Search for new phenomena in final states with large jet multiplicities and missing transverse momentum using √s = 13 TeV proton-proton collisions recorded by ATLAS in Run 2 of the LHC

JHEP10(2020)062 Published for SISSA by Springer Received:August 14, 2020 Accepted:September 9, 2020 Published:October 12, 2020 Search for new phenomena in final states with large jet multiplicities and missing transverse momentum using √s= 13 TeV proton-proton collisions recorded by ATLAS in Run 2 of the LHC The ATLAS collaboration E-mail: [email protected] Abstract: Results of a search for new particles decaying into eight or more jets and moderate missing transverse momentum are presented. The analysis uses 139 fb−1of proton-proton collision data at √s= 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. The selection rejects events containing isolated electrons or muons, and makes requirements according to the number of b-tagged jets and the scalar sum of masses of large-radius jets. The search extends previous analyses both in using a larger dataset and by employing improved jet and missing transverse momentum reconstruction methods which more cleanly separate signal from background processes. No evidence for physics beyond the Standard Model is found. The results are interpreted in the context of supersymmetry-inspired simplified models, significantly extending the limits on the gluino mass in those models. In particular, limits on the gluino mass are set at 2 TeV when the lightest neutralino is nearly massless in a model assuming a two-step cascade decay via the lightest chargino and second-lightest neutralino. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 2008.06032 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP10(2020)062 JHEP10(2020)062 Contents 1 Introduction 1 2 ATLAS detector 2 3 Datasets 3 3.1 Data 3 3.2 Monte Carlo simulations 4 4 Reconstruction and particle identification 7 5 Event selection 9 6 Background estimation 11 6.1 Multijet background 13 6.2 Leptonic backgrounds 16 6.3 Background normalisation corrections 17 6.4 Systematic uncertainties 18 7 Results and interpretation 23 8 Conclusion 28 The ATLAS collaboration 36 1 Introduction The Large Hadron Collider [1] (LHC) has produced a large dataset of proton-proton (pp) collisions at a centre-of-mass energy of 13 TeV enabling searches for new heavy particles predicted by theories such as supersymmetry (SUSY) [2–7]. Evidence for SUSY models may be sought through searches for the production of these heavy particles (such as gluinos) decaying, often via extended cascades, into lighter ones. If the lightest of these interacts only weakly and is stable then it can be an ideal dark-matter candidate. In R-parity-conserving (RPC) [8] SUSY models, the presence of a stable lightest supersymmetric particle (LSP) often leads to final states with significant missing transverse momentum (Emiss T), often accompanied by a large number of jets. Large jet multiplicities would also occur in events in which gluinos decay via R-parity-violating (RPV) [9] couplings on short (.ns) timescales. In this case the LSPs decay within the detector volume, so that the only invisible particles produced are neutrinos coming from SUSY particle decays, and hence the Emiss Tper event is generally smaller. A similar signature arises from any model in which cascade decays lead to the production of many jets, together with Emiss T either from dark-matter particles or neutrinos. – 1 – JHEP10(2020)062 This paper reports the results of an analysis of 139 fb−1of pp collision data recorded at √s= 13 TeV by the ATLAS experiment [10] throughout the entire Run-2 period of the LHC (2015–2018). It explores events with significant Emiss Tand at least eight jets with large transverse momentum (pT). Selected events are further classified into categories based on the presence of jets containing b-hadrons (b-jets) or on the sum of the masses of largeradius jets. The b-jet selection improves sensitivity to beyond-the-Standard-Model (BSM) signals with enhanced heavy-flavour decays. Given the unusually high jet multiplicities of the selected events, large jet masses can originate both from the capture of decay products from boosted heavy particles, including top quarks, and from accidental combinations [11]. The major backgrounds to the signal in this search are multijet production from QCD processes, top quark pair production (t¯ t) and Wboson production in association with jets (W+jets). Previous searches by ATLAS in similar final states were carried out on smaller LHC datasets recorded at √s= 7 TeV and √s= 8 TeV during 2011 and 2012 [12–14]. In addition, two searches were performed at √s= 13 TeV, one analysing the 2015 dataset [15] and one combining it with 2016 data [16] to achieve a total integrated luminosity of 36 fb−1. The current analysis extends those previous studies by including the complete Run-2 LHC dataset, by using an optimised selection tailored to the increased integrated luminosity, and also by incorporating several improved analysis methods which further increase sensitivity. One such development is the use of the particle-flow jet and Emiss Treconstruction algorithms recently developed for the ATLAS experiment [17]. These algorithms combine measurements of inner tracker and calorimeter energy deposits to improve the accuracy of the charged-hadron measurement, leading to improvements in the jet and Emiss Tresolution and stability against additional pp interactions in the same LHC bunch crossing. The analysis also employs an improved Emiss Tsignificance calculation [18], which accounts for the resolution of the reconstructed objects individually. This new definition increases the separation between events in which the Emiss Toriginates from weakly interacting particles and those in which Emiss Tis only due to detector resolution effects. The combination of the larger dataset and the developments in the analysis methodology provides this analysis with sensitivity over a significantly increased mass range. 2 ATLAS detector The ATLAS detector [10] is a multipurpose particle detector with a nearly 4πcoverage in solid angle.1It consists of an inner tracking detector (ID) surrounded by a thin superconducting solenoid providing a 2 T axial magnetic field, electromagnetic (EM) and hadronic 1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in the centre of the detector. The positive x-axis is defined by the direction from the interaction point to the centre of the LHC ring, with the positive y-axis pointing upwards, while the beam direction defines the z-axis. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the z-axis. The pseudorapidity ηis defined in terms of the polar angle θby η=−ln tan(θ/2). The rapidity is defined as y= 0.5 ln[(E+pz)/(E−pz)] where Edenotes the energy and pzis the component of the momentum along the beam direction. The transverse energy is defined to be ET=Ecos θ. The angular distance ∆Ris defined as p(∆y)2+ (∆φ)2. – 2 – JHEP10(2020)062 calorimeters, and a muon spectrometer. The inner tracking detector covers the pseudorapidity range |η|<2.5. It consists of silicon pixel, silicon microstrip, and transition radiation tracking detectors. A new inner pixel layer, the insertable B-layer [19,20], was added at a mean radius of 3.3 cm before the start of the 2015 data-taking period, improving the identification of b-jets. Lead/liquid-argon (LAr) sampling calorimeters provide EM energy measurements with high granularity. A steel/scintillator-tile hadron calorimeter covers the central pseudorapidity range (|η|<1.7). The endcap and forward regions are instrumented with LAr calorimeters for EM and hadronic energy measurements up to |η|= 4.9. The muon spectrometer surrounds the calorimeters and is based on three large air-core toroidal superconducting magnets with eight coils each. The field integral of the toroids ranges between 2.0 and 6.0 Tm across most of the detector. The muon spectrometer includes a system of precision tracking chambers and fast detectors for triggering. A two-level trigger system [21] is used to select events. The first-level trigger is implemented in hardware and uses a subset of the detector information to keep the accepted rate below 100 kHz. This is followed by a software-based trigger that reduces the accepted event rate to 1 kHz on average. 3 Datasets 3.1 Data The events considered in this paper correspond to 139 fb−1of pp LHC collision data collected between 2015 and 2018 by the ATLAS detector, at a centre-of-mass energy of 13 TeV and with a 25 ns proton bunch crossing interval. In 2015–2016 the average number of interactions per bunch crossing (pile-up) was hµi= 20, increasing to hµi= 38 in 2017 and hµi= 37 in 2018. The uncertainty in the combined 2015–2018 integrated luminosity is 1.7% [22], obtained using the LUCID-2 detector [23] for the primary luminosity measurements. Events were recorded using a variety of trigger selections. During both 2015 and 2016, events were selected by a trigger requiring at least six jets with ET>45 GeV and central pseudorapidity, |η|<2.4. Further events were triggered in 2015 by requiring the presence of at least five jets with ET>70 GeV and |η|<3.2, and in 2016 with a trigger requiring at least five jets with ET>65 GeV and |η|<2.4. In both 2017 and 2018, events were selected by triggers requiring at least five jets with ET>70 GeV and |η|<2.4, or seven jets with ET>45 GeV and |η|<2.4. Additional triggers were also used in 2017 and for some periods in 2018, which required at least six jets with ET>45 GeV and |η|<2.4. Due to their large trigger rates, these were set to select only a fraction of the events, approximately 15% of the two years’ data; such triggers are referred to as ‘prescaled’ triggers. The trigger jet calibration was improved in 2017–2018, resulting in substantially improved efficiency [24] after offline selection for the same trigger thresholds. The trigger selections are summarised later in table 1together with further event selections. Data quality requirements are imposed to ensure that only events in which the entire ATLAS detector was functioning well are used [25]. These, for example, exclude events containing data corruption in the ID and calorimeters, excessive noise and spurious jets produced by non-collision backgrounds [26,27]. – 3 – JHEP10(2020)062 3.2 Monte Carlo simulations Simulated events produced with several Monte Carlo (MC) event generators are used to predict yields for subdominant background contributions from Standard Model (SM) processes and for possible signals. All simulated events are overlaid with multiple pp collisions simulated with the soft QCD processes of Pythia 8.186 [28] using the A3 set of tuned parameters (A3 tune) [29] and the NNPDF2.3 LO parton distribution functions (PDFs) [30]. The simulated events are required to pass the trigger selections, and are weighted such that the pile-up conditions match those of the data. The response of the detector to particles was modelled with an ATLAS detector simulation [31] based on Geant4 [32] (full simulation), or using fast simulation based on a parameterisation of the performance of the ATLAS EM and hadronic calorimeters [33] and on Geant4 elsewhere. For the generation of t¯ tand single top quarks via the W t process and in the s-channel, matrix elements were calculated at next-to-leading order (NLO) using the Powheg- Box v2 generator [34–39] with the NNPDF3.0 NLO PDF set [40] in the five-flavour scheme. Electroweak t-channel single-top-quark events were generated using Powheg- Box v2, using the four-flavour scheme for the NLO matrix element calculations together with the fixed four-flavour PDF set NNPDF3.04f NLO. The diagram removal scheme [41] was used to prevent Wt events from being counted as t¯ tevents beyond leading order (LO). For these processes, the top quarks were decayed using MadSpin [42,43] preserving all spin correlations, while for all processes the parton shower, fragmentation, and the underlying event were simulated using Pythia 8.230 [44] with the NNPDF2.3 LO PDF set and the ATLAS A14 tune [45]. The top quark mass was set to 172.5 GeV. The hdamp parameter, which controls the pTof the first additional emission beyond the Born configuration in Powheg, was set to 1.5 times the mass of the top quark. The main effect of this parameter is to regulate the high-pTemission against which the t¯ tsystem recoils. The EvtGen v1.6.0 program [46] was used to model properties of the b- and c-hadron decays for this process and all others not simulated with Sherpa [47] unless otherwise stated. Simulated t¯ tevents are normalised to the cross-section calculated at next-to-next-to-leading order (NNLO) in perturbative QCD, including soft-gluon resummation to next-to-next-to-leading-logarithm (NNLL) accuracy [48]. The single-top-quark events for the W t channel are normalised using its approximate NNLO prediction [49,50], while the t- and s-channels are normalised using their NLO predictions [51,52]. Events containing t¯ tand additional heavy particles — comprising three or four top quarks, t¯ t+W,t¯ t+Zand t¯ t+WW production — were modelled using Mad- Graph5 aMC@NLO [53] for the matrix element calculation, interfaced to the Pythia 8 parton shower, hadronisation and underlying event model. The t¯ t+WW, three- and four-top-quark processes were simulated at LO in the strong coupling constant αS, using MadGraph5 aMC@NLO v2.2.2 interfaced to Pythia 8.186. The predicted production cross-sections were calculated to NLO as described in ref. [53] for these processes other than three-top-quark production, for which the cross-section was calculated to LO. The production of t¯ t+Wand t¯ t+Zevents was modelled using MadGraph5 aMC@NLO v2.3.3 – 4 – JHEP10(2020)062 at NLO with the NNPDF3.0 NLO PDF. Top quarks were decayed at LO using Mad- Spin to preserve spin correlations. Parton shower and hadronisation were modelled with Pythia 8.210. The cross-sections were calculated at NLO QCD and NLO EW accuracy using MadGraph5 aMC@NLO as reported in ref. [54]. In the case of t¯ t`` the cross-section is additionally scaled by an off-shell correction estimated at one-loop level in αS. For all processes, the A14 set of Pythia 8 parameters was used, together with the NNPDF2.3 LO PDF set. EvtGen v1.2.0 was used to model properties of the b- and c-hadron decays. The contribution from t¯ t+Hwas checked and found to be negligible. Events containing Wor Zbosons associated with jets were simulated using the Sherpa v2.2.1 generator. Matrix elements were calculated for up to two partons at NLO and four partons at LO using the Comix [55] and OpenLoops [56] matrix element generators and merged with the Sherpa parton shower [57] using the ME+PS@NLO prescription [58]. The NNPDF3.0 NNLO PDF set [40] was used in association with a tuning performed by the Sherpa authors. Diboson processes with one hadronically decaying boson accompanied by one charged lepton and one neutrino, two charged leptons or two neutrinos were simulated using Sherpa v2.1.1. The calculations include one additional parton at NLO for ZZ →2`+q¯q and ZZ →2ν+q¯qonly, and up to three additional partons at LO using the Comix and OpenLoops matrix element generators and merged with the Sherpa parton shower using the ME+PS@NLO prescription. The NNPDF3.0 NNLO PDF set was used in conjunction with a dedicated parton shower tuning developed by the Sherpa authors. Diboson processes with four charged leptons, three charged leptons and one neutrino, or two charged leptons and two neutrinos, are found to be negligible. Theoretical uncertainties are considered for all simulated samples. By far the most important process simulated in this analysis is t¯ tproduction, and several samples produced with different configurations, as explained below, are compared to estimate the uncertainty in this background. Samples were produced with the factorisation and renormalisation scales varied coherently up and down by a factor of two, and with parameters set to provide more/less radiation in the parton shower [59]. Additionally, to account for uncertainties from the parton shower modelling and generator choice, the nominal sample is compared to a sample generated with Powheg-Box interfaced to Herwig 7 [60] using the H7- UE tune [61] and the MMHT2014 LO PDF set [62], as well as samples generated with MadGraph5 aMC@NLO interfaced to Pythia 8. These alternative samples each use the NNPDF3.0 NLO PDF for the matrix element calculation. The comparison with samples which vary the amount of additional radiation contributes the largest uncertainty in the t¯ t signal region predictions. Similar alternative samples are used to assess the uncertainties in single-top-quark production, whereas uncertainties in other processes are handled via scale variations in the corresponding generator. Full simulation was used for all background MC samples, ensuring an accurate representation of detector effects. Further details of samples can be found in refs. [59,63,64]. A number of SUSY signal model samples were simulated using the ATLAS fast detector simulation [31] to allow the interpretation of the search results in terms of SUSY parameters. Substantial cross-sections are possible for production of gluinos. The resulting – 5 – JHEP10(2020)062 (a) Two-step decay (b) Off-shell top squarks (c) RPV Figure 1. Pseudo-Feynman diagrams for the different signal models used in this search. In (c), λ00 323 is one of the couplings of the third-generation squark to quarks in the RPV model. cascade decays result in a large multiplicity of jets, and may also exhibit an unusually high heavy-flavour content or atypically large jet masses. The first type of SUSY signal simulated is a simplified model, in which gluinos are pair-produced and then decay through an off-shell squark via the cascade: ˜g→q+ ¯q0+˜χ± 1q, q0∈ {u, d, s, c}, ˜χ± 1→W±+˜χ0 2, ˜χ0 2→Z+˜χ0 1, where the quarks are only permitted to be from the first two generations. The parameters of the model are the masses of the gluino, m˜g, and of the lightest neutralino, m˜χ0 1. The mass of the ˜χ± 1is constrained to be (m˜g+m˜χ0 1)/2, and the mass of the ˜χ0 2is set to (m˜χ± 1+m˜χ0 1)/2. A diagram of this ‘two-step’ simplified model is shown in figure 1(a). An additional signal model to which this analysis has significant sensitivity is gluinomediated top squark (˜ t1) production, in which top-quark-rich final states are produced as shown in figure 1(b). This model manifests itself as top quark production via either offshell or on-shell top squarks. In the off-shell model, pair-production of gluinos is followed by their decay with a 100% branching ratio into t¯ t+˜χ0 1, through a virtual top squark. Naturalness arguments for SUSY favour light gluinos, top squarks, and Higgsinos, so they motivate consideration of this final state. Permitting non-zero RPV couplings allows consideration of another variety of gluinomediated top squark production, wherein the last step of the decay proceeds via a baryonnumber-violating interaction: ˜ t1→¯s+¯ b(with charge conjugates implied). Such RPV models may give rise to final states with missing transverse momentum, for example from leptonic decays of Wbosons produced in top quark decays. The current analysis accepts final states with sufficiently low missing transverse momentum to be sensitive to these RPV scenarios. Figure 1(c) presents the RPV simplified model considered, for which the coupling strength induces prompt top squark decays. The signal samples were generated using MadGraph5 aMC@NLO interfaced to Pythia 8 with the A14 tune for the modelling of the parton shower, hadronisation and – 6 – JHEP10(2020)062 underlying event. The versions of the generators used for the two-step and RPV simplified models were MadGraph5 aMC@NLO v2.6.2 with Pythia 8.212, and for the gluinomediated top squark model MadGraph5 aMC@NLO v2.3.3 with Pythia 8.212. The matrix element calculation was performed at tree level and includes the emission of up to two additional partons. The PDF set used for the generation was NNPDF2.3 LO. The Evt- Gen v1.6.0 program was used to simulate properties of the b- and c-hadron decays. The matrix-element to parton-shower matching was done using the CKKW-L prescription [65], with a matching scale set to m˜g/4. Signal cross-sections were calculated to approximate NNLO in the strong coupling constant, adding the resummation of soft gluon emission at NNLL accuracy [66–73]. The nominal cross-section and its uncertainty were determined using the PDF4LHC15 mc PDF set, following the recommendations of ref. [74]. 4 Reconstruction and particle identification Primary vertices are reconstructed using at least two charged-particle tracks with pT> 500 MeV measured by the ID [75]. The primary vertex with the largest sum of squared track transverse momenta (Pp2 T) is designated as the hard-scatter vertex. Jets are reconstructed using the anti-kt[76–78] jet algorithm with radius parameter R= 0.4. It uses as inputs particle-flow objects. These are charged-particle tracks matched to the hard-scatter vertex with the requirement |z0sin θ|<2.0 mm, where z0is the longitudinal impact parameter, and calorimeter energy clusters surviving an energy subtraction algorithm that removes the calorimeter deposits of good-quality tracks originating from any vertex [17]. To eliminate jets containing a large energy contribution from pile-up, jets are tested for compatibility with the hard-scatter vertex with the jet vertex tagger (JVT) discriminant, utilising information from the ID tracks associated with the jet [79]. Any jets with 20 GeV < pT<120 GeV and |η|<2.4 for which JVT <0.5 are considered to originate from pile-up and are therefore rejected from the analysis. After the selection above is applied, only jets with pT>20 GeV and |η|<2.8 are considered in this analysis, with the exception of the Emiss Tcalculation, for which jets in the range 2.8≤ |η| ≤ 4.5 are also used. Hadronically decaying τ-leptons are not discriminated from other hadronic jets. Reconstructed R= 0.4 jets with pT>20 GeV and |η|<2.0 are reclustered to form large-radius jets using the anti-ktalgorithm with radius parameter R= 1.0 [80]. The input jets are required to pass an overlap removal procedure accounting for ambiguities between jets and leptons, as discussed below. Large-radius jets are retained for analysis if they have pT>100 GeV and |η|<1.5. Jets containing b-hadrons and which are within the ID acceptance (|η|<2.5) are identified as b-tagged jets using a multivariate algorithm that exploits the impact parameters of the charged-particle tracks, the presence of secondary vertices, and the reconstructed flight paths of b- and c-hadrons inside the jet [81]. The output of the algorithm is a single discriminant value which signifies the likelihood that the jet contains a b-hadron. This analysis considers jets to be b-tagged if the discriminant exceeds a threshold that results in an average identification efficiency of 70% for jets containing b-hadrons in simulated t¯ t – 7 – JHEP10(2020)062 events [82]. In the same event sample, a rejection factor of approximately 300 is reached for jets initiated by light quarks and gluons and 8.9 for jets initiated by charm quarks. Electron candidates are reconstructed from energy deposits in the EM calorimeter that are matched to charged-particle tracks in the ID [83]. ‘Baseline electrons’ are required to satisfy pT>7 GeV and |η|<2.47. They are identified using the ‘loose’ operating point provided by a likelihood-based algorithm [83]. Electrons with pT>20 GeV are defined as ‘signal electrons’ if they pass a ‘tight’ likelihood selection including impact parameter restrictions and the ‘GradientLoose’ isolation requirement [84] in addition to the ‘baseline electron’ preselection. To achieve additional rejection of background electrons from nonprompt sources, signal electron tracks must be matched to the hard-scatter vertex with a longitudinal impact parameter |z0sin θ|<0.5 mm and a transverse impact parameter significance |d0|/σ(d0)<5. Signal electrons are used in leptonic control regions, as described in section 6.2. The electron reconstruction and identification efficiency in simulated samples are corrected by factors determined by data-MC comparison using a given final state [84]. Photon candidates are identified using ‘tight’ criteria for lateral shower shapes in the first and second layers of the EM calorimeter, as well as for the degree of hadronic shower leakage [83]. Acceptance requirements of pT>40 GeV and |η|<2.37 are applied. Additionally, photons falling in the region 1.37 <|η|<1.52 are removed, to avoid a region of the calorimeter with limited instrumentation. Muon candidates are reconstructed from matching tracks in the ID and muon spectrometer, refined through a global fit which uses the hits from both subdetectors [85]. ‘Baseline muons’ must have pT>6 GeV and |η|<2.7, and satisfy the ‘medium’ identification criteria. Similarly to electrons, the longitudinal impact parameter z0relative to the hard-scatter vertex is required to satisfy |z0sin θ|<0.5 mm. Muons are characterised as ‘signal muons’ if they have a higher transverse momentum, pT>20 GeV, and satisfy the ‘GradientLoose’ isolation requirement [85], as well as a further transverse impact parameter restriction |d0|/σ(d0)<3. Signal muons are used in leptonic control regions, as described in section 6.2. Muon reconstruction and identification efficiencies in simulated samples are corrected with factors evaluated by a data-MC comparison [86]. To resolve the reconstruction ambiguities between electrons, muons, photons and jets, an overlap removal procedure is applied to baseline objects. First, any electron sharing an ID track with a muon is rejected. If it shares the same ID track with another electron, the one with lower pTis discarded. Next, photons with ∆R < 0.4 relative to an electron or a muon are discarded. Subsequently, non-b-tagged jets are rejected if they lie within ∆R= 0.2 of an electron or if the jet has no more than three tracks with pT>500 MeV, or contains an ID track matched to a muon such that pjet T<2pµ Tand the muon track has more than 70% of the sum of the transverse momenta of all tracks in the jet, such that the jet resembles radiation from the muon. Finally, electrons or muons with ∆R < 0.4 from a surviving jet are eliminated and jets with ∆R < 0.4 from photons are removed. The missing transverse momentum, Emiss T, is defined as the magnitude of the negative vector sum of the transverse momenta of baseline electrons and muons, photons and jets, which pass an overlap removal procedure, based on removing duplicated energy contributions and therefore distinct from that used for jet/lepton disambiguation. A ‘soft term’ – 8 – JHEP10(2020)062 ) miss T E(S 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Number of Events / Bin Width Data 2015-2018 Total background Multijets ll, ql→tt + jetsνl→W + jetsννll, →Z Single top + Xtt νqq ll, qq l→VV tbs (RPV) x100→t 1 t ~ →g ~ x100 0 1 χ ∼ qqWZ→g ~ ATLAS -1 = 13 TeV, 139 fbs VR-7ej50-2ib 0 1 2 3 4 5 6 7 8 9 10 ) miss T E(S 0.8 0.9 1 1.1 1.2 Data/Pred. (a) N50 jet = 7, Nb−jet ≥2 ) miss T E(S 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 Number of Events / Bin Width Data 2015-2018 Total background Multijets ll, ql→tt + jetsνl→W + jetsννll, →Z Single top + Xtt νqq ll, qq l→VV tbs (RPV) x100→t 1 t ~ →g ~ x100 0 1 χ ∼ qqWZ→g ~ ATLAS -1 = 13 TeV, 139 fbs VR-6ej80-0ib 0 1 2 3 4 5 6 7 8 9 10 ) miss T E(S 0.8 0.9 1 1.1 1.2 Data/Pred. (b) N80 jet = 6, Nb−jet ≥0 Figure 3. Distributions of S(Emiss T) for events in two of the validation regions. The upper panel shows the absolute yields for data (black points) and all background subcomponents (histograms), with the combination of statistical and systematic uncertainties shown by the hatched areas. The yields for two benchmark signal models are overlaid, representing 1.6 TeV gluinos decaying into Wand Zbosons and a 100 GeV neutralino via intermediate gauginos (long dashed histogram) or instead into tbs/tbd via a 600 GeV top squark through an R-parity-violating (RPV) coupling (short dashed histogram). Signal yields are scaled by a factor of 100 for visibility. The lower panel shows the ratio of the data yields to the total SM prediction. – 15 – JHEP10(2020)062 S(Emiss T) range 0–2 2–3 3–4 4–5 >5 N50 jet = 6, prescaled data TRl,prescale norm — — — TRprescale shape N50 jet = 7, full dataset TRh,prescale norm — — — VRNjet N50 jet = 7, full dataset TRl norm — — — TRshape N50 jet ≥8, full dataset TRh norm —QCR VRS(Emiss T)SR Table 5. Illustration of the main multijet template, control and validation regions in Njet and S(Emiss T) corresponding to the j50 signal regions. The template regions needed to derive predictions in the control and VRS(Emiss T)regions are not shown. For the row labelled ‘prescaled data’, a six-jet trigger was used that collected only a fraction of the Run 2 data. The superscript ‘prescale’ is used to indicate the template regions used to predict the VRNjet background using prescaled data. 6.2 Leptonic backgrounds The SM processes which produce multijet events with one or more leptons are categorised as leptonic backgrounds. Although events containing charged leptons (eor µ) are excluded from the signal regions, it is still possible for leptonic backgrounds to contaminate them. The veto only applies to events containing electrons or muons, and hence hadronically decaying τ-leptons (originating from top quark or Wboson decays) remain a source of background. Such τ-leptons are treated as jets within this analysis, so they may contribute to the jet count if they have sufficient pT, and the momentum lost through any associated neutrinos can also cause these events to enter the signal regions. Additionally, there are cases where the electrons or muons may fall outside of the detector acceptance, or be misreconstructed in the detector, increasing the Emiss Tof the event. The two largest leptonic backgrounds are from leptonically and semileptonically decaying t¯ t, and leptonically decaying Wbosons produced in association with jets. The estimation of these backgrounds employs the MC simulations described in section 3. To reduce normalisation and modelling uncertainties the background predictions are normalised to data using CRs. The control regions are designed to be kinematically similar to signal regions, but not to overlap with them. They are designed to enhance the contributions from particular backgrounds, in order to measure those backgrounds cleanly, while being comparatively sparse in signal contamination. Statistical orthogonality between the signal regions and the leptonic CRs is achieved by requiring exactly one electron or muon in CR events. To reduce statistical uncertainties, each Njet ≥msignal region has a corresponding (Njet ≥ m−1) control region, except in the case of signal regions with an N50 jet ≥8 requirement, where there are sufficiently large yields in the control region to match the signal region Njet ≥mrequirement. In order to increase the statistical precision in control regions with higher jet-multiplicity requirements, the S(Emiss T) threshold is reduced to 4 in the leptonic control regions. Two leptonic control regions are defined for each signal region: the first (WCR) includes ab-jet veto to enhance the contribution of W+jets backgrounds, and the second (TCR) requires at least one b-jet in the event, enriching the region with the t¯ tbackground. The – 16 – JHEP10(2020)062 Selection criterion Selection ranges Lepton multiplicity Exactly one signal eor µremaining after overlap removal Lepton pT>20 GeV mT<120 GeV Trigger Same as signal regions Jet pT,|η|Same as signal regions Njet (including lepton) ≥8, for N50 jet ≥8 signal regions; ≥(NSR jet −1), otherwise Nb-jet = 0 (WCR), ≥1 (TCR) MΣ JSame as signal regions S(Emiss T)>4 Table 6. Summary of the selections used to define the leptonic control regions. 8, 9, 10ij50 Nb-jet = 0 Nb-jet ≥1 CR definitions MΣ J≤340 GeV WCR1 TCR1 340 GeV < MΣ J≤500 GeV WCR2 TCR2 MΣ J>500 GeV WCR3 TCR3 Table 7. Control region subdivisions for the background fits for multi-bin signal selections. signal-region pTthresholds imposed on the jets also apply to the corresponding control regions. To simulate the effect of τ-leptons being reconstructed as jets in the signal regions, electrons and muons are also included as jets, for the purpose of the corresponding CR selection, provided that they pass the same pTand |η|requirements as the jets in the event. Finally, an upper bound on the transverse mass2computed with the lepton and Emiss Tis applied at 120 GeV in order to reduce contributions from signal processes. This variable has a kinematic endpoint at the Wboson mass for leptonically decaying on-shell Wbosons, but has no such bound when the lepton and Emiss Toriginate from different decays. The control region definitions are summarised in table 6. Additional requirements are placed on MΣ Jfor the control regions in the same manner as for the corresponding signal region selections. In the case of the single-bin selections (table 3), the same threshold is applied. For multi-bin selections, the control regions have three bins, corresponding to the same MΣ Jthresholds, as shown in table 7. The WCR and TCR are used as inputs to a fit that applies normalisation corrections to the t¯ tand W+jets background components, as described in section 6.3. 6.3 Background normalisation corrections Background estimates in the signal region are made more accurate by employing a background likelihood fit based on the control regions, using the methods described in ref. [89]. By means of this fit, the raw estimated yields for the major background components, in- 2The transverse mass is defined as mT=q2p` TEmiss T[1 −cos(∆φ(~pT`,~ Emiss T))], where p` Tis the lepton pT. – 17 – JHEP10(2020)062 cluding the multijet, t¯ t, and W+jets processes, are corrected for mismodelling. For the other background processes, which contribute of the order of 1% of the SR yields, the nominal MC predictions are used directly. The extent to which the background prediction is compatible with the signal region observation is quantified in the form of a p-value CLb, which is the probability of an upward fluctuation of the event yield relative to the signal region prediction no larger than that observed in data, given the background model. The multijet background, while estimated using a data-driven procedure, is incorporated in the simultaneous fit due to the dependence of the template prediction on the subtraction of other backgrounds which include the simulated t¯ tand W+jets estimates. Besides correcting for any residual mismodelling, fitting the multijet component handles the correlations between the systematic uncertainties of the different background components consistently. In the case of the single-bin regions (table 3), the normalisations of the background components are allowed to vary within their nominal uncertainties, described in section 6.4. For the multi-bin analysis channels (table 2), the additional information available to the fit permits a reduction in the uncertainties, as well as a modification of the event yields to better accommodate the control region measurements. To avoid artificially constraining systematic uncertainties in the background fit due to the high statistical precision, the multijet normalisation region QCR is limited to a single bin with Nb-jet ≥0 and MΣ J≥0. For illustration, the pre-fit yields for the SR-8ij50 leptonic CRs are shown in figure 4, demonstrating the extent of the observed mismodelling in the W+jets and t¯ tnormalisation. The fitted normalisation factors are summarised for all signal selections in figure 5, and are found to be consistent across the wide range of jet multiplicities probed. Figure 6shows the background modelling in the validation regions VRNjet and VRS(Emiss T) for the SR-8ij50 multi-bin analysis. Considering uncertainties, the data yields are in agreement with the predictions after applying the background normalisation factors. Similar levels of agreement are found for the single-bin signal regions, as can be seen in figure 7. In the VRS(Emiss T), there is a tendency for the background predictions to mildly overshoot the data, at the level of 10%. This is due to residual kinematic correlations causing the S(Emiss T) distribution not to be entirely independent of the jet multiplicity. Applying flavour-tagging and jet mass selections alters these correlations. In the validation and signal regions, an uncertainty based on the largest observed non-closure at lower jet multiplicities or smaller S(Emiss T) values is applied, and is found to cover the observed discrepancies. 6.4 Systematic uncertainties Systematic uncertainties from the following sources are assessed for the predicted background yields. Experimental systematic uncertainties chiefly include uncertainties in the energy or momentum scales of reconstructed jets and leptons or the missing transverse momentum, as well as the uncertainty in the total integrated luminosity and the magnitude of pile-up corrections. Theoretical uncertainties are assessed by varying the scales (renormalisation, factorisation, resummation, shower matching) at which cross-sections are calculated or by comparison of an ensemble of matrix element and parton shower programs used to generate the predictions. Each source of theoretical uncertainty is correlated across – 18 – JHEP10(2020)062 10 2 10 3 10 4 10 5 10 Events Data Total background ll, ql→tt + jetsνl→W + jetsννll, →Z Single top +Xtt νqqll, qql→VV ATLAS -1 = 13 TeV, 139 fbs WCR-8ij50, pre-fit < 340ΣMJ < 500Σ340 < MJ > 500ΣMJ 0.5 0.75 1 1.25 1.5 Data / Pred. (a) WCR, pre-fit 10 2 10 3 10 4 10 5 10 Events Data Total background ll, ql→tt + jetsνl→W + jetsννll, →Z Single top +Xtt νqqll, qql→VV ATLAS -1 = 13 TeV, 139 fbs TCR-8ij50, pre-fit < 340ΣMJ < 500Σ340 < MJ > 500ΣMJ 0.5 0.75 1 1.25 1.5 Data / Pred. (b) TCR, pre-fit Figure 4. Pre-fit yields in the (a) W+jets and (b) t¯ tbackground normalisation regions for the SR-8ij50 multi-bin analysis. The upper panel shows the absolute yields for data (black points) and all background subcomponents (histograms), with the combination of statistical and systematic uncertainties shown by the hatched areas. The lower panel shows the ratio of the data yields to the total SM prediction. the signal, control and validation region selections, but assessed separately for each background process. Additional uncertainties account for potential inaccuracies in the data-driven multijet estimate. The effects of residual kinematic correlations are estimated by modifying the HT-binning procedure used in the multijet estimate. A comparison between the nominal prediction and an alternative prediction assuming a broader resolution for flavour-tagged jets measured in data is used to estimate the impact of different flavour composition in – 19 – JHEP10(2020)062 8ij50, multi-bin 9ij50, multi-bin 10ij50, multi-bin 8ij50-0ib-MJ500 9ij50-0ib-MJ340 10ij50-0ib-MJ340 10ij50-0ib-MJ500 10ij50-1ib-MJ500 11ij50-0ib-MJ0 12ij50-2ib-MJ0 9ij80-0ib-MJ0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Normalisation factor ll, ql→tt + jetsνl→W Multijets ATLAS -1 = 13 TeV, 139 fbs Figure 5. Summary of the fitted normalisation factors for the t¯ t,W+jets and multijet backgrounds in all signal regions. The error bars indicate the combination of statistical and systematic uncertainties in the corresponding factors. Two pairs of SRs, namely the SR-8ij50 and SR-9ij50 multi-bin regions and the SR-10ij50-0ib-MJ500 and SR-10ij50-1ib-MJ500 single-bin regions share leptonic control regions, and therefore have highly correlated normalisation factors for the W+jets and t¯ t background components. the template and signal regions. Finally, a conservative uncertainty is assessed from the observed non-closure of the prediction in validation regions. The dominant sources of systematic uncertainty are listed in table 8. The total uncertainty in the predicted signal region yield is in the range 6–14% for the multi-bin signal regions, and 7–66% for the less constrained single-bin regions. In both cases the largest uncertainties arise in the regions with the highest requirements on the jet multiplicity (and in the single-bin case also on the b-jet multiplicity or MΣ J) since in those cases the supporting measurements are more statistically limited and so provide less precise constraints on the background predictions. In each of the three multi-bin signal regions the most important uncertainty is the closure systematic uncertainty associated with the multijet template estimate (3–6%), chiefly because of the dominance of this background component. In the single-bin signal regions the background normalisations are less constrained by the fit, and the statistical uncertainties associated with the multijet and t¯ tcontrol regions tend to dominate (4–27% for multijets, 6–14% for t¯ t). The theoretical uncertainty in the t¯ tbackground predictions is also found to be significant, with that from final-state radiation contributing up to 17% in the single-bin fits. The impact on the total yield in multi-bin signal regions is ≤5%. The Z+jets component has a theoretical uncertainty of up to 14% in the single-bin regions while it is at most 6% in the multi-bin signal regions. Most experimental systematic uncertainties affecting the reconstructed objects have insignificant impacts, being substantially reduced due to correlations between the MC- based predictions and the data-driven multijet template. The largest observed effects are – 20 – JHEP10(2020)062 1 10 2 10 3 10 4 10 5 10 6 10 Events Data Total background Multijets ll, ql→tt + jetsνl→W + jetsννll, →Z Single top +Xtt νqqll, qql→VV ATLAS -1 = 13 TeV, 139 fbs -8ij50, post-fit Njet VR < 340 Σ 0eb, MJ < 340 Σ 1eb, MJ < 340 Σ 2ib, MJ < 500 Σ 0eb, 340 < MJ < 500 Σ 1eb, 340 < MJ < 500 Σ 2ib, 340 < MJ > 500 Σ 0eb, MJ > 500 Σ 1eb, MJ > 500 Σ 2ib, MJ 0.5 0.75 1 1.25 1.5 Data / Pred. (a) VRNjet 1 10 2 10 3 10 4 10 5 10 Events Data Total background Multijets ll, ql→tt + jetsνl→W + jetsννll, →Z Single top +Xtt νqqll, qql→VV ATLAS -1 = 13 TeV, 139 fbs -8ij50, post-fit S(ETmiss) VR < 340 Σ 0eb, MJ < 340 Σ 1eb, MJ < 340 Σ 2ib, MJ < 500 Σ 0eb, 340 < MJ < 500 Σ 1eb, 340 < MJ < 500 Σ 2ib, 340 < MJ > 500 Σ 0eb, MJ > 500 Σ 1eb, MJ > 500 Σ 2ib, MJ 0.5 0.75 1 1.25 1.5 Data / Pred. (b) VRS(Emiss T) Figure 6. Post-fit event yields in validation regions for the SR-8ij50 multi-bin analysis. The upper panel shows the absolute yields for data (black points) and all background subcomponents (histograms), with the combination of statistical and systematic uncertainties shown by the hatched areas. The lower panel shows the ratio of the data yields to the total SM prediction. – 21 – JHEP10(2020)062 2 10 3 10 4 10 5 10 6 10 7 10 Events Total background Data Multijets ll, ql→tt + jetsνl→W + jetsννll, →Z Single top +Xtt νqq ll, l→VV ATLAS -1 = 13 TeV, 139 fbs , post-fit Njet VR 8ij50-0ib-MJ500 9ij50-0ib-MJ340 10ij50-0ib-MJ340 10ij50-0ib-MJ500 10ij50-1ib-MJ500 11ij50-0ib-MJ0 12ij50-2ib-MJ0 9ij80-0ib-MJ0 0.5 0.75 1 1.25 1.5 Data / Pred. (a) VRNjet 1 10 2 10 3 10 4 10 5 10 Events Total background Data Multijets ll, ql→tt + jetsνl→W + jetsννll, →Z Single top +Xtt νqq ll, l→VV ATLAS -1 = 13 TeV, 139 fbs , post-fit S(ETmiss) VR 8ij50-0ib-MJ500 9ij50-0ib-MJ340 10ij50-0ib-MJ340 10ij50-0ib-MJ500 10ij50-1ib-MJ500 11ij50-0ib-MJ0 12ij50-2ib-MJ0 9ij80-0ib-MJ0 0.5 0.75 1 1.25 1.5 Data / Pred. (b) VRS(Emiss T) Figure 7. Post-fit event yields in validation regions summarised for all single-bin signal regions. The upper panel shows the absolute yields for data (black points) and all background subcomponents (histograms), with the combination of statistical and systematic uncertainties shown by the hatched areas. The lower panel shows the ratio of the data yields to the total SM prediction. – 22 – JHEP10(2020)062 Signal region Total syst. Dominant systematic uncertainties SR-8ij50-0ib-MJ500 16% Emiss Tsoft, L 7% Emiss Tsoft, T 7% Z+jets PS 5% SR-9ij50-0ib-MJ340 16% Emiss Tsoft, T 9% Emiss Tsoft, L 9% Z+jets PS 4% SR-10ij50-0ib-MJ340 20% t¯ tFSR 9% MC stat. 9% Emiss Tsoft, L 8% SR-10ij50-0ib-MJ500 27% t¯ tFSR 17% MC stat. 12% Emiss Tsoft, L 9% SR-10ij50-1ib-MJ500 24% MC stat. 14% Emiss Tsoft, L 10% Emiss Tsoft, T 10% SR-11ij50 27% MC stat. 18% t¯ tFSR 14% t¯ tnorm 6% SR-12ij50-2ib 70% MC stat. 62% MJ norm 25% MJ HTbinning 13% SR-9ij80 21% MC stat. 14% Z+jets PS 14% Z+jets match 7% SR-8ij50 multi-bin 6% MJ closure 3% JES flavour 3% JES flavour 2% SR-9ij50 multi-bin 7% MJ closure 4% Z+jets PS 3% Emiss Tsoft, L 3% SR-10ij50 multi-bin 14% Z+jets PS 6% MJ closure 6% t¯ tFSR 5% Table 8. The total systematic uncertainties are shown for each of the single-bin signal regions, and also for the multi-bin signal regions, together with the three dominant contributions for each. The individual uncertainties can be (anti-)correlated, and do not necessarily sum in quadrature to the total background uncertainty. For the multi-bin signal regions the uncertainties are those found after summing the expected yields over the corresponding MΣ Jand b-jet multiplicity bins of table 2. Within the table ‘MC stat.’ indicates the statistical uncertainty of the simulated event yield in the SR, ‘MJ’ indicates the uncertainty in the multijet background, ‘closure’ indicates the uncertainty from the multijet template method closure, ‘norm’ is the result of statistical uncertainties from the CRs, ‘JES flavour’ indicates the effect of uncertainties in the jet energy scale due to differences between quark- and gluon-initiated jets, ‘Emiss Tsoft, L/T’ indicate two sources, longitudinal and transverse, of uncertainty in the soft component of the missing transverse momentum, ‘MJ HTbinning’ relates to the parameters of the binning of the multijet template in HT, ‘FSR’ indicates final-state radiation, ‘match’ indicates the matrix element/parton shower matching scale, and ‘PS’ is the uncertainty from varying the scale at which the strong coupling constant is calculated for parton shower emissions in the MC simulation. due to the uncertainties in the jet energy scale, which can have an effect of up to 3% due to the large jet activity in the events selected by this analysis, and in the soft term of the missing transverse momentum (≤10%). In the least populated SRs, there can be a large statistical uncertainty in the predictions from simulation. 7 Results and interpretation Data yields are shown graphically for all signal regions in figure 8. The background predicted to have the largest yield in all signal regions comes from multijet production. The relative contribution of the remaining backgrounds depends on the signal region. The t¯ t process generally provides the second-largest contribution, and tends to form a larger fraction of the total background for higher jet-multiplicity requirements. As the requirement on the number of b-tagged jets increases, it can be seen that the relative contributions of W+jets, Z+jets and multi-boson backgrounds decrease compared to those from t¯ tand single-top-quark production processes. A breakdown of the yields in the single-bin regions is given in table 9. For illustration, the full S(Emiss T) distributions for several signal regions are shown in figure 9. The – 23 – JHEP10(2020)062 data yields are found to be consistent with the background predictions within the assessed statistical and systematic uncertainties, with no significant excesses over the SM expectation. Mild deviations from the SM expectation are observed, with a tendency for the background to be overestimated in the higher jet-multiplicity regions, which is consistent with the trends observed in the corresponding validation regions. For interpretation, the likelihood fits for background estimation (section 6.3) are extended to include the signal region, and thereby perform two forms of hypothesis test using a profile-likelihood-ratio test statistic [90], quantifying the significance of any observed excesses or the lack thereof. Firstly, the discovery test discriminates between the null hypothesis stating that the SR measurement is consistent with only SM contributions and an alternative hypothesis postulating a positive signal. Secondly, assuming a specific signal model, one may also form an exclusion test of the signal-plus-background hypothesis, where an observation significantly smaller than the combination of SM and SUSY processes would lead to rejection of the signal model. This provides the exclusion p-value p1, the probability of observing at most the observed event yield when assuming that the signal is present with its nominal crosssection. A complementary p-value for the background observation CLbis defined as the probability of observing at most the observed yield under the background-only hypothesis. Points in the SUSY parameter space are considered excluded if the CLsparameter [91], computed as p1/(1 −CLb) is smaller than 0.05. This protects against spurious exclusion of signals due to observing SR event counts significantly smaller than those predicted. While not strictly defining a frequentist confidence level, these are referred to as 95% confidence level (CL) limits. The impact of the particle-flow reconstruction was assessed in the single-bin signal regions by applying the same event selection but instead using calorimeter-based hadronic reconstruction. As a result of the improved jet and Emiss Tresolution, the multijet background is found to be reduced by 30–50%, corresponding to two standard deviations when considering statistical uncertainties as well as the systematic uncertainties of the jet energy scale and multijet estimate closure, resulting in an overall 20% lower total background yield in most regions. Consequently, the expected sensitivity to new physics signals is improved by up to 30% (one standard deviation), quantified in terms of the upper limit on BSM events. The single-bin signal region event yields are used to derive model-independent constraints on the production of BSM particles. Table 10 shows the observed 95% CL limits on the visible cross-section hσi95 obs as well as the observed (expected) limits on the number of BSM signal events S95 obs (S95 exp) in each signal region. The discovery p-value p(s= 0), defined as the probability of observing at least the observed event yield when assuming that no signal is present, is calculated, as is the corresponding Gaussian significance Z. For signal regions in which the predictions exceed the data, the value of p(s= 0) is capped at 0.5. The smallest background p-value CLbcomputed in any region is 0.16, while the smallest discovery p-value is 0.41, and hence all observations are compatible with the SM- only hypothesis. The most stringent limits observed are for the SR-12ij50-2ib selection, for which visible cross-sections greater than 40 ab are excluded. Constraints on sparticle production in several benchmark parameter planes are shown in figure 10. 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Duckeck114, O.A. Ducu27b, D. Duda115, A. Dudarev36, A.C. Dudder100, E.M. Duffield18, M. D’uffizi101, L. Duflot65, M. D¨uhrssen36, C. D¨ulsen181, M. Dumancic179, A.E. Dumitriu27b, A.K. Duncan57, M. Dunford61a, A. Duperrin102, H. Duran Yildiz4a, M. D¨uren56, A. Durglishvili158b, D. Duschinger48, B. Dutta46, D. Duvnjak1, G.I. Dyckes136, M. Dyndal36, S. Dysch101, B.S. Dziedzic85, M.G. Eggleston49, T. Eifert8, G. Eigen17, K. Einsweiler18, T. Ekelof171, H. El Jarrari35e, V. Ellajosyula171, M. Ellert171, F. Ellinghaus181, A.A. Elliot93, N. Ellis36, J. Elmsheuser29, M. Elsing36, D. Emeliyanov143, A. Emerman39, Y. Enari162, M.B. Epland49, J. Erdmann47, A. Ereditato20, P.A. Erland85, M. Errenst36, M. Escalier65, C. Escobar173, O. Estrada Pastor173, E. Etzion160, H. Evans66, M.O. Evans155, A. Ezhilov137, F. Fabbri57, L. Fabbri23b,23a, V. Fabiani119, G. Facini177, R.M. Faisca Rodrigues Pereira139a, R.M. Fakhrutdinov123, S. Falciano73a, P.J. Falke24, S. Falke36, J. Faltova142, Y. Fang15a, Y. Fang15a, G. Fanourakis44, M. Fanti69a,69b, M. Faraj67a,67c,q, A. Farbin8, A. Farilla75a, E.M. Farina71a,71b, T. Farooque107, S.M. Farrington50, P. Farthouat36, F. Fassi35e, P. Fassnacht36, D. Fassouliotis9, M. Faucci Giannelli50, W.J. Fawcett32, L. Fayard65, O.L. Fedin137,o, W. Fedorko174, A. Fehr20, M. Feickert172, L. Feligioni102, A. Fell148, C. Feng60b, M. Feng49, M.J. Fenton170, A.B. Fenyuk123, S.W. Ferguson43, J. Ferrando46, A. Ferrante172, A. Ferrari171, P. Ferrari120, R. Ferrari71a, D.E. Ferreira de Lima61b, A. Ferrer173, D. Ferrere54, C. Ferretti106, F. Fiedler100, A. Filipˇciˇc92, F. Filthaut119, K.D. Finelli25, – 38 – JHEP10(2020)062 M.C.N. Fiolhais139a,139c,a, L. Fiorini173, F. Fischer114, J. Fischer100, W.C. Fisher107, T. Fitschen21, I. Fleck150, P. Fleischmann106, T. Flick181, B.M. Flierl114, L. Flores136, L.R. Flores Castillo63a, F.M. Follega76a,76b, N. Fomin17, J.H. Foo166, G.T. Forcolin76a,76b, B.C. Forland66, A. Formica144, F.A. F¨orster14, A.C. Forti101, E. Fortin102, M.G. Foti134, D. Fournier65, H. Fox90, P. Francavilla72a,72b, S. Francescato73a,73b, M. Franchini23b,23a, S. Franchino61a, D. Francis36, L. Franco5, L. Franconi20, M. Franklin59, G. Frattari73a,73b, A.N. Fray93, P.M. Freeman21, B. Freund110, W.S. Freund81b, E.M. Freundlich47, D.C. Frizzell128, D. Froidevaux36, J.A. Frost134, M. Fujimoto126, C. Fukunaga163, E. Fullana Torregrosa173, T. Fusayasu116, J. Fuster173, A. Gabrielli23b,23a, A. Gabrielli36, S. Gadatsch54, P. Gadow115, G. Gagliardi55b,55a, L.G. Gagnon110, G.E. Gallardo134, E.J. Gallas134, B.J. Gallop143, G. Galster40, R. Gamboa Goni93, K.K. Gan127, S. Ganguly179, J. Gao60a, Y. Gao50, Y.S. Gao31,l, F.M. Garay Walls146a, C. Garc´ıa173, J.E. Garc´ıa Navarro173, J.A. Garc´ıa Pascual15a, C. Garcia-Argos52, M. Garcia-Sciveres18, R.W. Gardner37, N. Garelli152, S. Gargiulo52, C.A. Garner166, V. Garonne133, S.J. Gasiorowski147, P. Gaspar81b, A. Gaudiello55b,55a, G. Gaudio71a, I.L. Gavrilenko111, A. Gavrilyuk124, C. Gay174, G. Gaycken46, E.N. Gazis10, A.A. Geanta27b, C.M. Gee145, C.N.P. Gee143, J. Geisen97, M. Geisen100, C. Gemme55b, M.H. Genest58, C. Geng106, S. Gentile73a,73b, S. George94, T. Geralis44, L.O. Gerlach53, P. Gessinger-Befurt100, G. Gessner47, S. Ghasemi150, M. Ghasemi Bostanabad175, M. Ghneimat150, A. Ghosh65, A. Ghosh78, B. Giacobbe23b, S. Giagu73a,73b, N. Giangiacomi23b,23a, P. Giannetti72a, A. Giannini70a,70b, G. Giannini14, S.M. Gibson94, M. Gignac145, D.T. Gil84b, D. Gillberg34, G. Gilles181, D.M. Gingrich3,al, M.P. Giordani67a,67c, P.F. Giraud144, G. Giugliarelli67a,67c, D. Giugni69a, F. Giuli74a,74b, S. Gkaitatzis161, I. Gkialas9,g, E.L. Gkougkousis14, P. Gkountoumis10, L.K. Gladilin113, C. Glasman99, J. Glatzer14, P.C.F. Glaysher46, A. Glazov46, G.R. Gledhill131, I. Gnesi41b,b, M. Goblirsch-Kolb26, D. Godin110, S. Goldfarb105, T. Golling54, D. Golubkov123, A. Gomes139a,139b, R. Goncalves Gama53, R. Gon¸calo139a,139c, G. Gonella131, L. Gonella21, A. Gongadze80, F. Gonnella21, J.L. Gonski39, S. Gonz´alez de la Hoz173, S. Gonzalez Fernandez14, C. Gonzalez Renteria18, R. Gonzalez Suarez171, S. Gonzalez-Sevilla54, G.R. Gonzalvo Rodriguez173, L. Goossens36, N.A. Gorasia21, P.A. Gorbounov124, H.A. Gordon29, B. Gorini36, E. Gorini68a,68b, A. Goriˇsek92, A.T. Goshaw49, M.I. Gostkin80, C.A. Gottardo119, M. Gouighri35b, A.G. Goussiou147, N. Govender33c, C. Goy5, I. Grabowska-Bold84a, E.C. Graham91, J. Gramling170, E. Gramstad133, S. Grancagnolo19, M. Grandi155, V. Gratchev137, P.M. Gravila27f , F.G. Gravili68a,68b, C. Gray57, H.M. Gray18, C. Grefe24, K. Gregersen97, I.M. Gregor46, P. Grenier152, K. Grevtsov46, C. Grieco14, N.A. Grieser128, A.A. Grillo145, K. Grimm31,k, S. Grinstein14,w, J.-F. Grivaz65, S. Groh100, E. Gross179, J. Grosse-Knetter53, Z.J. Grout95, C. Grud106, A. Grummer118, J.C. Grundy134, L. Guan106, W. Guan180, C. Gubbels174, J. Guenther36, A. Guerguichon65, J.G.R. Guerrero Rojas173, F. Guescini115, D. Guest170, R. Gugel100, T. Guillemin5, S. Guindon36, U. Gul57, J. Guo60c, W. Guo106, Y. Guo60a, Z. Guo102, R. Gupta46, S. Gurbuz12c, G. Gustavino128, M. Guth52, P. Gutierrez128, C. Gutschow95, C. Guyot144, C. Gwenlan134, C.B. Gwilliam91, E.S. Haaland133, A. Haas125, C. Haber18, H.K. Hadavand8, A. Hadef60a, M. Haleem176, J. Haley129, J.J. Hall148, G. Halladjian107, G.D. Hallewell102, K. Hamano175, H. Hamdaoui35e, M. Hamer24, G.N. Hamity50, K. Han60a,v, L. Han60a, S. Han18, Y.F. Han166, K. Hanagaki82,t, M. Hance145, D.M. Handl114, M.D. Hank37, R. Hankache135, E. Hansen97, J.B. Hansen40, J.D. Hansen40, M.C. Hansen24, P.H. Hansen40, E.C. Hanson101, K. Hara168, T. Harenberg181, S. Harkusha108, P.F. Harrison177, N.M. Hartman152, N.M. Hartmann114, Y. Hasegawa149, A. Hasib50, S. Hassani144, S. Haug20, R. Hauser107, L.B. Havener39, M. Havranek141, C.M. Hawkes21, R.J. Hawkings36, S. Hayashida117, D. Hayden107, C. Hayes106, R.L. Hayes174, C.P. Hays134, J.M. Hays93, H.S. Hayward91, S.J. Haywood143, F. He60a, – 39 – JHEP10(2020)062 M.P. Heath50, V. Hedberg97, S. Heer24, A.L. Heggelund133, C. Heidegger52, K.K. Heidegger52, W.D. Heidorn79, J. Heilman34, S. Heim46, T. Heim18, B. Heinemann46,aj, J.G. Heinlein136, J.J. Heinrich131, L. Heinrich36, J. Hejbal140, L. Helary61b, A. Held125, S. Hellesund133, C.M. Helling145, S. Hellman45a,45b, C. Helsens36, R.C.W. Henderson90, Y. Heng180, L. Henkelmann32, A.M. Henriques Correia36, H. Herde26, Y. Hern´andez Jim´enez33e, H. Herr100, M.G. Herrmann114, T. Herrmann48, G. Herten52, R. Hertenberger114, L. Hervas36, T.C. Herwig136, G.G. Hesketh95, N.P. Hessey167a, H. Hibi83, A. Higashida162, S. Higashino82, E. Hig´on-Rodriguez173, K. Hildebrand37, J.C. Hill32, K.K. Hill29, K.H. Hiller46, S.J. Hillier21, M. Hils48, I. Hinchliffe18, F. Hinterkeuser24, M. Hirose132, S. Hirose52, D. Hirschbuehl181, B. Hiti92, O. Hladik140, D.R. Hlaluku33e, J. Hobbs154, N. Hod179, M.C. Hodgkinson148, A. Hoecker36, D. Hohn52, D. Hohov65, T. Holm24, T.R. Holmes37, M. Holzbock114, L.B.A.H. Hommels32, T.M. Hong138, J.C. Honig52, A. H¨onle115, B.H. Hooberman172, W.H. Hopkins6, Y. Horii117, P. Horn48, L.A. Horyn37, S. Hou157, A. Hoummada35a, J. Howarth57, J. Hoya89, M. Hrabovsky130, J. Hrdinka77, J. Hrivnac65, A. Hrynevich109, T. Hryn’ova5, P.J. Hsu64, S.-C. Hsu147, Q. Hu29, S. Hu60c, Y.F. Hu15a,15d,an, D.P. Huang95, Y. Huang60a, Y. Huang15a, Z. Hubacek141, F. Hubaut102, M. Huebner24, F. Huegging24, T.B. Huffman134, M. Huhtinen36, R. Hulsken58, R.F.H. Hunter34, P. Huo154, N. Huseynov80,ac, J. Huston107, J. Huth59, R. Hyneman106, S. Hyrych28a, G. Iacobucci54, G. Iakovidis29, I. Ibragimov150, L. Iconomidou-Fayard65, P. Iengo36, R. Ignazzi40, O. Igonkina120,y,∗, R. Iguchi162, T. Iizawa54, Y. Ikegami82, M. Ikeno82, D. Iliadis161, N. Ilic119,166,ab, F. Iltzsche48, H. Imam35a, G. Introzzi71a,71b, M. Iodice75a, K. Iordanidou167a, V. Ippolito73a,73b, M.F. Isacson171, M. Ishino162, W. Islam129, C. Issever19,46, S. Istin159, F. Ito168, J.M. Iturbe Ponce63a, R. Iuppa76a,76b, A. Ivina179, H. Iwasaki82, J.M. Izen43, V. Izzo70a, P. Jacka140, P. Jackson1, R.M. Jacobs46, B.P. Jaeger151, V. Jain2, G. J¨akel181, K.B. Jakobi100, K. Jakobs52, T. Jakoubek179, J. Jamieson57, K.W. Janas84a, R. Jansky54, M. Janus53, P.A. Janus84a, G. Jarlskog97, A.E. Jaspan91, N. Javadov80,ac, T. Jav˚urek36, M. Javurkova103, F. Jeanneau144, L. Jeanty131, J. Jejelava158a, P. Jenni52,c, N. Jeong46, S. J´ez´equel5, H. Ji180, J. Jia154, H. Jiang79, Y. Jiang60a, Z. Jiang152, S. Jiggins52, F.A. Jimenez Morales38, J. Jimenez Pena115, S. Jin15c, A. Jinaru27b, O. Jinnouchi164, H. Jivan33e, P. Johansson148, K.A. Johns7, C.A. Johnson66, R.W.L. Jones90, S.D. Jones155, T.J. Jones91, J. Jongmanns61a, J. Jovicevic36, X. Ju18, J.J. Junggeburth115, A. Juste Rozas14,w, A. Kaczmarska85, M. Kado73a,73b, H. Kagan127, M. Kagan152, A. Kahn39, C. Kahra100, T. Kaji178, E. Kajomovitz159, C.W. Kalderon29, A. Kaluza100, A. Kamenshchikov123, M. Kaneda162, N.J. Kang145, S. Kang79, Y. Kano117, J. Kanzaki82, L.S. Kaplan180, D. Kar33e, K. Karava134, M.J. Kareem167b, I. Karkanias161, S.N. Karpov80, Z.M. Karpova80, V. Kartvelishvili90, A.N. Karyukhin123, A. Kastanas45a,45b, C. Kato60d,60c, J. Katzy46, K. Kawade149, K. Kawagoe88, T. Kawaguchi117, T. Kawamoto144, G. Kawamura53, E.F. Kay175, S. Kazakos14, V.F. Kazanin122b,122a, R. Keeler175, R. Kehoe42, J.S. Keller34, E. Kellermann97, D. Kelsey155, J.J. Kempster21, J. Kendrick21, K.E. Kennedy39, O. Kepka140, S. Kersten181, B.P. Kerˇsevan92, S. Ketabchi Haghighat166, M. Khader172, F. Khalil-Zada13, M. Khandoga144, A. Khanov129, A.G. Kharlamov122b,122a, T. Kharlamova122b,122a, E.E. Khoda174, A. Khodinov165, T.J. Khoo54, G. Khoriauli176, E. Khramov80, J. Khubua158b, S. Kido83, M. Kiehn54, C.R. Kilby94, E. Kim164, Y.K. Kim37, N. Kimura95, B.T. King91,∗, A. Kirchhoff53, D. Kirchmeier48, J. Kirk143, A.E. Kiryunin115, T. Kishimoto162, D.P. Kisliuk166, V. Kitali46, C. Kitsaki10, O. Kivernyk24, T. Klapdor-Kleingrothaus52, M. Klassen61a, C. Klein34, M.H. Klein106, M. Klein91, U. Klein91, K. Kleinknecht100, P. Klimek121, A. Klimentov29, T. Klingl24, T. Klioutchnikova36, F.F. Klitzner114, P. Kluit120, S. Kluth115, E. Kneringer77, E.B.F.G. Knoops102, A. Knue52, D. Kobayashi88, T. Kobayashi162, M. Kobel48, M. Kocian152, T. Kodama162, P. Kodys142, D.M. Koeck155, P.T. Koenig24, T. Koffas34, N.M. K¨ohler36, – 40 – JHEP10(2020)062 9Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 Bahcesehir University(a), Faculty of Engineering and Natural Sciences, Istanbul; Istanbul Bilgi University(b), Faculty of Engineering and Natural Sciences, Istanbul; Department of Physics(c), Bogazici University, Istanbul; Department of Physics Engineering(d), Gaziantep University, Gaziantep; Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de F´ısica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 Institute of High Energy Physics(a), Chinese Academy of Sciences, Beijing; Physics Department(b), Tsinghua University, Beijing; Department of Physics(c), Nanjing University, Nanjing; University of Chinese Academy of Science (UCAS)(d), Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA; United States of America 19 Institut f¨ur Physik, Humboldt Universit¨at zu Berlin, Berlin; Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern; Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom 22 Facultad de Ciencias y Centro de Investigaci´ones(a), Universidad Antonio Nari˜no, Bogot´a; Departamento de F´ısica(b), Universidad Nacional de Colombia, Bogot´a, Colombia; Colombia 23 INFN Bologna and Universita’ di Bologna(a), Dipartimento di Fisica; INFN Sezione di Bologna(b); Italy 24 Physikalisches Institut, Universit¨at Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 Transilvania University of Brasov(a), Brasov; Horia Hulubei National Institute of Physics and Nuclear Engineering(b), Bucharest; Department of Physics(c), Alexandru Ioan Cuza University of Iasi, Iasi; National Institute for Research and Development of Isotopic and Molecular Technologies(d), Physics Department, Cluj-Napoca; University Politehnica Bucharest(e), Bucharest; West University in Timisoara(f), Timisoara; Romania 28 Faculty of Mathematics(a), Physics and Informatics, Comenius University, Bratislava; Department of Subnuclear Physics(b), Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Departamento de F´ısica, Universidad de Buenos Aires, Buenos Aires; Argentina 31 California State University, CA; United States of America 32 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 33 Department of Physics(a), University of Cape Town, Cape Town; iThemba Labs(b), Western Cape; Department of Mechanical Engineering Science(c), University of Johannesburg, Johannesburg; University of South Africa(d), Department of Physics, Pretoria; School of Physics(e), University of the Witwatersrand, Johannesburg; South Africa 34 Department of Physics, Carleton University, Ottawa ON; Canada 35 Facult´e des Sciences Ain Chock(a), R´eseau Universitaire de Physique des Hautes Energies — Universit´e Hassan II, Casablanca; Facult´e des Sciences(b), Universit´e Ibn-Tofail, K´enitra; Facult´e des Sciences Semlalia(c), Universit´e Cadi Ayyad, LPHEA-Marrakech; Facult´e des Sciences(d), Universit´e Mohamed Premier and LPTPM, Oujda; Facult´e des sciences(e), Universit´e Mohammed V, Rabat; Morocco 36 CERN, Geneva; Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America – 47 – JHEP10(2020)062 38 LPC, Universit´e Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 39 Nevis Laboratory, Columbia University, Irvington NY; United States of America 40 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 41 Dipartimento di Fisica(a), Universit`a della Calabria, Rende; INFN Gruppo Collegato di Cosenza(b), Laboratori Nazionali di Frascati; Italy 42 Physics Department, Southern Methodist University, Dallas TX; United States of America 43 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 44 National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece 45 Department of Physics(a), Stockholm University; Oskar Klein Centre(b), Stockholm; Sweden 46 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 47 Lehrstuhl f¨ur Experimentelle Physik IV, Technische Universit¨at Dortmund, Dortmund; Germany 48 Institut f¨ur Kern- und Teilchenphysik, Technische Universit¨at Dresden, Dresden; Germany 49 Department of Physics, Duke University, Durham NC; United States of America 50 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 51 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 52 Physikalisches Institut, Albert-Ludwigs-Universit¨at Freiburg, Freiburg; Germany 53 II. Physikalisches Institut, Georg-August-Universit¨at G¨ottingen, G¨ottingen; Germany 54 D´epartement de Physique Nucl´eaire et Corpusculaire, Universit´e de Gen`eve, Gen`eve; Switzerland 55 Dipartimento di Fisica(a), Universit`a di Genova, Genova; INFN Sezione di Genova(b); Italy 56 II. Physikalisches Institut, Justus-Liebig-Universit¨at Giessen, Giessen; Germany 57 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom 58 LPSC, Universit´e Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 59 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America 60 Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics(a), University of Science and Technology of China, Hefei; Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE)(b), Shandong University, Qingdao; School of Physics and Astronomy(c), Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai; Tsung-Dao Lee Institute(d), Shanghai; China 61 Kirchhoff-Institut f¨ur Physik(a), Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg; Physikalisches Institut(b), Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg; Germany 62 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 63 Department of Physics(a), Chinese University of Hong Kong, Shatin, N.T., Hong Kong; Department of Physics(b), University of Hong Kong, Hong Kong; Department of Physics and Institute for Advanced Study(c), Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 64 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 65 IJCLab, Universit´e Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France 66 Department of Physics, Indiana University, Bloomington IN; United States of America 67 INFN Gruppo Collegato di Udine(a), Sezione di Trieste, Udine; ICTP(b), Trieste; Dipartimento Politecnico di Ingegneria e Architettura(c), Universit`a di Udine, Udine; Italy 68 INFN Sezione di Lecce(a); Dipartimento di Matematica e Fisica(b), Universit`a del Salento, Lecce; Italy 69 INFN Sezione di Milano(a); Dipartimento di Fisica(b), Universit`a di Milano, Milano; Italy 70 INFN Sezione di Napoli(a); Dipartimento di Fisica(b), Universit`a di Napoli, Napoli; Italy 71 INFN Sezione di Pavia(a); Dipartimento di Fisica(b), Universit`a di Pavia, Pavia; Italy 72 INFN Sezione di Pisa(a); Dipartimento di Fisica E. Fermi(b), Universit`a di Pisa, Pisa; Italy 73 INFN Sezione di Roma(a); Dipartimento di Fisica(b), Sapienza Universit`a di Roma, Roma; Italy 74 INFN Sezione di Roma Tor Vergata(a); Dipartimento di Fisica(b), Universit`a di Roma Tor Vergata, Roma; Italy 75 INFN Sezione di Roma Tre(a); Dipartimento di Matematica e Fisica(b), Universit`a Roma Tre, Roma; Italy – 48 – JHEP10(2020)062 76 INFN-TIFPA(a); Universit`a degli Studi di Trento(b), Trento; Italy 77 Institut f¨ur Astro- und Teilchenphysik, Leopold-Franzens-Universit¨at, Innsbruck; Austria 78 University of Iowa, Iowa City IA; United States of America 79 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 80 Joint Institute for Nuclear Research, Dubna; Russia 81 Departamento de Engenharia El´etrica(a), Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora; Universidade Federal do Rio De Janeiro COPPE/EE/IF(b), Rio de Janeiro; Universidade Federal de S˜ao Jo˜ao del Rei (UFSJ)(c), S˜ao Jo˜ao del Rei; Instituto de F´ısica(d), Universidade de S˜ao Paulo, S˜ao Paulo; Brazil 82 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 83 Graduate School of Science, Kobe University, Kobe; Japan 84 AGH University of Science and Technology(a), Faculty of Physics and Applied Computer Science, Krakow; Marian Smoluchowski Institute of Physics(b), Jagiellonian University, Krakow; Poland 85 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 86 Faculty of Science, Kyoto University, Kyoto; Japan 87 Kyoto University of Education, Kyoto; Japan 88 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 89 Instituto de F´ısica La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 90 Physics Department, Lancaster University, Lancaster; United Kingdom 91 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 92 Department of Experimental Particle Physics, Joˇzef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 93 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 94 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 95 Department of Physics and Astronomy, University College London, London; United Kingdom 96 Louisiana Tech University, Ruston LA; United States of America 97 Fysiska institutionen, Lunds universitet, Lund; Sweden 98 Centre de Calcul de l’Institut National de Physique Nucl´eaire et de Physique des Particules (IN2P3), Villeurbanne; France 99 Departamento de F´ısica Teorica C-15 and CIAFF, Universidad Aut´onoma de Madrid, Madrid; Spain 100 Institut f¨ur Physik, Universit¨at Mainz, Mainz; Germany 101 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 102 CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille; France 103 Department of Physics, University of Massachusetts, Amherst MA; United States of America 104 Department of Physics, McGill University, Montreal QC; Canada 105 School of Physics, University of Melbourne, Victoria; Australia 106 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 107 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 108 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus 109 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus 110 Group of Particle Physics, University of Montreal, Montreal QC; Canada 111 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia 112 National Research Nuclear University MEPhI, Moscow; Russia 113 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 114 Fakult¨at f¨ur Physik, Ludwig-Maximilians-Universit¨at M¨unchen, M¨unchen; Germany 115 Max-Planck-Institut f¨ur Physik (Werner-Heisenberg-Institut), M¨unchen; Germany 116 Nagasaki Institute of Applied Science, Nagasaki; Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan – 49 – JHEP10(2020)062 118 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 119 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands 120 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 121 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 122 Budker Institute of Nuclear Physics and NSU(a), SB RAS, Novosibirsk; Novosibirsk State University Novosibirsk(b); Russia 123 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia 124 Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National Research Centre “Kurchatov Institute”, Moscow; Russia 125 Department of Physics, New York University, New York NY; United States of America 126 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 127 Ohio State University, Columbus OH; United States of America 128 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 129 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 130 Palack´y University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 131 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 132 Graduate School of Science, Osaka University, Osaka; Japan 133 Department of Physics, University of Oslo, Oslo; Norway 134 Department of Physics, Oxford University, Oxford; United Kingdom 135 LPNHE, Sorbonne Universit´e, Universit´e de Paris, CNRS/IN2P3, Paris; France 136 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 137 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg; Russia 138 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 139 Laborat´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas — LIP(a), Lisboa; Departamento de F´ısica(b), Faculdade de Ciˆencias, Universidade de Lisboa, Lisboa; Departamento de F´ısica(c), Universidade de Coimbra, Coimbra; Centro de F´ısica Nuclear da Universidade de Lisboa(d), Lisboa; Departamento de F´ısica(e), Universidade do Minho, Braga; Departamento de F´ısica Te´orica y del Cosmos(f), Universidad de Granada, Granada (Spain); Dep F´ısica and CEFITEC of Faculdade de Ciˆencias e Tecnologia(g), Universidade Nova de Lisboa, Caparica; Instituto Superior T´ecnico(h), Universidade de Lisboa, Lisboa; Portugal 140 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 141 Czech Technical University in Prague, Prague; Czech Republic 142 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 143 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 144 IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette; France 145 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 146 Departamento de F´ısica(a), Pontificia Universidad Cat´olica de Chile, Santiago; Universidad Andres Bello(b), Department of Physics, Santiago; Instituto de Alta Investigaci´on(c), Universidad de Tarapac´a; Departamento de F´ısica(d), Universidad T´ecnica Federico Santa Mar´ıa, Valpara´ıso; Chile 147 Department of Physics, University of Washington, Seattle WA; United States of America 148 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 149 Department of Physics, Shinshu University, Nagano; Japan 150 Department Physik, Universit¨at Siegen, Siegen; Germany 151 Department of Physics, Simon Fraser University, Burnaby BC; Canada – 50 – JHEP10(2020)062 152 SLAC National Accelerator Laboratory, Stanford CA; United States of America 153 Physics Department, Royal Institute of Technology, Stockholm; Sweden 154 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 155 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 156 School of Physics, University of Sydney, Sydney; Australia 157 Institute of Physics, Academia Sinica, Taipei; Taiwan 158 E. Andronikashvili Institute of Physics(a), Iv. Javakhishvili Tbilisi State University, Tbilisi; High Energy Physics Institute(b), Tbilisi State University, Tbilisi; Georgia 159 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 160 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 161 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 162 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan 163 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan 164 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 165 Tomsk State University, Tomsk; Russia 166 Department of Physics, University of Toronto, Toronto ON; Canada 167 TRIUMF(a), Vancouver BC; Department of Physics and Astronomy(b), York University, Toronto ON; Canada 168 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 169 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 170 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 171 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 172 Department of Physics, University of Illinois, Urbana IL; United States of America 173 Instituto de F´ısica Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 174 Department of Physics, University of British Columbia, Vancouver BC; Canada 175 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 176 Fakult¨at f¨ur Physik und Astronomie, Julius-Maximilians-Universit¨at W¨urzburg, W¨urzburg; Germany 177 Department of Physics, University of Warwick, Coventry; United Kingdom 178 Waseda University, Tokyo; Japan 179 Department of Particle Physics, Weizmann Institute of Science, Rehovot; Israel 180 Department of Physics, University of Wisconsin, Madison WI; United States of America 181 Fakult¨at f¨ur Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universit¨at Wuppertal, Wuppertal; Germany 182 Department of Physics, Yale University, New Haven CT; United States of America aAlso at Borough of Manhattan Community College, City University of New York, New York NY; United States of America bAlso at Centro Studi e Ricerche Enrico Fermi; Italy cAlso at CERN, Geneva; Switzerland dAlso at CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille; France eAlso at D´epartement de Physique Nucl´eaire et Corpusculaire, Universit´e de Gen`eve, Gen`eve; Switzerland fAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain gAlso at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece – 51 – JHEP10(2020)062 hAlso at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America iAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America jAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel kAlso at Department of Physics, California State University, East Bay; United States of America lAlso at Department of Physics, California State University, Fresno; United States of America mAlso at Department of Physics, California State University, Sacramento; United States of America nAlso at Department of Physics, King’s College London, London; United Kingdom oAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia pAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland qAlso at Dipartimento di Matematica, Informatica e Fisica, Universit`a di Udine, Udine; Italy rAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russia sAlso at Giresun University, Faculty of Engineering, Giresun; Turkey tAlso at Graduate School of Science, Osaka University, Osaka; Japan uAlso at Hellenic Open University, Patras; Greece vAlso at IJCLab, Universit´e Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France wAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain xAlso at Institut f¨ur Experimentalphysik, Universit¨at Hamburg, Hamburg; Germany yAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands zAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia; Bulgaria aa Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary ab Also at Institute of Particle Physics (IPP), Vancouver; Canada ac Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan ad Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain ae Also at Joint Institute for Nuclear Research, Dubna; Russia af Also at Louisiana Tech University, Ruston LA; United States of America ag Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia ah Also at National Research Nuclear University MEPhI, Moscow; Russia ai Also at Physics Department, An-Najah National University, Nablus; Palestine aj Also at Physikalisches Institut, Albert-Ludwigs-Universit¨at Freiburg, Freiburg; Germany ak Also at The City College of New York, New York NY; United States of America al Also at TRIUMF, Vancouver BC; Canada am Also at Universita di Napoli Parthenope, Napoli; Italy an Also at University of Chinese Academy of Sciences (UCAS), Beijing; China ∗Deceased – 52 –