Measurements of the production cross-section for a Z boson in association with b-jets in proton-proton collisions at s√ = 13 TeV with the ATLAS detector

JHEP07(2020)044 Published for SISSA by Springer Received:March 27, 2020 Accepted:June 15, 2020 Published:July 7, 2020 Measurements of the production cross-section for a Z boson in association with b-jets in proton-proton collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: This paper presents a measurement of the production cross-section of a Z boson in association with b-jets, in proton-proton collisions at √s= 13 TeV with the ATLAS experiment at the Large Hadron Collider using data corresponding to an integrated luminosity of 35.6 fb−1. Inclusive and differential cross-sections are measured for events containing a Zboson decaying into electrons or muons and produced in association with at least one or at least two b-jets with transverse momentum pT>20 GeV and rapidity |y|<2.5. Predictions from several Monte Carlo generators based on leading-order (LO) or next-to-leading-order (NLO) matrix elements interfaced with a parton-shower simulation and testing different flavour schemes for the choice of initial-state partons are compared with measured cross-sections. The 5-flavour number scheme predictions at NLO accuracy agree better with data than 4-flavour number scheme ones. The 4-flavour number scheme predictions underestimate data in events with at least one b-jet. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 2003.11960 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP07(2020)044 JHEP07(2020)044 Contents 1 Introduction 1 2 The ATLAS detector 3 3 Data set and simulated event samples 4 3.1 Data set description 4 3.2 Simulated event samples for signal and background processes 4 3.3 Theoretical predictions 6 4 Event selection 9 4.1 Correction factors applied to simulation and corresponding uncertainties 11 5 Background estimation 12 5.1 Extraction of the cross-section for Z-boson production in association with light-jets and c-jets 14 6 Kinematic distributions 17 7 Correction to particle level 18 8 Uncertainties in the cross-section measurements 21 9 Results 23 9.1 Inclusive cross-sections 24 9.2 Differential cross-sections for Z+≥1b-jet 24 9.3 Differential cross-sections for Z+≥2b-jets 29 10 Conclusion 33 The ATLAS collaboration 41 1 Introduction The measurement of the production rate of a Zboson in association with jets originating from b-quarks1(Z+b-jets) in proton-proton (pp) collisions provides an important test of perturbative quantum chromodynamics (pQCD). Current predictions for Z+b-jets production are known at next-to-leading-order (NLO) accuracy in pQCD, and they can be derived in either a 4-flavour number scheme (4FNS) or a 5-flavour number scheme (5FNS) [1–4]. 1Unless otherwise mentioned, it is implicitly assumed that b-quark refers to both b-quark and ¯ b-antiquark. – 1 – JHEP07(2020)044 In the 4FNS, b-quarks do not contribute to the parton distribution functions (PDFs) of the proton and, in QCD, they only appear in a massive final state due to gluon splitting (g→bb). In the 5FNS, b-quark density is allowed in the initial state via a b-quark PDF, with the b-quark typically being massless. Therefore, in the 5FNS the Z+b-jets crosssection is sensitive to the b-quark PDF and can be used to constrain it. The ambiguity among the schemes is an intrinsic property of the calculation and is expected to reduce with the inclusion of higher order perturbative corrections [3]. Furthermore, the measurement of Z+b-jets production provides a benchmark to test predictions from Monte Carlo (MC) simulations. These are commonly used to estimate the background contribution of Z+b-jet events to other topologies, such as the production of a Higgs boson decaying into a b-quark pair in association with a Zboson, or in searches for physics beyond the SM with signatures containing leptons and b-jets in the final state. The Z+b-jets processes occur more rarely than the production of Z-boson events with inclusive jets (Z+jets) and they are more challenging to measure. The b-jets are identified by exploiting the long lifetime of b-hadrons produced in the quark hadronisation, and a higher level of background affects the measurement. The background is mainly composed of events with a Zboson associated with light-flavour jets or c-jets,2misidentified as b-jets, and events from the dileptonic decay of a t¯ tpair. Inclusive and differential cross-sections of Z+b-jets production have been measured in proton-antiproton collisions at the centre-of-mass energy of √s= 1.96 TeV by the CDF and D0 experiments [5–8] and at the Large Hadron Collider (LHC) [9] in √s= 7 TeV pp collisions by the ATLAS and CMS experiments [10–15], as well as in √s= 8 TeV pp collisions by the CMS experiment [16,17]. The CMS experiment also recently released a measurement of the ratio of Z+b-jets to Z+jets cross-sections and the ratio of Z+c-jets to Z+b-jets cross-sections for events with at least one b-jet or one c-jet in √s= 13 TeV pp collisions [18]. This paper presents a measurement of the inclusive and differential production crosssections of a Zboson, decaying into electrons or muons, in association with at least one or at least two b-jets using 35.6 fb−1of pp collision data collected by the ATLAS experiment at √s= 13 TeV in 2015 and 2016. For events with at least one b-jet, the differential crosssections are presented as a function of the transverse momentum3(pT) and the absolute value of the rapidity (|y|) of the leading b-jet, the pTand the |y|of the Zboson (Z pT and Z|y|), and as a function of observables correlating the Zboson with the leading b-jet, namely the azimuthal angle between them (∆φZb), the absolute value of their rapidity difference (∆yZb), and their angular separation (∆RZb). For events with at least two bjets, the differential cross-sections are presented as a function of the pTof the Zboson 2Ac-jet is a jet originating from a c-quark. 3ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θas η=−ln tan(θ/2). Angular separation is measured in units of ∆R≡p(∆η)2+ (∆φ)2. When dealing with massive jets and particles, the rapidity y=1 2ln E+pz E−pzis used, in which Eis the jet or particle energy and pzis the z-component of the jet or particle momentum. – 2 – JHEP07(2020)044 and as a function of observables built using the two leading b-jets, namely their pT(pT,bb), their invariant mass (mbb), pT,bb divided by their invariant mass (pT,bb/mbb), the azimuthal angle between them (∆φbb), the absolute value of their rapidity difference (∆ybb), and their angular separation (∆Rbb). The higher √sleads to a large increase in the measured cross-section in comparison with previous ATLAS publications. This allows more extreme regions of phase space to be explored and new measurements to be performed in the rare two-b-jets configuration (i.e. pT,bb and pT,bb/mbb). Previous ATLAS measurements were compared with MC predictions based on leading-order matrix elements interfaced with a parton-shower simulation, which showed substantial mismodelling. Recent advances in this field permit this paper to compare the data with the latest MC predictions using next-to-leading-order matrix elements, which are expected to provide a better description of the data. The experimental apparatus is described in section 2, and details of the data sample and the MC simulations are provided in section 3. The object definitions and the event selection at detector level are presented in section 4. Backgrounds that do not contain a real Zboson are estimated via MC simulations and validated in control regions in data or via data-driven techniques, while backgrounds containing a real Zboson and jets not originating from bquarks are estimated with a fit to data distributions sensitive to the flavour of the jet (flavour fit); both are described in section 5. Distributions of the kinematic variables are presented in section 6. After background subtraction, the data are unfolded to particle level in a fiducial phase space, which is detailed in section 7. Systematic uncertainties in the unfolded data are discussed in section 8. The results are presented in section 9, and conclusions are drawn in section 10. 2 The ATLAS detector The ATLAS detector [19] at the LHC covers nearly the entire solid angle around the collision point. It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets. The inner-detector system (ID) is immersed in a 2 T axial magnetic field and provides charged-particle tracking in the range |η|<2.5. The high-granularity silicon pixel detector covers the vertex region and provides four measurements for most tracks, the first hit normally being in the insertable B-layer [20,21]. It is followed by the silicon microstrip tracker, which provides eight measurements per track. These silicon detectors are complemented by the transition radiation tracker (TRT), which enables radially extended track reconstruction up to |η|= 2.0. The TRT also provides electron identification information based on the fraction of hits (typically 30 in total) with an energy deposit above the transition-radiation threshold. The calorimeter system covers the pseudorapidity range |η|<4.9. Within the region |η|<3.2, electromagnetic calorimetry is provided by barrel and endcap high-granularity lead/liquid-argon (LAr) calorimeters, with an additional thin LAr presampler covering |η|<1.8 to correct for energy loss in material upstream of the calorimeters. Hadronic – 3 – JHEP07(2020)044 calorimetry is provided by the steel/scintillator-tile calorimeter, segmented into three barrel structures within |η|<1.7, and two copper/LAr hadronic endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements, respectively. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by the superconducting air-core toroid magnets. The field integral of the toroid magnets ranges between 2.0 and 6.0 T m across most of the detector. The precision chambers cover the region |η|<2.7 with three layers of monitored drift tubes, complemented by cathode-strip chambers in the forward region, where the background is highest. The muon trigger system covers the range |η|<2.4 with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. Interesting events are accepted by the first-level trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the highlevel trigger [22]. The first-level trigger accepts events from the 40 MHz bunch crossings at a rate below 100 kHz, which the high-level trigger further reduces in order to record events to disk at about 1 kHz rate. 3 Data set and simulated event samples 3.1 Data set description The data used in this measurement were recorded in 2015 and 2016 with the ATLAS detector at the LHC in pp collisions at √s= 13 TeV. The candidate events were selected by either a single-electron or single-muon trigger that imposed a minimum transverse energy (transverse momentum) threshold for the electron (muon) channel and quality and isolation requirements, which depended on the LHC running conditions. The threshold in 2015 was 24 (20) GeV for the electrons (muons), satisfying loose isolation requirements. Due to the higher instantaneous luminosity in 2016, the threshold was increased to 26 GeV for both the electrons and the muons, and a more restrictive isolation requirement was imposed on both leptons along with more restrictive identification requirements for electrons. Triggers with higher thresholds but with no isolation requirement or with loosened identification criteria were also used to increase the efficiency. Crossings of proton bunches occurred every 25 ns, the collisions achieved a peak instantaneous luminosity of 1.37 ×1034 cm−2s−1, and the mean number of pp interactions per bunch crossing (pile-up) was hµi= 24. After applying criteria to ensure good ATLAS detector operation, the total integrated luminosity amounts to 35.6 fb−1. The uncertainty in the combined 2015-2016 integrated luminosity is 2.1% [23], obtained using the LUCID-2 detector [24] for the primary luminosity measurements. 3.2 Simulated event samples for signal and background processes MC simulations are used to describe signal events, to estimate the contribution of background processes, to unfold the data yield to the particle level, to estimate systematic uncertainties, and to compare predictions with the unfolded data distributions. – 4 – JHEP07(2020)044 An overview of all signal and background processes and the generators used for the production of nominal results is given in table 1together with the theory uncertainty in the normalisation cross-sections corresponding to PDFs and scale variations. Inclusive Z(→``, ` =e, µ) production in association with both lightand heavy-flavour jets was simulated using the Sherpa v2.2.1 [25] generator. In this set-up, matrix elements at NLO for up to two partons, and matrix elements at LO for up to four partons, were calculated with the Comix [26] and OpenLoops [27,28] libraries. They were matched with the Sherpa parton shower [29] using the MEPS@NLO prescription [30–33]. Sherpa uses the 5FNS with massless band c-quarks in the matrix element, but massive quarks in the parton shower. Samples were generated using the NNPDF3.0nnlo PDF set [34], along with the dedicated set of tuned parton-shower parameters developed by the Sherpa authors. In section 9, where several predictions are compared with the unfolded data, these samples are shown with their uncertainties and are referred to as Sherpa 5FNS (NLO). The uncertainties account for missing higher orders and are evaluated [35] using seven variations of the QCD factorisation and renormalisation scales in the matrix elements by factors of 0.5 and 2 and avoiding variations in opposite directions. Additional Z(→``) samples were produced with the LO matrix-element generator Alpgen v2.14 [36], interfaced with Pythia v6.426 [37] to model parton showers, using the parameter values of the Perugia2011C tune [38] for simulating the underlying event, and the CTEQ6L1 PDF set [39]. Matrix elements were calculated for up to five partons, and merged using the MLM prescription [40] with a matching scale of 15 GeV. Alpgen uses the 4FNS with massive band c-quarks in the matrix element and in the parton shower of Pythia. The matrix elements for the production of Z+b¯ band Z+c¯cevents are explicitly included and a heavy-flavour overlap procedure is used to remove the double counting, between the matrix element and the parton shower, of heavy quarks from gluon splitting. The properties of band c-hadron decays were simulated with EvtGen v1.2.0 [41], as was done in all generated samples where the parton shower was simulated with Pythia. Photos++ v3.52 [42,43] was used to simulate QED final-state radiation (FSR). The Alpgen samples are used in the analysis to estimate systematic uncertainties in the unfolding procedure and in backgrounds containing a genuine Zboson. In section 9these samples are referred to as Alpgen +Py6 4FNS (LO). Samples of Z(→ττ), W(→`ν), and W(→τν) events were simulated with Sherpa, using the same set-up adopted for the signal samples. The Z-boson and W-boson samples are normalised to the inclusive next-to-next-toleading-order (NNLO) cross-section predictions provided by the FEWZ 3.1 program [44– 47] with the CT14 PDF set. The K-factor applied to the Zsamples to match the NNLO prediction is 0.975 for Sherpa and 1.196 for Alpgen. The production of t¯ tevents with at least one Wboson decaying leptonically was modelled using the Powheg-Box [48–51] v2 generator at NLO with the NNPDF3.0NLO [34] PDF set. The hdamp parameter, which regulates the high-pTemissions against which the t¯ t system recoils, is set to 1.5 mtop [52]. The events were interfaced with Pythia v8.230 [53] using the A14 tune [54]. The t¯ tsample is normalised to the theory prediction at NNLO in QCD including the resummation of next-to-next-to-leading logarithmic (NNLL) softgluon terms [55–61]. Four additional t¯ tsamples were simulated to evaluate the un- – 5 – JHEP07(2020)044 certainty in this process, as described in [52]. One sample was produced with MadGraph5 aMC@NLO [62] and the same parton-shower model of the nominal t¯ tsample in order to estimate the uncertainty due to the modelling of the hard scattering process. A second Powheg-Box sample showered with Herwig 7.13 [63,64] was generated to evaluate the uncertainty due to the modelling of the parton shower and hadronization processes. A third sample was produced to simulate higher energy radiation with the factorisation and renormalisation scales changed by a factor of 0.5 while simultaneously increasing the hdamp value to 3.0 mtop and using the upper variation of the initial state radiation (ISR) from the A14 tune. The last sample simulates the lower energy radiation. It was generated with the renormalisation and factorisation scales varied by a factor of 2.0 while keeping the hdamp value at 1.5 mtop and using the ISR downward variation in the parton shower. The last two samples are also used to estimate the impact of FSR with parton-shower weights that vary the renormalisation scale for QCD emission in the FSR by factors of 0.5 and 2.0. Single-top-quark events in the Wt-, sand t-channels were generated using the Powheg-Box v1 generator interfaced with Pythia v6.4 [37]; the latter simulates parton showers, fragmentation, and the underlying event using the Perugia 2012 tune [38]. The CT10 PDF set was used [65]. The single-top samples for the tand s-channels are normalised to cross-sections from NLO predictions [66,67], while the Wt-channel sample is normalised to cross-sections from approximate NNLO predictions [68]. Diboson processes (W W,WZ, and ZZ) with one of the bosons decaying hadronically and the other leptonically were generated using Sherpa v2.2.1 with the CT10nlo PDF set. The matrix element includes up to one parton at NLO and up to three additional partons at LO. The samples are normalised to the NLO predictions [69]. Simulated events for qq →V H(→b¯ b) with V=Wor Zplus zero or one jet production at NLO were generated with the Powheg-Box v2 + GoSam + MiNLO generator [51,70– 72] with the NNPDF3.0NLO PDF set. The contribution from gg →ZH(→b¯ b) production was simulated using the LO Powheg-Box v2 matrix-element generator. The samples of simulated events include all final states where the Higgs boson decays into b¯ band the vector boson into a leptonic final state. The mass of the Higgs boson is set to 125 GeV and the H→b¯ bbranching fraction is set to 58%. The qq →V H(→b¯ b) cross-section is calculated at NNLO (QCD) and NLO (EW), while the gg →ZH cross-section is calculated at NLO+NLL (QCD). Generated events were processed with the ATLAS detector simulation [76], based on Geant4 [77], to simulate the detector response to final-state particles. To account for the effects of pile-up, multiple overlaid pp collisions were simulated with the soft QCD processes of Pythia v8.186 using the A2 tune [78] and the MSTW2008LO PDF set [79]. The distribution of the average number of interactions per bunch crossing in the simulation is weighted to reflect that in the data. Simulated events are processed with the same reconstruction algorithms as for the data. 3.3 Theoretical predictions In addition to particle-level predictions from the fully simulated Sherpa and Alpgen samples described above, unfolded results from data are compared with six other predictions listed in table 2. – 6 – JHEP07(2020)044 Process Generator Order of Reference Normalisation cross-section normalisation cross-section calculation uncertainty Z→`` (`=e, µ, τ )Sherpa NNLO [44–47] 5% with 66 < m`` <116 GeV W→`ν (`=e, µ, τ)Sherpa NNLO [44–47] 5% t¯ tPowheg-Box NNLO + NNLL [55–61] 6% (mtop = 172.5 GeV) Single top Powheg-Box NLO 6% (t-, Wt-, s-channel) (mtop = 172.5 GeV) Dibosons Z(→``) + Z(→qq), Sherpa NLO [69] 5% W(→`ν) + W(→qq) ) Higgs qq →Z(→``) + H(→b¯ b)Powheg-Box NNLO QCD + NLO EW [73–75] 3% gg →Z(→``) + H(→b¯ b) NLO + NLL qq →W(→`ν) + H(→b¯ b) NNLO QCD + NLO EW Table 1. Signal and background MC samples: the generator programs used in the simulation are listed in the second column, the order of the QCD calculation and the reference used for the calculations of the normalisation cross section are reported in the third and fourth columns. The normalisation cross-section uncertainty in the final column corresponds to PDFs and scale variations. Two particle-level predictions (using specific parton-shower and matching predictions) were produced with the Sherpa v2.2.7 generator using NLO matrix elements [80]. The first sample, referred to as Sherpa Zbb 4FNS (NLO), includes Z+b¯ bevents generated in the 4FNS at NLO with massive b-quarks. It is interesting to compare this sample, which contains two b-quarks in the matrix elements, with the unfolded data even in the case of distributions with at least one b-jet, to understand if there are regions of the phase space that can be described with such a configuration. The second sample, referred to as Sherpa Fusing 4FNS+5FNS (NLO), contains the matrix elements at NLO for up to two partons, and matrix elements at LO for up to three partons. It includes both Z+b¯ bevents generated in the 4FNS at NLO with massive b-quarks, and Z+jets events generated in the 5FNS at NLO. They are combined according to the procedure described in ref. [81]. The combination is achieved by means of a dedicated heavy-flavour overlap removal procedure, the fusing technique, that acts as an additional step after the multijet merging algorithms. This procedure combines the advantages of inclusive 5FNS calculations with the higher precision of 4FNS calculations in regions of phase space where the b-quark mass sets a relevant scale. The two Sherpa samples use the NNPDF3.0nnlo PDF set with αS(mZ) = 0.118 and the corresponding number of active quark flavours. Masses of cand b-quarks are taken into account in the parton shower in all Sherpa samples. Results are also compared with predictions from the LO matrix-element generator MadGraph5 aMC@NLO v2.2.2 [62] interfaced with Pythia v8.186 [53] with the A14 tune [54] to model the parton shower and underlying event. The matrix element includes up – 7 – JHEP07(2020)044 Generator Npartons max FNS PDF Parton NLO LO set Shower Z+jets (including Z+band Z+bb) Sherpa 5FNS (NLO) 2 4 5 NNPDF3.0nnlo Sherpa Sherpa Fusing 4FNS+5FNS (NLO) 2 3 5 (*) NNPDF3.0nnlo Sherpa Alpgen +Py6 4FNS (LO) — 5 4 CTEQ6L1 Pythia v6.426 Alpgen +Py6 (rew. NNPDF3.0lo) — 5 4 NNPDF3.0lo Pythia v6.426 MGaMC +Py8 5FNS (LO) — 4 5 NNPDF3.0nlo Pythia v8.186 MGaMC +Py8 5FNS (NLO) 1 — 5 NNPDF3.0nnlo Pythia v8.186 Z+bb Sherpa Zbb 4FNS (NLO) 2 — 4 NNPDF3.0nnlo Sherpa MGaMC +Py8 Zbb 4FNS (NLO) 2 — 4 NNPDF3.0nnlo Pythia v8.186 Table 2. Summary of theoretical predictions for the signal, including the maximum number of partons at each order in αS, the flavour number scheme (FNS), the PDFs set and the parton shower. (*) Details of the merging between 4FNS and 5FNS in Sherpa Fusing 4FNS+5FNS (NLO) are available in ref. [81]. to four partons. Additional jets are produced by the parton shower, which uses the CKKWL merging procedure [82], with a matching scale of 30 GeV. MadGraph5 aMC@NLO uses the 5FNS with massless band c-quarks in the matrix element, and massive quarks in the parton shower. The NNPDF3.0nlo PDF set is used with αS(mZ) = 0.118. This prediction is referred to as MGaMC +Py8 5FNS (LO). Two additional predictions were produced with MadGraph5 aMC@NLO v2.6.2, using matrix-element calculations with NLO accuracy. The first sample includes Z+jets events generated in the 5FNS with up to one parton at NLO, and massless band cquarks; the second sample includes Z+b¯ bevents generated in the 4FNS at NLO, and massive b-quarks. Both samples were generated using the NNPDF3.0nnlo PDF set with αS= 0.118. They were interfaced to the Pythia v8.186 parton shower using the FxFx merging scheme [83], with a matching scale of 25 GeV. As in the previous case, massive cand b-quarks are produced in the parton shower. The first sample is referred to as MGaMC +Py8 5FNS (NLO); the second is referred to as MGaMC +Py8 Zbb 4FNS (NLO). An additional Alpgen prediction is used to test the sensitivity of the measurements to the parton structure of the proton. The Alpgen samples presented in section 3.2 are reweighted to the NNPDF3.0lo PDF set, using the prescriptions reported in ref. [84]. These predictions are referred to as Alpgen +Py6 (rew. NNPDF3.0lo). The predictions of LO MC generators, such as Alpgen +Py6 4FNS (LO) and MGaMC +Py8 5FNS (LO), with up to four or five partons in the matrix element, are still an interesting case to study as they allow comparison with the predictions of MC generators at NLO accuracy and with a smaller number of partons in the matrix element. Furthermore, they provide a benchmark in common with past analyses, such as in ref. [11]. – 8 – JHEP07(2020)044 Generator Signal Z+jets background Signal Z+jets background Signal + Z+jets SF SF post-fit yield post-fit yield post-fit yield Sherpa 1.109 ±0.003 0.861 ±0.004 309 650 ±810 166 640 ±650 476 290 ±750 Alpgen 1.480 ±0.004 1.015 ±0.002 297 670 ±740 178 100 ±400 475 810 ±480 Table 4. Scale factors obtained for the fitted signal and Z+jet background for Sherpa and Alpgen fits, the total post-fit yields, and the statistical uncertainty, estimated with pseudo-experiments, from the fit for the 1-tag signal region. Generator Signal Z+ jets background Signal Z+ jets background Signal + Z+jets SF SF post-fit yield post-fit yield post-fit yield Sherpa 1.18 ±0.01 1.08 ±0.04 23 440 ±250 4780 ±180 28 220 ±200 Alpgen 1.18 ±0.01 1.30 ±0.05 23 650 ±240 4550 ±180 28 200 ±200 Table 5. Scale factors obtained for the fitted signal and Z+jet background for Sherpa and Alpgen fits, the total post-fit yields, and the statistical uncertainty, estimated with pseudo-experiments, from the fit for the 2-tag signal region. selection (procedure referred to as the truth-tagging). This probability is computed on the basis of the per-jet probabilities, which are assumed to be independent of each other [102]. As for the fit in the 2-tag region, the normalisations of the signal and of the Z+jets background are also free to float, while the normalisation of the other backgrounds is fixed to their estimate. Tables 4and 5show the normalisation scale factors in the 1and 2-tag regions obtained from the fit, together with the post-fit yields for the signal and Z+jet background samples generated with Sherpa or Alpgen. There is good agreement between the sum of the signal and background post-fit yields of Sherpa and Alpgen. The differences between Sherpa and Alpgen in the modelling of the Z+jet backgrounds after the flavour fit are taken into account in the systematic uncertainties as described below. The statistical uncertainty is estimated with pseudo-experiments. Figure 2shows the b-tagging discriminant bins after the fit in the 1-tag and 2-tag regions. In the upper panel of each figure, data are compared with the fit results obtained using templates derived from Sherpa samples for signal and Z+jet backgrounds. The lower panel shows the ratio of post-fit predictions to data using the Sherpa or Alpgen samples for signal and Z+jet backgrounds. The Z+jets backgrounds predicted by Sherpa and corrected for the normalisation factor obtained from the fit are used as the nominal estimate in this analysis. Systematic uncertainties due to the object selection efficiencies and calibrations, discussed in section 4.1, affect the normalisation and the shape of Z+jets backgrounds. They are assessed by repeating the fit with the templates varied according to each of the systematic uncertainties. The fit is also repeated for each of the uncertainties affecting the t¯ tand other backgrounds detailed above. An additional systematic uncertainty (referred to as the flavour fit uncertainty) in the normalisation of the Z+jets backgrounds is estimated by repeating the fit after separating the Z+cfrom the Z+ltemplate in the 1-tag region, – 15 – JHEP07(2020)044 20 40 60 80 100 120 140 160 180 200 3 10× Events 1 b-jet≥ee) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+b-jets (Sherpa) Z+c-jets Z+light-jets Top quark Diboson, VH 1 2 3 b-tag discriminant bin 0.8 1 1.2 Pred. / Data Alpgen Sherpa 50 100 150 200 250 300 3 10× Events 1 b-jet≥) + µµ→Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+b-jets (Sherpa) Z+c-jets Z+light-jets Top quark Diboson, VH 1 2 3 b-tag discriminant bin 0.8 1 1.2 Pred. / Data Alpgen Sherpa 2 4 6 8 10 3 10× Events 2 b-jets≥ee) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+2 b-jets (Sherpa) Z+1 b-jet Z+c-jets Z+light-jets Top quark Diboson, VH 1-1 1-2 2-2 1-3 2-3 3-3 b-tag discriminant bin 0.8 1 1.2 Pred. / Data Alpgen Sherpa 2 4 6 8 10 12 14 3 10× Events 2 b-jets≥) + µµ→Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+2 b-jets (Sherpa) Z+1 b-jet Z+c-jets Z+light-jets Top quark Diboson, VH 1-1 1-2 2-2 1-3 2-3 3-3 b-tag discriminant bin 0.8 1 1.2 Pred. / Data Alpgen Sherpa Figure 2. Post-fit b-tagging discriminant distributions for the electron (left) and muon (right) channels in the 1-tag (top) and 2-tag (bottom) signal regions. The lower panels display the ratios of the predictions to data using the signal and Z+jet background simulation either from Sherpa (red) or Alpgen (blue). Systematic and statistical uncertainties for the predicted distributions are combined in the hatched band, and the statistical uncertainty, estimated with pseudo-experiments, is shown on the data points. The systematic uncertainties account for both the detector-level uncertainties and the theory uncertainty of the non-Zbackgrounds. and after separating the Z+bfrom the Z+cand Z+ltemplates in the 2-tag region. An uncertainty affecting the shape and rate of the Z+jets background is derived by taking the difference between the post-fit Z+jets background evaluations using Sherpa and Alpgen samples. Another uncertainty accounts for potential jet-jet correlations that are not covered by the truth-tagging procedure which mitigates the large statistical fluctuations in the 2-tag region for Z+l. A 20% uncertainty is derived by taking the largest difference between the double-tagged event yields obtained with or without the weighting procedure – 16 – JHEP07(2020)044 1 10 2 10 3 10 4 10 5 10 Entries / GeV 1 b-jet≥ll) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+b-jets (Sherpa) Z+c-jets Z+light-jets Top quark Diboson, VH 200 400 600 800 [GeV] T Leading b-jet p 0.6 0.8 1 1.2 1.4 Pred. / Data Z+jets Validation Region 1 10 2 10 3 10 4 10 5 10 Entries / GeV 1 b-jet≥ll) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+b-jets (Sherpa) Z+c-jets Z+light-jets Top quark Diboson, VH 0 200 400 600 800 1000 [GeV] T Z p 0.6 0.8 1 1.2 1.4 Pred. / Data Z+jets Validation Region Figure 3. The pTof the leading b-jet (left) and of the Zboson (right) for events with at least one b-jet in the Z+jets validation region defined in table 3. Post-fit distributions for signal and Z+ jets backgrounds are shown. Systematic and statistical uncertainties for the predicted distributions are combined in the hatched band, and the statistical uncertainty is shown on the data points. The uncertainty in the predictions includes only the flavour-tagging efficiency uncertainty and flavour-fit uncertainty. being applied to simulated samples of Z+bb,Z+cc,W+bb, and W+cc.6These samples suffer less from statistical limitations. The test is done with both the Sherpa and Alpgen samples. The post-fit estimate of the Sherpa Z+jets background is validated in a region defined by applying the full signal event selection with the exception of b-tagging requirements. Events with at least one b-jet, with the b-tagging discriminant output in the b-jet efficiency range of 70%–77% and light-flavour jet (c-jet) misidentification rates of 0.51% (7.7%), are selected to provide a sample enriched in c-jets and light-flavour jets. As an example, figure 3 shows the pTof the leading b-jet and the pTof the Zboson in this region. The Z+land Z+cbackgrounds constitute 50% and 28% of the total prediction, respectively. Agreement between data and estimated backgrounds is observed within uncertainties. These include the uncertainties due to the flavour fit and b-tagging efficiency, and the statistical uncertainties of the predictions and data. The normalisation factors of the signal samples, shown in tables 4and 5, are applied in figures 2and 3in this section to demonstrate the robustness of this procedure, while in the following sections, post-fit normalisation factors are applied only to Z+jets background. 6 Kinematic distributions After the signal selection criteria are applied, the measured and expected distributions are compared at the detector level. The Z+jets background is shown for the normalisation factors derived from the flavour fit. Pre-fit distributions are used for the signal samples. 6Simulated Z+jets events are categorised as Z+cc (W+cc) if they belong to the Z+c(W+c) category and have at least two c-jets. – 17 – JHEP07(2020)044 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Entries / 2 GeV 1 b-jet≥ll) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+b-jets (Sherpa) Z+c-jets Z+light-jets Top quark Diboson, VH 80 85 90 95 100 105 [GeV] ll m 0.6 0.8 1 1.2 1.4 Pred. / Data Alpgen Sherpa 1− 10 1 10 2 10 3 10 4 10 5 10 Entries / GeV 1 b-jet≥ll) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+b-jets (Sherpa) Z+c-jets Z+light-jets Top quark Diboson, VH 0 200 400 600 800 1000 [GeV] T Z p 0.6 0.8 1 1.2 1.4 Pred. / Data Alpgen Sherpa Figure 4. Distribution of events passing the signal selection as a function of m`` (left) and pT,Z (right) for events with at least one b-jet. The lower panels display the ratio of the predictions for signal plus background to data using either Sherpa (red) or Alpgen +Pythia6 (blue) as the signal simulation. The statistical uncertainty of the data is shown as black error bars and the total uncertainty of the prediction as a hatched band. The latter consists of the statistical uncertainty and all systematic uncertainties from the predictions. Figure 4shows, as an example, the distributions of the m`` and pTof the Zboson for events in the 1-tag region. Figure 5shows the pTof the Zboson and the ∆Rbb distributions for events in the 2-tag region. The uncertainty bands include the statistical uncertainties of the simulated sample, the event-selection uncertainties described in section 4(omitting the common luminosity uncertainty), and the background uncertainties described in section 5. Both generators do not describe precisely the data in the full range of the measurement, although the Sherpa generator provides the best agreement with data. The total numbers of selected events in data and in predictions are presented in table 6, together with the prediction of each process, expressed as a fraction of the total number of predicted events. 7 Correction to particle level The signal event yields are determined by subtracting the estimated background contributions from the data. The resulting distributions are corrected for detector-level effects to the fiducial phase space at particle level defined in table 7. The procedure, based on simulated samples, corrects for Z-boson, jet, and b-jet selection efficiencies, resolution effects, and small differences between the fiducial and detector-level phase spaces. The pre-fit distributions of the Sherpa signal samples are used to perform the unfolding procedure. The signal samples for the simulation of Zevents with at least one or at least two b-jets are defined in section 4. Particle-level objects are selected with requirements close to the corresponding requirements for reconstructed signal candidate objects, in order to limit – 18 – JHEP07(2020)044 2− 10 1− 10 1 10 2 10 3 10 4 10 Entries / GeV 2 b-jets≥ll) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+2 b-jets (Sherpa) Z+1 b-jet Z+c-jets Z+light-jets Top quark Diboson, VH 0 200 400 600 800 1000 [GeV] T Z p 0.6 0.8 1 1.2 1.4 Pred. / Data Alpgen Sherpa 2 4 6 8 10 12 3 10× Entries / 0.4 2 b-jets≥ll) + →Z( ATLAS -1 = 13 TeV, 35.6 fbs Data Syst. Unc.⊕MC Stat. Z+2 b-jets (Sherpa) Z+1 b-jet Z+c-jets Z+light-jets Top quark Diboson, VH 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 bb R∆ 0.6 0.8 1 1.2 1.4 Pred. / Data Alpgen Sherpa Figure 5. Distribution of events passing the signal selection as a function of pT,Z (left) and ∆Rbb (right) for events with at least two b-jets. The lower panels display the ratio of the predictions for signal plus background to data using either Sherpa (red) or Alpgen +Pythia6 (blue) as the signal simulation. The statistical uncertainty of the data is shown as black error bars and the total uncertainty of the prediction as the hatched band. The latter consists of the statistical uncertainty and all systematic uncertainties from the predictions. 1-tag region Signal Z+b,Z+bb 59% Backgrounds Z+c18% Z+l18% Top 4% Diboson, V H 1% Others <1% Total predicted 470 000 ±650 Data 499 645 2-tag region Signal Z+bb 60% Backgrounds Z+b9% Z+c5% Z+l < 1% Top 23% Diboson, V H 2% Others 1% Total predicted 33 070 ±180 Data 36 548 Table 6. The expected size of the signal and backgrounds, expressed as a fraction of the total number of predicted events for inclusive b-jet multiplicities for the signal selection. The signal and Z+jets background predictions are from the Sherpa generator, with the Z+jets background estimate obtained after applying the normalisation scale factors obtained from the flavour fit. The total numbers of predicted and observed events are also shown. The uncertainty in the total predicted number of events is statistical only. the dependence of the measurement on theoretical predictions. In this definition, the lepton kinematic variables are computed using final-state leptons from the Z-boson decay. Photons radiated by the boson decay products within a cone of size ∆R= 0.1 around the direction of a final-state lepton are added to the lepton, and the sum is referred to as the ‘dressed’ lepton. Particle-level jets are identified by applying the anti-ktalgorithm with – 19 – JHEP07(2020)044 Kinematic variable Acceptance cut Lepton pTpT>27 GeV Lepton η|η|<2.5 m`` m`` = 91 ±15 GeV b-jet pTpT>20 GeV b-jet rapidity |y|<2.5 b-jet-lepton angular distance ∆R(b-jet, `)>0.4 Table 7. Kinematic criteria defining the fiducial phase space of the measurement at particle level. R= 0.4 to all final-state particles with a lifetime longer than 30 ps, excluding the dressed Z-boson decay products. A jet is identified as b-tagged if it lies within ∆R= 0.3 of one or more weakly decaying b-hadrons with pT>5 GeV. If a b-hadron matches more than one jet, only the closest jet in ∆Ris labelled as a b-jet. The correction of differential distributions is implemented using an iterative Bayesian method of unfolding [103] with two iterations. Simulated events are used to generate a response matrix for each distribution to account for bin-to-bin migration effects between the detector-level and particle-level distributions. The matrix is filled with the events that pass both the detector-level and particle-level selections. The particle-level prediction is used as the initial prior to determine the first estimate of the unfolded data distribution. For the second iteration, the new estimate of unfolded data is obtained using the backgroundsubtracted data and an unfolding matrix, which is derived on the basis of the Bayes’ theorem from the response matrix and the current prior. The background-subtracted data are corrected for the expected fraction of events which pass the detector-level selection, but not the particle-level one (unmatched-events), before entering the iterative unfolding. For each bin of each differential distribution, the unfolded event yields are divided by the integrated luminosity of the data sample and by the bin width, to obtain the cross-section measurement. The differential cross-section measurement of a given observable in the i-th bin is given by: σi=1 iLXUijfjNbsD j, where Lis the integrated luminosity, iis the reconstruction efficiency in i-th bin, NbsD j is the number of background-subtracted data events in the j-th bin, fjis the factor that corrects for unmatched events in the j-th bin, and Uij is the element (i, j) of the unfolding matrix calculated after two iterations, using the updated prior from the first iteration and the response matrix. The measurement of the inclusive cross-section for Z-boson events with at least one or at least two b-jets is obtained by applying a particle-level correction to the number of events in data with at least one or at least two b-jets, after background subtraction. The correction, which is applied as a divisor of the background-subtracted data, is derived from the ratio of the total number of reconstructed events in the detector-level phase space to the number of particle-level events in the fiducial phase space. It is 0.399 ±0.001 for Z- – 20 – JHEP07(2020)044 Source of uncertainty Z(→``) + ≥1b-jet Z(→``) + ≥2b-jets [%] [%] b-jet tagging efficiency 7.0 14 b-jet mistag rate 2.4 1.1 Jet 2.4 5.0 Lepton 0.8 1.2 Emiss T0.6 1.3 Z+cand Z+lbackgrounds 4.5 1.1 Top background 0.5 3.8 Other backgrounds <0.1 0.1 Pile-up 1.7 2.6 Unfolding 3.8 4.1 Luminosity 2.3 2.9 Total [%] 10 16 Table 8. Relative systematic uncertainties in the measured production cross-sections of Z(→ ``) + ≥1b-jet and Z(→``) + ≥2b-jets events. The “Jet” term includes the JES, JER and JVT uncertainties. The “Lepton” term includes the lepton trigger, efficiency, scale and resolution uncertainties. The “Z+cand Z+lbackgrounds” term also includes the Z+ 1bbackground in the Z+≥2b-jets measurement. boson events with at least one b-jet and 0.258 ±0.002 for Z-boson events with at least two b-jets, using Sherpa signal samples and quoting the statistical error. Since the electron and muon decay channels are combined to increase the precision of the signal fits to data, the corrections and response matrices are made using electron and muon signal samples to obtain combined particle-level yields. To validate this procedure, the analysis is performed for each of the two lepton channels separately. The results obtained from the individual channels are compatible within 1.4σand 1.6σwith the inclusive cross-section of Z-boson events with at least one b-jet and at least two b-jets, respectively. This comparison uses only the sum in quadrature of the statistical and uncorrelated systematic uncertainties. The differential cross-section measurements in the two channels also agree over the full range of each distribution. 8 Uncertainties in the cross-section measurements Table 8summarises the systematic uncertainties of the inclusive Z+b-jets cross-sections in the oneand two-b-tag regions. Figure 6shows as an example the breakdown of the systematic uncertainties in the cross-section as a function of Z-boson pTfor events with at least one b-jet and as a function of ∆Rbb for events with at least two b-jets. The systematic uncertainties in the cross-sections associated with the detector-level uncertainty sources described in section 4.1 are derived for each observable by propagating systematic shifts from each source through both the response matrices (unfolding factor) and the subtracted background contributions into the unfolded data for the differential (in- – 21 – JHEP07(2020)044 0 200 400 600 800 1000 [GeV] T Z p 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Relative systematic uncertainty b-jet tagging efficiency b-jet tagging mis-tag rate Jet Lepton miss T E Z+c, Z+l background top background Other backgrounds Pile-up Unfolding Luminosity Total ATLAS -1 = 13 TeV, 35.6 fbs 1 b-jet≥ll) + →Z( 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 bb R∆ 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Relative systematic uncertainty b-jet tagging efficiency b-jet tagging mis-tag rate Jet Lepton miss T E Z+c, Z+l background top background Other backgrounds Pile-up Unfolding Luminosity Total ATLAS -1 = 13 TeV, 35.6 fbs 2 b-jets≥ll) + →Z( Figure 6. Relative systematic uncertainties in the fiducial cross-section as a function of the Zboson pTin events with at least one b-jet (left) and as a function of the ∆Rbetween the two leading b-jets in events with at least two b-jets (right). The total uncertainty is shown in black while the different components listed in table 8are shown in different colours. clusive) cross-section measurements. The dominant source of uncertainty is the modelling of the b-tagging efficiency. Its impact on the inclusive cross-section ranges from 7.0% for Z-boson events with at least one b-jet to 14% for Z-boson events with at least two b-jets. Its effect on differential cross-section measurements ranges from 5% to 10% for Z-boson events with at least one b-jet and from 10% to 15% for Z-boson events with at least two b-jets. The impact of the mistag rate of cand light-jets is smaller; it is 2.4% for Z-boson events with at least one b-jet and 1% for Z-boson events with at least two b-jets. The uncertainty from each background source is determined by applying shifts to the subtracted background contributions and to the nominal response matrices or unfolding factors. The sources of uncertainty considered for Z+land Z+c(and Z+ 1bin the Z+≥2b-jets measurement), t¯ tand single-top, diboson and other minor backgrounds are described in section 5. The dominant uncertainty in the background to events with at least one b-jet originates from Z+jets events. This uncertainty contributes 4.5% to the uncertainty in the inclusive cross-section. An uncertainty of 3.7% derives from the difference between the modelling in Alpgen and Sherpa, while 2.6% is due to the flavour fit uncertainty. The impact of this uncertainty on the differential cross-sections ranges from a few per cent up to 25% in the extreme corners of the phase space. For a Z-boson pTvalue of about 500 GeV, the difference between the modelling in Alpgen and Sherpa contributes 18% to this uncertainty, and the flavour fit uncertainty is 12%. In contrast, the uncertainty in the estimation of background from t¯ tevents is the dominant source of uncertainty in the background to Z-boson events with at least two b-jets. It contributes 3.8% to the inclusive cross-section and ranges from 1% to 9% in the differential cross-sections. The uncertainty due to modelling of the Z+b-jets signal samples in the events with at least one and at least two b-jets are also accounted for. This is evaluated for each observable – 22 – JHEP07(2020)044 by reweighting the generator-level distribution in the Sherpa samples to provide a better description of the data at detector level. The modified Sherpa samples are then used to emulate data and are unfolded with the nominal simulated sample. An additional source accounts for the possible mismodelling of an observable that is not one of the unfolded observables (i.e. a hidden variable). This uncertainty is evaluated by reweighting, in the Sherpa samples, the generator-level distribution of the leading lepton’s pT, which is one of the observables showing the largest mismodelling, to provide a better description of the data at detector level. The modified Sherpa samples are used to unfold the data. The effect of the hidden variable’s mismodelling is negligible for all considered variables and all bins. A third uncertainty source accounts for the different hadronisation and parton-shower models used for the signal simulation. This uncertainty is evaluated by unfolding the Alpgen signal samples, which emulate the background-subtracted data, with the Sherpa signal samples. The generator-level distributions from the Alpgen samples are first reweighted to agree with Sherpa in order to remove effects related to shape differences. The difference between the generator-level distribution and the unfolded Alpgen reweighted distribution is taken as the uncertainty. For the inclusive cross-section, the modelling uncertainty is estimated by replacing the unfolding factor computed with Sherpa with the one computed with Alpgen. The dependence on the size of the simulated sample is derived using pseudoexperiments, and the spread of the results is taken as an uncertainty. The statistical term is typically less than a few per cent. It reaches 5% in the last bin of the ∆Rbb distribution and 15% only in the last bin of the ∆ybb distribution. The total unfolding uncertainty in the inclusive cross-sections is at the level of 4% in each of the two signal regions. In the differential distributions it is less than 5% in the 1-tag region and at a level of 5%–10% in the 2-tag region, except in some bins of the angular variables and in the tail of the pTand mbb distributions, where it reaches 20%. 9 Results The inclusive and differential cross-section measurements for Z+≥1b-jet and Z+≥ 2b-jets are shown in figures 7–15. The statistical uncertainty of the data is propagated through the unfolding by using 1000 pseudo-experiments, repeating the flavour fit for each of them. The statistical uncertainty in the inclusive cross-sections of Z+≥1b-jet and Z+≥2b-jets is 0.3% and 0.8% respectively. As mentioned in section 8, the systematic uncertainties are propagated through the unfolding via the response matrices or the unfolding factors and via the variation of the subtracted background. The measurements are compared with the predictions from Sherpa 5FNS (NLO),Alpgen +Py6 4 FNS (LO), Sherpa Fusing 4FNS+5FNS (NLO),Sherpa Zbb 4FNS (NLO),MGaMC +Py8 5FNS (LO),MGaMC +Py8 Zbb 4FNS (NLO) and MGaMC +Py8 5FNS (NLO). Theoretical uncertainties of Sherpa 5FNS (NLO), computed as described in section 3, are shown in the comparison with data. In this section, all predictions are normalised to their own cross-section to allow an unbiased comparison among different generators.7 7The NNLO cross-section K-factor applied to the inclusive Alpgen and Sherpa samples in previous sections is removed. – 23 – JHEP07(2020)044 6 8 10 12 14 16 18 20 22 24 1 b-jet) [pb]≥(Z + σ Data (stat.) Data (stat.+syst.) 1 b-jet≥ll) + →Z( Sherpa 5FNS (NLO) MGaMC+Py8 Zbb 4FNS (NLO) MGaMC+Py8 5FNS (NLO) Sherpa Zbb 4FNS (NLO) Sherpa Fusing 4FNS+5FNS (NLO) Alpgen+Py6 4FNS (LO) Alpgen+Py6 (rew. NNPDF3.0lo) MGaMC+Py8 5FNS (LO) 0.23 pb± 1.08 ± 0.03 ±10.90 ATLAS -1 =13 TeV, 35.6 fbs 0.5 1 1.5 2 2.5 3 3.5 2 b-jets) [pb]≥(Z + σ Data (stat.) Data (stat.+syst.) 2 b-jets≥ll) + →Z( Sherpa 5FNS (NLO) MGaMC+Py8 Zbb 4FNS (NLO) MGaMC+Py8 5FNS (NLO) Sherpa Zbb 4FNS (NLO) Sherpa Fusing 4FNS+5FNS (NLO) Alpgen+Py6 4FNS (LO) Alpgen+Py6 (rew. NNPDF3.0lo) MGaMC+Py8 5FNS (LO) 0.03 pb± 0.21 ± 0.01 ±1.32 ATLAS -1 =13 TeV, 35.6 fbs Figure 7. Measured cross-sections for Z+≥1b-jet (left) and Z+≥2b-jets (right). The data are compared with the predictions from Sherpa 5FNS (NLO),Alpgen +Py6 4 FNS (LO), Sherpa Fusing 4FNS+5FNS (NLO),Sherpa Zbb 4FNS (NLO),MGaMC +Py8 5FNS (LO),MGaMC +Py8 Zbb 4FNS (NLO) and MGaMC +Py8 5FNS (NLO). The yellow band corresponds to the statistical uncertainty of the data, and the green band to statistical and systematic uncertainties of the data, added in quadrature. The error bars on the Sherpa 5FNS (NLO) predictions correspond to the statistical and theoretical uncertainties added in quadrature. Only statistical uncertainties are shown for the other predictions. 9.1 Inclusive cross-sections The measured inclusive cross-sections for Z+≥1b-jet and Z+≥2b-jets, shown in figure 7, are 10.90 ±0.03(stat.) ±1.08(syst.) ±0.25(lumi.) pb and 1.32 ±0.01(stat.) ± 0.21(syst.) ±0.04(lumi.) pb, respectively. The 4FNS MC predictions are systematically lower than data in the inclusive one-b-jet case, both for MC generators with LO matrix elements, as implemented in Alpgen +Py6 4FNS (LO), and for Zbb predictions at NLO, as implemented in Sherpa Zbb 4FNS (NLO) and MGaMC +Py8 Zbb 4FNS (NLO). The 4FNS predictions agree well with data in the inclusive two-b-jet case. Even though the LO Alpgen +Py6 4FNS (LO) underestimates the data, the predictions and data agree within two standard deviations (2σ) of the experimental uncertainty. Use of the NNPDF3.0lo PDF set in Alpgen predictions gives better agreement with data because of a higher acceptance in the fiducial region. The 5FNS simulations, in general, adequately predict the inclusive cross-sections for both Z+≥1b-jet and Z+≥2b-jets. Overall, this is consistent with the results presented in the ATLAS measurement at √s= 7 TeV [11]. 9.2 Differential cross-sections for Z+≥1b-jet The differential cross-section measurements for the Z+≥1b-jet process are shown in figures 8–11. Each distribution is presented and discussed in detail in this section. The distributions of the transverse momentum of the Zboson and of the jets probe pQCD over a wide range of scales and provide important input to the background prediction for other SM processes, including Higgs boson production and searches beyond the SM. The differential cross-section as a function of the Z-boson pTfor events with at least one b-jet is shown in figure 8(left). In the low pTregion, up to 100 GeV, where soft radiative effects – 24 – JHEP07(2020)044 Figure 14. Measured cross-section as a function of pTof the Zboson (left) and of the di-b-jet system (pT,bb) (right) in events with at least two b-jets. The data are compared with the predictions from Sherpa 5FNS (NLO),Alpgen +Py6 4 FNS (LO),Sherpa Fusing 4FNS+5FNS (NLO), Sherpa Zbb 4FNS (NLO),MGaMC +Py8 5FNS (LO),MGaMC +Py8 Zbb 4FNS (NLO) and MGaMC +Py8 5FNS (NLO). The error bars correspond to the statistical uncertainty, and the hatched bands to the data statistical and systematic uncertainties added in quadrature. The red band corresponds to the statistical and theoretical uncertainties of Sherpa 5FNS (NLO) added in quadrature. Only statistical uncertainties are shown for the other predictions. predictions provide a quite good model of the shape of this observable’s distribution up to about 300 GeV, while the other predictions show various discrepancies in this region. This is particularly evident for MGaMC +Py8 Zbb 4FNS (NLO), and it is consistent with the mismodelling observed at low ∆Rbb, the region dominated by gluon splitting. In the high mass range all predictions underestimate the data, resulting in a sizeable mismodelling. Hence the use of these predictions for the background estimate in searches for physics beyond the SM in this final state could be problematic. The differential cross-sections as a function of the Z-boson pTand of the pTof the di-b-jet system (pT,bb) for events with at least two b-jets are shown in figure 14. Most of the predictions agree with data within the large experimental uncertainties, which are about 25% in most of the bins, and large statistical uncertainties of the predictions, which for some MC samples reach 25% in the highest bins. Alpgen shows a harder Z-boson pT spectrum than data, as was observed in the distribution of events with at least one b-jet. The Zbb simulation at NLO with 4FNS, as implemented in MGaMC +Py8 Zbb 4FNS (NLO) and Sherpa Zbb 4FNS (NLO), shows better agreement with data with respect – 31 – JHEP07(2020)044 Figure 15. Measured cross-section as a function of the pTof the di-b-jet system divided by its invariant mass (pT,bb/mbb) in events with at least two b-jets. The data are compared with the predictions from Sherpa 5FNS (NLO),Alpgen +Py6 4 FNS (LO),Sherpa Fusing 4FNS+5FNS (NLO),Sherpa Zbb 4FNS (NLO),MGaMC +Py8 5FNS (LO),MGaMC + Py8 Zbb 4FNS (NLO) and MGaMC +Py8 5FNS (NLO). The error bars correspond to the statistical uncertainty, and the hatched bands to the statistical and systematic uncertainties of the data, added in quadrature. The red band corresponds to the statistical and theoretical uncertainties of Sherpa 5FNS (NLO) added in quadrature. Only statistical uncertainties are shown for the other predictions. to the pTdistributions for events with at least one b-jet, but significant disagreement is still observed. Finally, the ratio of the pTof the di-b-jet system to its invariant mass (pT,bb/mbb) is sensitive to gluon splitting: a small value indicates a hard splitting and a large value is a consequence of soft splitting. The differential cross-section as a function of pT,bb/mbb is shown in figure 15.Sherpa 5FNS (NLO) and Sherpa Fusing 4FNS+5FNS (NLO) show quite good agreement with data, while MGaMC +Py8 Zbb 4FNS (NLO) agrees less well. – 32 – JHEP07(2020)044 10 Conclusion This paper presents a measurement of the cross-sections for Z-boson production in association with one or more b-jets in pp collisions at √s= 13 TeV. The analysed data correspond to an integrated luminosity of 35.6 fb−1recorded by the ATLAS detector at the LHC. The cross-sections are measured using the electron and muon decay modes of the Z boson in a fiducial phase space. In addition to the inclusive cross-sections, differential crosssections of several kinematic observables are measured, extending the range of jet transverse momenta to higher values than reported in previous ATLAS publications, which used data at lower centre-of-mass energies. The measurements are compared with predictions from a variety of Monte Carlo generators. In general, 5-flavour number scheme (5FNS) calculations at NLO accuracy predict the inclusive cross-sections well, while inclusive 4-flavour number scheme (4FNS) LO calculations largely underestimate the data. Predictions of Zbb at NLO accuracy agree with data only in the two-b-jets case, and underestimate the data in the case of events with at least one b-jet. Overall, Sherpa 5FNS (NLO), a 5FNS generator with matrix elements at NLO for up to two partons and matrix elements at LO for up to four partons, describes the various differential distributions within the experimental uncertainties. A significant discrepancy, common to all generators, is found for large values of mbb. The Sherpa Fusing 4FNS+5FNS (NLO) simulation, which combines 4FNS with 5FNS at NLO accuracy using a novel technique, agrees with Sherpa 5FNS (NLO), showing that in general at the scales tested by this measurement the effects of this merging are minor. A disagreement of about 20 30% is observed for large values of the leading b-jet transverse momentum, and for small angular separations between the Zboson and the leading b-jet. The 5FNS simulation with matrix elements for up to four partons at LO, as implemented in MGaMC +Py8 (LO), describes the data within the experimental uncertainties in most cases. In some cases this simulation is even better than predictions from MGaMC +Py8 5FNS (NLO), which has matrix elements with only one parton at NLO. This indicates the importance of simulations with several partons in the matrix element for a fair description of the data. The pure Zbb simulation at NLO in the 4FNS, as generated by Sherpa and MGaMC, shows significant deviations from the data even in the two-b-jets configuration, and this is more pronounced in MGaMC. This measurement provides essential input for the improvement of theoretical predictions and Monte Carlo generators of Z-boson production in association with b-jets, allowing a better quantitative understanding of perturbative QCD. Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST – 33 – JHEP07(2020)044 and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZˇ S, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, Marie Sk lodowska-Curie Actions and COST, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain; G¨oran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. 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Morvaj154, P. Moschovakos36, B. Moser120, M. Mosidze158b, T. Moskalets144, H.J. Moss148, J. Moss31,m, E.J.W. Moyse103, S. Muanza102, J. Mueller138, R.S.P. Mueller114, D. Muenstermann90, G.A. Mullier97, D.P. Mungo69a,69b, J.L. Munoz Martinez14, F.J. Munoz Sanchez101, P. Murin28b, W.J. Murray177,143, A. Murrone69a,69b, M. Muˇskinja18, C. Mwewa33a, A.G. Myagkov123,ah, A.A. Myers138, J. Myers131, M. Myska141, B.P. Nachman18, O. Nackenhorst47, A.Nag Nag48, K. Nagai134, K. Nagano82, Y. Nagasaka62, J.L. Nagle29, E. Nagy102, A.M. Nairz36, Y. Nakahama117, K. Nakamura82, T. Nakamura162, H. Nanjo132, F. Napolitano61a, R.F. Naranjo Garcia46, R. Narayan42, I. Naryshkin137, T. Naumann46, G. Navarro22a, P.Y. Nechaeva111, F. Nechansky46, T.J. Neep21, A. Negri71a,71b, M. Negrini23b, C. Nellist119, M.E. Nelson45a,45b, S. Nemecek140, M. Nessi36,d, M.S. Neubauer172, F. Neuhaus100, M. Neumann181, R. Newhouse174, P.R. Newman21, C.W. Ng138, Y.S. Ng19, Y.W.Y. Ng170, B. Ngair35e, H.D.N. Nguyen102, T. Nguyen Manh110, E. Nibigira38, R.B. Nickerson134, R. Nicolaidou144, D.S. Nielsen40, J. Nielsen145, N. Nikiforou11, V. Nikolaenko123,ah, I. Nikolic-Audit135, K. Nikolopoulos21, P. Nilsson29, H.R. Nindhito54, Y. Ninomiya82, A. Nisati73a, N. Nishu60c, R. Nisius115, I. Nitsche47, T. Nitta178, T. Nobe162, D.L. Noel32, Y. Noguchi86, I. Nomidis135, M.A. Nomura29, – 47 – JHEP07(2020)044 M. Nordberg36, J. Novak92, T. Novak92, O. Novgorodova48, R. Novotny141, L. Nozka130, K. Ntekas170, E. Nurse95, F.G. Oakham34,am, H. Oberlack115, J. Ocariz135, A. Ochi83, I. Ochoa39, J.P. Ochoa-Ricoux146a, K. O’Connor26, S. Oda88, S. Odaka82, S. Oerdek53, A. Ogrodnik84a, A. Oh101, S.H. Oh49, C.C. Ohm153, H. Oide164, M.L. Ojeda166, H. Okawa168, Y. Okazaki86, M.W. O’Keefe91, Y. Okumura162, T. Okuyama82, A. Olariu27b, L.F. Oleiro Seabra139a, S.A. Olivares Pino146a, D. Oliveira Damazio29, J.L. Oliver1, M.J.R. Olsson170, A. Olszewski85, J. Olszowska85, D.C. O’Neil151, A.P. O’neill134, A. Onofre139a,139e, P.U.E. Onyisi11, H. Oppen133, R.G. Oreamuno Madriz121, M.J. Oreglia37, G.E. Orellana89, D. Orestano75a,75b, N. Orlando14, R.S. Orr166, V. O’Shea57, R. Ospanov60a, G. Otero y Garzon30, H. Otono88, P.S. Ott61a, G.J. Ottino18, M. Ouchrif35d, J. Ouellette29, F. Ould-Saada133, A. Ouraou144, Q. Ouyang15a, M. Owen57, R.E. Owen21, V.E. Ozcan12c, N. Ozturk8, J. Pacalt130, H.A. Pacey32, K. Pachal49, A. Pacheco Pages14, C. Padilla Aranda14, S. Pagan Griso18, M. Paganini182, G. Palacino66, S. Palazzo50, S. Palestini36, M. Palka84b, D. Pallin38, P. Palni84a, I. Panagoulias10, C.E. Pandini36, J.G. Panduro Vazquez94, P. Pani46, G. Panizzo67a,67c, L. Paolozzi54, C. Papadatos110, K. Papageorgiou9,g, S. Parajuli42, A. Paramonov6, C. Paraskevopoulos10, D. Paredes Hernandez63b, S.R. Paredes Saenz134, B. Parida165, T.H. Park166, A.J. Parker31, M.A. Parker32, F. Parodi55b,55a, E.W. Parrish121, J.A. Parsons39, U. Parzefall52, L. Pascual Dominguez135, V.R. Pascuzzi18, J.M.P. Pasner145, F. Pasquali120, E. Pasqualucci73a, S. Passaggio55b, F. Pastore94, P. Pasuwan45a,45b, S. Pataraia100, J.R. Pater101, A. Pathak180,i, J. Patton91, T. Pauly36, J. Pearkes152, B. Pearson115, M. Pedersen133, L. Pedraza Diaz119, R. Pedro139a, T. Peiffer53, S.V. Peleganchuk122b,122a, O. Penc140, H. Peng60a, B.S. Peralva81a, M.M. Perego65, A.P. Pereira Peixoto139a, L. Pereira Sanchez45a,45b, D.V. Perepelitsa29, F. Peri19, L. Perini69a,69b, H. Pernegger36, S. Perrella139a, A. Perrevoort120, K. Peters46, R.F.Y. Peters101, B.A. Petersen36, T.C. Petersen40, E. Petit102, A. Petridis1, C. Petridou161, P. Petroff65, F. Petrucci75a,75b, M. Pettee182, N.E. Pettersson103, K. Petukhova142, A. Peyaud144, R. Pezoa146d, L. Pezzotti71a,71b, T. Pham105, F.H. Phillips107, P.W. Phillips143, M.W. Phipps172, G. Piacquadio154, E. Pianori18, A. Picazio103, R.H. Pickles101, R. Piegaia30, D. Pietreanu27b, J.E. Pilcher37, A.D. Pilkington101, M. Pinamonti67a,67c, J.L. Pinfold3, C. Pitman Donaldson95, M. Pitt160, L. Pizzimento74a,74b, M.-A. Pleier29, V. Pleskot142, E. Plotnikova80, P. Podberezko122b,122a, R. Poettgen97, R. Poggi54, L. Poggioli135, I. Pogrebnyak107, D. Pohl24, I. Pokharel53, G. Polesello71a, A. Poley18, A. Policicchio73a,73b, R. Polifka142, A. Polini23b, C.S. Pollard46, V. Polychronakos29, D. Ponomarenko112, L. Pontecorvo36, S. Popa27a, G.A. Popeneciu27d, L. Portales5, D.M. Portillo Quintero58, S. Pospisil141, K. Potamianos46, I.N. Potrap80, C.J. Potter32, H. Potti11, T. Poulsen97, J. Poveda173, T.D. Powell148, G. Pownall46, M.E. Pozo Astigarraga36, P. Pralavorio102, S. Prell79, D. Price101, M. Primavera68a, S. Prince104, M.L. Proffitt147, N. Proklova112, K. Prokofiev63c, F. Prokoshin80, S. Protopopescu29, J. Proudfoot6, M. Przybycien84a, D. Pudzha137, A. Puri172, P. Puzo65, J. Qian106, Y. Qin101, A. Quadt53, M. Queitsch-Maitland36, A. Qureshi1, M. Racko28a, F. Ragusa69a,69b, G. Rahal98, J.A. Raine54, S. Rajagopalan29, A. Ramirez Morales93, K. Ran15a,15d, T. Rashid65, D.M. Rauch46, F. Rauscher114, S. Rave100, B. Ravina148, I. Ravinovich179, J.H. Rawling101, M. Raymond36, A.L. Read133, N.P. Readioff58, M. Reale68a,68b, D.M. Rebuzzi71a,71b, G. Redlinger29, K. Reeves43, L. Rehnisch19, J. Reichert136, D. Reikher160, A. Reiss100, A. Rej150, C. Rembser36, A. Renardi46, M. Renda27b, M. Rescigno73a, S. Resconi69a, E.D. Resseguie18, S. Rettie95, B. Reynolds127, E. Reynolds21, O.L. Rezanova122b,122a, P. Reznicek142, E. Ricci76a,76b, R. Richter115, S. Richter46, E. Richter-Was84b, O. Ricken24, M. Ridel135, P. Rieck115, O. Rifki46, M. Rijssenbeek154, A. Rimoldi71a,71b, M. Rimoldi46, L. Rinaldi23b, G. Ripellino153, I. Riu14, P. Rivadeneira46, J.C. Rivera Vergara175, F. Rizatdinova129, E. Rizvi93, C. Rizzi36, R.T. Roberts101, S.H. Robertson104,ac, M. Robin46, D. Robinson32, C.M. Robles Gajardo146d, – 48 – JHEP07(2020)044 M. Robles Manzano100, A. Robson57, A. Rocchi74a,74b, E. Rocco100, C. Roda72a,72b, S. Rodriguez Bosca173, D. Rodriguez Rodriguez173, A.M. Rodr´ıguez Vera167b, S. Roe36, O. Røhne133, R. R¨ohrig115, R.A. Rojas146d, B. Roland52, C.P.A. Roland66, J. Roloff29, A. Romaniouk112, M. Romano23b,23a, N. Rompotis91, M. Ronzani125, L. Roos135, S. Rosati73a, G. Rosin103, B.J. Rosser136, E. Rossi46, E. Rossi75a,75b, E. Rossi70a,70b, L.P. Rossi55b, L. Rossini69a,69b, R. Rosten14, M. Rotaru27b, B. Rottler52, D. Rousseau65, G. Rovelli71a,71b, A. Roy11, D. Roy33e, A. Rozanov102, Y. Rozen159, X. Ruan33e, F. R¨uhr52, A. Ruiz-Martinez173, A. Rummler36, Z. Rurikova52, N.A. Rusakovich80, H.L. Russell104, L. Rustige38,47, J.P. Rutherfoord7, E.M. R¨uttinger148, M. Rybar39, G. Rybkin65, E.B. Rye133, A. Ryzhov123, J.A. Sabater Iglesias46, P. Sabatini53, S. Sacerdoti65, H.F-W. Sadrozinski145, R. Sadykov80, F. Safai Tehrani73a, B. Safarzadeh Samani155, M. Safdari152, P. Saha121, S. Saha104, M. Sahinsoy61a, A. Sahu181, M. Saimpert36, M. Saito162, T. Saito162, H. Sakamoto162, D. Salamani54, G. Salamanna75a,75b, J.E. Salazar Loyola146d, A. Salnikov152, J. Salt173, A. Salvador Salas14, D. Salvatore41b,41a, F. Salvatore155, A. Salvucci63a,63b,63c, A. Salzburger36, J. Samarati36, D. Sammel52, D. Sampsonidis161, D. Sampsonidou161, J. S´anchez173, A. Sanchez Pineda67a,36,67c, H. Sandaker133, C.O. Sander46, I.G. Sanderswood90, M. Sandhoff181, C. Sandoval22a, D.P.C. Sankey143, M. Sannino55b,55a, Y. Sano117, A. Sansoni51, C. Santoni38, H. Santos139a,139b, S.N. Santpur18, A. Santra173, A. Sapronov80, J.G. Saraiva139a,139d, O. Sasaki82, K. Sato168, F. Sauerburger52, E. Sauvan5, P. Savard166,am, R. Sawada162, C. Sawyer143, L. Sawyer96,ag, C. Sbarra23b, A. Sbrizzi23a, T. Scanlon95, J. Schaarschmidt147, P. Schacht115, B.M. Schachtner114, D. Schaefer37, L. Schaefer136, J. Schaeffer100, S. Schaepe36, U. Sch¨afer100, A.C. Schaffer65, D. Schaile114, R.D. Schamberger154, E. Schanet114, N. Scharmberg101, V.A. Schegelsky137, D. Scheirich142, F. Schenck19, M. Schernau170, C. Schiavi55b,55a, L.K. Schildgen24, Z.M. Schillaci26, E.J. Schioppa68a,68b, M. Schioppa41b,41a, K.E. Schleicher52, S. Schlenker36, K.R. Schmidt-Sommerfeld115, K. Schmieden36, C. Schmitt100, S. Schmitt46, S. Schmitz100, J.C. Schmoeckel46, L. Schoeffel144, A. Schoening61b, P.G. Scholer52, E. Schopf134, M. Schott100, J.F.P. Schouwenberg119, J. Schovancova36, S. Schramm54, F. Schroeder181, A. Schulte100, H-C. Schultz-Coulon61a, M. Schumacher52, B.A. Schumm145, Ph. Schune144, A. Schwartzman152, T.A. Schwarz106, Ph. Schwemling144, R. Schwienhorst107, A. Sciandra145, G. Sciolla26, M. Scodeggio46, M. Scornajenghi41b,41a, F. Scuri72a, F. Scutti105, L.M. Scyboz115, C.D. Sebastiani73a,73b, P. Seema19, S.C. Seidel118, A. Seiden145, B.D. Seidlitz29, T. Seiss37, C. Seitz46, J.M. Seixas81b, G. Sekhniaidze70a, S.J. Sekula42, N. Semprini-Cesari23b,23a, S. Sen49, C. Serfon29, L. Serin65, L. Serkin67a,67b, M. Sessa60a, H. Severini128, S. Sevova152, F. Sforza55b,55a, A. Sfyrla54, E. Shabalina53, J.D. Shahinian145, N.W. Shaikh45a,45b, D. Shaked Renous179, L.Y. Shan15a, M. Shapiro18, A. Sharma134, A.S. Sharma1, P.B. Shatalov124, K. Shaw155, S.M. Shaw101, M. Shehade179, Y. Shen128, A.D. Sherman25, P. Sherwood95, L. Shi157, S. Shimizu82, C.O. Shimmin182, Y. Shimogama178, M. Shimojima116, I.P.J. Shipsey134, S. Shirabe164, M. Shiyakova80,aa, J. Shlomi179, A. Shmeleva111, M.J. Shochet37, J. Shojaii105, D.R. Shope128, S. Shrestha127, E.M. Shrif33e, E. Shulga179, P. Sicho140, A.M. Sickles172, E. Sideras Haddad33e, O. Sidiropoulou36, A. Sidoti23b,23a, F. Siegert48, Dj. Sijacki16, M.Jr. Silva180, M.V. Silva Oliveira81a, S.B. Silverstein45a, S. Simion65, R. Simoniello100, C.J. Simpson-allsop21, S. Simsek12b, P. Sinervo166, V. Sinetckii113, S. Singh151, M. Sioli23b,23a, I. Siral131, S.Yu. Sivoklokov113, J. Sj¨olin45a,45b, A. Skaf53, E. Skorda97, P. Skubic128, M. Slawinska85, K. Sliwa169, R. Slovak142, V. Smakhtin179, B.H. Smart143, J. Smiesko28b, N. Smirnov112, S.Yu. Smirnov112, Y. Smirnov112, L.N. Smirnova113,s, O. Smirnova97, J.W. Smith53, M. Smizanska90, K. Smolek141, A. Smykiewicz85, A.A. Snesarev111, H.L. Snoek120, I.M. Snyder131, S. Snyder29, R. Sobie175,ac, A. Soffer160, A. Søgaard50, F. Sohns53, C.A. Solans Sanchez36, E.Yu. Soldatov112, U. Soldevila173, A.A. Solodkov123, A. Soloshenko80, – 49 – JHEP07(2020)044 O.V. Solovyanov123, V. Solovyev137, P. Sommer148, H. Son169, W. Song143, W.Y. Song167b, A. Sopczak141, A.L. Sopio95, F. Sopkova28b, C.L. Sotiropoulou72a,72b, S. Sottocornola71a,71b, R. Soualah67a,67c,f, A.M. Soukharev122b,122a, D. South46, S. Spagnolo68a,68b, M. Spalla115, M. Spangenberg177, F. Span`o94, D. Sperlich52, T.M. Spieker61a, G. Spigo36, M. Spina155, D.P. Spiteri57, M. Spousta142, A. Stabile69a,69b, B.L. Stamas121, R. Stamen61a, M. Stamenkovic120, E. Stanecka85, B. Stanislaus134, M.M. Stanitzki46, M. Stankaityte134, B. Stapf120, E.A. Starchenko123, G.H. Stark145, J. Stark58, P. Staroba140, P. Starovoitov61a, S. St¨arz104, R. Staszewski85, G. Stavropoulos44, M. Stegler46, P. Steinberg29, A.L. Steinhebel131, B. Stelzer151, H.J. Stelzer138, O. Stelzer-Chilton167a, H. Stenzel56, T.J. Stevenson155, G.A. Stewart36, M.C. Stockton36, G. Stoicea27b, M. Stolarski139a, S. Stonjek115, A. Straessner48, J. Strandberg153, S. Strandberg45a,45b, M. Strauss128, T. Strebler102, P. Strizenec28b, R. Str¨ohmer176, D.M. Strom131, R. Stroynowski42, A. Strubig50, S.A. Stucci29, B. Stugu17, J. Stupak128, N.A. Styles46, D. Su152, W. Su60c, S. Suchek61a, V.V. Sulin111, M.J. Sullivan91, D.M.S. Sultan54, S. Sultansoy4c, T. Sumida86, S. Sun106, X. Sun101, K. Suruliz155, C.J.E. Suster156, M.R. Sutton155, S. Suzuki82, M. Svatos140, M. Swiatlowski167a, S.P. Swift2, T. Swirski176, A. Sydorenko100, I. Sykora28a, M. Sykora142, T. Sykora142, D. Ta100, K. Tackmann46,y, J. Taenzer160, A. Taffard170, R. Tafirout167a, R. Takashima87, K. Takeda83, T. Takeshita149, E.P. Takeva50, Y. Takubo82, M. Talby102, A.A. Talyshev122b,122a, K.C. Tam63b, N.M. Tamir160, J. Tanaka162, R. Tanaka65, S. Tapia Araya172, S. Tapprogge100, A. Tarek Abouelfadl Mohamed107, S. Tarem159, K. Tariq60b, G. Tarna27b,c, G.F. Tartarelli69a, P. Tas142, M. Tasevsky140, T. Tashiro86, E. Tassi41b,41a, A. Tavares Delgado139a, Y. Tayalati35e, A.J. Taylor50, G.N. Taylor105, W. Taylor167b, H. Teagle91, A.S. Tee90, R. Teixeira De Lima152, P. Teixeira-Dias94, H. Ten Kate36, J.J. Teoh120, S. Terada82, K. Terashi162, J. Terron99, S. Terzo14, M. Testa51, R.J. Teuscher166,ac, S.J. Thais182, N. Themistokleous50, T. Theveneaux-Pelzer46, F. Thiele40, D.W. Thomas94, J.O. Thomas42, J.P. Thomas21, E.A. Thompson46, P.D. Thompson21, E. Thomson136, E.J. Thorpe93, R.E. Ticse Torres53, V.O. Tikhomirov111,ai, Yu.A. Tikhonov122b,122a, S. Timoshenko112, P. Tipton182, S. Tisserant102, K. Todome23b,23a, S. Todorova-Nova142, S. Todt48, J. Tojo88, S. Tok´ar28a, K. Tokushuku82, E. Tolley127, R. Tombs32, K.G. Tomiwa33e, M. Tomoto117, L. Tompkins152, P. Tornambe103, E. Torrence131, H. Torres48, E. Torr´o Pastor147, C. Tosciri134, J. Toth102,ab, D.R. Tovey148, A. Traeet17, C.J. Treado125, T. Trefzger176, F. Tresoldi155, A. Tricoli29, I.M. Trigger167a, S. Trincaz-Duvoid135, D.A. Trischuk174, W. Trischuk166, B. Trocm´e58, A. Trofymov65, C. Troncon69a, F. Trovato155, L. Truong33c, M. Trzebinski85, A. Trzupek85, F. Tsai46, J.C-L. Tseng134, P.V. Tsiareshka108,af , A. Tsirigotis161,v, V. Tsiskaridze154, E.G. Tskhadadze158a, M. Tsopoulou161, I.I. Tsukerman124, V. Tsulaia18, S. Tsuno82, D. Tsybychev154, Y. Tu63b, A. Tudorache27b, V. Tudorache27b, T.T. Tulbure27a, A.N. Tuna59, S. Turchikhin80, D. Turgeman179, I. Turk Cakir4b,t, R.J. Turner21, R.T. Turra69a, P.M. Tuts39, S. Tzamarias161, E. Tzovara100, G. Ucchielli47, K. Uchida162, F. Ukegawa168, G. Unal36, A. Undrus29, G. Unel170, F.C. Ungaro105, Y. Unno82, K. Uno162, J. Urban28b, P. Urquijo105, G. Usai8, Z. Uysal12d, V. Vacek141, B. Vachon104, K.O.H. Vadla133, A. Vaidya95, C. Valderanis114, E. Valdes Santurio45a,45b, M. Valente54, S. Valentinetti23b,23a, A. Valero173, L. Val´ery46, R.A. Vallance21, A. Vallier36, J.A. Valls Ferrer173, T.R. Van Daalen14, P. Van Gemmeren6, I. Van Vulpen120, M. Vanadia74a,74b, W. Vandelli36, M. Vandenbroucke144, E.R. Vandewall129, A. Vaniachine165, D. Vannicola73a,73b, R. Vari73a, E.W. Varnes7, C. Varni55b,55a, T. Varol157, D. Varouchas65, K.E. Varvell156, M.E. Vasile27b, G.A. Vasquez175, F. Vazeille38, D. Vazquez Furelos14, T. Vazquez Schroeder36, J. Veatch53, V. Vecchio101, M.J. Veen120, L.M. Veloce166, F. Veloso139a,139c, S. Veneziano73a, A. Ventura68a,68b, N. Venturi36, A. Verbytskyi115, V. Vercesi71a, M. Verducci72a,72b, C.M. Vergel Infante79, C. Vergis24, – 50 – JHEP07(2020)044 W. Verkerke120, A.T. Vermeulen120, J.C. Vermeulen120, C. Vernieri152, M.C. Vetterli151,am, N. Viaux Maira146d, T. Vickey148, O.E. Vickey Boeriu148, G.H.A. Viehhauser134, L. Vigani61b, M. Villa23b,23a, M. Villaplana Perez3, E.M. Villhauer50, E. Vilucchi51, M.G. Vincter34, G.S. Virdee21, A. Vishwakarma46, C. Vittori23b,23a, I. Vivarelli155, M. Vogel181, P. Vokac141, S.E. von Buddenbrock33e, E. Von Toerne24, V. Vorobel142, K. Vorobev112, M. Vos173, J.H. Vossebeld91, M. Vozak101, N. Vranjes16, M. Vranjes Milosavljevic16, V. Vrba141, M. Vreeswijk120, R. Vuillermet36, I. Vukotic37, S. Wada168, P. Wagner24, W. Wagner181, J. Wagner-Kuhr114, S. Wahdan181, H. Wahlberg89, R. Wakasa168, V.M. Walbrecht115, J. Walder90, R. Walker114, S.D. Walker94, W. Walkowiak150, V. Wallangen45a,45b, A.M. Wang59, A.Z. Wang180, C. Wang60c, F. Wang180, H. Wang18, H. Wang3, J. Wang63a, J. Wang61b, P. Wang42, Q. Wang128, R.-J. Wang100, R. Wang60a, R. Wang6, S.M. Wang157, W.T. Wang60a, W. Wang15c, W.X. Wang60a, Y. Wang60a, Z. Wang60c, C. Wanotayaroj46, A. Warburton104, C.P. Ward32, D.R. Wardrope95, N. Warrack57, A. Washbrook50, A.T. Watson21, M.F. Watson21, G. Watts147, B.M. Waugh95, A.F. Webb11, C. Weber29, M.S. Weber20, S.A. Weber34, S.M. Weber61a, A.R. Weidberg134, J. Weingarten47, M. Weirich100, C. Weiser52, P.S. Wells36, T. Wenaus29, T. Wengler36, S. Wenig36, N. Wermes24, M.D. Werner79, M. Wessels61a, T.D. Weston20, K. Whalen131, N.L. Whallon147, A.M. Wharton90, A.S. White106, A. White8, M.J. White1, D. Whiteson170, B.W. Whitmore90, W. Wiedenmann180, C. Wiel48, M. Wielers143, N. Wieseotte100, C. Wiglesworth40, L.A.M. Wiik-Fuchs52, H.G. Wilkens36, L.J. Wilkins94, H.H. Williams136, S. Williams32, C. Willis107, S. Willocq103, P.J. Windischhofer134, I. Wingerter-Seez5, E. Winkels155, F. Winklmeier131, B.T. Winter52, M. Wittgen152, M. Wobisch96, A. Wolf100, T.M.H. Wolf120, R. Wolff102, R. W¨olker134, J. Wollrath52, M.W. Wolter85, H. Wolters139a,139c, V.W.S. Wong174, N.L. Woods145, S.D. Worm46, B.K. Wosiek85, K.W. Wo´zniak85, K. Wraight57, S.L. Wu180, X. Wu54, Y. Wu60a, T.R. Wyatt101, B.M. Wynne50, S. Xella40, Z. Xi106, L. Xia177, X. Xiao106, X. Xie60a, I. Xiotidis155, D. Xu15a, H. Xu60a, H. Xu60a, L. Xu29, T. Xu144, W. Xu106, Z. Xu60b, Z. Xu152, B. Yabsley156, S. Yacoob33a, K. Yajima132, D.P. Yallup95, N. Yamaguchi88, Y. Yamaguchi164, A. Yamamoto82, M. Yamatani162, T. Yamazaki162, Y. Yamazaki83, J. Yan60c, Z. Yan25, H.J. Yang60c,60d, H.T. Yang18, S. Yang60a, T. Yang63c, X. Yang60b,58, Y. Yang162, Z. Yang60a, W-M. Yao18, Y.C. Yap46, Y. Yasu82, E. Yatsenko60c,60d, H. Ye15c, J. Ye42, S. Ye29, I. Yeletskikh80, M.R. Yexley90, E. Yigitbasi25, P. Yin39, K. Yorita178, K. Yoshihara79, C.J.S. Young36, C. Young152, J. Yu79, R. Yuan60b,h, X. Yue61a, M. Zaazoua35e, B. Zabinski85, G. Zacharis10, E. Zaffaroni54, J. Zahreddine135, A.M. Zaitsev123,ah, T. Zakareishvili158b, N. Zakharchuk34, S. Zambito59, D. Zanzi36, D.R. Zaripovas57, S.V. Zeißner47, C. Zeitnitz181, G. Zemaityte134, J.C. Zeng172, O. Zenin123, T. ˇ Zeniˇs28a, D. Zerwas65, M. Zgubiˇc134, B. Zhang15c, D.F. Zhang15b, G. Zhang15b, J. Zhang6, Kaili. Zhang15a, L. Zhang15c, L. Zhang60a, M. Zhang172, R. Zhang180, S. Zhang106, X. Zhang60c, X. Zhang60b, Y. Zhang15a,15d, Z. Zhang63a, Z. Zhang65, P. Zhao49, Z. Zhao60a, A. Zhemchugov80, Z. Zheng106, D. Zhong172, B. Zhou106, C. Zhou180, H. Zhou7, M.S. Zhou15a,15d, M. Zhou154, N. Zhou60c, Y. Zhou7, C.G. Zhu60b, C. Zhu15a,15d, H.L. Zhu60a, H. Zhu15a, J. Zhu106, Y. Zhu60a, X. Zhuang15a, K. Zhukov111, V. Zhulanov122b,122a, D. Zieminska66, N.I. Zimine80, S. Zimmermann52, Z. Zinonos115, M. Ziolkowski150, L. ˇ Zivkovi´c16, G. Zobernig180, A. Zoccoli23b,23a, K. Zoch53, T.G. Zorbas148, R. Zou37, L. Zwalinski36 1Department of Physics, University of Adelaide, Adelaide; Australia 2Physics Department, SUNY Albany, Albany NY; United States of America 3Department of Physics, University of Alberta, Edmonton AB; Canada 4Department of Physics(a), Ankara University, Ankara; Istanbul Aydin University(b), Application and Research Center for Advanced Studies, Istanbul; Division of Physics(c), TOBB University of Economics and Technology, Ankara; Turkey – 51 – JHEP07(2020)044 5LAPP, Universit´e Grenoble Alpes, Universit´e Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7Department of Physics, University of Arizona, Tucson AZ; United States of America 8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 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 Universit`a 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; (b)iThemba Labs, 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 – 52 – JHEP07(2020)044 36 CERN, Geneva; Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 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 Kernund 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 – 53 – JHEP07(2020)044 75 INFN Sezione di Roma Tre(a); Dipartimento di Matematica e Fisica(b), Universit`a Roma Tre, Roma; Italy 76 INFN-TIFPA(a); Universit`a degli Studi di Trento(b), Trento; Italy 77 Institut f¨ur Astround 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 – 54 – JHEP07(2020)044 116 Nagasaki Institute of Applied Science, Nagasaki; Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 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 – 55 – JHEP07(2020)044 150 Department Physik, Universit¨at Siegen, Siegen; Germany 151 Department of Physics, Simon Fraser University, Burnaby BC; Canada 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 CERN, Geneva; Switzerland cAlso at CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille; France dAlso at D´epartement de Physique Nucl´eaire et Corpusculaire, Universit´e de Gen`eve, Gen`eve; Switzerland eAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain fAlso at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah; United Arab Emirates – 56 –