Search for new phenomena in events with two opposite-charge leptons, jets and missing transverse momentum in pp collisions at \sqrt{s} = 13 TeV with the ATLAS detector
EEUROPEAN ORGANISATION FOR NUCLEAR RESEARCH (CERN)
Submitted to: JHEP CERN-EP-2020-2433rd February 2021
Search for new phenomena in events with twoopposite-charge leptons, jets and missing transverse momentum in 𝒑 𝒑 collisions at √ 𝒔 =
13 TeV with the
ATLAS detector
The ATLAS Collaboration
The results of a search for direct pair production of top squarks and for dark matter in eventswith two opposite-charge leptons (electrons or muons), jets and missing transverse momentumare reported, using 139 fb − of integrated luminosity from proton–proton collisions at √ 𝑠 = ( ) GeV. © a r X i v : . [ h e p - e x ] F e b Introduction
The Standard Model (SM) of particle physics is extremely successful in describing the phenomena ofelementary particles and their interactions. Its predictive power has been proven with high precision by awide range of experiments. However, despite its success, several important questions remain unansweredwithin 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 isstrong evidence from a range of astrophysical observations. A weakly interacting dark-matter candidateparticle can be produced at the Large Hadron Collider (LHC) [3] in a variety of ways, as described, forexample, by supersymmetry (SUSY) [4–9] or DM models. At the LHC, one of the most promising modesis the production of DM particle pairs in association with on- or off-shell top quarks. Previous searchesfor DM candidates in association with a top quark pair have been performed by the ATLAS [10–16] andCMS [17–26] collaborations. However, those previous searches were statistically limited, or sensitiveonly up to limited particle masses. They also suffered from significant regions in which no limit couldbe 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 tocover the regions in which the previous ATLAS results had no sensitivity [27, 28]. It achieves this inpart by exploiting a larger dataset, corresponding to 139 fb − of proton–proton collision data collected bythe 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 𝐸 missT significance’ [29], lowering the lepton 𝑝 T thresholds, and optimising a dedicated selection to targetsignal 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 particlewhich couples both to the SM and to the dark sector [33–35]. The couplings of the mediator to the SMfermions are then severely restricted by precision flavour measurements. An ansatz that automaticallyrelaxes these constraints is Minimal Flavour Violation [36]. This assumption implies that the interactionbetween any new neutral spin-0 state and SM matter is proportional to the fermion masses via Yukawa-typecouplings. It follows that colour-neutral mediators would be produced mainly through loop-inducedgluon fusion or in association with heavy-flavour quarks. Here, the DM particles 𝜒 are assumed to be pairproduced through the exchange of a spin-0 mediator, which can be a colour-neutral scalar or pseudoscalarparticle (denoted by 𝜙 or 𝑎 , respectively), in association with a top quark pair: 𝑝 𝑝 → 𝜒 ¯ 𝜒𝑡 ¯ 𝑡 (Figure 1(a)).Alternatively, dark-matter particles are also predicted in supersymmetry, a space-time symmetry that foreach SM particle postulates the existence of a partner particle whose spin differs by one-half unit. Toavoid violation of baryon number ( 𝐵 ) and lepton number ( 𝐿 ) conservation, a multiplicative quantumnumber 𝑅 -parity [37], defined as 𝑅 = (− ) ( 𝐵 − 𝐿 )+ 𝑆 , is assumed to be conserved. SUSY particlesare then produced in pairs, and the lightest supersymmetric particle (LSP) is stable and, if only weaklyinteracting, a candidate for dark matter [38, 39]. In the framework of a generic 𝑅 -parity-conserving MinimalSupersymmetric Standard Model (MSSM) [40, 41], the supersymmetric scalar partners of right-handed andleft-handed quarks (squarks), ˜ 𝑞 R and ˜ 𝑞 L , can mix to form two mass eigenstates, ˜ 𝑞 and ˜ 𝑞 , with ˜ 𝑞 defined Following Ref. [34], couplings to 𝑊 and 𝑍 bosons, as well as explicit dimension-4 𝜙 – ℎ or 𝑎 – ℎ couplings, are set to zero in thissimplified model. In addition, the coupling of the mediator to the dark sector is not taken to be proportional to the mass of theDM candidates. t WWφ/ab νℓχχℓνb (a) ˜ t ˜ t WWpp ˜ χ b (cid:96) ν ˜ χ b (cid:96) ν (b) ˜ t ˜ tpp b (cid:96) ν ˜ χ b (cid:96) ν ˜ χ (c) ˜ t ˜ t t Wt Wpp ˜ χ b (cid:96) ν ˜ χ b (cid:96) ν (d) Figure 1: Diagrams representing the signal models targeted by the searches: (a) the spin-0 mediator models, wherethe mediator decays into a pair of dark-matter particles and is produced in association with a pair of top quarks( 𝑝 𝑝 → 𝜒 ¯ 𝜒𝑡 ¯ 𝑡 ), (b) the three-body ˜ 𝑡 decay mode into an on-shell 𝑊 boson, a 𝑏 -quark and the lightest neutralino(˜ 𝑡 → 𝑏𝑊 ˜ 𝜒 ), (c) the four-body ˜ 𝑡 decay mode (˜ 𝑡 → 𝑏 ¯ ℓ𝜈 ˜ 𝜒 ) where ¯ ℓ and 𝜈 are a anti-lepton with its neutrino and(d) the two-body ˜ 𝑡 decay into an on-shell top quark and the lightest neutralino (˜ 𝑡 → 𝑡 ˜ 𝜒 ). For all the diagrams (a-d)the distinction between particle and anti-particle is omitted. to be the lighter one. In the case of the supersymmetric partner of the top quark, ˜ 𝑡 , large mixing effectscan lead to one of the top squark mass eigenstates, ˜ 𝑡 , being significantly lighter than the other squarks.The charginos and neutralinos are mixtures of the bino, winos and Higgsinos that are superpartners ofthe U(1) and SU(2) gauge bosons and the Higgs bosons, respectively. Their mass eigenstates are referredto as ˜ 𝜒 ± 𝑖 ( 𝑖 = , ) and ˜ 𝜒 𝑗 ( 𝑗 = , , , ) in order of increasing mass. In a large variety of models, theLSP, which is the DM candidate, is the lightest neutralino ˜ 𝜒 . Searches for direct pair production of thetop squark and DM particles can be performed in final states with two leptons (electrons or muons) ofopposite electric charge, jets and missing transverse momentum (Figures 1(b)–1(d)). Depending on themass difference between the top squark and the lighter SUSY particles, different decay modes are relevant.For 𝑚 ( 𝑊 ) + 𝑚 ( 𝑏 ) < 𝑚 ( ˜ 𝑡 ) − 𝑚 ( ˜ 𝜒 ) < 𝑚 ( 𝑡 ) , the three-body decay ˜ 𝑡 → 𝑏𝑊 ˜ 𝜒 occurs through an off-shelltop quark (Figure 1(b)). For smaller mass differences, i.e. 𝑚 ( ˜ 𝑡 ) − 𝑚 ( ˜ 𝜒 ) < 𝑚 ( 𝑊 ) + 𝑚 ( 𝑏 ) , the four-bodydecay channel ˜ 𝑡 → 𝑏 𝑓 𝑓 (cid:48) ˜ 𝜒 , where 𝑓 and 𝑓 (cid:48) are two fermions from the off-shell ( 𝑊 ∗ ) decay, is assumedto occur (Figure 1(c)). In this search, 𝑓 and 𝑓 (cid:48) are a charged lepton and its associated anti-neutrino (orvice versa). For each of these two decay modes a dedicated event selection is performed to maximise thesensitivity. These selections are referred to as three-body and four-body selections in this paper. Direct pairproduction of top squarks which decay into an on-shell top quark and the lightest neutralino ˜ 𝑡 → 𝑡 ˜ 𝜒 , willoccur when 𝑚 ( ˜ 𝑡 ) − 𝑚 ( ˜ 𝜒 ) > 𝑚 ( 𝑡 ) (Figure 1(d)). The signature of the 𝑡 ¯ 𝑡 +DM process is similar to that ofthe simplified model shown in Figure 1(a), so the same selection is also used to constrain the ˜ 𝑡 → 𝑡 ˜ 𝜒 model 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 simulatedMonte Carlo (MC) samples used in the analysis are detailed in Section 3 and the object identificationis documented in Section 4. The search strategy, the SM background estimations, and the systematicuncertainties are discussed in Sections 5, 6 and 7. The results and their statistical interpretations arepresented in Sections 8 and 9. Finally, Section 10 presents the conclusions.3 ATLAS detector
The ATLAS detector [42] at the LHC covers nearly the entire solid angle around the collision point. Itconsists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic andhadronic calorimeters, and a muon spectrometer with three large superconducting toroidal magnets.The inner-detector system (ID) is immersed in a 2 T axial magnetic field and provides charged-particletracking in the range | 𝜂 | < .
5. The high-granularity silicon pixel detector covers the vertex region andtypically provides four measurements per track, the first hit normally being in the insertable B-layerinstalled before Run 2 [43, 44]. It is followed by the silicon microstrip tracker, which usually provideseight measurements per track. These silicon detectors are complemented by the transition radiation tracker(TRT), which enables radially extended track reconstruction up to | 𝜂 | = .
0. The TRT also provideselectron identification information based on the fraction of hits (typically 30 in total) above a higherenergy-deposit threshold corresponding to transition radiation.The calorimeter system covers the pseudorapidity range | 𝜂 | < .
9. Within the region | 𝜂 | < . | 𝜂 | < . | 𝜂 | < .
7, and two copper/LAr hadronic endcapcalorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimetermodules optimised for electromagnetic and hadronic measurements respectively.The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuringthe deflection of muons in a magnetic field generated by the superconducting air-core toroids. The fieldintegral of the toroids ranges between 2.0 and 6.0 T m across most of the detector. A set of precisionchambers covers the region | 𝜂 | < . | 𝜂 | < . The data used in this analysis were collected by the ATLAS detector during 𝑝 𝑝 collisions at a centre-of-massenergy of √ 𝑠 =
13 TeV from 2015 to 2018. The average number (cid:104) 𝜇 (cid:105) of 𝑝 𝑝 interactions per bunch crossing(pile-up) varies from 14 during 2015 to 38 during 2017–2018. Only events taken in stable beam conditions, ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detectorand 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 pointsupwards. Cylindrical coordinates ( 𝑟, 𝜙 ) are used in the transverse plane, 𝜙 being the azimuthal angle around the 𝑧 -axis. Thepseudorapidity is defined in terms of the polar angle 𝜃 as 𝜂 = − ln tan ( 𝜃 / ) , and the rapidity in terms of energy 𝐸 and momentum 𝑝 as 𝑦 = . [( 𝐸 + 𝑝 𝑧 )/( 𝐸 − 𝑝 𝑧 )] . Angular distance is measured in units of Δ 𝑅 ≡ √︁ ( Δ 𝑦 ) + ( Δ 𝜙 ) or Δ 𝑅 𝜂 ≡ √︁ ( Δ 𝜂 ) + ( Δ 𝜙 ) .A vector energy (cid:174) 𝐸 is defined by combining the energy deposited in the calorimeter with its deposit direction. − . Theuncertainty in the combined 2015–2018 integrated luminosity is 1 .
7% [46], obtained using the LUCID-2detector [47].The two-body and three-body selections use events accepted by a trigger that requires a minimum of twoelectrons, two muons, or an electron and a muon [45]. Different trigger-level thresholds for the transversemomentum of the leptons were used in different data-taking periods, ranging between 8 and 22 GeV.Tighter thresholds are applied in the lepton offline selection, to ensure that the trigger efficiency is ‘onplateau’ in all of the relevant kinematic region. Missing transverse momentum triggers [48] are used inthe four-body selection to increase the acceptance of low- 𝑝 T leptons. The missing transverse momentumtrigger threshold varied depending on data-taking conditions in the four years: 70 GeV for data collectedduring 2015; in the range 90–110 GeV for data collected during 2016, and 110 GeV for data collectedduring 2017 and 2018. Tighter offline requirements on the missing transverse momentum are definedaccordingly 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 Geant4 simulationfor all the other detector components [50] is used for the SUSY and DM signal samples. MC events arereconstructed using the same algorithms used for the data. To compensate for small residual differencesbetween data and simulation in the lepton reconstruction efficiency, energy scale, energy resolution, triggermodelling, and 𝑏 -tagging efficiency, the simulated events are reweighted using correction factors derivedfrom data [51–53].The events targeted by this analysis are characterised by two leptons with opposite electric charge, jets andmissing transverse momentum. The main SM background contributions are expected to come from topquark pair production ( 𝑡 ¯ 𝑡 ), associated production of a 𝑍 boson and a top quark pair ( 𝑡 ¯ 𝑡 𝑍 ), single-top decayin the 𝑊𝑡 production channel ( 𝑊𝑡 ), 𝑍 / 𝛾 ∗ + jets production and diboson processes ( 𝑉𝑉 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 andhadronisation parameters (tunes), and the cross-section order in 𝛼 s used 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 upto two extra partons using MadGraph5_aMC@NLO 2.6.2 [54]. MadGraph5_aMC@NLO was interfacedto 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 thetwo-body selection results. Signal cross-sections were calculated to next-to-leading order (NLO) in 𝛼 s ,adding the resummation of soft gluon emission at next-to-leading-logarithm accuracy (NLO+NLL) [57–63].The nominal cross-sections and their uncertainties were taken from an envelope of cross-section predictionsusing different PDF sets and factorisation and renormalisation scales, as described in Ref. [64]. Jet–partonmatching was performed following the CKKW-L prescription [65]. The A14 tune [66] was used for themodelling of parton showering, hadronisation and the underlying event. Parton luminosities were providedby the NNPDF2.3LO PDF set [67].The dark-matter signal samples were also generated from leading-order matrix elements, with up to oneextra parton, using MadGraph5_aMC@NLO 2.6.2 interfaced to Pythia 8.212. In the DM samplesgeneration the couplings of the scalar and pseudoscalar mediators to the SM and DM particles ( 𝑔 𝑞 and5 𝜒 ) are set to one. The kinematics of the mediator decay are not strongly dependent on the values of thecouplings; however, the particle kinematic distributions are sensitive to the nature of the mediator and tothe mediator and DM particle masses. The cross-sections were computed at NLO [68, 69].Inelastic 𝑝 𝑝 interactions were generated and overlaid onto the hard-scattering process to simulate the effectof multiple proton–proton interactions occurring during the same (in-time) or a nearby (out-of-time) bunchcrossing. These were produced using Pythia 8.186 [70] and EvtGen [71] with the NNPDF2.3LO set ofPDFs [67] and the A3 tune [72]. The MC samples were reweighted so that the distribution of the averagenumber of interactions per bunch crossing reproduces the observed distribution in the data. Table 1: Simulated signal and background event samples with the corresponding matrix element and parton shower(PS) generators, cross-section order in 𝛼 s used to normalise the event yield, and the generator and PS PDF sets used. Physics process Generator Parton shower Normalisation PDF (generator) PDF (PS)SUSY Signals MadGraph5_aMC@NLO [54]. Pythia 8.212 + MadSpin [55, 56] NLO+NLL [57–63] NNPDF2.3LO [67] NNPDF2.3LO [67](three-body, four-body)SUSY Signals (two-body) MadGraph5_aMC@NLO Pythia 8.212 NLO+NLL [57–63] NNPDF2.3LO NNPDF2.3LODM Signals (two-body) MadGraph5_aMC@NLO Pythia 8.212 NLO [68, 69] NNPDF2.3LO NNPDF2.3LO 𝑡 ¯ 𝑡 Powheg-Box v2 [73–75] Pythia 8.230 NNLO+NNLL [76] NNPDF3.0NLO [77] NNPDF2.3LO 𝑡 ¯ 𝑡 + 𝑉 ( 𝑉 = 𝑊, 𝑍 ) MadGraph5_aMC@NLO Pythia 8.210 NLO [54, 78] NNPDF3.0NLO NNPDF2.3LOSingle top Powheg-Box v2 [73–75, 79, 80] Pythia 8.230 NLO+NNLL [81–85] NNPDF3.0NLO NNPDF2.3LO 𝑍 / 𝛾 ∗ (→ ℓℓ ) +jets Sherpa 2.2.1 [86, 87] Sherpa 2.2.1 NNLO [88] NNPDF3.0NNLO [77] NNPDF3.0NNLO [77]Diboson 𝑉𝑉 ( 𝑉 = 𝑊, 𝑍 ) Sherpa 2.2.1 or 2.2.2 [86] Sherpa 2.2.1 or 2.2.2 NLO [89] NNPDF3.0NNLO NNPDF3.0NNLOTriboson
𝑉𝑉𝑉 ( 𝑉 = 𝑊, 𝑍 ) Sherpa 2.2.2 Sherpa 2.2.2 NLO [86, 89] NNPDF3.0NNLO NNPDF3.0NNLO 𝑡 ¯ 𝑡𝐻 Powheg-Box v2 [73, 74, 90] Pythia 8.230 NLO [54, 78] NNPDF3.0NLO NNPDF2.3LO 𝑡 ¯ 𝑡𝑊𝑊 MadGraph5_aMC@NLO Pythia 8.186 [70] 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
Candidate events are required to have a reconstructed vertex with at least two associated tracks, each with 𝑝 T >
500 MeV and originating from the beam collision region in the 𝑥 – 𝑦 plane. The primary vertex in theevent 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 increasinglystringent set of reconstruction quality criteria and kinematic selections, so that signal leptons are a subsetof the baseline leptons. Baseline leptons are used in the calculation of missing transverse momentum( p missT ), to resolve ambiguities between the analysis objects in the event, as described later, and for thefake/non-prompt (FNP) lepton background estimation described in Section 6. Signal leptons are used forthe final event selection.Baseline electron candidates are reconstructed from three-dimensional clusters of energy depositionin the electromagnetic calorimeter matched to ID tracks. These electron candidates are required tohave pseudorapidity | 𝜂 | < . 𝐸 T > . Loose likelihood-based identificationrequirement [51] with an additional condition on the number of hits in the B-layer. The tracks associatedwith electron candidates are required to have a longitudinal impact parameter relative to the primaryvertex | 𝑧 sin 𝜃 | < . 𝜃 is the track’s polar angle. The transverse impact parameter is defined as the distance of closest approach in the transverse plane between a track and thebeam-line. The longitudinal impact parameter corresponds to the 𝑧 -coordinate distance between the point along the track atwhich the transverse impact parameter is defined and the primary vertex. | 𝜂 | < . | 𝜂 | < . 𝑝 T > | 𝑧 sin 𝜃 | < . Medium identification requirement, defined in Ref. [52], based on thenumbers of hits in the different ID and MS subsystems, and on the significance of the charge-to-momentumratio 𝑞 / 𝑝 .Additional tighter selections are applied to the baseline lepton candidates to select the signal electrons ormuons. Signal electrons are required to satisfy a Medium likelihood-based identification requirement [51]and the track associated with a signal electron is required to have a significance | 𝑑 |/ 𝜎 ( 𝑑 ) <
5, where 𝑑 is the transverse impact parameter relative to the reconstructed primary vertex and 𝜎 ( 𝑑 ) is its uncertainty.Isolation criteria are applied to electrons by placing an upper limit on the sum of the transverse energyof the calorimeter energy clusters in a cone of size Δ 𝑅 𝜂 = √︁ ( Δ 𝜂 ) + ( Δ 𝜙 ) = . 𝑝 T of tracks within a cone of Δ 𝑅 𝜂 = . 𝜂 . This varies from 90% for 𝑝 T =
25 GeV to 99% for 𝑝 T =
60 GeV in events with a 𝑍 boson decaying into pair of electrons [51].For signal muons a significance in the transverse impact parameter | 𝑑 |/ 𝜎 ( 𝑑 ) < 𝑝 T of tracks inside a cone of Δ 𝑅 𝜂 = . 𝑝 T . In addition, the sum of the transverseenergy of the calorimeter energy clusters in a cone of Δ 𝑅 𝜂 = . 𝑝 T [52].Jets are reconstructed from three-dimensional clusters of energy in the calorimeter [91] using the anti- 𝑘 𝑡 jet clustering algorithm [92] as implemented in the FastJet package [93], with a radius parameter 𝑅 = . 𝑝 T >
20 GeV and | 𝜂 | < . To reduce the effects of pile-up, for jets with | 𝜂 | ≤ . 𝑝 T <
120 GeV a significant fraction of thetracks associated with each jet are required to have an origin compatible with the primary vertex, as definedby the jet vertex tagger (JVT) [95]. 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 impactedby detector noise and non-collision backgrounds, specific jet-quality requirements [96, 97] are applied,designed to provide an efficiency of selecting jets from proton–proton collisions above 99.5% (99.9%) for 𝑝 T > ( ) GeV.The MV2C10 boosted decision tree algorithm [53] identifies jets containing 𝑏 -hadrons (‘ 𝑏 -jets’) by usingquantities such as the impact parameters of associated tracks, and well-reconstructed secondary vertices. Aselection that provides 77% efficiency for tagging 𝑏 -jets in simulated 𝑡 ¯ 𝑡 events is used. The correspondingrejection factors against jets originating from 𝑐 -quarks, from 𝜏 -leptons, and from light quarks and gluonsin 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 procedureis applied to the baseline leptons and jets in the order which follows. First, the calo-tagged muons areremoved if sharing the track with electrons and, next, all electrons sharing an ID track with a muon areremoved. Jets which are not 𝑏 -tagged (with the tagging parameters corresponding to an efficiency of 85%) Hadronic 𝜏 -lepton decay products are treated as jets. Δ 𝑅 = √︁ ( Δ 𝑦 ) + ( Δ 𝜙 ) = . Δ 𝑅 = . 𝑝 T >
100 GeV. Finally, any leptoncandidate is removed in favour of a jet candidate if it lies a distance Δ 𝑅 < min ( . , . + / 𝑝 T ( ℓ )) fromthe jet, where 𝑝 T ( ℓ ) is the 𝑝 T of the lepton.The missing transverse momentum ( p missT ), with magnitude 𝐸 missT , is defined as the negative vector sumof the transverse momenta for all baseline electrons, photons, muons and jets. Low-momentum tracksfrom the primary vertex that are not associated with reconstructed analysis objects are also included in thecalculation. The 𝐸 missT value is adjusted for the calibration of the selected physics objects [98]. Linkedto the 𝐸 missT value is the ‘object-based 𝐸 missT significance’, called simply ‘ 𝐸 missT significance’ in this paper.This quantity measures the significance of 𝐸 missT based upon the transverse momentum resolution of allobjects used in the calculation of the p missT . It is defined as 𝐸 missT significance = | p missT | √︃ 𝜎 ( − 𝜌 ) where 𝜎 L is the (longitudinal) component parallel to the p missT of the total transverse momentum resolutionfor all objects in the event and the quantity 𝜌 LT is the correlation factor between the parallel and perpendicularcomponents of the transverse momentum resolution for each object. On an event-by-event basis, giventhe full event composition, 𝐸 missT significance evaluates the 𝑝 -value that the observed 𝐸 missT is consistentwith the null hypothesis of zero real 𝐸 missT , as further detailed in Ref. [29]. In this way 𝐸 missT significancehelps to separate events with true 𝐸 missT , arising from weakly interacting particles such as dark matter orneutralinos, from those where 𝐸 missT is consistent with particle mismeasurement, resolution or identificationinefficiencies, thus providing better background rejection. Different event selections are inspired by previous published strategies [27, 28] reoptimised to fully exploitthe larger available dataset. For all selections, an improvement in the sensitivity is obtained with theintroduction of the 𝐸 missT significance variable, which enables further optimisation of the selection variables.The four-body sensitivity also benefits from a reduction in the lepton 𝑝 T threshold in the region with smallmass differences Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) between ˜ 𝑡 and ˜ 𝜒 . The threshold for the muon (electron) 𝑝 T was loweredfrom 7 GeV to 4 GeV (4 . 𝑚 ℓℓ greater than 20 GeV condition is applied to remove leptons from Drell–Yan and low-mass resonances, whilein the four-body selection, given the softer 𝑝 T spectrum of the leptons, 𝑚 ℓℓ is required to be higher than10 GeV. Events with same flavour (SF) lepton pairs ( 𝑒 ± 𝑒 ∓ and 𝜇 ± 𝜇 ∓ ) with 𝑚 ℓℓ between 71.2 and 111.2 GeVare rejected to reduce the 𝑍 boson background, except for the four-body selection. No additional 𝑚 ℓℓ selection is imposed on the different flavour (DF) lepton pairs ( 𝑒 ± 𝜇 ∓ ). Different jet ( 𝑏 -jet) multiplicities,labelled as 𝑛 jets ( 𝑛 𝑏 − jets ), are required in the three selections, as detailed below.8 .1 Discriminators and kinematic variables Final event selections are obtained by separating signal from SM background using different kinematicvariables. Two variables are constructed from the 𝐸 missT and the 𝑝 T of the leading leptons and jets: 𝑅 ℓ = 𝐸 missT /( 𝑝 T ( ℓ ) + 𝑝 T ( ℓ )) and 𝑅 ℓ 𝑗 = 𝐸 missT / (cid:32) 𝐸 missT + 𝑝 T ( ℓ ) + 𝑝 T ( ℓ ) + ∑︁ 𝑖 = ,...,𝑁 ≤ 𝑝 T ( 𝑗 𝑖 ) (cid:33) where 𝑝 T ( ℓ ) and 𝑝 T ( ℓ ) are the leading and sub-leading lepton transverse momenta respectively and 𝑝 T ( 𝑗 𝑖 = ,...,𝑁 ≤ ) are the transverse momenta of the up to four leading jets, in decreasing order. For somebackgrounds, e.g. 𝑍 / 𝛾 ∗ + jets, the variable 𝑅 ℓ has a distribution that peaks at lower values than the signal,and it is thus used to reject those backgrounds. Similarly, 𝑅 ℓ 𝑗 is employed for its high rejection poweragainst multi-jet events.Another variable employed is p ℓℓ T , boost , which is defined as the vectorial sum of p missT and the leptons’transverse momentum vectors p T ( ℓ ) and p T ( ℓ ) . Its magnitude, 𝑝 ℓℓ T , boost , can be interpreted as themagnitude of the vector sum of all the transverse hadronic activity in the event. The azimuthal anglebetween the p missT vector and the p ℓℓ T , boost vector is defined as Δ 𝜙 boost . This variable is useful for selectingevents where the non hadronic component ( 𝑒 , 𝜇 , 𝜈 and 𝜒 or ˜ 𝜒 ) is collimated.The lepton-based stransverse mass [99, 100] is a kinematic variable used to bound the masses of a pair ofidentical particles which have each decayed into a visible and an invisible particle. This quantity is definedas 𝑚 T2 ( p T , , p T , , p missT ) = min q T , + q T , = p missT (cid:8) max [ 𝑚 T ( p T , , q T , ) , 𝑚 T ( p T , , q T , ) ] (cid:9) , where 𝑚 T indicates the transverse mass, p T , and p T , are the transverse momentum vectors of two visibleparticles, and q T , and q T , are transverse momentum vectors with p missT = q T , + q T , . The minimisationis performed over all the possible decompositions of p missT . In this paper, p T , and p T , are the transversemomentum vectors of the two leptons and 𝑚 T2 ( p T ( ℓ ) , p T ( ℓ ) , p missT ) is referred to simply as 𝑚 ℓℓ T2 . For the 𝑚 ℓℓ T2 calculation, the invisible particles are assumed to be massless. The 𝑚 ℓℓ T2 distribution is expected tohave an endpoint corresponding to the 𝑊 mass for backgrounds such as 𝑡 ¯ 𝑡 while it is expected to reachhigher values in the case of SUSY events, due to the presence of the neutralinos [101, 102].The three-body selection uses a number of ‘super-razor’ variables [103], which are derived with a series ofassumptions made in order to approximate the centre-of-mass energy frame (Razor Frame) of two parentparticles (i.e. top squarks) and the decay frames. Each parent particle is assumed to decay into a set ofvisible (only leptons are considered in this case) and invisible particles (i.e. neutrinos and neutralinos).These variables are 𝑅 𝑝 T , the Lorentz factor 𝛾 R + , the azimuthal angle Δ 𝜙 R 𝛽 and 𝑀 R Δ . The first variable is 𝑅 𝑝 T = | (cid:174) 𝐽 T |/(| (cid:174) 𝐽 T | + √ ˆ 𝑠 R / ) with (cid:174) 𝐽 T as the vector sum of the transverse momenta of the visible particles andthe missing transverse momentum, and √ ˆ 𝑠 R as an estimate of the system’s energy in the razor frame 𝑅 ,defined as the frame in which the two visible leptons have equal and opposite longitudinal momentum( 𝑝 z ). The value of | (cid:174) 𝐽 T | vanishes for events where leptons are the only visible particles, such as dibosonevents, leading to 𝑅 𝑝 T values that tend toward zero. Instead, in events that contain additional activity, suchas 𝑡 ¯ 𝑡 , this variable tends towards unity. The Lorentz factor, 𝛾 R + , is associated with the boost from therazor frame 𝑅 to the approximation of the two decay frames of the parent particles and is expected to have The transverse mass is defined by the equation 𝑚 T ( p T , q T ) = √︁ | p T || q T |( − cos ( Δ 𝜙 )) , where Δ 𝜙 is the angle betweenparticles of negligible mass with transverse momenta p T and q T . 𝛾 R + are otherwise expected when the two visible particles are collinear and have comparablemomentum. The azimuthal angle Δ 𝜙 R 𝛽 is defined between the razor boost from the laboratory to the 𝑅 frame and the sum of the visible momenta as evaluated in the 𝑅 frame. It is a good discriminator whenused in searches for signals from models with small mass differences between the massive pair-producedparticle and the invisible particle produced in the decay. Finally, the last variable is 𝑀 R Δ = √ ˆ 𝑠 R / 𝛾 R + ,which is particularly powerful in discriminating between signal events and 𝑡 ¯ 𝑡 and diboson background,since it has a kinematic end-point that is proportional to the mass-splitting between the parent particle andthe invisible particle. This selection targets the dark-matter signal model that assumes the production of a pair of dark-matterparticles through the exchange of a spin-0 mediator, in association with a pair of top quarks (Figure 1(a)).It is also used for a search for top squarks decaying into an on-shell top and neutralino (Figure 1(d)).For each event, the leading lepton, ℓ , is required to have 𝑝 T ( ℓ ) >
25 GeV, while for the sub-leadinglepton, ℓ , the requirement is 𝑝 T ( ℓ ) >
20 GeV. The event selection also requires at least one reconstructed 𝑏 -jet, Δ 𝜙 boost lower than 1.5 and 𝐸 missT significance greater than 12, and finally 𝑚 ℓℓ T2 greater than 110 GeV.Following the classification of the events, two sets of signal regions (SRs) are defined: a set of exclusiveSRs binned in the 𝑚 ℓℓ T2 variable, to maximise model-dependent search sensitivity, and a set of inclusive SRs,to be used for model-independent results. For the binned SRs, events are separated according to the leptonflavours, different flavour or same flavour, and by the range [ 𝑥, 𝑦 ) of the 𝑚 ℓℓ T2 interval: SR − DF − body [ 𝑥,𝑦 ) or SR − SF − body [ 𝑥,𝑦 ) . For the inclusive signal regions, referred to as SR − body [ 𝑥, ∞) with 𝑥 being the lower boundplaced on the 𝑚 ℓℓ T2 variable, DF and SF events are combined. The common definition of these two sets ofsignal regions is shown in Table 2. Table 2: Two-body selection. Common definition of the binned and the inclusive sets of signal regions. SR − body Leptons flavour DF SF 𝑝 T ( ℓ ) [GeV] > 𝑝 T ( ℓ ) [GeV] > 𝑚 ℓℓ [GeV] > | 𝑚 ℓℓ − 𝑚 𝑍 | [GeV] – > 𝑛 𝑏 − jets ≥ Δ 𝜙 boost [rad] < . 𝐸 missT significance > 𝑚 ℓℓ T2 [GeV] > .3 Three-body event selection The three-body decay mode of the top squark shown in Figure 1(b) is dominant in the region where 𝑚 ( ˜ 𝑡 ) > 𝑚 ( ˜ 𝜒 ) + 𝑚 ( 𝑊 ) + 𝑚 ( 𝑏 ) and 𝑚 ( ˜ 𝑡 ) < 𝑚 ( ˜ 𝜒 ) + 𝑚 ( 𝑡 ) . The signal kinematics in this region resemblethat of 𝑊𝑊 production when Δ 𝑚 ( ˜ 𝑡, ˜ 𝜒 ) ∼ 𝑚 ( 𝑊 ) and that of 𝑡 ¯ 𝑡 production when Δ 𝑚 ( ˜ 𝑡, ˜ 𝜒 ) ∼ 𝑚 ( 𝑡 ) . Thesignal selection was optimised to reject these dominant backgrounds while not degrading signal efficiency.The 𝑏 -jet multiplicity is highly dependent on the mass-splitting between the top squark and the neutralino, Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) = 𝑚 ( ˜ 𝑡 ) − 𝑚 ( ˜ 𝜒 ) , since for lower Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) the 𝑏 -jets have lower momentum and cannot bereconstructed efficiently. Accordingly, two orthogonal signal regions were defined: SR − body 𝑊 targeting Δ 𝑚 ( ˜ 𝑡, ˜ 𝜒 ) ∼ 𝑚 ( 𝑊 ) , applying a 𝑏 -jet veto, and SR − body 𝑡 targeting Δ 𝑚 ( ˜ 𝑡, ˜ 𝜒 ) ∼ 𝑚 ( 𝑡 ) , allowing for 𝑏 -jets.Separation between same-flavour and different-flavour events is also kept to optimise model-dependentsearch sensitivity, thus defining four different SRs: SR-DF − body 𝑊 , SR-SF − body 𝑊 , SR-DF − body 𝑡 and SR-SF − body 𝑡 . The signal regions make use of a common set of requirements on the 𝑝 T of the two leptons, 𝐸 missT significance and 𝛾 R + . The definitions of these regions are summarised in Table 3. Table 3: Three-body selection. Signal regions definition. SR − body 𝑊 SR − body 𝑡 Leptons flavour DF SF DF SF 𝑝 T ( ℓ ) [GeV] > > 𝑝 T ( ℓ ) [GeV] > > 𝑚 ℓℓ [GeV] > > | 𝑚 ℓℓ − 𝑚 𝑍 | [GeV] – >
20 – > 𝑛 𝑏 − jets = ≥ Δ 𝜙 R 𝛽 [rad] > . > . 𝐸 missT significance > > 𝛾 R + > . > . 𝑅 𝑝 T > . > . 𝑀 R Δ [GeV] > > In the kinematic region defined by 𝑚 ( ˜ 𝑡 ) < 𝑚 ( ˜ 𝜒 ) + 𝑚 ( 𝑏 ) + 𝑚 ( 𝑊 ) and 𝑚 ( ˜ 𝑡 ) > 𝑚 ( ˜ 𝜒 ) + 𝑚 ( 𝑏 ) , the topsquarks are assumed to decay via a four-body process through an off-shell top quark and 𝑊 boson as shownin Figure 1(c). In this region the final-state leptons from the virtual 𝑊 boson decay are expected to havelower momentum and can be efficiently selected when imposing both a lower and upper bound on the 𝑝 T of the leptons. A transverse momentum lower bound of 4.5 GeV (4 GeV) is applied for electrons (muons),together with an upper bound, which is optimised separately for the leading and the sub-leading leptons.Two separate signal regions are defined to cover different Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) ranges: the first one, SR − bodySmall Δ 𝑚 ,targets small values of Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) and requires 𝑝 T ( ℓ ) <
25 GeV and 𝑝 T ( ℓ ) <
10 GeV; the second one,SR − bodyLarge Δ 𝑚 , targets larger values of Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) and instead requires 𝑝 T ( ℓ ) >
10 GeV. This condition also11nsures orthogonality between the two SRs. The presence of an energetic initial-state radiation (ISR)jet recoiling against the system of the two top squarks is required, introducing an imbalance in the eventkinematics with an enhanced value of 𝐸 missT that allows signal events to be distinguished from SM processes.For this reason, for each event, the leading jet 𝑗 is considered to be a jet from ISR and required to have 𝑝 T >
150 GeV. A further reduction of the SM background is achieved with selections on 𝐸 missT significance, 𝑝 ℓℓ T , boost , 𝑅 ℓ and 𝑅 ℓ 𝑗 variables. An additional requirement is applied to improve the sub-leading leptonisolation, using the following isolation variable:min Δ 𝑅 ℓ , 𝑗 𝑖 = min 𝑗 𝑖 ∈[ jets ] Δ 𝑅 𝜂 ( ℓ , 𝑗 𝑖 ) where ‘[jets]’ contains all the jets in the event. This reduces the probability of lepton misidentification orselecting a lepton originating from heavy-flavour or 𝜋 / 𝐾 decays in jets. The definitions of these regionsare summarised in Table 4. Table 4: Four-body selection. Signal regions definition. SR − bodySmall Δ 𝑚 SR − bodyLarge Δ 𝑚 𝑝 T ( ℓ ) [GeV] < < 𝑝 T ( ℓ ) [GeV] < [ , ] 𝑚 ℓℓ [GeV] > 𝑝 T ( 𝑗 ) [GeV] > Δ 𝑅 ℓ , 𝑗 𝑖 > 𝐸 missT significance > 𝑝 ℓℓ T , boost [GeV] > 𝐸 missT [GeV] > 𝑅 ℓ > > 𝑅 ℓ 𝑗 > . > . The MC predictions for the dominant SM background processes are improved using a data-drivennormalisation procedure, while non-dominant processes are estimated directly using MC simulation.A simultaneous profile likelihood fit [104] is used to constrain the MC yields with the observed datain dedicated background control regions (CRs). The fit is performed using standard minimisationsoftware [105, 106] where the normalisations of the targeted backgrounds are allowed to float, whilethe MC simulation is used to describe the shape of kinematic variables. Systematic uncertainties thatcould affect the expected yields in the different regions are taken into account in the fit through nuisanceparameters. Each uncertainty source is described by a single nuisance parameter, and correlations betweennuisance parameters, background processes and selections are taken into account. A list of the systematicuncertainties considered in the fits is provided in Section 7. The SM background thus modelled is validatedin dedicated validation regions (VRs) which are disjoint from both the control and signal regions.Important sources of reducible background are events with jets which are misidentified as leptons. Thefake/non-prompt (FNP) lepton background comes from 𝜋 / 𝐾 and heavy-flavour hadron decays and photon12onversions. This is particularly important for the low- 𝑝 T leptons targeted by the four-body selection.The FNP background is mainly suppressed by the lepton isolation requirements described in Section 4,but a non-negligible residual contribution is expected. This is estimated from data using the ‘fake factor’method [107–110] which uses two orthogonal lepton definitions, labelled as ‘Id’ and ‘anti-Id’, to define acontrol data sample enriched in fake leptons. The Id lepton corresponds to the signal lepton identificationcriteria used in this analysis. Anti-Id electrons fail either the signal identification or isolation requirement,while anti-Id muons fail the isolation requirement. The sample used for the fake-factor computation isenriched in 𝑍 +jets events. Events with three leptons are selected, with the two same-flavour leptons ofopposite electric charge (SFOS leptons) identified as the 𝑍 boson decay products ( ℓ 𝑍 and ℓ 𝑍 , in orderof decreasing 𝑝 T ) satisfying the Id requirements, and the third unpaired lepton, called the probe lepton( ℓ probe ), satisfying either the Id or anti-Id criteria. The fake factor is defined as the ratio of the Id leptonyield to the anti-Id probe lepton yield. Residual contributions from processes producing prompt leptonsare subtracted using the MC predictions. Fake factors are measured separately for electrons and muons andas a function of the lepton 𝑝 T and 𝜂 . These are derived in the CR FNP region whose selection is summarisedin Table 5. The FNP estimates in each analysis region are derived by applying the fake factors to eventssatisfying that region’s criteria but replacing at least one of the signal leptons by an anti-Id one.
Table 5: FNP selection. Detailed definition of the CR
FNP region. CR FNP
Lepton multiplicity 3 | 𝑚 ℓℓ − 𝑚 𝑍 | [GeV] <
10 for SFOS pair 𝑝 T ( ℓ 𝑍 ) [GeV] > 𝑝 T ( ℓ 𝑍 ) [GeV] > 𝑝 T ( ℓ probe ) [GeV] > . ( . ) 𝑒 ( 𝜇 ) Δ 𝑅 𝜂 ( ℓ probe , ℓ 𝑖 ) > . 𝑚 T ( ℓ probe , 𝐸 missT ) [GeV] < 𝑝 T ( ℓ probe ) <
16 GeVor 𝐸 missT <
50 GeVThe three selections in this paper use different sets of CRs and VRs, specifically designed to be kinematicallysimilar to the respective SRs. The definitions of the regions used in each analysis and the results of the fitsare described in the following subsections.
The main background sources for the two-body selection are 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 with invisible decay of the 𝑍 boson.These processes are normalised to data in dedicated CRs: CR − body 𝑡 ¯ 𝑡 and CR 𝑡 ¯ 𝑡 𝑍 . The 𝑡 ¯ 𝑡 normalisationfactor is extracted from different-flavour dilepton events. In order to test the reliability of the 𝑡 ¯ 𝑡 backgroundprediction, two validation regions VR − body 𝑡 ¯ 𝑡, DF and VR − body 𝑡 ¯ 𝑡, SF are defined. The 𝑡 ¯ 𝑡 𝑍 production events withinvisible decay of the 𝑍 boson are expected to dominate the tail of the 𝑚 ℓℓ T2 distribution in the SRs andare normalised in the dedicated control region CR 𝑡 ¯ 𝑡 𝑍 . Given the difficulty in achieving sufficient purityfor this SM process because of the high contamination from 𝑡 ¯ 𝑡 events, a strategy based on a three-lepton13nal state is adopted. Events are selected if characterised by three charged leptons including at least onepair of SFOS leptons having invariant mass consistent with that of the 𝑍 boson ( | 𝑚 ℓℓ − 𝑚 𝑍 | <
20 GeV).If more than one pair is identified, the one with 𝑚 ℓℓ closest to the 𝑍 boson mass is chosen. Events arefurther required to have a jet multiplicity, 𝑛 jets , greater than or equal to three with at least two 𝑏 -taggedjets. These selections target 𝑡 ¯ 𝑡 𝑍 production with the 𝑍 boson decaying into two leptons and 𝑡 ¯ 𝑡 decayingin the semileptonic channel. In order to select 𝑡 ¯ 𝑡 𝑍 events whose kinematics, regardless of subsequent 𝑡 ¯ 𝑡 and 𝑍 decays, emulate the kinematics of this background in the SRs, the momenta of the two leptons ofthe SFOS pair ( p ( ℓ Z1 ) , p ( ℓ Z2 )) are vectorially added to the p missT , effectively treating them like the neutrinopair from the 𝑍 boson decay. A variable called 𝐸 missT , corr = (cid:12)(cid:12)(cid:0) p missT + p ( ℓ Z1 ) + p ( ℓ Z2 ) (cid:1) T (cid:12)(cid:12) is constructed. Eventscharacterised by high 𝑚 ℓℓ T2 in the SRs are emulated by requiring high 𝐸 missT , corr values in CR 𝑡 ¯ 𝑡 𝑍 . In order tocheck the 𝑡 ¯ 𝑡 𝑍 background estimation, the validation region VR − body 𝑡 ¯ 𝑡 𝑍 was defined. For this region, eventswith four leptons are selected and required to have at least one pair of SFOS leptons compatible with the 𝑍 boson decay. A variant of the 𝑚 T2 variable called 𝑚 ℓ T2 is defined from the p missT , corr = (cid:0) p missT + p ( ℓ Z1 ) + p ( ℓ Z2 ) (cid:1) T and the momenta of the remaining two leptons. The definition of the control and validation regions usedin the two-body selection is summarised in Table 6. The expected signal contamination in the CRs isgenerally below ∼ Table 6: Two-body selection. Control and validation regions definition. The common selection defined in Section 5also applies to all regions. CR − body 𝑡 ¯ 𝑡 CR 𝑡 ¯ 𝑡𝑍 VR − body 𝑡 ¯ 𝑡, DF VR − body 𝑡 ¯ 𝑡, SF VR − body 𝑡 ¯ 𝑡𝑍 Lepton multiplicity 2 3 2 4Lepton flavour DF at least one SFOS pair DF SF at least one SFOS pair 𝑝 T ( ℓ ) [GeV] > > > > 𝑝 T ( ℓ ) [GeV] > > > > 𝑝 T ( ℓ ) [GeV] – >
20 – > 𝑝 T ( ℓ ) [GeV] – – – > 𝑚 ℓℓ >
20 – >
20 – | 𝑚 ℓℓ − 𝑚 𝑍 | [GeV] – <
20 for at least one SFOS pair – > <
20 for the SFOS pair 𝑛 𝑏 − jets ≥ ≥ 𝑛 jets ≥ ≥ > Δ 𝜙 boost [rad] ≥ . < . 𝐸 missT significance > >
12 – 𝐸 missT , corr [GeV] – >
140 – – 𝑚 ℓℓ T2 [GeV] [100, 120] – [100, 110] – 𝑚 ℓ T2 [GeV] – – – >110 Figure 2 illustrates the modelling of the shape of two important variables after the background fit: (a)shows the Δ 𝜙 boost distribution with the CR − body 𝑡 ¯ 𝑡 selection, and (b) shows the 𝑚 ℓℓ distribution of the SFOSleptons in the CR 𝑡 ¯ 𝑡 𝑍 selection. Good agreement is found between the data and the background model forall of the selection variables.The results of the fit are reported in Table 7 for the two-body CRs and VRs. The normalisations for fittedbackgrounds are found to be consistent with the theoretical predictions when uncertainties are considered:the normalisation factors obtained from the fit for 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 are 0 . ± .
08 and 1 . ± .
14 respectively.14 E v en t s / . Data Standard Modeltt WtZtt FNPDiboson Others
ATLAS = 13 TeV, 139 fbs2 body selection tt2 body CR boost φ∆ D a t a / S M (a) E v en t s / G e V Data Standard ModelZtt FNPDiboson Others
ATLAS = 13 TeV, 139 fbs2 body selection Ztt CR
40 60 80 100 120 140 [GeV]
SFOSll m012 D a t a / S M (b) Figure 2: Two-body selection. Distributions of (a) Δ 𝜙 boost in CR − body 𝑡 ¯ 𝑡 and (b) 𝑚 ℓℓ of the two same-flavour andopposite-charge leptons candidate in CR 𝑡 ¯ 𝑡𝑍 , each after the background fit. The contributions from all SM backgroundsare shown as a histogram stack. “Others” includes the contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 ,and 𝑡𝑍 . The hatched bands represent the total statistical and detector-related systematic uncertainty. The rightmostbin 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 bandsrepresenting the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. Good agreement, within one standard deviation of the SM background prediction, is observed in the VRs(see Figure 3).
Table 7: Two-body selection. Background fit results for CR − body 𝑡 ¯ 𝑡 , CR 𝑡 ¯ 𝑡𝑍 , VR − body 𝑡 ¯ 𝑡, DF , VR − body 𝑡 ¯ 𝑡, SF and VR − body 𝑡 ¯ 𝑡𝑍 .“Others” includes contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. Combined statisticaland systematic uncertainties are given. Entries marked ‘–’ indicate a negligible background contribution (less than0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the totalbackground uncertainty. CR − body 𝑡 ¯ 𝑡 CR 𝑡 ¯ 𝑡𝑍 VR − body 𝑡 ¯ 𝑡, DF VR − body 𝑡 ¯ 𝑡, SF VR − body 𝑡 ¯ 𝑡𝑍 Observed events 230 247 45 38 26Total (post-fit) SM events 230 ±
15 246 ±
16 50 ±
15 42 ±
11 25 . ± . 𝑡 ¯ 𝑡 ±
17 – 44 ±
15 36 ±
11 –Post-fit, 𝑡 ¯ 𝑡𝑍 . ± .
23 170 ±
22 1 . ± . . ± . . ± . 𝑊𝑡 ± . ± . . ± . . ± . ± . ± .
25 0 . ± .
32 8 . ± . . ± . ±
12 1 . ± . . ± . . ± . . + . − . ± . + . − . . + . − . . + . − . E v en t s Data Standard Modeltt WtZtt FNP+jets γ Z/ DibosonOthers
ATLAS = 13 TeV, 139 fbs2 body selection ,SFtt2 body VR missT E012 D a t a / S M (a) E v en t s / G e V Data Standard ModelZtt FNPDiboson Others
ATLAS = 13 TeV, 139 fbs2 body selection Ztt2 body VR m012 D a t a / S M (b) Figure 3: Two-body selection. Distributions of the 𝐸 missT significance in (a) VR − body 𝑡 ¯ 𝑡, SF and (b) 𝑚 ℓ T2 in VR − body 𝑡 ¯ 𝑡𝑍 , eachafter the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others”includes contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bands representthe total statistical and detector-related systematic uncertainty. The rightmost bin of each plot includes overflowevents. In the upper panels, red arrows indicate the validation region selection criteria. The bottom panels showthe ratio of the observed data to the total SM background prediction, with hatched bands representing the totaluncertainty in the background prediction; red arrows show data outside the vertical-axis range. The dominant SM backgrounds in the three-body signal regions are diboson, 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 production.Dedicated CRs were defined, labelled as CR − body 𝑉 𝑉 and CR − body 𝑡 ¯ 𝑡 , which are kinematically close to the SRsand which have good purity in diboson and 𝑡 ¯ 𝑡 events respectively. The orthogonality between CRs and SRsis mainly ensured by the inversion of the Δ 𝜙 R 𝛽 cut. The normalisation of the 𝑡 ¯ 𝑡 𝑍 background is extractedusing the same control region CR 𝑡 ¯ 𝑡 𝑍 defined for the two-body selection in Section 6.1. Dedicated validationregions were defined to test the modelling of these processes: VR − body 𝑉 𝑉 for the diboson background, andVR ( ) − body 𝑡 ¯ 𝑡 and VR ( ) − body 𝑡 ¯ 𝑡 for the validation of the 𝑡 ¯ 𝑡 background, where VR ( ) − body 𝑡 ¯ 𝑡 is characterisedby a 𝑏 -jet veto while at least one 𝑏 -jet is required in VR ( ) − body 𝑡 ¯ 𝑡 . The definition of the control andvalidation regions is summarised in Table 8. The expected signal contamination is below 2% in the CRsand reaches a maximum of 10% in the VRs for a top squark mass of ∼
430 GeV.Table 9 shows the expected and observed numbers of events in each of the control and validation regionsafter the background fit. The normalisation factors extracted from the fit of the backgrounds for the diboson, 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 production processes are 0 . ± .
28, 0 . ± .
09 and 1 . ± .
15 respectively. The totalnumber of fitted background events in the validation regions is in agreement with the observed number ofdata events. Figure 4 shows the distributions of Δ 𝜙 R 𝛽 for the CR − body 𝑉 𝑉 and CR − body 𝑡 ¯ 𝑡 selections after thebackground fit, illustrating the MC modelling of the shape for this variable. Figure 5 shows distributions of 𝑅 𝑝 T in VR ( ) − body 𝑡 ¯ 𝑡 and VR ( ) − body 𝑡 ¯ 𝑡 , and of Δ 𝜙 R 𝛽 in VR − body 𝑉 𝑉 , after the background fit. Good agreement,within one standard deviation of the SM background prediction, is observed in the validation regions.16 able 8: Three-body selection. Control and validation regions definitions. The common selection defined in Section 5also applies to all regions. A further control region CR 𝑡 ¯ 𝑡𝑍 was defined previously in Table 7.CR − body 𝑡 ¯ 𝑡 CR − body 𝑉 𝑉 VR ( ) − body 𝑡 ¯ 𝑡 VR ( ) − body 𝑡 ¯ 𝑡 VR − body 𝑉 𝑉
Lepton flavour DF DF+SF DF DF DF+SF 𝑝 T ( ℓ ) [GeV] > > > > > 𝑝 T ( ℓ ) [GeV] > > > > > 𝑚 ℓℓ [GeV] > > > > > | 𝑚 ℓℓ − 𝑚 𝑍 | [GeV] – >
20 (SF only) – – >
20 (SF only) 𝑛 𝑏 − jets ≥ = = ≥ = 𝑀 R Δ [GeV] > > [ , ] [ , ] > 𝑅 𝑝 T – > . > . > . > . 𝛾 R + > 0.7 > . > . > . [ . , . ] 𝐸 missT significance > > > > > Δ 𝜙 R 𝛽 [rad] < . < . > . > . > . − body 𝑉 𝑉 , CR − body 𝑡 ¯ 𝑡 , CR 𝑡 ¯ 𝑡𝑍 , VR − body 𝑉 𝑉 , VR ( ) − body 𝑡 ¯ 𝑡 andVR ( ) − body 𝑡 ¯ 𝑡 . “Others” includes contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes.Combined statistical and systematic uncertainties are given. Entries marked ‘–’ indicate a negligible backgroundcontribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up inquadrature to the total background uncertainty.CR − body 𝑡 ¯ 𝑡 CR − body 𝑉 𝑉 CR 𝑡 ¯ 𝑡𝑍 VR ( ) − body 𝑡 ¯ 𝑡 VR ( ) − body 𝑡 ¯ 𝑡 VR − body 𝑉 𝑉
Observed events 192 169 247 41 137 84Total (post-fit) SM events 192 ±
14 169 ±
13 247 ±
16 38 . ± . ±
25 97 ± 𝑡 ¯ 𝑡 ±
14 65 ± − ± ±
24 44 ± 𝑡 ¯ 𝑡𝑍 . ± .
33 1 . ± .
31 172 ±
23 0 . + . − . . ± . . ± . . ± .
035 74 ±
21 16 ± ± . ± . ± 𝑊𝑡 . ± . . ± . − . ± . . ± . . ± . 𝑍 / 𝛾 ∗ + jets − ± − − − . + . − . Others 1 . ± .
21 3 . ± .
24 43 ±
12 0 . ± .
06 1 . ± .
18 1 . ± . . + . − . . ± . ± . + . − . . + . − . . ± . E v en t s / . r ad Data Standard ModelDiboson ttWt ZttFNP +jets γ Z/Others
ATLAS = 13 TeV, 139 fbs3 body selection VV3 body CR R β φ∆ D a t a / S M (a) −
10 110 E v en t s / . r ad Data Standard Modeltt WtZtt FNPDiboson Others
ATLAS = 13 TeV, 139 fbs3 body selection tt3 body CR R β φ∆ D a t a / S M (b) Figure 4: Three-body selection. Distributions of (a) Δ 𝜙 R 𝛽 in the CR − body 𝑉 𝑉 selection, and (b) in the CR − body 𝑡 ¯ 𝑡 selection,after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. “Others”includes contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bands representthe total statistical and detector-related systematic uncertainty. In the upper panels, red arrows indicate the controlregion 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 thevertical-axis range. E v en t s / . r ad Data Standard Modeltt WtZtt FNPDiboson Others
ATLAS = 13 TeV, 139 fbs3 body selection tt3 body VR2 PT R012 D a t a / S M (a) E v en t s / . r ad Data Standard ModelDiboson ttWt ZttFNP +jets γ Z/Others
ATLAS = 13 TeV, 139 fbs3 body selection VV3 body VR R β φ∆ D a t a / S M (b) Figure 5: Three-body selection. Distributions of (a) 𝑅 𝑝 T in the validation region VR ( ) − body 𝑡 ¯ 𝑡 and (b) Δ 𝜙 R 𝛽 in thevalidation region VR − body 𝑉 𝑉 , after the background fit. The contributions from all SM backgrounds are shown as ahistogram stack. “Others” includes contributions from
𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes.The hatched bands represent the total statistical and detector-related systematic uncertainty. The bottom panelsshow the ratio of the observed data to the total SM background prediction, with hatched bands representing the totaluncertainty in the background prediction. .3 Estimation of the backgrounds in the four-body selection The dominant irreducible SM background sources for the four-body selection are 𝑡 ¯ 𝑡 and diboson: thesebackgrounds are normalised in two dedicated background-enriched control regions labelled as CR − body 𝑡 ¯ 𝑡 and CR − body 𝑉 𝑉 . Some of the requirements defining the kinematics of the SRs are relaxed in order to allow theselection of 𝑡 ¯ 𝑡 events in CR − body 𝑡 ¯ 𝑡 , while the 𝑅 ℓ selection is adjusted to maintain complete orthogonalitywith the SRs. The diboson contribution in CR − body 𝑉 𝑉 is enhanced by limiting the number of jets in the eventand the sub-leading jet 𝑝 T , and by the additional veto on 𝑏 -jets. The background predictions are tested invalidation regions: VR − body 𝑡 ¯ 𝑡 for 𝑡 ¯ 𝑡 validation and VR − body 𝑉 𝑉 and VR − body 𝑉 𝑉 , ℓ for diboson validation, with thelatter two selecting, respectively, events with two and three leptons in the final state. For VR − body 𝑉 𝑉 , ℓ a newset of variables is defined in order to mimic the dibosons’ kinematics in the signal regions. The two SFOSleptons with an invariant mass closest to 𝑚 𝑍 are considered as the two leptons coming from the decayof the 𝑍 boson. The momentum of the lepton ( p ( ℓ Zpaired ) ) of the selected pair having the same electriccharge as the non-paired lepton is added to the p missT in order to define 𝐸 miss 𝑇 , ℓ, corr = (cid:12)(cid:12)(cid:12)(cid:16) p missT + p ( ℓ Zpaired ) (cid:17) T (cid:12)(cid:12)(cid:12) and 𝑅 ℓ, corr is defined as the ratio of 𝐸 miss 𝑇 , ℓ, corr to the sum of the transverse momenta of two remaining OSleptons. The invariant mass of the remaining two leptons, called 𝑚 ℓℓ, corr , is also used. The definition ofthe control and validation regions used in the four-body selection is summarised in Table 10. In the 𝑡 ¯ 𝑡 control region the signal contamination is ∼
1% or less. In CR − body 𝑉 𝑉 , the typical signal contaminationis about ∼ − − ∼
5% for a top squark mass of ∼
400 GeV andlightest-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 regionsafter the background fit. The normalisation factors extracted by the fit for the diboson and 𝑡 ¯ 𝑡 productionprocesses are 1 . ± .
25 and 0 . ± .
12 respectively. The distributions of 𝐸 missT in CR − body 𝑡 ¯ 𝑡 and 𝑅 ℓ inCR − body 𝑉 𝑉 , after the background fit, are shown in Figure 6. The distributions of 𝑝 T ( ℓ ) in VR − body 𝑡 ¯ 𝑡 , 𝑛 jets in VR − body 𝑉 𝑉 and 𝐸 miss 𝑇 , ℓ, corr in VR − body 𝑉 𝑉 , ℓ , after the background fit, are shown in Figure 7. Good agreementbetween data and the SM predictions is observed. 19 able 10: Four-body selection. Control and validation regions definition. The common selection defined in Section 5also applies to all regions.CR − body 𝑡 ¯ 𝑡 CR − body 𝑉 𝑉 VR − body 𝑡 ¯ 𝑡 VR − body 𝑉 𝑉 VR − body 𝑉 𝑉 , ℓ Lepton multiplicity 2 2 2 2 3Lepton flavour DF+SF DF+SF DF+SF DF+SF at least one SFOS pair 𝑝 T ( ℓ ) [GeV] < < < < < 𝑝 T ( ℓ ) [GeV] < < < < < 𝑝 T ( ℓ ) [GeV] – – – – < 𝑚 ℓℓ [GeV] > > > > > | 𝑚 ℓℓ − 𝑚 𝑍 | [GeV] – >
10 for SF only – >
10 for SF only - 𝐸 missT [GeV] > > > > > 𝑝 T ( 𝑗 ) [GeV] > > > > > Δ 𝑅 ℓ , 𝑗 𝑖 > > > > > 𝑛 jets – ≤ ≤ < 𝑛 𝑏 − jets ≥ = ≥ = = 𝑏 -tagged 𝑗 – – True – – 𝑝 T ( 𝑗 ) [GeV] – <
40 if 𝑗 exists – – – 𝐸 missT significance > > > > > 𝑝 ℓℓ T , boost [GeV] > > > >
280 – 𝑅 ℓ < < > [ , ] – 𝑅 ℓ 𝑗 – – [ . , . ] – – 𝐸 missT , ℓ, corr [GeV] – – – – > 𝑅 ℓ, corr – – – – > 𝑚 ℓℓ, corr [GeV] – – – – > − body 𝑡 ¯ 𝑡 , CR − body 𝑉 𝑉 , VR − body 𝑡 ¯ 𝑡 , VR − body 𝑉 𝑉 and VR − body 𝑉 𝑉 , ℓ .The ‘Others’ category contains the contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 . Combinedstatistical 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 quadratureto the total background uncertainty. CR − body 𝑡 ¯ 𝑡 CR − body 𝑉 𝑉 VR − body 𝑡 ¯ 𝑡 VR − body 𝑉 𝑉 VR − body 𝑉 𝑉 , ℓ Observed events 149 163 86 168 25Total (post-fit) SM events 149 ±
12 162 ±
13 86 ±
20 173 ±
14 27 ± 𝑡 ¯ 𝑡 ±
13 39 ±
13 41 ±
19 57 ±
14 –Post-fit, diboson 0 . ± . ±
18 1 . ± . ±
18 19 ± 𝑊𝑡 ± . ± . ± . ± . 𝑍 / 𝛾 ∗ + jets 0 . ± .
07 2 . ± . . ± . . ± .
35 – 𝑡 ¯ 𝑡𝑍 . ± .
34 0 . ± .
09 0 . ± .
17 0 . ± .
16 0 . ± . . ± .
17 0 . ± .
26 1 . ± .
20 1 . ± . . ± . . ± . ± . ± . ± . ± . [GeV] misst E E v en t s / G e V ATLAS CR -1 =13 TeV, 139 fbs Data Standard ModelFNP ttDiboson Wt*+jets g Z/ ZttOthers
250 300 350 400 450 500 550 [GeV] misst E D a t a / S M (a) R E v en t s / . ATLAS CR -1 =13 TeV, 139 fbs Data Standard ModelFNP ttDiboson Wt*+jets g Z/ ZttOthers R D a t a / S M (b) Figure 6: Four-body selection. Distributions of (a) 𝐸 missT in CR − body 𝑡 ¯ 𝑡 and (b) 𝑅 ℓ in CR − body 𝑉 𝑉 after the backgroundfit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributionsfrom
𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bands represent the total statistical anddetector-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 thetotal SM background prediction, with hatched bands representing the total uncertainty in the background prediction. [GeV] lt P E v en t s / G e V ATLAS VR -1 =13 TeV, 139 fbs Data Standard ModelFNP ttDiboson Wt*+jets g Z/ ZttOthers
10 20 30 40 50 60 ) [GeV] (l t p D a t a / S M (a) jets N E v en t s ATLAS VR -1 =13 TeV, 139 fbs Data Standard ModelFNP ttDiboson Wt*+jets g Z/ ZttOthers jets N D a t a / S M (b) [GeV] missT,corr E E v en t s / G e V ATLAS VR -1 =13 TeV, 139 fbs Data Standard ModelFNP DibosonZtt Others
250 300 350 400 450 500 [GeV] missT,1l,corr E D a t a / S M (c) Figure 7: Four-body selection. Distributions of (a) 𝑝 T ( ℓ ) in VR − body 𝑡 ¯ 𝑡 , (b) 𝑛 jets in VR − body 𝑉 𝑉 and (c) 𝐸 missT , ℓ, corr inVR − body 𝑉 𝑉 , ℓ after the background fit. The contributions from all SM backgrounds are shown as a histogram stack.“Others” includes contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bandsrepresent the total statistical and detector-related systematic uncertainty. The rightmost bin of each plot includesoverflow events. In the upper panels, red arrows indicate the validation region selection criteria. The bottom panelsshow the ratio of the observed data to the total SM background prediction, with hatched bands representing the totaluncertainty in the background prediction. Systematic uncertainties
Systematic uncertainties are evaluated for the signal and for the background predictions. The mainexperimental uncertainties in the yields of the reconstructed objects, the theoretical uncertainties in theprocesses’ yields, and the uncertainties related to the MC modelling of the SM backgrounds are describedin 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 energyresolution (JER). The JES and JER uncertainties are derived as a function of the 𝑝 T and 𝜂 of the jet, as wellas of the pile-up conditions and the jet-flavour composition of the selected jet sample [111]. Uncertaintiesassociated with the modelling of the 𝑏 -tagging efficiencies for 𝑏 -jets, 𝑐 -jets and light-flavour jets [112,113] are also considered. The systematic uncertainties related to the modelling of 𝐸 missT in the simulationare estimated by propagating the uncertainties in the energy and momentum scales of electrons, muons andjets, as well as the uncertainties in the resolution and scale of the soft term [114]. Other detector-relatedsystematic uncertainties, including those arising from lepton reconstruction efficiency, energy scale, energyresolution and in the modelling of the trigger efficiency [45, 51, 52, 115, 116], or the ones due to thepile-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 intouncertainties 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 hadronisationprocesses that convert partons into the hadronic final states. The uncertainties in the modelling of the 𝑡 ¯ 𝑡 background are estimated by varying the renormalisation and factorisation scales, as well as the amount ofinitial- and final-state radiation produced when generating the samples [117, 118]. Comparison between theyields obtained with Powheg and MadGraph5_aMC@NLO [117] is used to estimate uncertainties fromthe event generator choice. For 𝑡 ¯ 𝑡 𝑍 production, in the two-body and three-body selections, the effects ofQCD scale uncertainties are evaluated using seven-point variations of the factorisation and renormalisationscales [119]. Uncertainties for additional radiation contributions (ISR, FSR) are evaluated by comparingthe nominal sample with one obtained with a Pythia tune enhancing the radiation [55]. In the four-bodyselection, since the 𝑡 ¯ 𝑡 𝑍 background contribution is minor, a total theoretical error of 14%, coming fromthe cross-section uncertainty [120], is applied instead. For 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 production, the parton showeringand hadronisation uncertainties are covered by the difference between samples obtained using the twodifferent showering models implemented in Pythia and in Herwig. Single top quark production viathe 𝑊𝑡 -channel is a minor background in all the selections. An uncertainty in the acceptance due to theinterference between 𝑡 ¯ 𝑡 and 𝑊𝑡 production is assigned by comparing dedicated samples produced withPowheg and Pythia using the diagram removal (DR) and the diagram subtraction (DS) approaches [121].The modelling uncertainties for the diboson background are estimated using the seven-point variationsof the renormalisation and factorisation scales. Additional uncertainties in the resummation (QSF) andmatching (CKKM) scales between the matrix element generator and parton shower are computed byvarying the scale parameters in Sherpa [89]. For the other background processes which make minorcontributions a conservative uncertainty is applied. These minor backgrounds are mainly 𝑡 ¯ 𝑡𝑊 𝑍 and 𝑡𝑡𝑊 processes. A 30% uncertainty, driven by the DR versus DS difference for the 𝑡 ¯ 𝑡𝑊 𝑍 [122] process, isapplied in the two-body and three-body selections. For the four-body selection a 22% uncertainty isapplied for the uncertainty in the 𝑡 ¯ 𝑡𝑊 cross-section [120]. For all the processes mentioned above the PDFuncertainties [123] were evaluated and found to be negligible.Systematic uncertainties in the data-driven FNP background estimate are expected due to potentialdifferences in the FNP composition (heavy flavour, light flavour or photon conversions) between the regions23efined in Section 6 and the CR FNP used to extract the fake factor. A FNP systematic error is evaluated ineach of the regions by varying the FNP composition in the CR
FNP to match that of the considered analysisregion. The statistical error is also included by propagating the statistical uncertainty in the ratio used tocompute the fake factor. For the four-body selection, where the FNP lepton background is dominant, a FNPclosure uncertainty is also evaluated from the full difference between the data and the FNP predictions asobserved in a validation region with two same-charge leptons with kinematics similar to the four-bodyselection. The closure uncertainty ranges between 13% and 33% in the regions where the FNP backgroundis important.A 1.7% uncertainty in the luminosity measurement is considered for all signal and background estimatesthat are derived directly from MC simulations [46].Tables 12, 13 and 14 summarise the contributions from the different sources of systematic uncertainty inthe total SM background predictions for the two-body, three-body and four-body signal regions. The totalsystematic uncertainty ranges between 14% and 26%, with the dominant sources being the MC statisticalerror, 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 cross-section predictionsusing different PDF sets and factorisation and renormalisation scales as described in Ref. [64]. Theuncertainty in the DM production cross-section is derived from the scale variations and the PDF choices. TheSUSY 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, wherethe largest theory uncertainties are those resulting from radiation and are in the range 10% to 24% dependingon the mass difference 𝑚 ( ˜ 𝑡 ) − 𝑚 ( ˜ 𝜒 ) . For the DM signals the total systematic uncertainty is between 5%and 20%. 24 able 12: Two-body selection. Sources of systematic uncertainty in the SM background estimates, after thebackground fits, for the SF selection. The values are given as relative uncertainties in the total expected backgroundevent yields in the SRs. Entries marked ‘–’ indicate a contribution smaller than 1%. ‘MC statistical uncertainty’refers to the statistical uncertainty from the simulated event samples. ‘Other theoretical uncertainties’ represent thetheoretical uncertainty coming from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 contributions. The individualcomponents can be correlated and therefore do not necessarily add up in quadrature to the total systematic uncertainty. Signal Region SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ∞) Total SM background uncertainty 19% 20% 17% 15% 15% 20% 𝑉𝑉 theoretical uncertainties − .
4% 3 .
5% 4 .
9% 4 .
4% 7 . 𝑡 ¯ 𝑡 theoretical uncertainties 10% 11% 6 . − .
7% 2 . 𝑡 ¯ 𝑡𝑍 theoretical uncertainties 1 .
0% 2 .
2% 4 .
2% 5 .
2% 5 .
0% 11% 𝑡 ¯ 𝑡 – 𝑊𝑡 interference − − − − .
0% 5 . .
0% 1 .
4% 2 .
7% 2 .
5% 2 .
6% 1 . .
1% 5 .
4% 7 .
0% 7 .
7% 9 .
9% 8 . 𝑡 ¯ 𝑡 normalisation 7 .
6% 4 .
8% 1 . − − − 𝑡 ¯ 𝑡𝑍 normalisation 1 .
1% 3 .
2% 5 .
6% 7 .
2% 6 .
4% 4 . .
7% 9 .
6% 2 .
0% 3 .
4% 2 . .
6% 13% 7 .
0% 6 .
1% 3 .
6% 7 . 𝐸 missT modelling 2 .
9% 3 .
6% 1 .
0% 4 .
1% 2 .
7% 1 . .
6% 1 .
8% 1 .
8% 3 .
8% 3 .
7% 6 . .
0% 1 .
0% 1 .
0% 2 .
6% 3 .
0% 2 . − .
4% 1 .
0% 1 .
0% 1 . − Fake and non-prompt leptons − − . − .
8% 4 . able 13: Two-body selection. Sources of systematic uncertainty in the SM background estimates, after thebackground fits, for the DF selection. The values are given as relative uncertainties in the total expected backgroundevent yields in the SRs. Entries marked ‘–’ indicate a contribution smaller than 1%. ‘MC statistical uncertainty’refers to the statistical uncertainty from the simulated event samples. ‘Other theoretical uncertainties’ represent thetheoretical uncertainty coming from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 contributions. The individualcomponents can be correlated and therefore do not necessarily add up in quadrature to the total systematic uncertainty. Signal Region SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ∞) Total SM background uncertainty 20% 20% 15% 16% 14% 21% 𝑉𝑉 theoretical uncertainties 1 .
0% 1 .
3% 2 .
6% 1 .
0% 2 .
0% 1 . 𝑡 ¯ 𝑡 theoretical uncertainties 9 .
6% 12% 7 . − . − 𝑡 ¯ 𝑡𝑍 theoretical uncertainties 1 .
2% 2 .
0% 5 .
3% 6 .
6% 5 .
7% 16% 𝑡 ¯ 𝑡 – 𝑊𝑡 interference − − − − − − Other theoretical uncertainties 1 .
0% 1 .
2% 2 .
8% 3 .
2% 2 .
7% 3 . .
7% 5 .
0% 6 .
9% 8 .
2% 7 .
7% 6 . 𝑡 ¯ 𝑡 normalisation 7 .
2% 5 .
6% 1 . − − − 𝑡 ¯ 𝑡𝑍 normalisation 1 .
4% 2 .
8% 6 .
9% 9 .
1% 7 .
3% 7 . .
5% 10% 2 .
5% 6 .
1% 1 .
0% 2 . .
6% 6 .
2% 4 .
3% 5 .
3% 2 . 𝐸 missT modelling 3 .
5% 6 .
1% 1 .
0% 2 .
2% 2 .
2% 1 . .
5% 1 .
1% 1 .
6% 1 .
3% 1 .
3% 1 . .
0% 1 .
0% 1 .
3% 2 .
0% 1 .
0% 1 . − .
6% 1 . − . − Fake and non-prompt leptons − . − − .
1% 13% able 14: Three-body and four-body selections. Sources of systematic uncertainty in the SM background estimates,after the background fits. The values are given as relative uncertainties in the total expected background event yieldsin the SRs. Entries marked ‘–’ indicate a contribution smaller than 1%. ‘MC statistical uncertainty’ refers to thestatistical uncertainty from the simulated event samples. ‘Other theoretical uncertainties’ represent the theoreticaluncertainty coming from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 contributions. The individual componentscan be correlated and therefore do not necessarily add up in quadrature to the total systematic uncertainty. Signal Region SR-DF − body 𝑊 SR-SF − body 𝑊 SR-DF − body 𝑡 SR-SF − body 𝑡 SR − bodySmall Δ 𝑚 SR − bodyLarge Δ 𝑚 Total SM background uncertainty 18% 26% 18% 22% 25% 14% 𝑉𝑉 theoretical uncertainties 8 .
0% 10% 1 .
0% 1 .
5% 3 .
6% 4 . 𝑡 ¯ 𝑡 theoretical uncertainties 8 .
2% 6 .
6% 14% 8 .
6% 1 .
0% 6 . 𝑡 ¯ 𝑡𝑍 theoretical uncertainties − − .
2% 2 . − − 𝑡 ¯ 𝑡 – 𝑊𝑡 interference − . − . − . − − .
4% 1 . − − MC statistical uncertainty 5 .
8% 7 .
4% 5 .
6% 6 .
7% 3 .
3% 2 . 𝑉𝑉 normalisation 15% 20% 1 .
0% 2 .
0% 2 .
8% 8 . 𝑡 ¯ 𝑡 normalisation 2 .
3% 1 .
9% 4 .
9% 3 .
3% 1 .
0% 6 . 𝑡 ¯ 𝑡𝑍 normalisation − − .
1% 4 . − − Jet energy scale 5 .
5% 3 .
7% 3 .
8% 4 .
1% 1 .
0% 3 . .
3% 11% 9 .
0% 18% 1 .
3% 3 . .
3% 2 .
0% 1 .
0% 2 .
5% 1 .
3% 3 . 𝐸 missT modelling 1 .
1% 2 .
2% 3 .
0% 1 . − . .
1% 2 .
9% 1 .
6% 1 . − . .
0% 1 . − − . − Fake and non-prompt leptons 1 . − − .
6% 25% − Results
A set of simultaneous likelihood fits is performed, for each one of the three different selections, usingstandard minimisation software packages, HistFitter and pyhf [105, 106]. For the normalisation of thesemi-data-driven backgrounds, only the CRs are considered in the background fit, while for the computationof the exclusion limits both the CRs and SRs are included as constraining channels. The likelihood isa product of Poisson probability density functions (pdf), describing the observed number of events ineach CR/SR, and Gaussian pdf distributions that describe the nuisance parameters associated with allthe systematic uncertainties. Systematic uncertainties that are correlated between different samples areaccounted for in the fit configuration by using the same nuisance parameter. The uncertainties are appliedin each of the CRs and SRs and their effect is correlated for events across all regions in the fit.The results of the background fit are shown in Figures 8–10 for each of the three analysis selections. Ingeneral, good agreement, within about one standard deviation, is observed in all the SRs and VRs except inSR-DF − body 𝑊 where the data fluctuates well below the fit. E v en t s Data Standard Model Ztt ttDiboson FNPWt Z+jetsOthers P r e f i t P o s t f i t ControlRegions Validation Regions Signal Regions
ATLAS = 13 TeV, 139 fbs2 body selection tt bod y CR Z tt bod y CR , D F tt bod y V R , S F tt bod y V R Z tt bod y V R ) ↵ [ , bod y S R ) ↵ [ , bod y S R ) ↵ [ , bod y S R ) ↵ [ , bod y S R ) ↵ [ , bod y S R ) ↵ [ , bod y S R ) ↵ [ , bod y S R − − S i gn i f i c an c e tt µ Ztt µ X µ Figure 8: Two-body selection. Expected and observed yields are shown. The upper panel shows the observednumber of events in each of the CRs, VRs and the inclusive SRs defined in the two-body selection, together withthe expected SM backgrounds obtained before the fit in the CRs and after the fit in the VRs and SRs. “Others”includes contributions from
𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The shaded band representsthe total uncertainty in the expected SM background. The lower panel shows the normalisation factors 𝜇 𝑋 (left twobins) extracted in the CRs for the 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡𝑍 processes, while, for the VRs and the inclusive SRs (right bins), thesignificance as defined in Ref. [124]. E v en t s Data Standard ModelDiboson tt Ztt FNPWt Z+jetsOthers P r e f i t P o s t f i t Control Regions Validation Regions Signal Regions
ATLAS = 13 TeV, 139 fbs3 body selection tt bod y CR Z tt bod y CR VV bod y CR tt bod y V R tt bod y V R VV bod y V R bod y W S R D F bod y W S R S F bod y t S R D F bod y t S R S F − − S i gn i f i c an c e tt µ Ztt µ VV µ X µ Figure 9: Three-body selection. Expected and observed yields are shown. The upper panel shows the observednumber of events in each of the CRs,VRs and SRs defined in the three-body selection, together with the expected SMbackgrounds obtained before the fit in the CRs and after the fit in the VRs and SRs. “Others” includes contributionsfrom
𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The shaded band represents the total uncertainty inthe expected SM background. The lower panel shows the normalisation factors 𝜇 𝑋 (left three bins) extracted in theCRs for the 𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑍 and diboson processes, while, for the VRs and the SRs (right bins), the significance as defined inRef. [124]. E v en t s Data Standard ModelFNP ttDiboson WtZ+jets ZttOthers P r e f i t P o s t f i t Control Regions Validation Regions Signal Regions
ATLAS = 13 TeV, 139 fbs4 body selection tt4 body CR VV4 body CR tt4 body VR VV4 body VR VV,3L4 body VR m ∆ Small4 body SR m ∆ Large4 body SR − − S i gn i f i c an c e tt µ VV µ X µ Figure 10: Four-body selection. Expected and observed yields are shown. The upper panel shows the observednumber of events in each of the CRs, VRs and SRs defined in the four-body selection, together with the expected SMbackgrounds obtained before the fit in the CRs and after the fit in the VRs and SRs. “Others” includes contributionsfrom
𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The shaded band represents the total uncertainty in theexpected SM background. The lower panel shows the normalisation factors 𝜇 𝑋 (left two bins) extracted in the CRsfor the 𝑡 ¯ 𝑡 and diboson processes, while, for the VRs and the SRs (right bins), the significance as defined in Ref. [124]. .1 Two-body selection results The estimated SM yields in the binned and inclusive SRs defined in the two-body selection are obtainedwith a background fit which simultaneously determines the normalisations of the background contributionsfrom 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 . Figure 11 shows the 𝑚 ℓℓ T2 distribution for events satisfying all the selection criteria ofthe SR − body110 , ∞ (SF and DF) signal regions, after the background fit. Each bin corresponds to one of thebinned SRs. No significant excess over the SM prediction is observed, as can be seen from results shown inTables 15 and 16 for the binned SRs. E v en t s / B i n Data Standard Modeltt WtZtt FNPDiboson Others )= (150,1) GeV χ , φ : m( φ +tt )= (150,1) GeV χ +a: m(a,tt ATLAS = 13 TeV, 139 fbs2 body selection DF2 body SR
100 120 140 160 180 200 220 240 260 280 [GeV] T2 m012 D a t a / S M (a) E v en t s / B i n Data Standard Modeltt WtZtt FNP+jets γ Z/ DibosonOthers )= (150,1) GeV χ , φ : m( φ +tt )= (150,1) GeV χ +a: m(a,tt ATLAS = 13 TeV, 139 fbs2 body selection SF2 body SR
100 120 140 160 180 200 220 240 260 280 [GeV] T2 m012 D a t a / S M (b) Figure 11: Two-body selection. Distributions of 𝑚 ℓℓ T2 in SR − body110 , ∞ for (a) different-flavour and (b) same-flavour eventssatisfying the selection criteria of the given SR, except the one for the presented variable, after the backgroundfit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributionsfrom 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bands represent the total statistical andsystematic uncertainty. The rightmost bin of each plot includes overflow events. Reference dark-matter signal modelsare overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottompanels show the ratio of the observed data to the total SM background prediction, with hatched bands representingthe total uncertainty in the background prediction. able 15: Two-body selection. Background fit results for the different-flavour leptons binned SRs. The ‘Others’category contains the contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 . Combined statistical andsystematic uncertainties are given. Entries marked ‘–’ indicate a negligible background contribution (less than 0.001events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the totalbackground uncertainty. SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ) SR-DF − body [ , ∞) Observed events 19 13 5 1 1 3Fitted bkg. events 22 ± . ± . . ± . . ± .
45 3 . ± .
45 3 . ± . 𝑡 ¯ 𝑡 ± . ± . . ± . . + . − . . ± .
11 –Post-fit, 𝑡 ¯ 𝑡 + 𝑍 . ± . . ± . . ± . . ± . . ± . . ± . 𝑊𝑡 . ± .
27 0 . + . − . . ± .
012 – 0 . ± .
013 – 𝑍 / 𝛾 ∗ + jets – – – – – –Diboson 0 . ± .
27 0 . ± .
24 0 . ± .
16 0 . + . − . . ± .
13 0 . ± . . ± .
19 1 . ± .
28 1 . ± .
16 0 . ± .
12 0 . ± .
13 0 . ± . . + . − . . ± . . + . − . . + . − . . ± .
23 0 . ± . Table 16: Two-body selection. Background fit results for the same-flavour leptons binned SRs. The ‘Others’ categorycontains the contributions from
𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 . Combined statistical and systematicuncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature tothe total background uncertainty. SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ) SR-SF − body [ , ∞) Observed events 17 19 9 3 4 5Fitted bkg. events 18 . ± . . ± . . ± . . ± . . ± . ± 𝑡 ¯ 𝑡 . ± . . ± . . ± . . + . − . . ± .
08 0 . + . − . Post-fit, 𝑡 ¯ 𝑡 + 𝑍 . ± .
35 3 . ± . . ± . . ± . . ± .
45 1 . ± . 𝑊𝑡 . ± . . ± . . ± .
04 0 . + . − . . ± .
06 0 . ± . 𝑍 / 𝛾 ∗ + jets 0 . ± .
014 0 . ± .
003 0 . + . − . . ± .
13 0 . ± .
33 0 . ± . . ± .
20 1 . ± . . ± .
24 0 . ± . . ± .
28 0 . ± . . ± .
13 1 . ± .
21 0 . ± .
16 0 . ± .
11 0 . ± .
14 0 . ± . . + . − . . + . − . . ± .
06 0 . + . − . . ± .
12 0 . ± . The dominant background processes in the three-body selection are diboson, 𝑡 ¯ 𝑡 and 𝑡 ¯ 𝑡 𝑍 production, and theyields are determined with a simultaneous fit. Figure 12 shows the distributions of 𝑀 R Δ in SR − body 𝑊 (top)and in SR − body 𝑡 (bottom), for events satisfying all the selection criteria except the one for the presentedvariable, after the background fit. Table 17 shows the observed events in each signal region and the SMbackground estimates. No excess over the SM prediction is observed while a fluctuation of about − 𝜎 isobserved in SR-DF − body 𝑊 and is also visible in Figure 12(a).32 −
10 110 E v en t s / G e V ) = (475,385) GeV χ∼ , t~, m( t~ t~ ) = (475,310) GeV χ∼ , t~, m( t~ t~Data Standard ModelDiboson ttWt ZttFNP +jets γ Z/Others
ATLAS = 13 TeV, 139 fbs3 body selection SR DF
80 90 100 110 120 130 140 [GeV] R ∆ M012 D a t a / S M (a) −
10 110 E v en t s / G e V ) = (475,385) GeV χ∼ , t~, m( t~ t~ ) = (475,310) GeV χ∼ , t~, m( t~ t~Data Standard ModelDiboson ttWt ZttFNP +jets γ Z/Others
ATLAS = 13 TeV, 139 fbs3 body selection SR SF
80 90 100 110 120 130 140 [GeV] R ∆ M012 D a t a / S M (b) E v en t s / G e V ) = (475,385) GeV χ∼ , t~, m( t~ t~ ) = (475,310) GeV χ∼ , t~, m( t~ t~Data Standard Modeltt WtZtt FNPDiboson Others ATLAS = 13 TeV, 139 fbs3 body selection SR DF
80 90 100 110 120 130 140 [GeV] R ∆ M012 D a t a / S M (c) E v en t s / G e V ) = (475,385) GeV χ∼ , t~, m( t~ t~ ) = (475,310) GeV χ∼ , t~, m( t~ t~Data Standard Modeltt WtZtt FNP+jets γ Z/ DibosonOthers
ATLAS = 13 TeV, 139 fbs3 body selection SR SF
80 90 100 110 120 130 140 [GeV] R ∆ M012 D a t a / S M (d) Figure 12: Three-body selection. Distributions of 𝑀 R Δ in (a,b) SR − body 𝑊 and (c,d) SR − body 𝑡 for (left) same-flavourand (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presentedvariable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack.“Others” includes contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bandsrepresent 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 panelsindicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SMbackground prediction, with hatched bands representing the total uncertainty in the background prediction; redarrows show data outside the vertical-axis range. able 17: Three-body selection. Observed event yields and background fit results for the three-body selectionSRs. The ‘Others’ category contains contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 . Combinedstatistical 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 quadratureto the total background uncertainty. SR-DF − body 𝑊 SR-SF − body 𝑊 SR-DF − body 𝑡 SR-SF − body 𝑡 Observed events 1 5 5 5Total (post-fit) SM events 5 . ± . . ± . . ± . . ± . 𝑡 ¯ 𝑡 . ± . . ± .
32 3 . ± . . ± . 𝑡 ¯ 𝑡 + 𝑍 . ± .
034 0 . ± .
05 2 . ± . . ± . . ± . . ± . . ± .
09 0 . ± . 𝑊𝑡 . ± .
05 0 . ± .
030 0 . + . − . . ± . 𝑍 / 𝛾 ∗ +jets – 0 . ± .
019 – 0 . + . − . Others 0 . ± .
020 0 . ± .
05 0 . ± .
12 0 . ± . . ± .
09 0 . + . − . . + . − . . + . − . .3 Four-body selection results The estimated SM yields in SR − bodySmall Δ 𝑚 and SR − bodyLarge Δ 𝑚 are determined with a background fit that providesthe normalisation factors for 𝑡 ¯ 𝑡 and diboson production. Figure 13 shows the distributions of (a) 𝐸 missT inSR − bodySmall Δ 𝑚 and (b) 𝑅 ℓ 𝑗 in SR − bodyLarge Δ 𝑚 for events satisfying the selection criteria of the given SR, except theone for the presented variable, after the background fit. The background fit results are shown in Table 18.The observed yield in the SR is within one standard deviation of the background prediction. [GeV] misst E1 -
10 110 E v en t s / G e V ATLAS m D Small4-body SR -1 =13 TeV, 139 fbs Data Standard ModelFNP ttDiboson Wt*+jets g Z/ ZttOthers )=(400,380) GeV c~ , t~,m( t~ t~ )=(460,415) GeV c~ , t~,m( t~ t~
300 400 500 600 700 800 900 1000 [GeV] missT E D a t a / S M (a) R E v en t s / . ATLAS m D Large4-body SR -1 =13 TeV, 139 fbs Data Standard ModelFNP ttDiboson Wt*+jets g Z/ ZttOthers )=(400,380) GeV c~ , t~,m( t~ t~ )=(460,415) GeV c~ , t~,m( t~ t~ R D a t a / S M (b) Figure 13: Four-body selection. (a) distributions of 𝐸 missT in SR − bodySmall Δ 𝑚 and (b) distribution of 𝑅 ℓ 𝑗 in SR − bodyLarge Δ 𝑚 forevents satisfying the selection criteria of the given SR, except the one for the presented variable, after the backgroundfit. The contributions from all SM backgrounds are shown as a histogram stack. “Others” includes contributionsfrom 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 processes. The hatched bands represent the total statistical andsystematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair productionsignal 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 bandsrepresenting the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range. able 18: Four-body selection. Observed event yields and background fit results for SR − bodySmall Δ 𝑚 and SR − bodyLarge Δ 𝑚 . The‘Others’ category contains the contributions from 𝑉𝑉𝑉 , 𝑡 ¯ 𝑡𝑡 , 𝑡 ¯ 𝑡𝑡 ¯ 𝑡 , 𝑡 ¯ 𝑡𝑊 , 𝑡 ¯ 𝑡𝑊𝑊 , 𝑡 ¯ 𝑡𝑊 𝑍 , 𝑡 ¯ 𝑡𝐻 , and 𝑡𝑍 . Combined statisticaland systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add upin quadrature to the total background uncertainty. SR − bodySmall Δ 𝑚 SR − bodyLarge Δ 𝑚 Observed events 10 19Total (post-fit) SM events 12 . ± . . ± . 𝑡 ¯ 𝑡 . ± .
26 8 . ± . . ± . . ± . 𝑊𝑡 . ± .
08 2 . ± . 𝑍 / 𝛾 ∗ +jets 0 . ± .
023 0 . ± . 𝑡 ¯ 𝑡 𝑍 . ± .
010 0 . ± . . + . − . . ± . . ± . . ± . No excess is observed in the data relative to the expected background. The analysis results are thereforeinterpreted in terms of model-independent upper limits on the visible cross-section ( 𝜎 vis ) of new physics,defined as the 95% confidence level (CL) upper limit on the number of signal events ( 𝑆 ) divided bythe integrated luminosity, and in terms of exclusion limits in the plane of the masses parameters of oursimplified models. For the two-body selection the upper limits are derived using the inclusive SRs.The upper limits on 𝜎 vis are derived, in each SR, by performing a model-independent hypothesis test,which introduces a free signal as an additional process to be constrained by the observed yield. The CL s method [125] is used to derive all the exclusion confidence levels. Model-independent upper limits arepresented in Table 19. These limits assume negligible signal contamination in the CRs, resulting in a moreconservative result than from the model-dependent limits, where a small signal contamination is allowed inthe CRs.Model-dependent limits are computed for the various signal scenarios considered in the analysis. Thehypothesis tests are performed including the expected signal yield and its associated uncertainties in theCRs and SRs. All limits are quoted at 95% CL with the CL s method. When setting limits, the two-bodyselection binned SRs SR − DF − body [ 𝑥,𝑦 ) and SR − SF − body [ 𝑥,𝑦 ) regions are combined. Similarly, the SR-DF − body 𝑊 ,SR-SF − body 𝑊 , SR-DF − body 𝑡 , and SR-SF − body 𝑡 signal regions are combined for the three-body selection, andso are SR − bodySmall Δ 𝑚 and SR − bodyLarge Δ 𝑚 for the four-body selection.Limits for simplified models in which pair-produced ˜ 𝑡 decay with 100% branching ratio into a top quarkand ˜ 𝜒 are shown in the ˜ 𝑡 – ˜ 𝜒 mass plane in Figure 14(a) and in the 𝑚 ( ˜ 𝑡 ) – Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) plane in Figure 14(b).The exclusion contour is the envelope of the exclusion regions obtained separately for the three selections.Top squark masses up to 1 TeV are excluded for a massless lightest neutralino. Neutralino masses upto 500 GeV are excluded for 𝑚 ( ˜ 𝑡 ) above the top quark production kinematic limit. In the three-body36ecay region, top squark masses are excluded up to 600 GeV for Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) =
120 GeV, up to 550 GeVfor Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) close to the top quark mass and up to 430 GeV for Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) close to the 𝑊 boson mass.In the four-body decay region, top squark masses are excluded up to 540 GeV for Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) =
40 GeV.Top squark decay around the 𝑊 boson production kinematic limit is not fully excluded for 𝑚 ( ˜ 𝑡 ) above400 GeV because there the four-body and three-body decay exclusion regions do not overlap. The four-bodyselection loses sensitivity for Δ 𝑚 ( ˜ 𝑡, ˜ 𝜒 ) (cid:38) 𝑚 ( 𝑊 ) due to the upper bound of the sub-leading lepton 𝑝 T while, for the three body selection, the 𝑀 R Δ requirement suppresses the sensitivity for Δ 𝑚 ( ˜ 𝑡, ˜ 𝜒 ) (cid:46) 𝑚 ( 𝑊 ) because of the smaller mass splitting. The three-body and two-body overlap in the sensitivity providesexclusion coverage around the top quark production kinematic limit up to 𝑚 ( ˜ 𝑡 ) of 540 GeV. Table 19: Model-independent 95% CL upper limits on the visible cross-section ( 𝜎 vis ) of new physics, on the visiblenumber of signal events ( 𝑆 ), on the visible number of signal events ( 𝑆 ) given the expected number of backgroundevents (and ± 𝜎 excursions of the expected number), and the discovery 𝑝 -value ( 𝑝 ( 𝑠 = ) ), all calculated withpseudo-experiments, are shown for each of the SRs. The 𝑝 -value is reported as 0.5 if the observed yield is smallerthan that predicted. Selection S ignal Region 𝜎 vis [fb] 𝑆 𝑆 𝑝 ( 𝑠 = ) Two-body SR − body [ , ∞) .
21 29 . + − . − body [ , ∞) .
15 21 . + − . − body [ , ∞) .
10 13 . + − . − body [ , ∞) .
06 8 . + − . . − body [ , ∞) .
06 7 . . + . − . . − body [ , ∞) .
06 7 . . + . − . . − body [ , ∞) .
05 7 . . + . − . . − body 𝑊 .
023 3 . . + . − . . − body 𝑊 .
05 7 . . + . − . . − body 𝑡 .
04 5 . . + . − . . − body 𝑡 .
04 6 . . + . − . . − bodySmall Δ 𝑚 .
06 8 . . + . − . . − bodyLarge Δ 𝑚 .
08 11 . . + . − . .
00 400 500 600 700 800 900 1000 1100) [GeV] t~m(100200300400500600700 ) [ G e V ] c~ m ( ) = c~ , t ~ m ( D W + m b ) = m c~ , t ~ m ( D t ) = m c~ , t ~ m ( D ) exp s – Expected Limit ( )
SUSYtheory s – Observed Limit (, arXiv: 1710.11412 -1 ATLAS 36.1 fb c~ / bff' c~ / bW c~ t fi t~ production; t~ t~ ATLAS -1 =13 TeV, 139 fbs All limits at 95% CL (a)
300 400 500 600 700 800 900 1000 1100) [GeV] t~m(050100150200250 ) [ G e V ] c~ , t ~ m ( D W + m b ) = m c~ , t~ m( D t ) = m c~ , t~ m( D ) exp s – Expected Limit ( )
SUSYtheory s – Observed Limit (, arXiv: 1710.11412 -1 ATLAS 36.1 fb c~ / bff' c~ / bW c~ t fi t~ production; t~ 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 ˜ 𝑡 pair production, decaying via˜ 𝑡 → 𝑡 (∗) ˜ 𝜒 with 100% branching ratio, in the (a) 𝑚 ( ˜ 𝑡 ) – 𝑚 ( ˜ 𝜒 ) and (b) 𝑚 ( ˜ 𝑡 ) – Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) planes. The dashed linesand the shaded bands are the expected limits and their ± 𝜎 uncertainties. The thick solid lines are the observedlimits for the central value of the signal cross-section. The expected and observed limits do not include the effect ofthe theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit whenvarying the signal cross-section by ± 𝜎 of the theoretical uncertainty. 𝑔 = 𝑔 𝑞 = 𝑔 𝜒 =
1, denoted by 𝜎 obs / 𝜎 Th ( 𝑔 = . ) .These limits are obtained as a function of the mediator mass, assuming a specific DM particle mass of1 GeV. Both the scalar and pseudoscalar mediator cases are considered. The sensitivity is approximatelyconstant for mediator masses below 100 GeV and the models are excluded for scalar (pseudoscalar) mediatormasses up to 250 ( ) GeV when assuming 𝑔 =
10 20 30 40 50 100 200 300 ) [GeV] φ m( −
10 110 ( g = ) T h σ / ob s σ % C L li m i t on ATLAS = 13 TeV , 139 fbs2 body selection Scalar χχ → φ , φ + tt ) = 1 GeV χ = 1.0, m( χ = g q g= g Observed 95% CLExpected 95% CL σ ± Expected σ ± Expected (g=1.0) σ Theory unc. on (a)
10 20 30 40 50 100 200 300 m(a) [GeV] −
10 110 ( g = ) T h σ / ob s σ % C L li m i t on ATLAS = 13 TeV , 139 fbs2 body selection Pseudoscalar χχ → + a, a tt ) = 1 GeV χ = 1.0, m( χ = g q g= g Observed 95% CLExpected 95% CL σ ± Expected σ ± Expected (g=1.0) σ Theory unc. on (b)
Figure 15: Exclusion limits for (a) 𝑡 ¯ 𝑡 + 𝜙 scalar and (b) 𝑡 ¯ 𝑡 + 𝑎 pseudoscalar models as a function of the mediator massfor a DM particle mass of 𝑚 ( 𝜒 ) = 𝑔 = 𝑔 𝑞 = 𝑔 𝜒 =
1. Thesolid (dashed) lines shows the observed (expected) exclusion limits.
10 Conclusion
This paper reports the results of a search for direct top squark pair production and for dark matter in a finalstate containing two leptons with opposite electric charge, jets and missing transverse momentum. Thesearch uses an integrated luminosity of 139 fb − of proton–proton collisions at √ 𝑠 =
13 TeV, as collectedby 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 additionalintegrated luminosity and a new discriminating variable, the object-based 𝐸 missT significance. Moreover, inthe small- Δ 𝑚 ( ˜ 𝑡 , ˜ 𝜒 ) region, an important gain in sensitivity is also achieved by lowering the 𝑝 T thresholdfor lepton selection.The data are found to be consistent with the Standard Model predictions. Assuming direct ˜ 𝑡 pairproduction with both top squarks decaying in either the two-body channel ˜ 𝑡 → 𝑡 ˜ 𝜒 , the three-body channel˜ 𝑡 → 𝑏𝑊 ˜ 𝜒 , or the four-body channel ˜ 𝑡 → 𝑏ℓ𝜈 ˜ 𝜒 , constraints at 95% confidence level are placed on39he minimum ˜ 𝑡 and ˜ 𝜒 masses up to about 1 TeV and 500 GeV respectively. The results improve onthe previous ATLAS limits obtained in a two-lepton final state and provide unique sensitivity among theATLAS searches in the mass region where the decay ˜ 𝑡 → 𝑡 ˜ 𝜒 becomes kinematically allowed. For thedark-matter model, assuming spin-0 mediator production in association with a pair of top quarks and decaywith 100% branching ratio into a pair of dark-matter particles, scalar (pseudoscalar) mediator masses up toabout 250 ( ) GeV are excluded at 95% confidence level for mediator couplings 𝑔 𝑞 = 𝑔 𝜒 = cknowledgements We thank CERN for the very successful operation of the LHC, as well as the support staff from ourinstitutions 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, MPOCR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU,France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR,China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO,Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MESof 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 andCantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOEand NSF, United States of America. In addition, individual groups and members have received supportfrom 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; DFGand AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF andthe Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Banking Foundation, CERCA ProgrammeGeneralitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; GöranGustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.The crucial computing support from all WLCG partners is acknowledged gratefully, in particular fromCERN, 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 (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resourceproviders. Major contributors of computing resources are listed in Ref. [126].41 eferences [1] F. Zwicky,
Die Rotverschiebung von extragalaktischen Nebeln , Helv. Phys. Acta (1933) 110.[2] G. Bertone, D. Hooper and J. Silk, Particle dark matter: evidence, candidates and constraints ,Phys. Rept. (2005) 279, arXiv: hep-ph/0404175 .[3] L. Evans and P. Bryant,
LHC Machine , JINST (2008) S08001.[4] Y. Golfand and E. Likhtman, Extension of the Algebra of Poincare Group Generators and Violationof P Invariance , JETP Lett. (1971) 323, [Pisma Zh. Eksp. Teor. Fiz. (1971) 452].[5] D. Volkov and V. Akulov, Is the neutrino a goldstone particle? , Phys. Lett. B (1973) 109.[6] J. Wess and B. Zumino, Supergauge transformations in four dimensions , Nucl. Phys. B (1974) 39.[7] J. Wess and B. Zumino, Supergauge invariant extension of quantum electrodynamics , Nucl. Phys.B (1974) 1.[8] S. Ferrara and B. Zumino, Supergauge invariant Yang-Mills theories , Nucl. Phys. B (1974) 413.[9] A. Salam and J. Strathdee, Super-symmetry and non-Abelian gauges , Phys. Lett. B (1974) 353.[10] ATLAS Collaboration, Search for a scalar partner of the top quark in the all-hadronic 𝑡 ¯ 𝑡 plusmissing transverse momentum final state at √ 𝑠 = TeV with the ATLAS detector , Eur. Phys. J. C (2020) 737, arXiv: .[11] ATLAS Collaboration, ATLAS Run 1 searches for direct pair production of third-generation squarksat the Large Hadron Collider , Eur. Phys. J. C (2015) 510, arXiv: .[12] ATLAS Collaboration, Search for a scalar partner of the top quark in the jets plus missingtransverse momentum final state at √ 𝑠 = TeV with the ATLAS detector , JHEP (2017) 085,arXiv: .[13] ATLAS Collaboration, Search for top-squark pair production in final states with one lepton, jets,and missing transverse momentum using fb − of √ 𝑠 = TeV 𝑝 𝑝 collision data with the ATLASdetector , JHEP (2018) 108, arXiv: .[14] ATLAS Collaboration, Search for supersymmetry in final states with charm jets and missingtransverse momentum in TeV 𝑝 𝑝 collisions with the ATLAS detector , JHEP (2018) 050, arXiv: .[15] ATLAS Collaboration, Search for dark matter and other new phenomena in events with an energeticjet and large missing transverse momentum using the ATLAS detector , JHEP (2018) 126, arXiv: .[16] ATLAS Collaboration, Measurements of top-quark pair spin correlations in the 𝑒𝜇 channel at √ 𝑠 = TeV using 𝑝 𝑝 collisions in the ATLAS detector , Eur. Phys. J. C (2020) 754, arXiv: .[17] CMS Collaboration, Inclusive search for supersymmetry using razor variables in 𝑝 𝑝 collisions at √ 𝑠 = TeV , Phys. Rev. D (2017) 012003, arXiv: .[18] CMS Collaboration, A search for new phenomena in 𝑝 𝑝 collisions at √ 𝑠 = TeV in final stateswith missing transverse momentum and at least one jet using the 𝛼 T variable , Eur. Phys. J. C (2017) 294, arXiv: .[19] CMS Collaboration, Searches for pair production of third-generation squarks in √ 𝑠 = TeV 𝑝 𝑝 collisions , Eur. Phys. J. C (2017) 327, arXiv: .4220] CMS Collaboration, Search for direct production of supersymmetric partners of the top quarkin the all-jets final state in proton–proton collisions at √ 𝑠 = TeV , JHEP (2017) 005, arXiv: .[21] CMS Collaboration, Search for top squark pair production in 𝑝 𝑝 collisions at √ 𝑠 = TeV usingsingle lepton events , JHEP (2017) 019, arXiv: .[22] CMS Collaboration, Search for top squarks and dark matter particles in opposite-charge dileptonfinal states at √ 𝑠 = TeV , Phys. Rev. D (2018) 032009, arXiv: .[23] CMS Collaboration, Searches for physics beyond the standard model with the 𝑀 T variablein hadronic final states with and without disappearing tracks in proton–proton collisions at √ 𝑠 = TeV , Eur. Phys. J. C (2020) 3, arXiv: .[24] CMS Collaboration, Search for supersymmetry in proton–proton collisions at TeV in final stateswith jets and missing transverse momentum , JHEP (2019) 244, arXiv: .[25] CMS Collaboration, Search for direct top squark pair production in events with one lepton, jets,and missing transverse momentum at 13 TeV with the CMS experiment , JHEP (2020) 032, arXiv: .[26] CMS Collaboration, Search for Dark Matter Particles Produced in Association with a Top QuarkPair at √ 𝑠 = TeV , Phys. Rev. Lett. (2019) 011803, arXiv: .[27] ATLAS Collaboration,
Search for direct top squark pair production in final states with two leptonsin √ 𝑠 = TeV 𝑝 𝑝 collisions with the ATLAS detector , Eur. Phys. J. C (2017) 898, arXiv: .[28] ATLAS Collaboration, Search for dark matter produced in association with bottom or top quarksin √ 𝑠 = TeV 𝑝 𝑝 collisions with the ATLAS detector , Eur. Phys. J. C (2018) 18, arXiv: .[29] ATLAS Collaboration, Object-based missing transverse momentum significance in the ATLASDetector , ATLAS-CONF-2018-038, 2018, url: https://cds.cern.ch/record/2630948 .[30] J. Goodman and W. Shepherd,
LHC Bounds on UV-Complete Models of Dark Matter , 2011, arXiv: .[31] J. Alwall, P. Schuster and N. Toro,
Simplified models for a first characterization of new physics atthe LHC , Phys. Rev. D (2009) 075020, arXiv: .[32] D. Alves et al., Simplified models for LHC new physics searches , J. Phys. G (2012) 105005,arXiv: .[33] D. Abercrombie et al., Dark Matter benchmark models for early LHC Run-2 Searches: Report ofthe ATLAS/CMS Dark Matter Forum , Phys. Dark Univ. (2019) 100371, arXiv: .[34] M. R. Buckley, D. Feld and D. Goncalves, Scalar simplified models for dark matter , Phys. Rev. D (2015), arXiv: .[35] U. Haisch and E. Re, Simplified dark matter top-quark interactions at the LHC , JHEP (2015) 078,arXiv: .[36] G. D’Ambrosio, G. Giudice, G. Isidori and A. Strumia, Minimal Flavour Violation: an effectivefield theory approach , Nucl. Phys. B (2002) 155, arXiv: hep-ph/0207036 .[37] G. R. Farrar and P. Fayet,
Phenomenology of the production, decay, and detection of new hadronicstates associated with supersymmetry , Phys. Lett. B (1978) 575.4338] H. Goldberg, Constraint on the Photino Mass from Cosmology , Phys. Rev. Lett. (1983) 1419,Erratum: Phys. Rev. Lett. (2009) 099905.[39] J. Ellis, J. Hagelin, D. V. Nanopoulos, K. A. Olive and M. Srednicki, Supersymmetric relics fromthe big bang , Nucl. Phys. B (1984) 453.[40] P. Fayet,
Supersymmetry and weak, electromagnetic and strong interactions , Phys. Lett. B (1976) 159.[41] P. Fayet, Spontaneously broken supersymmetric theories of weak, electromagnetic and stronginteractions , Phys. Lett. B (1977) 489.[42] ATLAS Collaboration, The ATLAS Experiment at the CERN Large Hadron Collider , JINST (2008) S08003.[43] ATLAS Collaboration, ATLAS Insertable B-Layer Technical Design Report , ATLAS-TDR-19, 2010,url: https://cds.cern.ch/record/1291633 , ATLAS Insertable B-Layer Technical DesignReport Addendum , ATLAS-TDR-19-ADD-1, 2012,
URL : https://cds.cern.ch/record/1451888 .[44] B. Abbott et al., Production and integration of the ATLAS Insertable B-Layer , JINST (2018)T05008, arXiv: .[45] ATLAS Collaboration, Performance of the ATLAS trigger system in 2015 , Eur. Phys. J. C (2017) 317, arXiv: .[46] ATLAS Collaboration, Luminosity determination in 𝑝 𝑝 collisions at √ 𝑠 = TeV using the ATLASdetector at the LHC , ATLAS-CONF-2019-021, 2019, url: https://cds.cern.ch/record/2677054 .[47] G. Avoni et al.,
The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS ,JINST (2018) P07017.[48] ATLAS Collaboration, Performance of the missing transverse momentum triggers for the ATLASdetector during Run-2 data taking , JHEP (2020) 080, arXiv: .[49] S. Agostinelli et al., Geant4 – a simulation toolkit , Nucl. Instrum. Meth. A (2003) 250.[50] ATLAS Collaboration,
The ATLAS Simulation Infrastructure , Eur. Phys. J. C (2010) 823, arXiv: .[51] ATLAS Collaboration, Electron and photon performance measurements with the ATLAS detectorusing the 2015–2017 LHC proton–proton collision data , JINST (2019) P12006, arXiv: .[52] ATLAS Collaboration, Muon reconstruction performance of the ATLAS detector in proton–protoncollision data at √ 𝑠 = TeV , Eur. Phys. J. C (2016) 292, arXiv: .[53] ATLAS Collaboration, ATLAS 𝑏 -jet identification performance and efficiency measurement with 𝑡 ¯ 𝑡 events in 𝑝 𝑝 collisions at √ 𝑠 = TeV , Eur. Phys. J. C (2019) 970, arXiv: .[54] J. Alwall et al., The automated computation of tree-level and next-to-leading order differentialcross sections, and their matching to parton shower simulations , JHEP (2014) 079, arXiv: .[55] T. Sjöstrand et al., An introduction to PYTHIA 8.2 , Comput. Phys. Commun. (2015) 159, arXiv: . 4456] P. Artoisenet, R. Frederix, O. Mattelaer and R. Rietkerk,
Automatic spin-entangled decays of heavyresonances in Monte Carlo simulations , JHEP (2013) 015, arXiv: .[57] J. Debove, B. Fuks and M. Klasen, Threshold resummation for gaugino pair production at hadroncolliders , Nucl. Phys. B (2011) 51, arXiv: .[58] B. Fuks, M. Klasen, D. R. Lamprea and M. Rothering,
Gaugino production in proton-protoncollisions at a center-of-mass energy of TeV , JHEP (2012) 081, arXiv: .[59] B. Fuks, M. Klasen, D. R. Lamprea and M. Rothering, Precision predictions for electroweaksuperpartner production at hadron colliders with resummino , Eur. Phys. J. C (2013) 2480,arXiv: .[60] J. Fiaschi and M. Klasen, Neutralino-chargino pair production at NLO+NLL with resummation-improved parton density functions for LHC Run II , Phys. Rev. D (2018) 055014, arXiv: .[61] G. Bozzi, B. Fuks and M. Klasen, Threshold resummation for slepton-pair production at hadroncolliders , Nucl. Phys. B (2007) 157, arXiv: hep-ph/0701202 [hep-ph] .[62] B. Fuks, M. Klasen, D. R. Lamprea and M. Rothering,
Revisiting slepton pair production at theLarge Hadron Collider , JHEP (2014) 168, arXiv: .[63] J. Fiaschi and M. Klasen, Slepton pair production at the LHC in NLO+NLL with resummation-improved parton densities , JHEP (2018) 094, arXiv: .[64] C. Borschensky et al., Squark and gluino production cross sections in pp collisions at √ 𝑠 = , , and TeV , Eur. Phys. J. C (2014) 3174, arXiv: .[65] L. Lönnblad and S. Prestel, Merging multi-leg NLO matrix elements with parton showers , JHEP (2013) 166, arXiv: .[66] ATLAS Collaboration, ATLAS Pythia 8 tunes to TeV data , ATL-PHYS-PUB-2014-021, 2014,url: https://cds.cern.ch/record/1966419 .[67] R. D. Ball et al.,
Parton distributions with LHC data , Nucl. Phys. B (2013) 244, arXiv: .[68] O. Mattelaer and E. Vryonidou,
Dark matter production through loop-induced processes at theLHC: the s-channel mediator case , Eur. Phys. J. C (2015) 436, arXiv: .[69] M. Backović et al., Higher-order QCD predictions for dark matter production at the LHC insimplified models with s-channel mediators , Eur. Phys. J. C (2015) 482, arXiv: .[70] T. Sjöstrand, S. Mrenna and P. Z. Skands, A brief introduction to PYTHIA 8.1 , Comput. Phys.Commun. (2008) 852, arXiv: .[71] D. J. Lange,
The EvtGen particle decay simulation package , Nucl. Instrum. Meth. A (2001) 152.[72] ATLAS Collaboration,
The Pythia 8 A3 tune description of ATLAS minimum bias and inelasticmeasurements incorporating the Donnachie–Landshoff diffractive model , ATL-PHYS-PUB-2016-017, 2016, url: https://cds.cern.ch/record/2206965 .[73] S. Frixione, P. Nason and C. Oleari,
Matching NLO QCD computations with parton showersimulations: the POWHEG method , JHEP (2007) 070, arXiv: .[74] S. Alioli, P. Nason, C. Oleari and E. Re, A general framework for implementing NLO calculationsin shower Monte Carlo programs: the POWHEG BOX , JHEP (2010) 043, arXiv: . 4575] P. Nason, A new method for combining NLO QCD with shower Monte Carlo algorithms , JHEP (2004) 040, arXiv: hep-ph/0409146 .[76] M. Czakon and A. Mitov, Top++: A program for the calculation of the top-pair cross-section athadron colliders , Comput. Phys. Commun. (2014) 2930, arXiv: .[77] R. D. Ball et al.,
Parton distributions for the LHC run II , JHEP (2015) 040, arXiv: .[78] ATLAS Collaboration, Modelling of the 𝑡 ¯ 𝑡𝐻 and 𝑡 ¯ 𝑡𝑉 ( 𝑉 = 𝑊, 𝑍 ) processes for √ 𝑠 = TeV ATLASanalyses , ATL-PHYS-PUB-2016-005, 2016, url: https://cds.cern.ch/record/2120826 .[79] R. Frederix, E. Re and P. Torrielli,
Single-top 𝑡 -channel hadroproduction in the four-flavour schemewith POWHEG and aMC@NLO , JHEP (2012) 130, arXiv: .[80] S. Alioli, P. Nason, C. Oleari and E. Re, NLO single-top production matched with shower inPOWHEG: s- and t-channel contributions , JHEP (2009) 111, arXiv: .[81] M. Aliev et al., HATHOR - HAdronic Top and Heavy quarks crOss section calculatoR , Comput.Phys. Commun. (2011) 1034, arXiv: .[82] P. Kant et al.,
HATHOR for single top-quark production: Updated predictions and uncertaintyestimates for single top-quark production in hadronic collisions , Comput. Phys. Commun. (2015) 74, arXiv: .[83] N. Kidonakis,
Next-to-next-to-leading-order collinear and soft gluon corrections for t-channelsingle top quark production , Phys. Rev. D (2011) 091503, arXiv: .[84] N. Kidonakis, Two-loop soft anomalous dimensions for single top quark associated productionwith a W- or H- , Phys. Rev. D (2010) 054018, arXiv: .[85] N. Kidonakis, NNLL resummation for s-channel single top quark production , Phys. Rev. D (2010) 054028, arXiv: .[86] T. Gleisberg et al., Event generation with SHERPA 1.1 , JHEP (2009) 007, arXiv: .[87] ATLAS Collaboration, Monte Carlo Generators for the Production of a 𝑊 or 𝑍 / 𝛾 ∗ Bosonin Association with Jets at ATLAS in Run 2 , ATL-PHYS-PUB-2016-003, 2016, url: https://cds.cern.ch/record/2120133 .[88] R. Gavin, Y. Li, F. Petriello and S. Quackenbush,
FEWZ 2.0: A code for hadronic Z production atnext-to-next-to-leading order , Comput. Phys. Commun. (2011) 2388.[89] ATLAS Collaboration,
Multi-Boson Simulation for TeV ATLAS Analyses , ATL-PHYS-PUB-2017-005, 2017, url: https://cds.cern.ch/record/2261933 .[90] H. B. Hartanto, B. Jager, L. Reina and D. Wackeroth,
Higgs boson production in association withtop quarks in the POWHEG BOX , Phys. Rev. D (2015) 094003, arXiv: .[91] ATLAS Collaboration, Topological cell clustering in the ATLAS calorimeters and its performancein LHC Run 1 , Eur. Phys. J. C (2017) 490, arXiv: .[92] M. Cacciari, G. P. Salam and G. Soyez, The anti-k t jet clustering algorithm , JHEP (2008) 063,arXiv: .[93] M. Cacciari, G. P. Salam and G. Soyez, FastJet user manual , Eur. Phys. J. C (2012) 1896, arXiv: . 4694] ATLAS Collaboration, Jet energy scale measurements and their systematic uncertainties inproton–proton collisions at √ 𝑠 = TeV with the ATLAS detector , Phys. Rev. D (2017) 072002,arXiv: .[95] ATLAS Collaboration, Tagging and suppression of pileup jets with the ATLAS detector , ATLAS-CONF-2014-018, 2014, url: https://cds.cern.ch/record/1700870 .[96] ATLAS Collaboration,
Selection of jets produced in TeV proton–proton collisions with the ATLASdetector , ATLAS-CONF-2015-029, 2015, url: https://cds.cern.ch/record/2037702 .[97] ATLAS Collaboration,
Performance of pile-up mitigation techniques for jets in 𝑝 𝑝 collisionsat √ 𝑠 = TeV using the ATLAS detector , Eur. Phys. J. C (2016) 581, arXiv: .[98] ATLAS Collaboration, Performance of missing transverse momentum reconstruction with theATLAS detector using proton–proton collisions at √ 𝑠 = TeV , Eur. Phys. J. C (2018) 903,arXiv: .[99] C. G. Lester and D. J. Summers, Measuring masses of semi-invisibly decaying particles pairproduced at hadron colliders , Phys. Lett. B (1999) 99, arXiv: hep-ph/9906349 .[100] A. Barr, C. G. Lester and P. Stephens,
A variable for measuring masses at hadron colliders whenmissing energy is expected; m 𝑇 : the truth behind the glamour , J. Phys. G (2003) 2343, arXiv: hep-ph/0304226 .[101] W. S. Cho, K. Choi, Y. G. Kim and C. B. Park, Measuring superparticle masses at hadron colliderusing the transverse mass kink , JHEP (2008) 035, arXiv: .[102] M. Burns, K. Kong, K. T. Matchev and M. Park, Using subsystem 𝑀 𝑇 for complete massdeterminations in decay chains with missing energy at hadron colliders , JHEP (2009) 143,arXiv: .[103] M. R. Buckley, J. D. Lykken, C. Rogan and M. Spiropulu, Super-razor and searches for sleptonsand charginos at the LHC , Phys. Rev. D (2014) 055020, arXiv: .[104] G. Cowan, K. Cranmer, E. Gross and O. Vitells, Asymptotic formulae for likelihood-based tests ofnew physics , Eur. Phys. J. C (2011) 1554, arXiv: , Erratum:Eur. Phys. J. C (2013) 2501.[105] M. Baak et al., HistFitter software framework for statistical data analysis , Eur. Phys. J. C (2015) 153, arXiv: .[106] L. Heinrich, M. Feickert and G. Stark, pyhf , version 0.4.4, url: https://github.com/scikit-hep/pyhf .[107] ATLAS Collaboration, Measurement of the 𝑊𝑊 cross section in √ 𝑠 = TeV 𝑝 𝑝 collisions withATLAS , Phys. Rev. Lett. (2011) 041802, arXiv: .[108] ATLAS Collaboration,
Search for anomalous production of prompt same-sign lepton pairs andpair-produced doubly charged Higgs bosons with √ 𝑠 = TeV 𝑝 𝑝 collisions using the ATLASdetector , JHEP (2015) 041, arXiv: .[109] CMS Collaboration, Search for charged Higgs bosons with the H ± → 𝜏 ± 𝜈 𝜏 decay channel inproton-proton collisions at √ 𝑠 =
13 TeV, JHEP (2019) 142, arXiv: .[110] CMS Collaboration, Search for singly produced third-generation leptoquarks decaying to a 𝜏 lepton and a b quark in proton-proton collisions at √ 𝑠 =
13 TeV, JHEP (2018) 115, arXiv: . 47111] ATLAS Collaboration, Jet Calibration and Systematic Uncertainties for Jets Reconstructed in theATLAS Detector at √ 𝑠 = TeV , ATL-PHYS-PUB-2015-015, 2015, url: https://cds.cern.ch/record/2037613 .[112] ATLAS Collaboration,
Calibration of 𝑏 -tagging using dileptonic top pair events in a combinatoriallikelihood approach with the ATLAS experiment , ATLAS-CONF-2014-004, 2014, url: https://cds.cern.ch/record/1664335 .[113] ATLAS Collaboration, Calibration of the performance of 𝑏 -tagging for 𝑐 and light-flavour jets inthe 2012 ATLAS data , ATLAS-CONF-2014-046, 2014, url: https://cds.cern.ch/record/1741020 .[114] ATLAS Collaboration, Expected performance of missing transverse momentum reconstructionfor the ATLAS detector at √ 𝑠 = TeV , ATL-PHYS-PUB-2015-023, 2015, url: https://cds.cern.ch/record/2037700 .[115] ATLAS Collaboration,
Performance of electron and photon triggers in ATLAS during LHC Run 2 ,Eur. Phys. J. C (2020) 47, arXiv: .[116] ATLAS Collaboration, Performance of the ATLAS muon triggers in Run 2 , arXiv e-prints,arXiv:2004.13447 (2020) arXiv:2004.13447, arXiv: .[117] ATLAS Collaboration,
Simulation of top-quark production for the ATLAS experiment at √ 𝑠 = TeV , ATL-PHYS-PUB-2016-004, 2016, url: https://cds.cern.ch/record/2120417 .[118] ATLAS Collaboration,
Improvements in 𝑡 ¯ 𝑡 modelling using NLO+PS Monte Carlo generators forRun 2 , ATL-PHYS-PUB-2018-009, 2018, url: https://cds.cern.ch/record/2630327 .[119] ATLAS Collaboration, Modelling of the 𝑡 ¯ 𝑡𝐻 and 𝑡 ¯ 𝑡𝑉 ( 𝑉 = 𝑊, 𝑍 ) processes for √ 𝑠 = TeV ATLASanalyses , ATL-PHYS-PUB-2015-022, 2016, url: http://cds.cern.ch/record/2120826 .[120] ATLAS Collaboration,
Measurement of the 𝑡 ¯ 𝑡 𝑍 and 𝑡 ¯ 𝑡𝑊 cross sections in proton–proton collisionsat √ 𝑠 = TeV with the ATLAS detector , Phys. Rev. D (2019) 072009, arXiv: .[121] S. Frixione, E. Laenen, P. Motylinski, C. White and B. R. Webber, Single-top hadroproduction inassociation with a 𝑊 boson , JHEP (2008) 029, arXiv: .[122] O. Bessidskaia Bylund, ‘Modelling Wt and tWZ production at NLO for ATLAS analyses’, , 2016, arXiv: .[123] J. Butterworth et al., PDF4LHC recommendations for LHC Run II , J. Phys. G (2016) 023001,arXiv: .[124] R. D. Cousins, J. T. Linnemann and J. Tucker, Evaluation of three methods for calculating statisticalsignificance when incorporating a systematic uncertainty into a test of the background-onlyhypothesis for a Poisson process , Nucl. Instrum. Meth. A (2008) 480, arXiv: physics/0702156[physics.data-an] .[125] A. L. Read,
Presentation of search results: the
𝐶 𝐿 𝑆 technique , J. Phys. G (2002) 2693.[126] ATLAS Collaboration, ATLAS Computing Acknowledgements , ATL-SOFT-PUB-2020-001, url: https://cds.cern.ch/record/2717821 .48 he ATLAS Collaboration
G. Aad , B. Abbott , D.C. Abbott , A. Abed Abud , K. Abeling , D.K. Abhayasinghe ,S.H. Abidi , O.S. AbouZeid , N.L. Abraham , H. Abramowicz , H. Abreu , Y. Abulaiti ,B.S. Acharya , B. Achkar , L. Adam , C. Adam Bourdarios , L. Adamczyk , L. Adamek ,J. Adelman , A. Adiguzel , S. Adorni , T. Adye , A.A. Affolder , Y. Afik , C. Agapopoulou ,M.N. Agaras , A. Aggarwal , C. Agheorghiesei , J.A. Aguilar-Saavedra , A. Ahmad ,F. Ahmadov , W.S. Ahmed , X. Ai , G. Aielli , S. Akatsuka , M. Akbiyik , T.P.A. Åkesson ,E. Akilli , A.V. Akimov , K. Al Khoury , G.L. Alberghi , J. Albert , M.J. Alconada Verzini ,S. Alderweireldt , M. Aleksa , I.N. Aleksandrov , C. Alexa , T. Alexopoulos , A. Alfonsi ,F. Alfonsi , M. Alhroob , B. Ali , S. Ali , M. Aliev , G. Alimonti , C. Allaire ,B.M.M. Allbrooke , B.W. Allen , P.P. Allport , A. Aloisio , F. Alonso , C. Alpigiani ,E. Alunno Camelia , M. Alvarez Estevez , M.G. Alviggi , Y. Amaral Coutinho ,A. Ambler , L. Ambroz , C. Amelung , D. Amidei , S.P. Amor Dos Santos , S. Amoroso ,C.S. Amrouche , F. An , C. Anastopoulos , N. Andari , T. Andeen , J.K. Anders ,S.Y. Andrean , A. Andreazza , V. Andrei , C.R. Anelli , S. Angelidakis , A. Angerami ,A.V. Anisenkov , A. Annovi , C. Antel , M.T. Anthony , E. Antipov , M. Antonelli ,D.J.A. Antrim , F. Anulli , M. Aoki , J.A. Aparisi Pozo , M.A. Aparo , L. Aperio Bella ,N. Aranzabal , V. Araujo Ferraz , R. Araujo Pereira , C. Arcangeletti , A.T.H. Arce ,J-F. Arguin , S. Argyropoulos , J.-H. Arling , A.J. Armbruster , A. Armstrong , O. Arnaez ,H. Arnold , Z.P. Arrubarrena Tame , G. Artoni , H. Asada , K. Asai , S. Asai ,T. Asawatavonvanich , N. Asbah , E.M. Asimakopoulou , L. Asquith , J. Assahsah ,K. Assamagan , R. Astalos , R.J. Atkin , M. Atkinson , N.B. Atlay , H. Atmani ,P.A. Atmasiddha , K. Augsten , V.A. Austrup , G. Avolio , M.K. Ayoub , G. Azuelos ,D. Babal , H. Bachacou , K. Bachas , F. Backman , P. Bagnaia , M. Bahmani ,H. Bahrasemani , A.J. Bailey , V.R. Bailey , J.T. Baines , C. Bakalis , O.K. Baker ,P.J. Bakker , E. Bakos , D. Bakshi Gupta , S. Balaji , R. Balasubramanian , E.M. Baldin ,P. Balek , F. Balli , W.K. Balunas , J. Balz , E. Banas , M. Bandieramonte ,A. Bandyopadhyay , Sw. Banerjee , L. Barak , W.M. Barbe , E.L. Barberio , D. Barberis ,M. Barbero , G. Barbour , T. Barillari , M-S. Barisits , J. Barkeloo , T. Barklow , R. Barnea ,B.M. Barnett , R.M. Barnett , Z. Barnovska-Blenessy , A. Baroncelli , G. Barone , A.J. Barr ,L. Barranco Navarro , F. Barreiro , J. Barreiro Guimarães da Costa , U. Barron , S. Barsov ,F. Bartels , R. Bartoldus , G. Bartolini , A.E. Barton , P. Bartos , A. Basalaev , A. Basan ,A. Bassalat , M.J. Basso , R.L. Bates , S. Batlamous , J.R. Batley , B. Batool , M. Battaglia ,M. Bauce , F. Bauer , P. Bauer , H.S. Bawa , A. Bayirli , J.B. Beacham , T. Beau ,P.H. Beauchemin , F. Becherer , P. Bechtle , H.C. Beck , H.P. Beck , K. Becker , C. Becot ,A. Beddall , A.J. Beddall , V.A. Bednyakov , M. Bedognetti , C.P. Bee , T.A. Beermann ,M. Begalli , M. Begel , A. Behera , J.K. Behr , F. Beisiegel , M. Belfkir , A.S. Bell , G. Bella ,L. Bellagamba , A. Bellerive , P. Bellos , K. Beloborodov , K. Belotskiy , N.L. Belyaev ,D. Benchekroun , N. Benekos , Y. Benhammou , D.P. Benjamin , M. Benoit , J.R. Bensinger ,S. Bentvelsen , L. Beresford , M. Beretta , D. Berge , E. Bergeaas Kuutmann , N. Berger ,B. Bergmann , L.J. Bergsten , J. Beringer , S. Berlendis , G. Bernardi , C. Bernius ,F.U. Bernlochner , T. Berry , P. Berta , A. Berthold , I.A. Bertram , O. Bessidskaia Bylund ,N. Besson , S. Bethke , A. Betti , A.J. Bevan , J. Beyer , S. Bhatta , D.S. Bhattacharya ,P. Bhattarai , V.S. Bhopatkar , R. Bi , R.M. Bianchi , O. Biebel , D. Biedermann , R. Bielski ,K. Bierwagen , N.V. Biesuz , M. Biglietti , T.R.V. Billoud , M. Bindi , A. Bingul ,49. Bini , S. Biondi , C.J. Birch-sykes , M. Birman , T. Bisanz , J.P. Biswal ,D. Biswas , A. Bitadze , C. Bittrich , K. Bjørke , T. Blazek , I. Bloch , C. Blocker , A. Blue ,U. Blumenschein , G.J. Bobbink , V.S. Bobrovnikov , S.S. Bocchetta , D. Bogavac ,A.G. Bogdanchikov , C. Bohm , V. Boisvert , P. Bokan , T. Bold , A.E. Bolz ,M. Bomben , M. Bona , J.S. Bonilla , M. Boonekamp , C.D. Booth , A.G. Borbély ,H.M. Borecka-Bielska , L.S. Borgna , A. Borisov , G. Borissov , D. Bortoletto , D. Boscherini ,M. Bosman , J.D. Bossio Sola , K. Bouaouda , J. Boudreau , E.V. Bouhova-Thacker ,D. Boumediene , A. Boveia , J. Boyd , D. Boye , I.R. Boyko , A.J. Bozson , J. Bracinik ,N. Brahimi , G. Brandt , O. Brandt , F. Braren , B. Brau , J.E. Brau ,W.D. Breaden Madden , K. Brendlinger , R. Brener , L. Brenner , R. Brenner , S. Bressler ,B. Brickwedde , D.L. Briglin , D. Britton , D. Britzger , I. Brock , R. Brock , G. Brooijmans ,W.K. Brooks , E. Brost , P.A. Bruckman de Renstrom , B. Brüers , D. Bruncko , A. Bruni ,G. Bruni , M. Bruschi , N. Bruscino , L. Bryngemark , T. Buanes , Q. Buat ,P. Buchholz , A.G. Buckley , I.A. Budagov , M.K. Bugge , O. Bulekov , B.A. Bullard ,T.J. Burch , S. Burdin , C.D. Burgard , A.M. Burger , B. Burghgrave , J.T.P. Burr , C.D. Burton ,J.C. Burzynski , V. Büscher , E. Buschmann , P.J. Bussey , J.M. Butler , C.M. Buttar ,J.M. Butterworth , P. Butti , W. Buttinger , C.J. Buxo Vazquez , A. Buzatu ,A.R. Buzykaev , G. Cabras , S. Cabrera Urbán , D. Caforio , H. Cai , V.M.M. Cairo ,O. Cakir , N. Calace , P. Calafiura , G. Calderini , P. Calfayan , G. Callea , L.P. Caloba ,A. Caltabiano , S. Calvente Lopez , D. Calvet , S. Calvet , T.P. Calvet , M. Calvetti ,R. Camacho Toro , S. Camarda , D. Camarero Munoz , P. Camarri , M.T. Camerlingo ,D. Cameron , C. Camincher , S. Campana , M. Campanelli , A. Camplani , V. Canale ,A. Canesse , M. Cano Bret , J. Cantero , T. Cao , Y. Cao , M. Capua , R. Cardarelli ,F. Cardillo , G. Carducci , I. Carli , T. Carli , G. Carlino , B.T. Carlson ,E.M. Carlson , L. Carminati , R.M.D. Carney , S. Caron , E. Carquin , S. Carrá ,G. Carratta , J.W.S. Carter , T.M. Carter , M.P. Casado , A.F. Casha , E.G. Castiglia ,F.L. Castillo , L. Castillo Garcia , V. Castillo Gimenez , N.F. Castro , A. Catinaccio ,J.R. Catmore , A. Cattai , V. Cavaliere , V. Cavasinni , E. Celebi , F. Celli , K. Cerny ,A.S. Cerqueira , A. Cerri , L. Cerrito , F. Cerutti , A. Cervelli , S.A. Cetin , Z. Chadi ,D. Chakraborty , J. Chan , W.S. Chan , W.Y. Chan , J.D. Chapman , B. Chargeishvili ,D.G. Charlton , T.P. Charman , M. Chatterjee , C.C. Chau , S. Che , S. Chekanov ,S.V. Chekulaev , G.A. Chelkov , B. Chen , C. Chen , C.H. Chen , H. Chen , H. Chen ,J. Chen , J. Chen , J. Chen , S. Chen , S.J. Chen , X. Chen , Y. Chen , Y-H. Chen ,H.C. Cheng , H.J. Cheng , A. Cheplakov , E. Cheremushkina , R. Cherkaoui El Moursli ,E. Cheu , K. Cheung , T.J.A. Chevalérias , L. Chevalier , V. Chiarella , G. Chiarelli ,G. Chiodini , A.S. Chisholm , A. Chitan , I. Chiu , Y.H. Chiu , M.V. Chizhov , K. Choi ,A.R. Chomont , Y. Chou , Y.S. Chow , L.D. Christopher , M.C. Chu , X. Chu ,J. Chudoba , J.J. Chwastowski , L. Chytka , D. Cieri , K.M. Ciesla , V. Cindro , I.A. Cioară ,A. Ciocio , F. Cirotto , Z.H. Citron , M. Citterio , D.A. Ciubotaru , B.M. Ciungu ,A. Clark , P.J. Clark , S.E. Clawson , C. Clement , L. Clissa , Y. Coadou ,M. Cobal , A. Coccaro , J. Cochran , R. Coelho Lopes De Sa , H. Cohen , A.E.C. Coimbra ,B. Cole , A.P. Colijn , J. Collot , P. Conde Muiño , S.H. Connell , I.A. Connelly ,S. Constantinescu , F. Conventi , A.M. Cooper-Sarkar , F. Cormier , K.J.R. Cormier ,L.D. Corpe , M. Corradi , E.E. Corrigan , F. Corriveau , M.J. Costa , F. Costanza ,D. Costanzo , G. Cowan , J.W. Cowley , J. Crane , K. Cranmer , R.A. Creager ,S. Crépé-Renaudin , F. Crescioli , M. Cristinziani , V. Croft , G. Crosetti , A. Cueto ,T. Cuhadar Donszelmann , H. Cui , A.R. Cukierman , W.R. Cunningham , S. Czekierda ,50. Czodrowski , M.M. Czurylo , M.J. Da Cunha Sargedas De Sousa , J.V. Da Fonseca Pinto ,C. Da Via , W. Dabrowski , F. Dachs , T. Dado , S. Dahbi , T. Dai , C. Dallapiccola ,M. Dam , G. D’amen , V. D’Amico , J. Damp , J.R. Dandoy , M.F. Daneri , M. Danninger ,V. Dao , G. Darbo , O. Dartsi , A. Dattagupta , T. Daubney , S. D’Auria , C. David ,T. Davidek , D.R. Davis , I. Dawson , K. De , R. De Asmundis , M. De Beurs ,S. De Castro , N. De Groot , P. de Jong , H. De la Torre , A. De Maria , D. De Pedis ,A. De Salvo , U. De Sanctis , M. De Santis , A. De Santo , J.B. De Vivie De Regie ,D.V. Dedovich , A.M. Deiana , J. Del Peso , Y. Delabat Diaz , D. Delgove , F. Deliot ,C.M. Delitzsch , M. Della Pietra , D. Della Volpe , A. Dell’Acqua , L. Dell’Asta ,M. Delmastro , C. Delporte , P.A. Delsart , S. Demers , M. Demichev , G. Demontigny ,S.P. Denisov , L. D’Eramo , D. Derendarz , J.E. Derkaoui , F. Derue , P. Dervan , K. Desch ,K. Dette , C. Deutsch , M.R. Devesa , P.O. Deviveiros , F.A. Di Bello , A. Di Ciaccio ,L. Di Ciaccio , C. Di Donato , A. Di Girolamo , G. Di Gregorio , A. Di Luca ,B. Di Micco , R. Di Nardo , K.F. Di Petrillo , R. Di Sipio , C. Diaconu , F.A. Dias ,T. Dias Do Vale , M.A. Diaz , F.G. Diaz Capriles , J. Dickinson , M. Didenko , E.B. Diehl ,J. Dietrich , S. Díez Cornell , C. Diez Pardos , A. Dimitrievska , W. Ding , J. Dingfelder ,S.J. Dittmeier , F. Dittus , F. Djama , T. Djobava , J.I. Djuvsland , M.A.B. Do Vale ,M. Dobre , D. Dodsworth , C. Doglioni , J. Dolejsi , Z. Dolezal , M. Donadelli , B. Dong ,J. Donini , A. D’onofrio , M. D’Onofrio , J. Dopke , A. Doria , M.T. Dova , A.T. Doyle ,E. Drechsler , E. Dreyer , T. Dreyer , A.S. Drobac , D. Du , T.A. du Pree , Y. Duan ,F. Dubinin , M. Dubovsky , A. Dubreuil , E. Duchovni , G. Duckeck , O.A. Ducu ,D. Duda , A. Dudarev , A.C. Dudder , E.M. Duffield , M. D’uffizi , L. Duflot , M. Dührssen ,C. Dülsen , M. Dumancic , A.E. Dumitriu , M. Dunford , S. Dungs , A. Duperrin ,H. Duran Yildiz , M. Düren , A. Durglishvili , D. Duschinger , B. Dutta , D. Duvnjak ,G.I. Dyckes , M. Dyndal , S. Dysch , B.S. Dziedzic , M.G. Eggleston , T. Eifert , G. Eigen ,K. Einsweiler , T. Ekelof , H. El Jarrari , V. Ellajosyula , M. Ellert , F. Ellinghaus ,A.A. Elliot , N. Ellis , J. Elmsheuser , M. Elsing , D. Emeliyanov , A. Emerman , Y. Enari ,M.B. Epland , J. Erdmann , A. Ereditato , P.A. Erland , M. Errenst , M. Escalier , C. Escobar ,O. Estrada Pastor , E. Etzion , G. Evans , H. Evans , M.O. Evans , A. Ezhilov , F. Fabbri ,L. Fabbri , V. Fabiani , G. Facini , R.M. Fakhrutdinov , S. Falciano , P.J. Falke , S. Falke ,J. Faltova , Y. Fang , Y. Fang , G. Fanourakis , M. Fanti , M. Faraj , A. Farbin ,A. Farilla , E.M. Farina , T. Farooque , S.M. Farrington , P. Farthouat , F. Fassi ,P. Fassnacht , D. Fassouliotis , M. Faucci Giannelli , W.J. Fawcett , L. Fayard , O.L. Fedin ,W. Fedorko , A. Fehr , M. Feickert , L. Feligioni , A. Fell , C. Feng , M. Feng ,M.J. Fenton , A.B. Fenyuk , S.W. Ferguson , J. Ferrando , A. Ferrari , P. Ferrari , R. Ferrari ,D.E. Ferreira de Lima , A. Ferrer , D. Ferrere , C. Ferretti , F. Fiedler , A. Filipčič ,F. Filthaut , K.D. Finelli , M.C.N. Fiolhais , L. Fiorini , F. Fischer , J. Fischer ,W.C. Fisher , T. Fitschen , I. Fleck , P. Fleischmann , T. Flick , B.M. Flierl , L. Flores ,L.R. Flores Castillo , F.M. Follega , N. Fomin , J.H. Foo , G.T. Forcolin , B.C. Forland ,A. Formica , F.A. Förster , A.C. Forti , E. Fortin , M.G. Foti , D. Fournier , H. Fox ,P. Francavilla , S. Francescato , M. Franchini , S. Franchino , D. Francis , L. Franco ,L. Franconi , M. Franklin , G. Frattari , A.N. Fray , P.M. Freeman , B. Freund ,W.S. Freund , E.M. Freundlich , D.C. Frizzell , D. Froidevaux , J.A. Frost , M. Fujimoto ,C. Fukunaga , E. Fullana Torregrosa , T. Fusayasu , J. Fuster , A. Gabrielli , A. Gabrielli ,S. Gadatsch , P. Gadow , G. Gagliardi , L.G. Gagnon , G.E. Gallardo , E.J. Gallas ,B.J. Gallop , R. Gamboa Goni , K.K. Gan , S. Ganguly , J. Gao , Y. Gao , Y.S. Gao ,F.M. Garay Walls , C. García , J.E. García Navarro , J.A. García Pascual , C. Garcia-Argos ,51. Garcia-Sciveres , R.W. Gardner , N. Garelli , S. Gargiulo , C.A. Garner , V. Garonne ,S.J. Gasiorowski , P. Gaspar , A. Gaudiello , G. Gaudio , P. Gauzzi , I.L. Gavrilenko ,A. Gavrilyuk , C. Gay , G. Gaycken , E.N. Gazis , A.A. Geanta , C.M. Gee , C.N.P. Gee ,J. Geisen , M. Geisen , C. Gemme , M.H. Genest , C. Geng , S. Gentile , S. George ,T. Geralis , L.O. Gerlach , P. Gessinger-Befurt , G. Gessner , M. Ghasemi Bostanabad ,M. Ghneimat , A. Ghosh , A. Ghosh , B. Giacobbe , S. Giagu , N. Giangiacomi ,P. Giannetti , A. Giannini , G. Giannini , S.M. Gibson , M. Gignac , D.T. Gil , B.J. Gilbert ,D. Gillberg , G. Gilles , N.E.K. Gillwald , D.M. Gingrich , M.P. Giordani , P.F. Giraud ,G. Giugliarelli , D. Giugni , F. Giuli , S. Gkaitatzis , I. Gkialas , E.L. Gkougkousis ,P. Gkountoumis , L.K. Gladilin , C. Glasman , J. Glatzer , P.C.F. Glaysher , A. Glazov ,G.R. Gledhill , I. Gnesi , M. Goblirsch-Kolb , D. Godin , S. Goldfarb , T. Golling ,D. Golubkov , A. Gomes , R. Goncalves Gama , R. Gonçalo , G. Gonella ,L. Gonella , A. Gongadze , F. Gonnella , J.L. Gonski , S. González de la Hoz ,S. Gonzalez Fernandez , R. Gonzalez Lopez , C. Gonzalez Renteria , R. Gonzalez Suarez ,S. Gonzalez-Sevilla , G.R. Gonzalvo Rodriguez , L. Goossens , N.A. Gorasia , P.A. Gorbounov ,H.A. Gordon , B. Gorini , E. Gorini , A. Gorišek , A.T. Goshaw , M.I. Gostkin ,C.A. Gottardo , M. Gouighri , A.G. Goussiou , N. Govender , C. Goy , I. Grabowska-Bold ,E.C. Graham , J. Gramling , E. Gramstad , S. Grancagnolo , M. Grandi , V. Gratchev ,P.M. Gravila , F.G. Gravili , C. Gray , H.M. Gray , C. Grefe , K. Gregersen , I.M. Gregor ,P. Grenier , K. Grevtsov , C. Grieco , N.A. Grieser , A.A. Grillo , K. Grimm , S. Grinstein ,J.-F. Grivaz , S. Groh , E. Gross , J. Grosse-Knetter , Z.J. Grout , C. Grud , A. Grummer ,J.C. Grundy , L. Guan , W. Guan , C. Gubbels , J. Guenther , A. Guerguichon ,J.G.R. Guerrero Rojas , F. Guescini , D. Guest , R. Gugel , A. Guida , T. Guillemin ,S. Guindon , J. Guo , W. Guo , Y. Guo , Z. Guo , R. Gupta , S. Gurbuz , G. Gustavino ,M. Guth , P. Gutierrez , C. Gutschow , C. Guyot , C. Gwenlan , C.B. Gwilliam ,E.S. Haaland , A. Haas , C. Haber , H.K. Hadavand , A. Hadef , M. Haleem , J. Haley ,J.J. Hall , G. Halladjian , G.D. Hallewell , K. Hamano , H. Hamdaoui , M. Hamer ,G.N. Hamity , K. Han , L. Han , L. Han , S. Han , Y.F. Han , K. Hanagaki , M. Hance ,D.M. Handl , M.D. Hank , R. Hankache , E. Hansen , J.B. Hansen , J.D. Hansen ,M.C. Hansen , P.H. Hansen , E.C. Hanson , K. Hara , T. Harenberg , S. Harkusha ,P.F. Harrison , N.M. Hartman , N.M. Hartmann , Y. Hasegawa , A. Hasib , S. Hassani ,S. Haug , R. Hauser , M. Havranek , C.M. Hawkes , R.J. Hawkings , S. Hayashida ,D. Hayden , C. Hayes , R.L. Hayes , C.P. Hays , J.M. Hays , H.S. Hayward , S.J. Haywood ,F. He , Y. He , M.P. Heath , V. Hedberg , A.L. Heggelund , N.D. Hehir , C. Heidegger ,K.K. Heidegger , W.D. Heidorn , J. Heilman , S. Heim , T. Heim , B. Heinemann ,J.G. Heinlein , J.J. Heinrich , L. Heinrich , J. Hejbal , L. Helary , A. Held , S. Hellesund ,C.M. Helling , S. Hellman , C. Helsens , R.C.W. Henderson , L. Henkelmann ,A.M. Henriques Correia , H. Herde , Y. Hernández Jiménez , H. Herr , M.G. Herrmann ,T. Herrmann , G. Herten , R. Hertenberger , L. Hervas , G.G. Hesketh , N.P. Hessey , H. Hibi ,S. Higashino , E. Higón-Rodriguez , K. Hildebrand , J.C. Hill , K.K. Hill , K.H. Hiller ,S.J. Hillier , M. Hils , I. Hinchliffe , F. Hinterkeuser , M. Hirose , S. Hirose , D. Hirschbuehl ,B. Hiti , O. Hladik , J. Hobbs , R. Hobincu , N. Hod , M.C. Hodgkinson , A. Hoecker ,D. Hohn , D. Hohov , T. Holm , T.R. Holmes , M. Holzbock , L.B.A.H. Hommels , T.M. Hong ,J.C. Honig , A. Hönle , B.H. Hooberman , W.H. Hopkins , Y. Horii , P. Horn , L.A. Horyn ,S. Hou , A. Hoummada , J. Howarth , J. Hoya , M. Hrabovsky , J. Hrivnac , A. Hrynevich ,T. Hryn’ova , P.J. Hsu , S.-C. Hsu , Q. Hu , S. Hu , Y.F. Hu , D.P. Huang , X. Huang ,Y. Huang , Y. Huang , Z. Hubacek , F. Hubaut , M. Huebner , F. Huegging , T.B. Huffman ,52. Huhtinen , R. Hulsken , R.F.H. Hunter , N. Huseynov , J. Huston , J. Huth , R. Hyneman ,S. Hyrych , G. Iacobucci , G. Iakovidis , I. Ibragimov , L. Iconomidou-Fayard , P. Iengo ,R. Ignazzi , R. Iguchi , T. Iizawa , Y. Ikegami , M. Ikeno , N. Ilic , F. Iltzsche , H. Imam ,G. Introzzi , M. Iodice , K. Iordanidou , V. Ippolito , M.F. Isacson , M. Ishino ,W. Islam , C. Issever , S. Istin , J.M. Iturbe Ponce , R. Iuppa , A. Ivina , J.M. Izen ,V. Izzo , P. Jacka , P. Jackson , R.M. Jacobs , B.P. Jaeger , V. Jain , G. Jäkel , K.B. Jakobi ,K. Jakobs , T. Jakoubek , J. Jamieson , K.W. Janas , R. Jansky , M. Janus , P.A. Janus ,G. Jarlskog , A.E. Jaspan , N. Javadov , T. Javůrek , M. Javurkova , F. Jeanneau , L. Jeanty ,J. Jejelava , P. Jenni , N. Jeong , S. Jézéquel , J. Jia , Z. Jia , H. Jiang , Y. Jiang , Z. Jiang ,S. Jiggins , F.A. Jimenez Morales , J. Jimenez Pena , S. Jin , A. Jinaru , O. Jinnouchi ,H. Jivan , P. Johansson , K.A. Johns , C.A. Johnson , E. Jones , R.W.L. Jones , S.D. Jones ,T.J. Jones , J. Jovicevic , X. Ju , J.J. Junggeburth , A. Juste Rozas , A. Kaczmarska ,M. Kado , H. Kagan , M. Kagan , A. Kahn , C. Kahra , T. Kaji , E. Kajomovitz ,C.W. Kalderon , A. Kaluza , A. Kamenshchikov , M. Kaneda , N.J. Kang , S. Kang ,Y. Kano , J. Kanzaki , L.S. Kaplan , D. Kar , K. Karava , M.J. Kareem , I. Karkanias ,S.N. Karpov , Z.M. Karpova , V. Kartvelishvili , A.N. Karyukhin , E. Kasimi , A. Kastanas ,C. Kato , J. Katzy , K. Kawade , K. Kawagoe , T. Kawaguchi , T. Kawamoto , G. Kawamura ,E.F. Kay , F.I. Kaya , S. Kazakos , V.F. Kazanin , J.M. Keaveney , R. Keeler ,J.S. Keller , E. Kellermann , D. Kelsey , J.J. Kempster , J. Kendrick , K.E. Kennedy , O. Kepka ,S. Kersten , B.P. Kerševan , S. Ketabchi Haghighat , F. Khalil-Zada , M. Khandoga ,A. Khanov , A.G. Kharlamov , T. Kharlamova , E.E. Khoda , T.J. Khoo ,G. Khoriauli , E. Khramov , J. Khubua , S. Kido , M. Kiehn , E. Kim , Y.K. Kim ,N. Kimura , A. Kirchhoff , D. Kirchmeier , J. Kirk , A.E. Kiryunin , T. Kishimoto ,D.P. Kisliuk , V. Kitali , C. Kitsaki , O. Kivernyk , T. Klapdor-Kleingrothaus , M. Klassen ,C. Klein , M.H. Klein , M. Klein , U. Klein , K. Kleinknecht , P. Klimek , A. Klimentov ,F. Klimpel , T. Klingl , T. Klioutchnikova , F.F. Klitzner , P. Kluit , S. Kluth , E. Kneringer ,E.B.F.G. Knoops , A. Knue , D. Kobayashi , M. Kobel , M. Kocian , T. Kodama , P. Kodys ,D.M. Koeck , P.T. Koenig , T. Koffas , N.M. Köhler , M. Kolb , I. Koletsou , T. Komarek ,T. Kondo , K. Köneke , A.X.Y. Kong , A.C. König , T. Kono , V. Konstantinides ,N. Konstantinidis , B. Konya , R. Kopeliansky , S. Koperny , K. Korcyl , K. Kordas ,G. Koren , A. Korn , I. Korolkov , E.V. Korolkova , N. Korotkova , O. Kortner , S. Kortner ,V.V. Kostyukhin , A. Kotsokechagia , A. Kotwal , A. Koulouris ,A. Kourkoumeli-Charalampidi , C. Kourkoumelis , E. Kourlitis , V. Kouskoura , R. Kowalewski ,W. Kozanecki , A.S. Kozhin , V.A. Kramarenko , G. Kramberger , D. Krasnopevtsev ,M.W. Krasny , A. Krasznahorkay , D. Krauss , J.A. Kremer , J. Kretzschmar , K. Kreul ,P. Krieger , F. Krieter , S. Krishnamurthy , A. Krishnan , M. Krivos , K. Krizka ,K. Kroeninger , H. Kroha , J. Kroll , J. Kroll , K.S. Krowpman , U. Kruchonak , H. Krüger ,N. Krumnack , M.C. Kruse , J.A. Krzysiak , A. Kubota , O. Kuchinskaia , S. Kuday ,D. Kuechler , J.T. Kuechler , S. Kuehn , T. Kuhl , V. Kukhtin , Y. Kulchitsky , S. Kuleshov ,Y.P. Kulinich , M. Kuna , A. Kupco , T. Kupfer , O. Kuprash , H. Kurashige ,L.L. Kurchaninov , Y.A. Kurochkin , A. Kurova , M.G. Kurth , E.S. Kuwertz , M. Kuze ,A.K. Kvam , J. Kvita , T. Kwan , C. Lacasta , F. Lacava , D.P.J. Lack , H. Lacker ,D. Lacour , E. Ladygin , R. Lafaye , B. Laforge , T. Lagouri , S. Lai , I.K. Lakomiec ,J.E. Lambert , S. Lammers , W. Lampl , C. Lampoudis , E. Lançon , U. Landgraf ,M.P.J. Landon , V.S. Lang , J.C. Lange , R.J. Langenberg , A.J. Lankford , F. Lanni ,K. Lantzsch , A. Lanza , A. Lapertosa , J.F. Laporte , T. Lari , F. Lasagni Manghi ,M. Lassnig , V. Latonova , T.S. Lau , A. Laudrain , A. Laurier , M. Lavorgna ,53.D. Lawlor , M. Lazzaroni , B. Le , E. Le Guirriec , A. Lebedev , M. LeBlanc ,T. LeCompte , F. Ledroit-Guillon , A.C.A. Lee , C.A. Lee , G.R. Lee , L. Lee , S.C. Lee ,S. Lee , B. Lefebvre , H.P. Lefebvre , M. Lefebvre , C. Leggett , K. Lehmann , N. Lehmann ,G. Lehmann Miotto , W.A. Leight , A. Leisos , M.A.L. Leite , C.E. Leitgeb , R. Leitner ,K.J.C. Leney , T. Lenz , S. Leone , C. Leonidopoulos , A. Leopold , C. Leroy , R. Les ,C.G. Lester , M. Levchenko , J. Levêque , D. Levin , L.J. Levinson , D.J. Lewis , B. Li ,B. Li , C-Q. Li , F. Li , H. Li , H. Li , J. Li , K. Li , L. Li , M. Li , Q.Y. Li ,S. Li , X. Li , Y. Li , Z. Li , Z. Li , Z. Li , Z. Li , Z. Liang , M. Liberatore ,B. Liberti , K. Lie , S. Lim , C.Y. Lin , K. Lin , R.A. Linck , R.E. Lindley , J.H. Lindon ,A. Linss , A.L. Lionti , E. Lipeles , A. Lipniacka , T.M. Liss , A. Lister , J.D. Little , B. Liu ,B.X. Liu , H.B. Liu , J.B. Liu , J.K.K. Liu , K. Liu , M. Liu , M.Y. Liu , P. Liu ,X. Liu , Y. Liu , Y. Liu , Y.L. Liu , Y.W. Liu , M. Livan , A. Lleres ,J. Llorente Merino , S.L. Lloyd , C.Y. Lo , E.M. Lobodzinska , P. Loch , S. Loffredo ,T. Lohse , K. Lohwasser , M. Lokajicek , J.D. Long , R.E. Long , I. Longarini , L. Longo ,I. Lopez Paz , A. Lopez Solis , J. Lorenz , N. Lorenzo Martinez , A.M. Lory , A. Lösle ,X. Lou , X. Lou , A. Lounis , J. Love , P.A. Love , J.J. Lozano Bahilo , M. Lu , Y.J. Lu ,H.J. Lubatti , C. Luci , F.L. Lucio Alves , A. Lucotte , F. Luehring , I. Luise ,L. Luminari , B. Lund-Jensen , N.A. Luongo , M.S. Lutz , D. Lynn , H. Lyons , R. Lysak ,E. Lytken , F. Lyu , V. Lyubushkin , T. Lyubushkina , H. Ma , L.L. Ma , Y. Ma ,D.M. Mac Donell , G. Maccarrone , C.M. Macdonald , J.C. MacDonald , J. Machado Miguens ,R. Madar , W.F. Mader , M. Madugoda Ralalage Don , N. Madysa , J. Maeda , T. Maeno ,M. Maerker , V. Magerl , N. Magini , J. Magro , D.J. Mahon , C. Maidantchik ,A. Maio , K. Maj , O. Majersky , S. Majewski , Y. Makida , N. Makovec ,B. Malaescu , Pa. Malecki , V.P. Maleev , F. Malek , D. Malito , U. Mallik , C. Malone ,S. Maltezos , S. Malyukov , J. Mamuzic , G. Mancini , J.P. Mandalia , I. Mandić ,L. Manhaes de Andrade Filho , I.M. Maniatis , J. Manjarres Ramos , K.H. Mankinen , A. Mann ,A. Manousos , B. Mansoulie , I. Manthos , S. Manzoni , A. Marantis , G. Marceca ,L. Marchese , G. Marchiori , M. Marcisovsky , L. Marcoccia , C. Marcon , M. Marjanovic ,Z. Marshall , M.U.F. Martensson , S. Marti-Garcia , C.B. Martin , T.A. Martin , V.J. Martin ,B. Martin dit Latour , L. Martinelli , M. Martinez , P. Martinez Agullo ,V.I. Martinez Outschoorn , S. Martin-Haugh , V.S. Martoiu , A.C. Martyniuk , A. Marzin ,S.R. Maschek , L. Masetti , T. Mashimo , R. Mashinistov , J. Masik , A.L. Maslennikov ,L. Massa , P. Massarotti , P. Mastrandrea , A. Mastroberardino , T. Masubuchi ,D. Matakias , A. Matic , N. Matsuzawa , P. Mättig , J. Maurer , B. Maček ,D.A. Maximov , R. Mazini , I. Maznas , S.M. Mazza , J.P. Mc Gowan , S.P. Mc Kee ,T.G. McCarthy , W.P. McCormack , E.F. McDonald , A.E. McDougall , J.A. Mcfayden ,G. Mchedlidze , M.A. McKay , K.D. McLean , S.J. McMahon , P.C. McNamara ,C.J. McNicol , R.A. McPherson , J.E. Mdhluli , Z.A. Meadows , S. Meehan , T. Megy ,S. Mehlhase , A. Mehta , B. Meirose , D. Melini , B.R. Mellado Garcia , J.D. Mellenthin ,M. Melo , F. Meloni , A. Melzer , E.D. Mendes Gouveia , A.M. Mendes Jacques Da Costa ,H.Y. Meng , L. Meng , X.T. Meng , S. Menke , E. Meoni , S. Mergelmeyer ,S.A.M. Merkt , C. Merlassino , P. Mermod , L. Merola , C. Meroni , G. Merz ,O. Meshkov , J.K.R. Meshreki , J. Metcalfe , A.S. Mete , C. Meyer , J-P. Meyer ,M. Michetti , R.P. Middleton , L. Mijović , G. Mikenberg , M. Mikestikova , M. Mikuž ,H. Mildner , A. Milic , C.D. Milke , D.W. Miller , L.S. Miller , A. Milov , D.A. Milstead ,A.A. Minaenko , I.A. Minashvili , L. Mince , A.I. Mincer , B. Mindur , M. Mineev ,Y. Minegishi , Y. Mino , L.M. Mir , M. Mironova , T. Mitani , J. Mitrevski , V.A. Mitsou ,54. Mittal , O. Miu , A. Miucci , P.S. Miyagawa , A. Mizukami , J.U. Mjörnmark ,T. Mkrtchyan , M. Mlynarikova , T. Moa , S. Mobius , K. Mochizuki , P. Moder ,P. Mogg , S. Mohapatra , R. Moles-Valls , K. Mönig , E. Monnier , A. Montalbano ,J. Montejo Berlingen , M. Montella , F. Monticelli , S. Monzani , N. Morange ,A.L. Moreira De Carvalho , D. Moreno , M. Moreno Llácer , C. Moreno Martinez ,P. Morettini , M. Morgenstern , S. Morgenstern , D. Mori , M. Morii , M. Morinaga ,V. Morisbak , A.K. Morley , G. Mornacchi , A.P. Morris , L. Morvaj , P. Moschovakos ,B. Moser , M. Mosidze , T. Moskalets , P. Moskvitina , J. Moss , E.J.W. Moyse ,S. Muanza , J. Mueller , R.S.P. Mueller , D. Muenstermann , G.A. Mullier , J.J. Mullin ,D.P. Mungo , J.L. Munoz Martinez , F.J. Munoz Sanchez , P. Murin , W.J. Murray ,A. Murrone , J.M. Muse , M. Muškinja , C. Mwewa , A.G. Myagkov , A.A. Myers ,G. Myers , J. Myers , M. Myska , B.P. Nachman , O. Nackenhorst , A.Nag Nag , K. Nagai ,K. Nagano , Y. Nagasaka , J.L. Nagle , E. Nagy , A.M. Nairz , Y. Nakahama , K. Nakamura ,T. Nakamura , H. Nanjo , F. Napolitano , R.F. Naranjo Garcia , R. Narayan , I. Naryshkin ,M. Naseri , T. Naumann , G. Navarro , P.Y. Nechaeva , F. Nechansky , T.J. Neep , A. Negri ,M. Negrini , C. Nellist , C. Nelson , M.E. Nelson , S. Nemecek , M. Nessi ,M.S. Neubauer , F. Neuhaus , M. Neumann , R. Newhouse , P.R. Newman , C.W. Ng ,Y.S. Ng , Y.W.Y. Ng , B. Ngair , H.D.N. Nguyen , T. Nguyen Manh , E. Nibigira ,R.B. Nickerson , R. Nicolaidou , D.S. Nielsen , J. Nielsen , M. Niemeyer , N. Nikiforou ,V. Nikolaenko , I. Nikolic-Audit , K. Nikolopoulos , P. Nilsson , H.R. Nindhito , A. Nisati ,N. Nishu , R. Nisius , I. Nitsche , T. Nitta , T. Nobe , D.L. Noel , Y. Noguchi , I. Nomidis ,M.A. Nomura , M. Nordberg , J. Novak , T. Novak , O. Novgorodova , R. Novotny , L. Nozka ,K. Ntekas , E. Nurse , F.G. Oakham , J. Ocariz , A. Ochi , I. Ochoa , J.P. Ochoa-Ricoux ,K. O’Connor , S. Oda , S. Odaka , S. Oerdek , A. Ogrodnik , A. Oh , C.C. Ohm , H. Oide ,R. Oishi , M.L. Ojeda , H. Okawa , Y. Okazaki , M.W. O’Keefe , Y. Okumura , A. Olariu ,L.F. Oleiro Seabra , S.A. Olivares Pino , D. Oliveira Damazio , J.L. Oliver , M.J.R. Olsson ,A. Olszewski , J. Olszowska , Ö.O. Öncel , D.C. O’Neil , A.P. O’neill , A. Onofre ,P.U.E. Onyisi , H. Oppen , R.G. Oreamuno Madriz , M.J. Oreglia , G.E. Orellana ,D. Orestano , N. Orlando , R.S. Orr , V. O’Shea , R. Ospanov , G. Otero y Garzon ,H. Otono , P.S. Ott , G.J. Ottino , M. Ouchrif , J. Ouellette , F. Ould-Saada , A. Ouraou ,Q. Ouyang , M. Owen , R.E. Owen , V.E. Ozcan , N. Ozturk , J. Pacalt , H.A. Pacey ,K. Pachal , A. Pacheco Pages , C. Padilla Aranda , S. Pagan Griso , G. Palacino , S. Palazzo ,S. Palestini , M. Palka , P. Palni , C.E. Pandini , J.G. Panduro Vazquez , P. Pani , G. Panizzo ,L. Paolozzi , C. Papadatos , K. Papageorgiou , S. Parajuli , A. Paramonov , C. Paraskevopoulos ,D. Paredes Hernandez , S.R. Paredes Saenz , B. Parida , T.H. Park , A.J. Parker , M.A. Parker ,F. Parodi , E.W. Parrish , J.A. Parsons , U. Parzefall , L. Pascual Dominguez , V.R. Pascuzzi ,J.M.P. Pasner , F. Pasquali , E. Pasqualucci , S. Passaggio , F. Pastore , P. Pasuwan ,S. Pataraia , J.R. Pater , A. Pathak , J. Patton , T. Pauly , J. Pearkes , M. Pedersen ,L. Pedraza Diaz , R. Pedro , T. Peiffer , S.V. Peleganchuk , O. Penc , C. Peng ,H. Peng , B.S. Peralva , M.M. Perego , A.P. Pereira Peixoto , L. Pereira Sanchez ,D.V. Perepelitsa , E. Perez Codina , L. Perini , H. Pernegger , S. Perrella , A. Perrevoort ,K. Peters , R.F.Y. Peters , B.A. Petersen , T.C. Petersen , E. Petit , V. Petousis , C. Petridou ,F. Petrucci , M. Pettee , N.E. Pettersson , K. Petukhova , A. Peyaud , R. Pezoa ,L. Pezzotti , T. Pham , P.W. Phillips , M.W. Phipps , G. Piacquadio , E. Pianori ,A. Picazio , R.H. Pickles , R. Piegaia , D. Pietreanu , J.E. Pilcher , A.D. Pilkington ,M. Pinamonti , J.L. Pinfold , C. Pitman Donaldson , M. Pitt , L. Pizzimento , A. Pizzini ,M.-A. Pleier , V. Plesanovs , V. Pleskot , E. Plotnikova , P. Podberezko , R. Poettgen ,55. Poggi , L. Poggioli , I. Pogrebnyak , D. Pohl , I. Pokharel , G. Polesello , A. Poley ,A. Policicchio , R. Polifka , A. Polini , C.S. Pollard , V. Polychronakos , D. Ponomarenko ,L. Pontecorvo , S. Popa , G.A. Popeneciu , L. Portales , D.M. Portillo Quintero , S. Pospisil ,K. Potamianos , I.N. Potrap , C.J. Potter , H. Potti , T. Poulsen , J. Poveda , T.D. Powell ,G. Pownall , M.E. Pozo Astigarraga , A. Prades Ibanez , P. Pralavorio , M.M. Prapa , S. Prell ,D. Price , M. Primavera , M.L. Proffitt , N. Proklova , K. Prokofiev , F. Prokoshin ,S. Protopopescu , J. Proudfoot , M. Przybycien , D. Pudzha , A. Puri , P. Puzo ,D. Pyatiizbyantseva , J. Qian , Y. Qin , A. Quadt , M. Queitsch-Maitland , G. Rabanal Bolanos ,M. Racko , F. Ragusa , G. Rahal , J.A. Raine , S. Rajagopalan , A. Ramirez Morales ,K. Ran , D.F. Rassloff , D.M. Rauch , F. Rauscher , S. Rave , B. Ravina , I. Ravinovich ,M. Raymond , A.L. Read , N.P. Readioff , M. Reale , D.M. Rebuzzi , G. Redlinger ,K. Reeves , D. Reikher , A. Reiss , A. Rej , C. Rembser , A. Renardi , M. Renda ,M.B. Rendel , A.G. Rennie , S. Resconi , E.D. Resseguie , S. Rettie , B. Reynolds ,E. Reynolds , O.L. Rezanova , P. Reznicek , E. Ricci , R. Richter , S. Richter ,E. Richter-Was , M. Ridel , P. Rieck , O. Rifki , M. Rijssenbeek , A. Rimoldi ,M. Rimoldi , L. Rinaldi , T.T. Rinn , G. Ripellino , I. Riu , P. Rivadeneira ,J.C. Rivera Vergara , F. Rizatdinova , E. Rizvi , C. Rizzi , S.H. Robertson , M. Robin ,D. Robinson , C.M. Robles Gajardo , M. Robles Manzano , A. Robson , A. Rocchi ,C. Roda , S. Rodriguez Bosca , A. Rodriguez Rodriguez , A.M. Rodríguez Vera , S. Roe ,J. Roggel , O. Røhne , R. Röhrig , R.A. Rojas , B. Roland , C.P.A. Roland , J. Roloff ,A. Romaniouk , M. Romano , N. Rompotis , M. Ronzani , L. Roos , S. Rosati , G. Rosin ,B.J. Rosser , E. Rossi , E. Rossi , E. Rossi , L.P. Rossi , L. Rossini , R. Rosten ,M. Rotaru , B. Rottler , D. Rousseau , G. Rovelli , A. Roy , D. Roy , A. Rozanov ,Y. Rozen , X. Ruan , T.A. Ruggeri , F. Rühr , A. Ruiz-Martinez , A. Rummler , Z. Rurikova ,N.A. Rusakovich , H.L. Russell , L. Rustige , J.P. Rutherfoord , E.M. Rüttinger , M. Rybar ,G. Rybkin , E.B. Rye , A. Ryzhov , J.A. Sabater Iglesias , P. Sabatini , L. Sabetta ,S. Sacerdoti , H.F-W. Sadrozinski , R. Sadykov , F. Safai Tehrani , B. Safarzadeh Samani ,M. Safdari , P. Saha , S. Saha , M. Sahinsoy , A. Sahu , M. Saimpert , M. Saito , T. Saito ,H. Sakamoto , D. Salamani , G. Salamanna , A. Salnikov , J. Salt , A. Salvador Salas ,D. Salvatore , F. Salvatore , A. Salvucci , A. Salzburger , J. Samarati , D. Sammel ,D. Sampsonidis , D. Sampsonidou , J. Sánchez , A. Sanchez Pineda , H. Sandaker ,C.O. Sander , I.G. Sanderswood , M. Sandhoff , C. Sandoval , D.P.C. Sankey , M. Sannino ,Y. Sano , A. Sansoni , C. Santoni , H. Santos , S.N. Santpur , A. Santra , K.A. Saoucha ,A. Sapronov , J.G. Saraiva , O. Sasaki , K. Sato , F. Sauerburger , E. Sauvan , P. Savard ,R. Sawada , C. Sawyer , L. Sawyer , I. Sayago Galvan , C. Sbarra , A. Sbrizzi ,T. Scanlon , J. Schaarschmidt , P. Schacht , D. Schaefer , L. Schaefer , U. Schäfer ,A.C. Schaffer , D. Schaile , R.D. Schamberger , E. Schanet , C. Scharf , N. Scharmberg ,V.A. Schegelsky , D. Scheirich , F. Schenck , M. Schernau , C. Schiavi , L.K. Schildgen ,Z.M. Schillaci , E.J. Schioppa , M. Schioppa , K.E. Schleicher , S. Schlenker ,K.R. Schmidt-Sommerfeld , K. Schmieden , C. Schmitt , S. Schmitt , L. Schoeffel ,A. Schoening , P.G. Scholer , E. Schopf , M. Schott , J.F.P. Schouwenberg , J. Schovancova ,S. Schramm , F. Schroeder , A. Schulte , H-C. Schultz-Coulon , M. Schumacher ,B.A. Schumm , Ph. Schune , A. Schwartzman , T.A. Schwarz , Ph. Schwemling ,R. Schwienhorst , A. Sciandra , G. Sciolla , F. Scuri , F. Scutti , L.M. Scyboz ,C.D. Sebastiani , K. Sedlaczek , P. Seema , S.C. Seidel , A. Seiden , B.D. Seidlitz , T. Seiss ,C. Seitz , J.M. Seixas , G. Sekhniaidze , S.J. Sekula , N. Semprini-Cesari , S. Sen ,C. Serfon , L. Serin , L. Serkin , M. Sessa , H. Severini , S. Sevova , F. Sforza ,56. Sfyrla , E. Shabalina , J.D. Shahinian , N.W. Shaikh , D. Shaked Renous , L.Y. Shan ,M. Shapiro , A. Sharma , A.S. Sharma , P.B. Shatalov , K. Shaw , S.M. Shaw , M. Shehade ,Y. Shen , A.D. Sherman , P. Sherwood , L. Shi , C.O. Shimmin , Y. Shimogama ,M. Shimojima , J.D. Shinner , I.P.J. Shipsey , S. Shirabe , M. Shiyakova , J. Shlomi ,A. Shmeleva , M.J. Shochet , J. Shojaii , D.R. Shope , S. Shrestha , E.M. Shrif , M.J. Shroff ,E. Shulga , P. Sicho , A.M. Sickles , E. Sideras Haddad , O. Sidiropoulou , A. Sidoti ,F. Siegert , Dj. Sijacki , M.Jr. Silva , M.V. Silva Oliveira , S.B. Silverstein , S. Simion ,R. Simoniello , C.J. Simpson-allsop , S. Simsek , P. Sinervo , V. Sinetckii , S. Singh ,S. Sinha , M. Sioli , I. Siral , S.Yu. Sivoklokov , J. Sjölin , A. Skaf , E. Skorda ,P. Skubic , M. Slawinska , K. Sliwa , V. Smakhtin , B.H. Smart , J. Smiesko , N. Smirnov ,S.Yu. Smirnov , Y. Smirnov , L.N. Smirnova , O. Smirnova , E.A. Smith , H.A. Smith ,M. Smizanska , K. Smolek , A. Smykiewicz , A.A. Snesarev , H.L. Snoek , I.M. Snyder ,S. Snyder , R. Sobie , A. Soffer , A. Søgaard , F. Sohns , C.A. Solans Sanchez ,E.Yu. Soldatov , U. Soldevila , A.A. Solodkov , A. Soloshenko , O.V. Solovyanov ,V. Solovyev , P. Sommer , H. Son , A. Sonay , W. Song , W.Y. Song , A. Sopczak ,A.L. Sopio , F. Sopkova , S. Sottocornola , R. Soualah , A.M. Soukharev , D. South ,S. Spagnolo , M. Spalla , M. Spangenberg , F. Spanò , D. Sperlich , T.M. Spieker ,G. Spigo , M. Spina , D.P. Spiteri , M. Spousta , A. Stabile , B.L. Stamas , R. Stamen ,M. Stamenkovic , A. Stampekis , E. Stanecka , B. Stanislaus , M.M. Stanitzki , M. Stankaityte ,B. Stapf , E.A. Starchenko , G.H. Stark , J. Stark , P. Staroba , P. Starovoitov , S. Stärz ,R. Staszewski , G. Stavropoulos , M. Stegler , P. Steinberg , A.L. Steinhebel , B. Stelzer ,H.J. Stelzer , O. Stelzer-Chilton , H. Stenzel , T.J. Stevenson , G.A. Stewart , M.C. Stockton ,G. Stoicea , M. Stolarski , S. Stonjek , A. Straessner , J. Strandberg , S. Strandberg ,M. Strauss , T. Strebler , P. Strizenec , R. Ströhmer , D.M. Strom , R. Stroynowski ,A. Strubig , S.A. Stucci , B. Stugu , J. Stupak , N.A. Styles , D. Su , W. Su ,X. Su , N.B. Suarez , V.V. Sulin , M.J. Sullivan , D.M.S. Sultan , S. Sultansoy , T. Sumida ,S. Sun , X. Sun , C.J.E. Suster , M.R. Sutton , S. Suzuki , M. Svatos , M. Swiatlowski ,S.P. Swift , T. Swirski , A. Sydorenko , I. Sykora , M. Sykora , T. Sykora , D. Ta ,K. Tackmann , J. Taenzer , A. Taffard , R. Tafirout , E. Tagiev , R.H.M. Taibah ,R. Takashima , K. Takeda , T. Takeshita , E.P. Takeva , Y. Takubo , M. Talby ,A.A. Talyshev , K.C. Tam , N.M. Tamir , J. Tanaka , R. Tanaka , S. Tapia Araya ,S. Tapprogge , A. Tarek Abouelfadl Mohamed , S. Tarem , K. Tariq , G. Tarna ,G.F. Tartarelli , P. Tas , M. Tasevsky , E. Tassi , G. Tateno , A. Tavares Delgado ,Y. Tayalati , A.J. Taylor , G.N. Taylor , W. Taylor , H. Teagle , A.S. Tee ,R. Teixeira De Lima , P. Teixeira-Dias , H. Ten Kate , J.J. Teoh , K. Terashi , J. Terron ,S. Terzo , M. Testa , R.J. Teuscher , N. Themistokleous , T. Theveneaux-Pelzer , D.W. Thomas ,J.P. Thomas , E.A. Thompson , P.D. Thompson , E. Thomson , E.J. Thorpe , V.O. Tikhomirov ,Yu.A. Tikhonov , S. Timoshenko , P. Tipton , S. Tisserant , K. Todome ,S. Todorova-Nova , S. Todt , J. Tojo , S. Tokár , K. Tokushuku , E. Tolley , R. Tombs ,K.G. Tomiwa , M. Tomoto , L. Tompkins , P. Tornambe , E. Torrence , H. Torres ,E. Torró Pastor , M. Toscani , C. Tosciri , J. Toth , D.R. Tovey , A. Traeet , C.J. Treado ,T. Trefzger , F. Tresoldi , A. Tricoli , I.M. Trigger , S. Trincaz-Duvoid , D.A. Trischuk ,W. Trischuk , B. Trocmé , A. Trofymov , C. Troncon , F. Trovato , L. Truong , M. Trzebinski ,A. Trzupek , F. Tsai , P.V. Tsiareshka , A. Tsirigotis , V. Tsiskaridze , E.G. Tskhadadze ,M. Tsopoulou , I.I. Tsukerman , V. Tsulaia , S. Tsuno , D. Tsybychev , Y. Tu , A. Tudorache ,V. Tudorache , A.N. Tuna , S. Turchikhin , D. Turgeman , I. Turk Cakir , R.J. Turner ,R. Turra , P.M. Tuts , S. Tzamarias , E. Tzovara , K. Uchida , F. Ukegawa , G. Unal ,57. Unal , A. Undrus , G. Unel , F.C. Ungaro , Y. Unno , K. Uno , J. Urban , P. Urquijo ,G. Usai , Z. Uysal , V. Vacek , B. Vachon , K.O.H. Vadla , T. Vafeiadis , A. Vaidya ,C. Valderanis , E. Valdes Santurio , M. Valente , S. Valentinetti , A. Valero , L. Valéry ,R.A. Vallance , A. Vallier , J.A. Valls Ferrer , T.R. Van Daalen , P. Van Gemmeren , S. Van Stroud ,I. Van Vulpen , M. Vanadia , W. Vandelli , M. Vandenbroucke , E.R. Vandewall ,D. Vannicola , R. Vari , E.W. Varnes , C. Varni , T. Varol , D. Varouchas , K.E. Varvell ,M.E. Vasile , G.A. Vasquez , F. Vazeille , D. Vazquez Furelos , T. Vazquez Schroeder , J. Veatch ,V. Vecchio , M.J. Veen , L.M. Veloce , F. Veloso , S. Veneziano , A. Ventura ,A. Verbytskyi , V. Vercesi , M. Verducci , C.M. Vergel Infante , C. Vergis , W. Verkerke ,A.T. Vermeulen , J.C. Vermeulen , C. Vernieri , P.J. Verschuuren , M.C. Vetterli ,N. Viaux Maira , T. Vickey , O.E. Vickey Boeriu , G.H.A. Viehhauser , L. Vigani ,M. Villa , M. Villaplana Perez , E.M. Villhauer , E. Vilucchi , M.G. Vincter , G.S. Virdee ,A. Vishwakarma , C. Vittori , I. Vivarelli , M. Vogel , P. Vokac , J. Von Ahnen ,S.E. von Buddenbrock , E. Von Toerne , V. Vorobel , K. Vorobev , M. Vos , J.H. Vossebeld ,M. Vozak , N. Vranjes , M. Vranjes Milosavljevic , V. Vrba , M. Vreeswijk , N.K. Vu ,R. Vuillermet , I. Vukotic , S. Wada , P. Wagner , W. Wagner , J. Wagner-Kuhr , S. Wahdan ,H. Wahlberg , R. Wakasa , V.M. Walbrecht , J. Walder , R. Walker , S.D. Walker ,W. Walkowiak , V. Wallangen , A.M. Wang , A.Z. Wang , C. Wang , C. Wang , H. Wang ,H. Wang , J. Wang , P. Wang , Q. Wang , R.-J. Wang , R. Wang , R. Wang , S.M. Wang ,W.T. Wang , W. Wang , W.X. Wang , Y. Wang , Z. Wang , C. Wanotayaroj , A. Warburton ,C.P. Ward , R.J. Ward , N. Warrack , A.T. Watson , M.F. Watson , G. Watts , B.M. Waugh ,A.F. Webb , C. Weber , M.S. Weber , S.A. Weber , S.M. Weber , Y. Wei , A.R. Weidberg ,J. Weingarten , M. Weirich , C. Weiser , P.S. Wells , T. Wenaus , B. Wendland , T. Wengler ,S. Wenig , N. Wermes , M. Wessels , T.D. Weston , K. Whalen , A.M. Wharton , A.S. White ,A. White , M.J. White , D. Whiteson , B.W. Whitmore , W. Wiedenmann , C. Wiel , M. Wielers ,N. Wieseotte , C. Wiglesworth , L.A.M. Wiik-Fuchs , H.G. Wilkens , L.J. Wilkins ,D.M. Williams , H.H. Williams , S. Williams , S. Willocq , P.J. Windischhofer ,I. Wingerter-Seez , E. Winkels , F. Winklmeier , B.T. Winter , M. Wittgen , M. Wobisch ,A. Wolf , R. Wölker , J. Wollrath , M.W. Wolter , H. Wolters , V.W.S. Wong ,A.F. Wongel , N.L. Woods , S.D. Worm , B.K. Wosiek , K.W. Woźniak , K. Wraight , S.L. Wu ,X. Wu , Y. Wu , J. Wuerzinger , T.R. Wyatt , B.M. Wynne , S. Xella , J. Xiang , X. Xiao ,X. Xie , I. Xiotidis , D. Xu , H. Xu , H. Xu , L. Xu , R. Xu , T. Xu , W. Xu , Y. Xu ,Z. Xu , Z. Xu , B. Yabsley , S. Yacoob , D.P. Yallup , N. Yamaguchi , Y. Yamaguchi ,A. Yamamoto , M. Yamatani , T. Yamazaki , Y. Yamazaki , J. Yan , Z. Yan , H.J. Yang ,H.T. Yang , S. Yang , T. Yang , X. Yang , X. Yang , Y. Yang , Z. Yang , W-M. Yao ,Y.C. Yap , H. Ye , J. Ye , S. Ye , I. Yeletskikh , M.R. Yexley , E. Yigitbasi , P. Yin , K. Yorita ,K. Yoshihara , C.J.S. Young , C. Young , J. Yu , R. Yuan , X. Yue , M. Zaazoua ,B. Zabinski , G. Zacharis , E. Zaffaroni , J. Zahreddine , A.M. Zaitsev , T. Zakareishvili ,N. Zakharchuk , S. Zambito , D. Zanzi , S.V. Zeißner , C. Zeitnitz , G. Zemaityte , J.C. Zeng ,O. Zenin , T. Ženiš , D. Zerwas , M. Zgubič , B. Zhang , D.F. Zhang , G. Zhang , J. Zhang ,K. Zhang , L. Zhang , L. Zhang , M. Zhang , R. Zhang , S. Zhang , X. Zhang , X. Zhang ,Y. Zhang , Z. Zhang , Z. Zhang , P. Zhao , Y. Zhao , Z. Zhao , A. Zhemchugov ,Z. Zheng , D. Zhong , B. Zhou , C. Zhou , H. Zhou , M. Zhou , N. Zhou , Y. Zhou ,C.G. Zhu , C. Zhu , H.L. Zhu , H. Zhu , J. Zhu , Y. Zhu , X. Zhuang , K. Zhukov ,V. Zhulanov , D. Zieminska , N.I. Zimine , S. Zimmermann , Z. Zinonos , M. Ziolkowski ,L. Živković , G. Zobernig , A. Zoccoli , K. Zoch , T.G. Zorbas , R. Zou , L. Zwalinski .58 Department of Physics, University of Adelaide, Adelaide; Australia. Physics Department, SUNY Albany, Albany NY; United States of America. Department of Physics, University of Alberta, Edmonton AB; Canada. ( 𝑎 ) Department of Physics, Ankara University, Ankara; ( 𝑏 ) Istanbul Aydin University, Application andResearch Center for Advanced Studies, Istanbul; ( 𝑐 ) Division of Physics, TOBB University of Economicsand Technology, Ankara; Turkey. LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France. High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America. Department of Physics, University of Arizona, Tucson AZ; United States of America. Department of Physics, University of Texas at Arlington, Arlington TX; United States of America. Physics Department, National and Kapodistrian University of Athens, Athens; Greece. Physics Department, National Technical University of Athens, Zografou; Greece. Department of Physics, University of Texas at Austin, Austin TX; United States of America. ( 𝑎 ) Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; ( 𝑏 ) Istanbul BilgiUniversity, Faculty of Engineering and Natural Sciences, Istanbul; ( 𝑐 ) Department of Physics, BogaziciUniversity, Istanbul; ( 𝑑 ) Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey. Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan. Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona;Spain. ( 𝑎 ) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; ( 𝑏 ) Physics Department,Tsinghua University, Beijing; ( 𝑐 ) Department of Physics, Nanjing University, Nanjing; ( 𝑑 ) University ofChinese Academy of Science (UCAS), Beijing; China. Institute of Physics, University of Belgrade, Belgrade; Serbia. Department for Physics and Technology, University of Bergen, Bergen; Norway. Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA;United States of America. Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany. Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University ofBern, Bern; Switzerland. School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom. ( 𝑎 ) Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño,Bogotá; ( 𝑏 ) Departamento de Física, Universidad Nacional de Colombia, Bogotá, Colombia; Colombia. ( 𝑎 ) INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica; ( 𝑏 ) INFN Sezione di Bologna; Italy. Physikalisches Institut, Universität Bonn, Bonn; Germany. Department of Physics, Boston University, Boston MA; United States of America. Department of Physics, Brandeis University, Waltham MA; United States of America. ( 𝑎 ) Transilvania University of Brasov, Brasov; ( 𝑏 ) Horia Hulubei National Institute of Physics and NuclearEngineering, Bucharest; ( 𝑐 ) Department of Physics, Alexandru Ioan Cuza University of Iasi,Iasi; ( 𝑑 ) National Institute for Research and Development of Isotopic and Molecular Technologies, PhysicsDepartment, Cluj-Napoca; ( 𝑒 ) University Politehnica Bucharest, Bucharest; ( 𝑓 ) West University in Timisoara,Timisoara; Romania. ( 𝑎 ) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; ( 𝑏 ) Department ofSubnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; SlovakRepublic. Physics Department, Brookhaven National Laboratory, Upton NY; United States of America. Departamento de Física, Universidad de Buenos Aires, Buenos Aires; Argentina. California State University, CA; United States of America.59 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom. ( 𝑎 ) Department of Physics, University of Cape Town, Cape Town; ( 𝑏 ) iThemba Labs, WesternCape; ( 𝑐 ) Department of Mechanical Engineering Science, University of Johannesburg,Johannesburg; ( 𝑑 ) University of South Africa, Department of Physics, Pretoria; ( 𝑒 ) School of Physics,University of the Witwatersrand, Johannesburg; South Africa. Department of Physics, Carleton University, Ottawa ON; Canada. ( 𝑎 ) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - UniversitéHassan II, Casablanca; ( 𝑏 ) Faculté des Sciences, Université Ibn-Tofail, Kénitra; ( 𝑐 ) Faculté des SciencesSemlalia, Université Cadi Ayyad, LPHEA-Marrakech; ( 𝑑 ) Moroccan Foundation for Advanced ScienceInnovation and Research (MAScIR), Rabat; ( 𝑒 ) LPMR, Faculté des Sciences, Université Mohamed Premier,Oujda; ( 𝑓 ) Faculté des sciences, Université Mohammed V, Rabat; Morocco. CERN, Geneva; Switzerland. Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America. LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France. Nevis Laboratory, Columbia University, Irvington NY; United States of America. Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark. ( 𝑎 ) Dipartimento di Fisica, Università della Calabria, Rende; ( 𝑏 ) INFN Gruppo Collegato di Cosenza,Laboratori Nazionali di Frascati; Italy. Physics Department, Southern Methodist University, Dallas TX; United States of America. Physics Department, University of Texas at Dallas, Richardson TX; United States of America. National Centre for Scientific Research "Demokritos", Agia Paraskevi; Greece. ( 𝑎 ) Department of Physics, Stockholm University; ( 𝑏 ) Oskar Klein Centre, Stockholm; Sweden. Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany. Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund; Germany. Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany. Department of Physics, Duke University, Durham NC; United States of America. SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom. INFN e Laboratori Nazionali di Frascati, Frascati; Italy. Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany. II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany. Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland. ( 𝑎 ) Dipartimento di Fisica, Università di Genova, Genova; ( 𝑏 ) INFN Sezione di Genova; Italy. II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany. SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom. LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France. Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States ofAmerica. ( 𝑎 ) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics,University of Science and Technology of China, Hefei; ( 𝑏 ) Institute of Frontier and Interdisciplinary Scienceand Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University,Qingdao; ( 𝑐 ) School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for ParticleAstrophysics and Cosmology (MOE), SKLPPC, Shanghai; ( 𝑑 ) Tsung-Dao Lee Institute, Shanghai; China. ( 𝑎 ) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; ( 𝑏 ) PhysikalischesInstitut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany. Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan. ( 𝑎 ) Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; ( 𝑏 ) Departmentof Physics, University of Hong Kong, Hong Kong; ( 𝑐 ) Department of Physics and Institute for Advanced60tudy, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China. Department of Physics, National Tsing Hua University, Hsinchu; Taiwan. IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France. Department of Physics, Indiana University, Bloomington IN; United States of America. ( 𝑎 ) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; ( 𝑏 ) ICTP, Trieste; ( 𝑐 ) DipartimentoPolitecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy. ( 𝑎 ) INFN Sezione di Lecce; ( 𝑏 ) Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy. ( 𝑎 ) INFN Sezione di Milano; ( 𝑏 ) Dipartimento di Fisica, Università di Milano, Milano; Italy. ( 𝑎 ) INFN Sezione di Napoli; ( 𝑏 ) Dipartimento di Fisica, Università di Napoli, Napoli; Italy. ( 𝑎 ) INFN Sezione di Pavia; ( 𝑏 ) Dipartimento di Fisica, Università di Pavia, Pavia; Italy. ( 𝑎 ) INFN Sezione di Pisa; ( 𝑏 ) Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy. ( 𝑎 ) INFN Sezione di Roma; ( 𝑏 ) Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy. ( 𝑎 ) INFN Sezione di Roma Tor Vergata; ( 𝑏 ) Dipartimento di Fisica, Università di Roma Tor Vergata,Roma; Italy. ( 𝑎 ) INFN Sezione di Roma Tre; ( 𝑏 ) Dipartimento di Matematica e Fisica, Università Roma Tre, Roma;Italy. ( 𝑎 ) INFN-TIFPA; ( 𝑏 ) Università degli Studi di Trento, Trento; Italy. Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria. University of Iowa, Iowa City IA; United States of America. Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America. Joint Institute for Nuclear Research, Dubna; Russia. ( 𝑎 ) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz deFora; ( 𝑏 ) Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; ( 𝑐 ) Instituto de Física,Universidade de São Paulo, São Paulo; Brazil. KEK, High Energy Accelerator Research Organization, Tsukuba; Japan. Graduate School of Science, Kobe University, Kobe; Japan. ( 𝑎 ) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science,Krakow; ( 𝑏 ) Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland. Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland. Faculty of Science, Kyoto University, Kyoto; Japan. Kyoto University of Education, Kyoto; Japan. Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka ;Japan. Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina. Physics Department, Lancaster University, Lancaster; United Kingdom. Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom. Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics,University of Ljubljana, Ljubljana; Slovenia. School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom. Department of Physics, Royal Holloway University of London, Egham; United Kingdom. Department of Physics and Astronomy, University College London, London; United Kingdom. Louisiana Tech University, Ruston LA; United States of America. Fysiska institutionen, Lunds universitet, Lund; Sweden. Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3),Villeurbanne; France. Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain.
Institut für Physik, Universität Mainz, Mainz; Germany.61 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom.
CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France.
Department of Physics, University of Massachusetts, Amherst MA; United States of America.
Department of Physics, McGill University, Montreal QC; Canada.
School of Physics, University of Melbourne, Victoria; Australia.
Department of Physics, University of Michigan, Ann Arbor MI; United States of America.
Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States ofAmerica.
B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus.
Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus.
Group of Particle Physics, University of Montreal, Montreal QC; Canada.
P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia.
National Research Nuclear University MEPhI, Moscow; Russia.
D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow;Russia.
Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany.
Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany.
Nagasaki Institute of Applied Science, Nagasaki; Japan.
Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan.
Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States ofAmerica.
Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen;Netherlands.
Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam;Netherlands.
Department of Physics, Northern Illinois University, DeKalb IL; United States of America. ( 𝑎 ) Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk; ( 𝑏 ) Novosibirsk State UniversityNovosibirsk; Russia.
Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia.
Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National ResearchCentre "Kurchatov Institute", Moscow; Russia.
Department of Physics, New York University, New York NY; United States of America.
Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan.
Ohio State University, Columbus OH; United States of America.
Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; UnitedStates of America.
Department of Physics, Oklahoma State University, Stillwater OK; United States of America.
Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic.
Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America.
Graduate School of Science, Osaka University, Osaka; Japan.
Department of Physics, University of Oslo, Oslo; Norway.
Department of Physics, Oxford University, Oxford; United Kingdom.
LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris; France.
Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America.
Konstantinov Nuclear Physics Institute of National Research Centre "Kurchatov Institute", PNPI, St.Petersburg; Russia.
Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of62merica. ( 𝑎 ) Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa; ( 𝑏 ) Departamento deFísica, Faculdade de Ciências, Universidade de Lisboa, Lisboa; ( 𝑐 ) Departamento de Física, Universidadede Coimbra, Coimbra; ( 𝑑 ) Centro de Física Nuclear da Universidade de Lisboa, Lisboa; ( 𝑒 ) Departamento deFísica, Universidade do Minho, Braga; ( 𝑓 ) Departamento de Física Teórica y del Cosmos, Universidad deGranada, Granada (Spain); ( 𝑔 ) Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia,Universidade Nova de Lisboa, Caparica; ( ℎ ) Instituto Superior Técnico, Universidade de Lisboa, Lisboa;Portugal.
Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic.
Czech Technical University in Prague, Prague; Czech Republic.
Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic.
Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom.
IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France.
Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; UnitedStates of America. ( 𝑎 ) Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; ( 𝑏 ) Universidad AndresBello, Department of Physics, Santiago; ( 𝑐 ) Instituto de Alta Investigación, Universidad deTarapacá; ( 𝑑 ) Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile.
Universidade Federal de São João del Rei (UFSJ), São João del Rei; Brazil.
Department of Physics, University of Washington, Seattle WA; United States of America.
Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom.
Department of Physics, Shinshu University, Nagano; Japan.
Department Physik, Universität Siegen, Siegen; Germany.
Department of Physics, Simon Fraser University, Burnaby BC; Canada.
SLAC National Accelerator Laboratory, Stanford CA; United States of America.
Physics Department, Royal Institute of Technology, Stockholm; Sweden.
Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States ofAmerica.
Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom.
School of Physics, University of Sydney, Sydney; Australia.
Institute of Physics, Academia Sinica, Taipei; Taiwan. ( 𝑎 ) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; ( 𝑏 ) HighEnergy Physics Institute, Tbilisi State University, Tbilisi; Georgia.
Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel.
Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel.
Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece.
International Center for Elementary Particle Physics and Department of Physics, University of Tokyo,Tokyo; Japan.
Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan.
Department of Physics, Tokyo Institute of Technology, Tokyo; Japan.
Tomsk State University, Tomsk; Russia.
Department of Physics, University of Toronto, Toronto ON; Canada. ( 𝑎 ) TRIUMF, Vancouver BC; ( 𝑏 ) Department of Physics and Astronomy, York University, Toronto ON;Canada.
Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and AppliedSciences, University of Tsukuba, Tsukuba; Japan.
Department of Physics and Astronomy, Tufts University, Medford MA; United States of America.63 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States ofAmerica.
Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden.
Department of Physics, University of Illinois, Urbana IL; United States of America.
Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain.
Department of Physics, University of British Columbia, Vancouver BC; Canada.
Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada.
Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany.
Department of Physics, University of Warwick, Coventry; United Kingdom.
Waseda University, Tokyo; Japan.
Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel.
Department of Physics, University of Wisconsin, Madison WI; United States of America.
Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische UniversitätWuppertal, Wuppertal; Germany.
Department of Physics, Yale University, New Haven CT; United States of America. 𝑎 Also at Borough of Manhattan Community College, City University of New York, New York NY; UnitedStates of America. 𝑏 Also at Center for High Energy Physics, Peking University; China. 𝑐 Also at Centro Studi e Ricerche Enrico Fermi; Italy. 𝑑 Also at CERN, Geneva; Switzerland. 𝑒 Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France. 𝑓 Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève;Switzerland. 𝑔 Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain. ℎ Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece. 𝑖 Also at Department of Physics and Astronomy, Michigan State University, East Lansing MI; UnitedStates of America. 𝑗 Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States ofAmerica. 𝑘 Also at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel. 𝑙 Also at Department of Physics, California State University, East Bay; United States of America. 𝑚 Also at Department of Physics, California State University, Fresno; United States of America. 𝑛 Also at Department of Physics, California State University, Sacramento; United States of America. 𝑜 Also at Department of Physics, King’s College London, London; United Kingdom. 𝑝 Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia. 𝑞 Also at Department of Physics, University of Fribourg, Fribourg; Switzerland. 𝑟 Also at Dipartimento di Matematica, Informatica e Fisica, Università di Udine, Udine; Italy. 𝑠 Also at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russia. 𝑡 Also at Giresun University, Faculty of Engineering, Giresun; Turkey. 𝑢 Also at Graduate School of Science, Osaka University, Osaka; Japan. 𝑣 Also at Hellenic Open University, Patras; Greece. 𝑤 Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain. 𝑥 Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany. 𝑦 Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy ofSciences, Sofia; Bulgaria. 𝑧 Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest;Hungary. 64 𝑎 Also at Institute of Particle Physics (IPP); Canada. 𝑎𝑏 Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan. 𝑎𝑐 Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain. 𝑎𝑑 Also at Istanbul University, Dept. of Physics, Istanbul; Turkey. 𝑎𝑒 Also at Joint Institute for Nuclear Research, Dubna; Russia. 𝑎 𝑓
Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia. 𝑎𝑔 Also at National Research Nuclear University MEPhI, Moscow; Russia. 𝑎ℎ Also at Physics Department, An-Najah National University, Nablus; Palestine. 𝑎𝑖 Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany. 𝑎 𝑗
Also at The City College of New York, New York NY; United States of America. 𝑎𝑘 Also at TRIUMF, Vancouver BC; Canada. 𝑎𝑙 Also at Universita di Napoli Parthenope, Napoli; Italy. 𝑎𝑚 Also at University of Chinese Academy of Sciences (UCAS), Beijing; China. ∗∗