwa33e, M. Tomoto82,117, L. Tompkins153, P. Tornambe103, E. Torrence131, H. Torres48, E. Torró Pastor174, M. Toscani30, C. Tosciri134, J. Toth102,z, D.R. Tovey149, A. Traeet17, C.J. Treado125, T. Trefzger177, F. Tresoldi156, A. Tricoli29, I.M. Trigger168a, S. Trincaz-Duvoid135, D.A. Trischuk175, W. Trischuk167, B. Trocmé58, A. Trofymov65, C. Troncon69a, F. Trovato156, L. Truong33c, M. Trzebinski85, A. Trzupek85, F. Tsai46, P.V. Tsiareshka108,ae, A. Tsirigotis162,v, V. Tsiskaridze155, E.G. Tskhadadze159a, M. Tsopoulou162, I.I. Tsukerman124, V. Tsulaia18, S. Tsuno82, D. Tsybychev155, Y. Tu63b, A. Tudorache27b, V. Tudorache27b, A.N. Tuna36, S. Turchikhin80, D. Turgeman180, I. Turk Cakir4b,t, R.J. Turner21, R. Turra69a, P.M. Tuts39, S. Tzamarias162, E. Tzovara100, K. Uchida163, F. Ukegawa169, G. Unal36, M. Unal11, A. Undrus29, G. Unel171, F.C. Ungaro105, Y. Unno82, K. Uno163, J. Urban28b, P. Urquijo105, G. Usai8, Z. Uysal12d, V. Vacek141, B. Vachon104, K.O.H. Vadla133, T. Vafeiadis36, A. Vaidya95, C. Valderanis114, E. Valdes Santurio45a,45b, M. Valente168a, S. Valentinetti23b,23a, A. Valero174, L. Valéry46, R.A. Vallance21, A. Vallier36, J.A. Valls Ferrer174, T.R. Van Daalen14, P. Van Gemmeren6, S. Van Stroud95, I. Van Vulpen120, M. Vanadia74a,74b, W. Vandelli36, M. Vandenbroucke144, E.R. Vandewall129, D. Vannicola73a,73b, R. Vari73a, E.W. Varnes7, C. Varni55b,55a, T. Varol158, D. Varouchas65, K.E. Varvell157, M.E. Vasile27b, G.A. Vasquez176, F. Vazeille38, D. Vazquez Furelos14, T. Vazquez Schroeder36, J. Veatch53, V. Vecchio101, M.J. Veen120, L.M. Veloce167, F. Veloso139a,139c, S. Veneziano73a, A. Ventura68a,68b, A. Verbytskyi115, V. Vercesi71a, M. Verducci72a,72b, C.M. Vergel Infante79, C. Vergis24, W. Verkerke120, A.T. Vermeulen120, J.C. Vermeulen120, C. Vernieri153, P.J. Verschuuren94, M.C. Vetterli152,ak, N. Viaux Maira146d, T. Vickey149, O.E. Vickey Boeriu149, G.H.A. Viehhauser134, L. Vigani61b, M. Villa23b,23a, M. Villaplana Perez174, E.M. Villhauer50, E. Vilucchi51, M.G. Vincter34, G.S. Virdee21, A. Vishwakarma50, C. Vittori23b,23a, I. Vivarelli156, M. Vogel182, P. Vokac141, J. Von Ahnen46, S.E. von Buddenbrock33e, E. Von Toerne24, V. Vorobel142, K. Vorobev112, – 54 – JHEP04(2021)165 M. Vos174, J.H. Vossebeld91, M. Vozak101, N. Vranjes16, M. Vranjes Milosavljevic16, V. Vrba141,∗, M. Vreeswijk120, N.K. Vu102, R. Vuillermet36, I. Vukotic37, S. Wada169, P. Wagner24, W. Wagner182, J. Wagner-Kuhr114, S. Wahdan182, H. Wahlberg89, R. Wakasa169, V.M. Walbrecht115, J. Walder143, R. Walker114, S.D. Walker94, W. Walkowiak151, V. Wallangen45a,45b, A.M. Wang59, A.Z. Wang181, C. Wang60a, C. Wang60c, H. Wang18, H. Wang3, J. Wang63a, P. 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Zwalinski36 1Department of Physics, University of Adelaide, Adelaide, Australia 2Physics Department, SUNY Albany, Albany NY, U.S.A. 3Department of Physics, University of Alberta, Edmonton AB, Canada 4 (a)Department of Physics, Ankara University, Ankara; (b)Istanbul Aydin University, Application and Research Center for Advanced Studies, Istanbul; (c)Division of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL, U.S.A. 7Department of Physics, University of Arizona, Tucson AZ, U.S.A. 8Department of Physics, University of Texas at Arlington, Arlington TX, U.S.A. 9Physics Department, National and Kapodistrian University of Athens, Athens, Greece 10 Physics Department, National Technical University of Athens, Zografou, Greece – 55 – JHEP04(2021)165 11 Department of Physics, University of Texas at Austin, Austin TX, U.S.A. 12 (a)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; (b)Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul; (c)Department of Physics, Bogazici University, Istanbul; (d)Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; (b)Physics Department, Tsinghua University, Beijing; (c)Department of Physics, Nanjing University, Nanjing; (d)University of Chinese Academy of Science (UCAS), Beijing, China 16 Institute of Physics, University of Belgrade, Belgrade, Serbia 17 Department for Physics and Technology, University of Bergen, Bergen, Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA, U.S.A. 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, U.K. 22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá; (b)Departamento de Física, Universidad Nacional de Colombia, Bogotá, Colombia, Colombia 23 (a)INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica; (b)INFN Sezione di Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston MA, U.S.A. 26 Department of Physics, Brandeis University, Waltham MA, U.S.A. 27 (a)Transilvania University of Brasov, Brasov; (b)Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; (d)National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca; (e)University Politehnica Bucharest, Bucharest; (f)West University in Timisoara, Timisoara, Romania 28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; (b)Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY, U.S.A. 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31 California State University, CA, U.S.A. 32 Cavendish Laboratory, University of Cambridge, Cambridge, U.K. 33 (a)Department of Physics, University of Cape Town, Cape Town; (b)iThemba Labs, Western Cape; (c)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg; (d)University of South Africa, Department of Physics, Pretoria; (e)School of Physics, University of the Witwatersrand, Johannesburg, South Africa 34 Department of Physics, Carleton University, Ottawa ON, Canada 35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca; (b)Faculté des Sciences, Université Ibn-Tofail, Kénitra; (c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech; (d)Moroccan Foundation for Advanced Science Innovation and Research (MAScIR), Rabat; (e)LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda; (f)Faculté des sciences, Université Mohammed V, Rabat, Morocco 36 CERN, Geneva, Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL, U.S.A. 38 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France – 56 –