PHYSICAL REVIEW D 99, 052004 (2019) Properties of g → bb̄ at small opening p angles ffiffi in pp collisions with the ATLAS detector at s = 13 TeV M. Aaboud et al.* (ATLAS Collaboration) (Received 24 December 2018; published 14 March 2019) The fragmentation of high-energy gluons at small opening angles is largely unconstrained by present measurements. Gluon splitting to b-quark pairs is a unique probe into the properties of gluon fragmentation because identified b-tagged jets provide a proxy for the quark daughters of the initial gluon. In this study, pffiffiffi key differential distributions related to the g → bb̄ process are measured using 33 fb−1 of s ¼ 13 TeV pp collision data recorded by the ATLAS experiment at the LHC in 2016. Jets constructed from charged- particle tracks, clustered with the anti-kt jet algorithm with radius parameter R ¼ 0.2, are used to probe angular scales below the R ¼ 0.4 jet radius. The observables are unfolded to particle level in order to facilitate direct comparisons with predictions from present and future simulations. Multiple significant differences are observed between the data and parton shower Monte Carlo predictions, providing input to improve these predictions of the main source of background events in analyses involving boosted Higgs bosons decaying into b-quarks. DOI: 10.1103/PhysRevD.99.052004 I. INTRODUCTION significant modification to the massless QCD splitting functions by screening the soft-emission singularity. The search for highly Lorentz-boosted Higgs bosons produced by Standard Model (SM) processes [1] or by Trijet measurements from LEP [13–15] and SLD [16] beyond-the-SM (BSM) processes [2–7] is of crucial provide valuable information about the rate of g → bb̄, but importance at the Large Hadron Collider (LHC). As the have not explored the differential properties of the frag- branching ratio for the Higgs boson to decay into bottom mentation in the small opening-angle regime. Previous quark pairs dominates the total decay rate, the boosted measurements that include the bb̄ final state at the Spp̄S, H → bb̄ channel can be the most sensitive to BSM effects Tevatron, and LHC using inclusive [17–36], multijet [37– entering at high Higgs boost. Algorithms for identifying 39], and associated production [40–49] topologies have jets resulting from bottom quark fragmentation are very focused on well-separated quark pairs (dominated by fixed- powerful, so the main background for searches with order instead of parton-splitting effects) and were limited in boosted Higgs bosons contains b-quarks. The main con- their kinematic reach due in part to small datasets and low tribution to this background is gluon splitting to bb̄ pairs at momentum transfers. small opening angles since the angle between the b-quarks The high transverse momentum and low angular sepa- in H → bb̄ scales with the Higgs boson mass (mH ) and ration regime for g → bb̄ can be probed at the LHC using momentum (pH ) as mH =pH . The g → bb̄ process also b-tagged small-radius jets within large-radius jets. This contributes to many other important SM measurements and topology is used to calibrate b-tagging in dense environ- searches by providing a source of additional real b-quark ments [50–52] and is studied phenomenologically [53,54]. jets that can fake a signal for b-quarks originating from The measurement shown in this paper builds on these other processes (see e.g., Refs. [8–12]). studies by using data collected by the ATLAS detector from pffiffiffi The modeling of g → bb̄ fragmentation is complex s ¼ 13 TeV pp collisions in order to perform a differ- and provides a useful probe of quantum chromodynamics ential cross-section measurement of g → bb̄ inside jets at (QCD). The large mass of the b-quark introduces a high transverse momentum—see Fig. 1 for a representative Feynman diagram. Small-radius jets built from charged- * particle tracks are used as proxies for b-quarks and can be Full author list given at the end of the article. used as precision probes of the small opening-angle regime. Published by the American Physical Society under the terms of This paper is organized as follows. After a brief the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to introduction to the ATLAS detector in Sec. II, the data the author(s) and the published article’s title, journal citation, and simulations used for the measurement are documented and DOI. Funded by SCOAP3. in Sec. III. Section IV describes the event selection and 2470-0010=2019=99(5)=052004(26) 052004-1 © 2019 CERN, for the ATLAS Collaboration M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) utilizes partial detector information to reduce the accepted event rate to 100 kHz. The high-level trigger is based on software and accepts events at a rate of 1 kHz. III. DATASETS This measurement uses the dataset of pp collisions recorded by the ATLAS detector in 2016, corresponding to FIG. 1. A representative diagram for the high-pT g → bb̄ −1 pffiffiffi luminosity of 33 fb at a center-of-mass an integrated process studied in this paper. energy of s ¼ 13 TeV. Events are considered only if they were collected during stable beam conditions and satisfy Sec. V lists and motivates the observables to be measured. data quality requirements. Due to the high instantaneous The key challenge in the measurement is the estimation of luminosity and the large total inelastic proton-proton background processes, which is performed using a data- scattering cross section, on average there are about 25 driven approach illustrated in Sec. VI. The data are simultaneous (pileup) collisions in each bunch crossing. unfolded to correct for detector effects to allow direct The measurement presented in this paper uses a variety comparisons to particle-level predictions. This procedure is of Monte Carlo (MC) simulated event samples to correct explained in Sec. VII and the associated systematic for detector effects as well as for direct comparisons with uncertainties are detailed in Sec. VIII. The results are the unfolded data. Inclusive jet events were generated at presented in Sec. IX and the paper concludes with Sec. X. leading order in the strong coupling constant, αS , with PYTHIA 8.1 [58] using a 2 → 2 matrix element, the II. ATLAS DETECTOR NNPDF2.3LO parton distribution function (PDF) set The ATLAS detector [55] is a multipurpose particle [59], and a set of generator parameter values called the detector with a forward/backward-symmetric cylindrical A14 tune [60]. The EVTGEN [61] program was used to geometry. The detector has a nearly 4π coverage in solid model the heavy-flavor decays to agree with experimental angle1 and consists of an inner tracking detector, electro- data. Additional inclusive jet events were simulated using a magnetic and hadronic calorimeters, and a muon spec- different generator in order to study the impact of modeling trometer. The inner detector (ID) is surrounded by a uncertainties related to both the perturbative and nonper- superconducting solenoid providing a 2 T magnetic field turbative aspects of fragmentation. SHERPA 2.1 [62] generates and covers a pseudorapidity range of jηj < 2.5. The ID is events using multileg 2 → 2 and 2 → 3 matrix elements, composed of silicon pixel and microstrip detectors as well which are matched to parton showers following the Catani- pffiffiffi as a transition radiation tracker. For the LHC s ¼ 13 TeV Krauss-Kuhn-Webber (CKKW) prescription [63]. These run, the silicon pixel detector has been upgraded to include SHERPA events were simulated using the CT10 PDF set an additional layer close to the beam interaction point [56]. [64] and the default parameter tune in SHERPA. The lead/liquid-argon electromagnetic sampling calorime- Energy depositions from particles in MC event samples ters measure electromagnetic energies with high granularity interacting with the detector and the subsequent detector for the pseudorapidity region of jηj < 3.2. Hadron energies readout were modeled using a full simulation of the ATLAS are measured by a hadronic (steel/scintillator tile) calori- detector [65] implemented in Geant4 [66]. The effects of meter with jηj < 1.7. The forward and end cap regions pileup were simulated with unbiased pp collisions using between 1.5 < jηj < 4.9 are instrumented with liquid-argon PYTHIA 8.1 and overlaid on the nominal inclusive jet events. calorimeters for both the electromagnetic and hadronic IV. OBJECT AND EVENT SELECTION measurements. Surrounding the calorimeters, the muon spectrometer includes three large superconducting toroidal Section IVA describes the definition of collision verti- magnets with eight coils each. The muon spectrometer has a ces, charged-particle tracks, jets, and b-jets. Following the system of precision tracking chambers covering jηj < 2.7 discussion of objects, Section IV B describes the particle- and fast trigger chambers covering jηj < 2.4. A two-level level definition of the measurement phase space and the trigger system is used to select events for further analysis detector-level selections used to create an enriched sample [57]. The first-level trigger is implemented in hardware and of jets resulting from g → bb̄. 1 A. Object reconstruction, identification ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the center of the and association detector and the z-axis along the beam pipe. The x-axis points Collision vertices are reconstructed from ID tracks. Each from the IP to the center of the LHC ring, and the y-axis points vertex is required to be associated with at least two tracks upward. Cylindrical coordinates (r, ϕ) are used in the transverse plane, ϕ being the azimuthal angle around the beam pipe. The with pT > 0.4 GeV. The primary hard-scattering vertex P of pseudorapidity is defined in terms of the polar angle as the event is chosen to be the vertex having the highest p2T η ¼ − ln tanðpolar angle=2Þ. calculated using all associated tracks. Particle-level events 052004-2 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) in simulation are considered before the addition of pileup being a b-jet, c-jet, or light-flavor jet. The selected working and therefore there is no ambiguity in selecting the collision point corresponds to a b-jet efficiency, a c-jet efficiency, vertex. and a light-flavor-jet rejection of ϵb ¼ 60%; ϵc ¼ 15%, and Calorimeter jets are built from calibrated topological 1=ϵlight ¼ 480, respectively, as measured in tt̄ events for jets calorimeter-cell clusters [67] using the anti-kt [68] algo- with pT > 10 GeV and jηj < 2.5. A pT - and η-dependent rithm with radius parameter R ¼ 1.0 as implemented in scale factor is applied to MC events to account for the FastJet [69]. Jets are groomed using a trimming procedure measured efficiency difference between data and MC events [70]. This procedure reclusters the constituents of a jet into at the chosen working point [76]. This scale factor is subjets with a smaller radius of size Rsub ¼ 0.2 and removes consistent with unity for b-jets with uncertainties ranging those subjets with a low fraction of the full jet momentum from a few to 10% and the scale factor for light-flavor jets is f cut ¼ 0.05. Following jet grooming, the mass and momen- between 1.5 and 2 with an uncertainty that ranges from tum of the resulting jets are corrected so that the detector- about 30%–50%. level values match the particle-level values on average [71]. Particle-level track-jets are tagged as b-jets if there is a These large-radius jets are proxies for the gluons. b-hadron from the simulated event record with pT > 5 GeV Smaller-radius jets are used as proxies for the b-quarks that is ghost-associated with the jet. If instead a hadron originating from the gluons. For this purpose, jets are containing a c-quark from the event record with pT > 5 GeV clustered using tracks as inputs (track-jets). Track-jets can be matched to the jet by ghost-association, it is declared a clustered from tracks with pT > 500 MeV that have been c-jet. All other jets are declared to be light-flavor jets. well matched to the primary vertex2 and are composed of at least one pixel detector hit and at least six hits in the silicon B. Event selection tracker [72] are constructed using the anti-kt algorithm with At detector level events are selected using single-jet R ¼ 0.2. Track-jets are required to have at least two tracks. triggers. In the first-level trigger, a sliding-window algorithm Four-vectors are calculated for each track, assuming the based on low-granularity calorimeter towers records events mass of the charged pion. Small-radius jets are matched to with transverse energy greater than 100 GeV. In the high- large-radius jets via ghost association [73]. This matching level trigger, R ¼ 1.0 anti-kt jets are formed from calibrated procedure creates ghost versions of the small-radius jets calorimeter-cell clusters and the pT threshold is 420 GeV. with the same direction but infinitesimal pT . Jet clustering This trigger scheme is fully efficient for calibrated offline jets is repeated and small-radius jets are assigned to the large- with pT > 450 GeV and jηj < 2 and therefore these kin- radius jet that contains their ghosted version. Since the jet ematic requirements are used to select jets for the measure- finding algorithm is infrared safe, the four-momenta of the ment. The offline analysis requires the highest-pT jets are unaffected by the addition of ghosts. calorimeter jet to have at least two associated track-jets with Particle-level jets are clustered using the same algorithms pT > 10 GeV and jηj < 2.5. If there are more than two as for detector-level jets, except the inputs to jet finding are track-jets, only the leading two are used for subsequent all stable particles (cτ > 10 mm) excluding all muons and analysis. In order to enhance the g → bb̄ purity, the leading neutrinos. The same trimming algorithm applied to calo- track-jet associated with the selected calorimeter jet must be rimeter jets is also applied to the large-radius particle-level b-tagged by the MV2c10 algorithm at the 60% efficiency jets. Particle-level track-jets are formed from all stable working point. Requiring both track-jets to be b-tagged charged particles that have pT > 500 MeV and jηj < 2.5, increases the purity but degrades the precision of the back- excluding muons. ground fit described in Sec. VI and so only one is required. Track-jets that are likely to have originated from the fragmentation of a b-quark (b-jet) are identified using the MV2c10 algorithm [74,75], which is a combination of three baseline algorithms IP3D, SV, and JetFitter. The IP3D algorithm uses log-likelihood ratios of the three- dimensional signed impact parameter significance of tracks associated with jets. The SV tagger reconstructs the secon- dary decay vertices of b-hadrons. The JetFitter algorithm reconstructs the topology of detached vertices along the b-hadron decay axis. Finally, the MV2c10 algorithm combines the outputs of the baseline algorithms with a boosted decision tree and assigns a probability of a jet 2 The transverse impact parameter must be less than 2 mm and the longitudinal impact parameter multiplied by the sine of the FIG. 2. Schematic diagrams illustrating the ΔRðb; bÞ and polar angle must be less than 3 mm. Δθppg;gbb observables. In this example, the gluon is emitted at η ¼ 0. 052004-3 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) 0.7 1 ATLAS Simulation ATLAS Simulation 0.00 0.00 0.00 0.00 0.02 0.97 0.06 0.00 0.00 0.02 0.91 0.8 0.8 Pr(Detector-level | Particle-level) Pr(Detector-level | Particle-level) 0.6 0.8 Particle-level Δθgpp,gbb/ π 0.00 0.00 0.00 0.03 0.95 0.02 0.00 0.07 0.02 0.90 0.01 Particle-level ΔR(b,b) 0.5 0.6 0.6 0.6 0.00 0.00 0.04 0.94 0.02 0.00 0.00 0.01 0.97 0.01 0.00 0.4 0.4 0.4 0.4 0.00 0.04 0.93 0.02 0.00 0.00 0.02 0.90 0.02 0.06 0.00 0.3 0.2 0.2 0.2 0.06 0.89 0.05 0.00 0.00 0.00 0.91 0.02 0.00 0.00 0.07 0.92 0.08 0.00 0.00 0.00 0.00 0.2 0 0 0.2 0.3 0.4 0.5 0.6 0.7 0 0.2 0.4 0.6 0.8 1 Detector-level ΔR(b,b) Detector-level Δθgpp,gbb/ π 0.5 0 ATLAS Simulation ATLAS Simulation 0.00 0.01 0.03 0.18 0.78 0.8 0.00 0.00 0.00 0.08 0.92 Pr(Detector-level | Particle-level) Pr(Detector-level | Particle-level) 0.4 0.6 Particle-level log(m /p ) 0.00 0.02 0.16 0.58 0.24 T −1 Particle-level z(p ) bb T 0.6 0.3 0.00 0.00 0.08 0.86 0.06 0.01 0.13 0.62 0.18 0.05 0.4 0.00 0.09 0.84 0.07 0.00 0.4 0.2 −2 0.10 0.80 0.10 0.00 0.00 0.09 0.68 0.19 0.03 0.01 0.2 0.1 0.2 0.79 0.19 0.01 0.00 0.00 0.76 0.21 0.02 0.00 0.00 0 −3 0 0 0.1 0.2 0.3 0.4 0.5 −3 −2 −1 0 Detector-level z(p ) Detector-level log(m /p ) T bb T FIG. 3. The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as Prðdetector-leveljparticle-levelÞ, in simulation for ΔRðb; bÞ (top left), Δθppg;gbb (top right), zðpT Þ (bottom left), and logðmbb =pT Þ (bottom right). The small antidiagonal component for Δθppg;gbb is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so Δθppg;gbb ↦ π − Δθppg;gbb . At particle level events are required to have at least one the b-quarks, and the orientation of the gluon splitting large-radius jet with pT > 450 GeV. The leading jet needs relative to the gluon production plane. The first of these to have at least two associated particle-level track-jets with quantities is probed by measuring the ΔRðb; bÞ ¼ pT > 10 GeV. Both of the associated small-radius jets pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Δϕðb; bÞ2 þ Δηðb; bÞ2 between track-jets. Momentum must be tagged as b-jets. sharing is explored using the quantity zðpT Þ ¼ pT;2 = This inclusive event selection produces a sample where ðpT;1 þ pT;2 Þ, where pT;1 and pT;2 are the transverse QCD scattering processes dominate. momenta of the leading and subleading track-jets, respec- tively. A quantity sensitive to the relative orientation of the gluon splitting is Δθppg;gbb , which is the angle between the V. OBSERVABLES plane spanned by the beam line and the vector sum of the The kinematic properties of the g → bb̄ process are two track-jets and the plane spanned by the two track-jets. characterized by three quantities: the opening angle The angular quantities ΔRðb; bÞ and Δθppg;gbb are depicted between the b-quarks, the momentum sharing between in Fig. 2. 052004-4 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) In addition to these quantities, the dimensionless mass produce consistent results. The leading sd0 is not as well logðmbb =pT Þ is also measured, where the mass and pT in modeled as ssub 2 d0 and therefore the χ resulting from the fit the logarithm are computed from the four-vector sum of the procedure described below is slightly worse. The value of two track-jets. The bb̄ mass is an important observable for ssub d0 does not have a strong dependence on jet pT , so the fit measurements and searches with Higgs and Z bosons. is performed inclusively. This choice was validated by Track-jets, in contrast to the calorimeter-based subjets from using pT -binned fits, which produce results consistent with trimming, are used due to their excellent angular resolution; the inclusive approach. in simulation, there is little difference between using the A binned maximum-likelihood fit to ssub d0 ðj1 Þ and sd0 ðj2 Þ sub directions from the track-jets, the full jets (including neutrals), or the b-hadrons from the b-quark fragmentation. is performed to extract the flavor fractions. Given the There is some discrepancy between these different objects flavors of track-jets j1 and j2 (pT;1 ≥ pT;2 ), ssub d0 ðj1 Þ and for the energy-dependent observables, but the track-jets are ssub d0 ðj 2 Þ are well approximated as being statistically inde- still useful due to their excellent momentum resolution in pendent (linear correlation is less than 5%). Therefore, d0 ðj1 Þ; sd0 ðj2 ÞÞ can be the gluon pT range probed in this measurement. The the probability distribution pðssub sub excellent angular and momentum resolutions are presented approximated by the product of marginals pðssub d0 ðj1 ÞÞ× in Fig. 3, which shows the detector response for all four observables targeted with this measurement. pðssub d0 ðj 2 ÞÞ. This approximation reduces a two-dimensional fit to a simultaneous fit of two one-dimensional distribu- VI. BACKGROUND ESTIMATION tions. In order to increase the robustness of the fit, flavor combinations with similar templates are merged. The three After the event selection, the contribution from large- templates used for each bin of the target observable are BB radius jets that do not have two associated track-jets (signal), B, and L þ C. The BB template only includes containing b-hadron is subtracted from data, as described events where both jets are labeled as b-jets using MC below, before correcting for detector effects. The fraction of particle-level flavor labeling. The B template is an aggre- background events may not be well modeled by the gation of bl and bc events in which the leading track-jets simulation, so correction factors are determined from data are labeled as b and the subleading track-jets are labeled as template fits to the impact parameter distribution and light-flavor or c in particle-level flavor labeling. This applied for each bin of the four target observables of the template also includes cases in which a g → bb̄ splitting analysis prior to subtraction.3 In each bin of the target was fully contained inside one small-radius track-jet and observable distributions, the distribution of the signed the second track-jet is due to a light quark or gluon. The rest impact parameter significance sd0 is fitted to data using of the events are merged into the L þ C template. The templates from simulation while letting the fraction of each nominal results use fits from PYTHIA templates. Uncer- flavor component float in the fit [50,51]. For a given track, tainties in the templates, in particular resulting from the sd0 ¼ sj jd0 j=σðd0 Þ, where d0 is the transverse impact template merging scheme and from the choice of generator, parameter relative to the beam line and σðd0 Þ is the are described in Sec. VIII A. uncertainty in d0 from the track fit and the variable sj is Examples of the flavor-fraction determination fits are the sign of d0 with respect to the jet axis: sj ¼ þ1 if shown in Fig. 4 for one bin of ΔRðb; bÞ. The template sinðϕjet − ϕtrack Þ · d0 > 0 and sj ¼ −1 otherwise. The binning was chosen to have enough sensitivity in the tails transverse impact parameter itself is signed, with while also having sufficient simulation statistics to populate signðd0 Þ ¼ signð ⃗pT;track × ð⃗rIP;xy − r⃗ PV;xy ÞÞ, where r⃗ IP;xy the templates; for reference, the MC statistical uncertainty and r⃗ PV;xy are the locations of the track impact parameter is shown in the error bands. Since the leading track-jet is and primary vertex, respectively, in the transverse plane. required to be b-tagged, the distribution of ssub d0 ðj1 Þ is Due to the long lifetime of b-hadrons, the values of sd0 broader and shifted toward more positive values than the for tracks in b-jets tend to be larger than those for tracks in distribution of ssub d0 ðj2 Þ. In contrast, the subleading jet is c-jets and light-flavor jets. Therefore, the distribution of sd0 most often a light-flavor jet for both the B and L þ C categories and therefore ssub d0 ðj2 Þ is nearly symmetric about can be used to extract the fractions of b-jets, c-jets, and light-flavor jets using templates from simulation. For each zero. The BB template is similar in shape for the leading track-jet ji , the sd0 from the track with the second largest and subleading track-jets. For this particular fit, the χ 2 per jsd0 j, called ssub d0 ðji Þ, is used for the extraction. The leading degree of freedom improves from 72=22 (prefit) to 13.5=22 and third-leading sd0 values (ordered by jsd0 j) are used as a (postfit). As a result of the fit, the background fraction validation of the flavor-fraction fitting procedure and changes from 79.6% to 82.8%. A comparison of the prefit and postfit flavor fractions for all bins of ΔR as 3 As these fits are performed at detector level, there is no well as the other observables is presented in Fig. 5. Except statistical correlation between bins. Therefore, the fits are for Δθppg;gbb and the highest bin of zðpT Þ the PYTHIA performed independently for each observable bin. simulation prediction for the shape of the flavor-fraction 052004-5 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) 3 50 ×10 ×103 60 ATLAS ATLAS 45 0.25 < ΔR(b,b) < 0.3 0.25 < ΔR(b,b) < 0.3 s = 13 TeV, Lint = 33 fb-1 s = 13 TeV, Lint = 33 fb-1 40 50 Total (lead, j1) χ2 = 13.5 (9.5) / 19 DoF Total (sublead, j2 ) χ2 = 13.5 (4.0) / 19 DoF 35 Component (pre-fit %, post-fit %) Component (pre-fit %, post-fit %) 40 Events Events 30 L+C (45%, 34%) L+C (45%, 34%) 25 B (34%, 50%) 30 B (34%, 50%) 20 BB (20%, 17%) BB (20%, 17%) MC Uncertainty 20 MC Uncertainty 15 10 Data Data 10 5 0 0 1.2 1.2 Data/MC Data/MC 1.1 1.1 1 1 0.9 0.9 0.8 0.8 −40 −20 0 20 40 60 −40 −20 0 20 40 60 ssub d (j ) ssub d (j ) 0 1 0 2 FIG. 4. The distribution of ssub d0 in data and in simulation, postfit, for the higher-pT track-jet (left) and for the lower-pT track-jet (right) in the bin 0.25 < ΔRðb; bÞ < 0.3. The three components are the signal double-b (BB), the background single b (B), and the background non-b components (L þ C). Percentages reported in the legend indicate the prefit and postfit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the postfit simulation. 1 1 ATLAS s = 13 TeV, Lint = 33 fb-1 ATLAS s = 13 TeV, Lint = 33 fb-1 Data (post-fit) MC (pre-fit) Data (post-fit) MC (pre-fit) 0.8 BB BB 0.8 BB BB B B B B Flavor Fraction Flavor Fraction 0.6 L+C L+C 0.6 L+C L+C 0.4 0.4 0.2 0.2 0 0 0.2 0.3 0.4 0.5 0.6 0.7 0 0.2 0.4 0.6 0.8 1 ΔR(b,b) Δθppg,gbb / π 1 1 ATLAS s = 13 TeV, Lint = 33 fb-1 ATLAS s = 13 TeV, Lint = 33 fb-1 Data (post-fit) MC (pre-fit) Data (post-fit) MC (pre-fit) 0.8 BB BB 0.8 BB BB B B B B Flavor Fraction Flavor Fraction 0.6 L+C L+C 0.6 L+C L+C 0.4 0.4 0.2 0.2 0 0 0 0.1 0.2 0.3 0.4 0.5 −3 −2 −1 0 z(p ) log(m / p ) T bb T FIG. 5. The prefit (MC) and postfit (data) flavor fractions for ΔRðb; bÞ (top left), Δθppg;gbb (top right), zðpT Þ (bottom left), and logðmbb =pT Þ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit’s systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit’s statistical and systematic uncertainties on the final results is presented in Table I. 052004-6 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) distribution is accurate for the BB fraction. In all cases A. Object reconstruction the BB yield is slightly overestimated. The flavor frac- Each object used in the analysis has an associated tions from B and L þ C are inverted between PYTHIA and uncertainty. These uncertainties affect the acceptance data. factors and the response matrix, as well as the background fit templates. VII. UNFOLDING After subtracting the background from the detector-level 1. Calorimeter jets distributions, as described in Sec. VI, the data are The energies of large-radius jets are shifted and smeared corrected for resolution and acceptance effects. The to account for uncertainties in both the bias and variance of fiducial volume of the measurement is described by the the reconstructed energy. Jet energy scale uncertainties are particle-level object and event selection in Sec. IV. First, determined by comparing calorimeter-based and tracker- the data are corrected for events that pass the detector- based jet energy measurements in inclusive dijet events, level selection but not the particle-level selection using the and range from 2% to 6% [79]. simulations introduced in Sec. III. Then, the iterative Bayes (IB) unfolding technique [77] is used to correct 2. Flavor tagging for the detector resolution in events that pass both the Data/MC corrections (“scale factors”) and uncertainties detector-level and particle-level selections. The IB method in the b-tagging efficiencies and c-jet misidentification is applied with four iterations implemented in the rates are determined from tt̄ events [80]. Light-flavor RooUnfold framework [78]. After the application of the misidentification rates are studied using dijet events. The response matrix, a final correction is applied to account for b-jet uncertainties are 5%–10%, while the c-jet uncertain- events that pass the particle-level but not detector-level ties are 20% and light-flavor jet uncertainties are about selection. Uncertainties in the unfolding procedure are 50%. Uncertainties for track-jets with pT > 300 GeV are described in Sec. VIII. extrapolated from low-pT jets as there are too few events with high-pT jets for a proper calibration. Extrapolation VIII. UNCERTAINTIES uncertainties are evaluated by varying quantities such as impact parameter resolutions and descriptions of the Systematic and statistical uncertainties were assessed detector material. The extrapolated uncertainties are added for each aspect of the analysis, including the back- in quadrature on top of the data-based pT - and η-dependent ground subtraction, acceptance and efficiency correction uncertainties and range from 20% to 100% depending on factors, response matrix, and unfolding method. For the pT and the flavor of the jet. As the flavor fractions are each uncertainty, a component of the analysis chain is constrained in situ, there is a significant reduction from the varied and then the entire procedure including the back- prior flavor-tagging uncertainty values described above. In ground subtraction is repeated. Table I provides a sum- particular, there is little sensitivity to the background scale mary of the systematic uncertainties for each observable. factors and there is no sensitivity to inclusive flavor-tagging The jet energy scale, the unfolding, and the theoretical scale factors for b-jets. Residual ΔRðb; bÞ-dependent scale modeling uncertainties dominate. Further details about factor uncertainties that account for differences in the each category are provided in Sec. VIII A for the uncer- modeling of isolated versus nonisolated b-jets are derived tainties associated with each analysis object, in Sec. VIII B from dedicated performance studies [50,51]. for the background fit procedure uncertainty, and in Sec. VIII C for the unfolding method and theoretical 3. Tracking modeling uncertainties. Systematic uncertainties are estimated for the track reconstruction efficiency, fake rate, and track parameter TABLE I. Summary of systematic uncertainty sizes for each scales and resolutions. The main source of inclusive observable for the normalized differential cross sections. tracking inefficiency is multiple scattering in the ID, so ΔRðb; bÞ Δθppg;gbb zðpT Þ logðmbb =pT Þ the uncertainty is set by the accuracy with which the ID material is simulated [81]. This leads to a 0.5% uncertainty Calorimeter jet energy 2–3% 2–5% 3–6% 2–4% for jηj < 0.1, which grows to 2.7% at the end of the ID Flavor tagging 1–2% <1% <1% 1–2% Tracking 1–2% 2–3% 2–4% 1–2% acceptance. An additional source of inefficiency arises Background fit 3–6% 1% 2–9% 2–5% inside the high-multiplicity environment in the cores of Unfolding method <1% <1% 1–4% 1% jets due to silicon pixel and microstrip cluster merging. The Theoretical modeling 2–11% 2–14% 3–9% 4–12% uncertainty in the modeling of this density-induced ineffi- ciency is about 0.8% [82,83]. Fake tracks are due to Statistical 1% 1% 1% 1% combinations from multiple charged particles. The track Total 5–13% 5–15% 7–13% 6–13% selection described in Sec. IV reduces the contribution of 052004-7 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) fake tracks to much less than 1% with a relative uncertainty described above. The rest of the contributions are evaluated that is about 30% [84]. The track parameters that are most by comparing the result using PYTHIA with the result using relevant for this analysis are pT and d0 . Weak modes in the the alternative SHERPA sample described in Sec. VI. This ID alignment cause a bias in the track sagitta that is comparison is decomposed into components corresponding corrected for using a dedicated calibration and the cali- to varying only the response matrix or only the initial/final bration uncertainty is propagated through to the measure- correction factors. Varying only one component at a time is ment [72]. The modeling of the d0 scale and resolution was possible by reweighting a component of the PYTHIA studied in Z → μþ μ− events at low pT and in dijet events at simulation to match the SHERPA simulation and then high pT [72] and is used to assign an uncertainty to the evaluating the relative difference in the unfolded result. modeling of these important track properties. All of the components are added in quadrature to determine the total uncertainty due to fragmentation modeling. Each B. Background fit component is treated as uncorrelated because the uncer- tainty is based on only two fragmentation models and In addition to the fit validations described in Sec. VI, therefore a potential reduced uncertainty from exploiting several aspects of the fit are varied in order to assess the potentially unphysical correlations between kinematic uncertainty in the extracted flavor fractions. properties impacting acceptance and substructure attributes impacting the response is avoided. 1. Fit range The nominal flavor fraction fit is performed for IX. RESULTS d0 ∈ ½−40; 70. In order to assess the impact of this ssub The unfolded results along with multiple parton shower choice and the sensitivity to the ssub d0 tails, the fit is repeated MC predictions are presented in Fig. 6. By construction, while excluding the left and right tails of the distributions, 0.2 ≤ ΔR ≤ 1.0 and the peak around 0.3 is due to the radius d0 ∈ ½−30; 70 and sd0 ∈ ½−40; 60. corresponding to ssub sub of the track-jets. Furthermore, 0 ≤ zðpT Þ ≤ 0.5, with some distortions to the natural distribution at low values due to 2. Template merging scheme the pT threshold applied to the small-radius track-jets. Merging background components to form three aggre- The SHERPA predictions are generally more accurate gated templates fixes the relative fractions of the template than those from PYTHIA, although there are significant subcomponents. The sensitivity of the fitted flavor fractions differences between both generators and the data at low to this choice is estimated by varying each merged back- mass, low zðpT Þ and for all Δθppg;gbb . The Δθppg;gbb ground component up or down by a factor of 2. The fit distribution in data appears to be inverted with respect to range has a bigger impact on the uncertainty than the the one from PYTHIA (with a minimum instead of maxi- merging variations of the flavor fractions. mum at π=2) while SHERPA predicts a relatively uniform distribution. For comparison, the figure contains a version C. Unfolding method and theoretical modeling of PYTHIA4 with the azimuthal asymmetries induced by gluon polarization turned off.5 This sample appears to be An uncertainty resulting from the unfolding method closer to SHERPA and also to the data, consistent with the described in Sec. VII is determined by unfolding the studies in Ref. [86], which also suggest a good agreement prediction from a different simulation with the nominal with simulations that include higher-order effects. In procedure. The alternative simulation is constructed by general, the properties of gluon polarization inside unpo- reweighting the nominal particle-level spectrum so that the larized hadrons are largely unconstrained by experimental simulated detector-level spectrum, obtained by propagating data (see e.g., Ref. [87] and references therein). This and the reweighted particle-level spectrum through the response future measurements of Δθppg;gbb may provide a new way matrix, agrees well with the data. The modified detector- to extract pT -dependent parton distributions in order to level distribution is unfolded with the nominal response better understand proton structure and further improve the matrix and the difference between this and the reweighted precision of various cross-section calculations [88]. particle-level spectrum is an indication of the bias due to the In addition to studying gluon production properties, g → unfolding method (in particular, the choice of prior) [85]. bb̄ provides a handle on gluon fragmentation. Due to the The unfolded result can depend on the modeling of jet large b-quark mass and in general the large mbb mass that is fragmentation through the background fit, the prior, the response matrix, and the correction factors. The sd0 dis- 4 tribution does not strongly depend on the properties of the Due to the available settings in PYTHIA, different versions were used for these comparisons. It was checked that there is no jet radiation pattern, but an uncertainty is determined by significant difference in Δθppg;gbb between PYTHIA versions 8.186 taking the fitted background using templates from SHERPA and 8.230. instead of PYTHIA. Variations in the prior are already 5 TimeShower:phiPolAsym=off and TimeShower: accounted for in the data-driven nonclosure uncertainty phiPolAsymHard=off. 052004-8 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) 6 ATLAS ATLAS s = 13 TeV, Lint = 33 fb-1 1.5 s = 13 TeV, Lint = 33 fb-1 /π (1/ σ) dσ/dΔR(b,b) gpp,gbb 2016 Data Total Uncertainty 4 Sherpa 2.1 (1/ σ) dσ/dΔθ Pythia 8.230 (A14 + Var2±) 1 Pythia 8.230 (A14, m2 /4) bb 2016 Data 2 0.5 Total Uncertainty Sherpa 2.1 Pythia 8.230 (A14 + Var2±) Pythia 8.230 (A14, no g pol.) 0 0 1.2 1.2 MC/Data MC/Data 1 1 0.8 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0 0.2 0.4 0.6 0.8 1 ΔR(b,b) Δθgpp,gbb/ π 4 ATLAS 2016 Data ATLAS 1.5 Total Uncertainty s = 13 TeV, Lint = 33 fb-1 Sherpa 2.1 s = 13 TeV (1/ σ) dσ/dlog(m /p ) T Pythia 8.230 (A14 + Var2±) Lint = 33 fb-1 (1/ σ) dσ/dz(p ) 3 Pythia 8.230 (A14, m2 /4) bb T bb 1 2 2016 Data Total Uncertainty 0.5 1 Sherpa 2.1 Pythia 8.230 (A14 + Var2±) Pythia 8.230 (A14, m2 /4) bb 0 0 1.5 1.5 MC/Data MC/Data 1 1 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 −3 −2 −1 0 z(p ) = p / (p +p ) log(m /p ) T T,2 T,1 T,2 bb T FIG. 6. The unfolded distribution of ΔRðb; bÞ (top left), Δθppg;gbb (top right), zðpT Þ (bottom left), and logðmbb =pT Þ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Sec. VIII). These data are compared with predictions from the PYTHIA and SHERPA MC simulations. The bands for the PYTHIA prediction represented by a square indicate the Var2 variations (dominated by a 10% variation in the final-state shower αs ). The additional set of PYTHIA markers use m2bb =4 for the renormalization scale. possible after splitting, there are many formally equivalent For example, the Var2 þ A14 variation, which increases model choices in describing gluon fragmentation. For the final-state shower αS ðM Z Þ value to 0.139, moves the example, the scale at which the strong coupling constant prediction further from the data in nearly all measurement acts (renormalization scale) may be better described as bins. Related variations (not shown) such as using m2bb scaling with m2bb instead of the PYTHIA default p2T;bb . To instead of p2T;bb as the renormalization scale, adding addi- illustrate the sensitivity of ΔRðb; bÞ, zðpT Þ and logðm=pT Þ tional phase-space factors, or suppressing high-mass bb̄ to fragmentation settings in PYTHIA, the plots of Fig. 6 pairs, are not significantly different from the nominal show the final-state radiation variations of the A14 tune PYTHIA setup.7 (indicated as an uncertainty band in the plot) as well as a different way to treat the b-quark mass in the QCD splitting kernels indicated by the m2bb =4 variation (m2bb =4 instead of 7 Variations of TimeShower:weightGluonToQuark p2T;bb for the renormalization scale).6 No variation describes and TimeShower:scaleGluonToQuark; when Time- Shower:weightGluonToQuark=1, then the g → bb̄ kernel all of the data, but some variations are worse than others. is weighted by an extra β phase-space factor; when Time- Shower:weightGluonToQuark=5, this kernel is re- weighted to αS ðm2bb Þ instead of αS ðp2T Þ; when TimeShower: 6 TimeShower:weightGluonToQuark=5 and Time- weightGluonToQuark=8, there is an additional factor that Shower:scaleGluonToQuark=0.25. suppresses the rate of high-mass bb̄ pairs. 052004-9 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) X. CONCLUSION IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, This paper presents a measurement of various properties −1 Greece; RGC, Hong Kong SAR, China; ISF and pffiffiffig → bb̄ at high pT and low ΔRðb; bÞ from 33 fb of of Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, s ¼ 13 TeV pp collisions recorded by the ATLAS Japan; CNRST, Morocco; NWO, Netherlands; RCN, detector at the LHC. A flavor-fraction fit is used to remove Norway; MNiSW and NCN, Poland; FCT, Portugal; contributions from processes other than g → bb̄. The fitted MNE/IFA, Romania; MES of Russia and NRC KI, fractions significantly disagree with the prefit PYTHIA Russian Federation; JINR; MESTD, Serbia; MSSR, predictions and suggest that further studies could improve Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South the modeling of analyses sensitive to these fractions. The Africa; MINECO, Spain; SRC and Wallenberg Foundation, measured properties are unfolded to correct for the detector Sweden; SERI, SNSF and Cantons of Bern and Geneva, acceptance and resolution for direct comparison with Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, particle-level models. Comparisons are made at the particle United Kingdom; and DOE and NSF, United States of level between the distributions and various models of jet America. In addition, individual groups and members have formation. Simulations from the SHERPA event generator received support from BCKDF, CANARIE, CRC and generally provide a better model than PYTHIA, especially Compute Canada, Canada; COST, ERC, ERDF, Horizon for the Δθppg;gbb observable, which is sensitive to the 2020, and Marie Skłodowska-Curie Actions, European modeling of the gluon polarization. The particle-level Union; Investissements d’ Avenir Labex and Idex, ANR, spectra are publicly available [89] for further interpretation France; DFG and AvH Foundation, Germany; Herakleitos, and can be used to validate QCD MC predictions and tune Thales and Aristeia programs cofinanced by EU-ESF and their models’ free parameters. the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; ACKNOWLEDGMENTS and The Royal Society and Leverhulme Trust, United We thank CERN for the very successful operation of the Kingdom. The crucial computing support from all LHC, as well as the support staff from our institutions WLCG partners is acknowledged gratefully, in particular without whom ATLAS could not be operated efficiently. from CERN, the ATLAS Tier-1 facilities at TRIUMF We acknowledge the support of ANPCyT, Argentina; (Canada); NDGF (Denmark, Norway, Sweden); CC- YerPhI, Armenia; ARC, Australia; BMWFW and FWF, IN2P3 (France); KIT/GridKA (Germany); INFN-CNAF Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and (Italy); NL-T1 (Netherlands); PIC (Spain); ASGC FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; (Taiwan); RAL (UK); BNL (USA); the Tier-2 facilities CONICYT, Chile; CAS, MOST and NSFC, China; worldwide; and large non-WLCG resource providers. COLCIENCIAS, Colombia; MSMT CR, MPO CR and Major contributors of computing resources are listed VSC CR, Czech Republic; DNRF and DNSRC, Denmark; in Ref. 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Guillemin,5 S. Guindon,35 U. Gul,55 J. Guo,58c W. Guo,103 Y. Guo,58a,t Z. Guo,99 R. Gupta,44 S. Gurbuz,12c G. Gustavino,125 P. Gutierrez,125 C. Gutschow,92 C. Guyot,142 M. P. Guzik,81a C. Gwenlan,132 C. B. Gwilliam,88 A. Haas,122 C. Haber,18 H. K. Hadavand,8 N. Haddad,34e A. Hadef,58a S. Hageböck,35 M. Hagihara,166 M. Haleem,174 J. Haley,126 G. Halladjian,104 G. D. Hallewell,99 K. Hamacher,179 P. Hamal,127 K. Hamano,173 H. Hamdaoui,34e A. Hamilton,32a G. N. Hamity,146 K. Han,58a,aj L. Han,58a S. Han,15d K. Hanagaki,79,v M. Hance,143 052004-15 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) D. M. Handl,112 B. Haney,134 R. Hankache,133 P. Hanke,59a E. Hansen,94 J. B. Hansen,39 J. D. Hansen,39 M. C. Hansen,24 P. H. Hansen,39 K. Hara,166 A. S. Hard,178 T. Harenberg,179 S. Harkusha,105 P. F. Harrison,175 N. M. Hartmann,112 Y. Hasegawa,147 A. Hasib,48 S. Hassani,142 S. Haug,20 R. Hauser,104 L. Hauswald,46 L. B. Havener,38 M. Havranek,139 C. M. Hawkes,21 R. J. Hawkings,35 D. Hayden,104 C. 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Tudorache,27b V. Tudorache,27b T. T. Tulbure,27a A. N. Tuna,57 S. Turchikhin,77 D. Turgeman,177 I. Turk Cakir,4b,u R. J. Turner,21 R. T. Turra,66a P. M. Tuts,38 S. Tzamarias,159 E. Tzovara,97 G. Ucchielli,45 I. Ueda,79 M. Ughetto,43a,43b F. Ukegawa,166 G. Unal,35 A. Undrus,29 G. Unel,168 F. C. Ungaro,102 Y. Unno,79 K. Uno,160 J. Urban,28b P. Urquijo,102 G. Usai,8 J. Usui,79 L. Vacavant,99 V. Vacek,139 B. Vachon,101 K. O. H. Vadla,131 A. Vaidya,92 C. Valderanis,112 E. Valdes Santurio,43a,43b M. Valente,52 S. Valentinetti,23b,23a A. Valero,171 L. Valéry,44 R. A. Vallance,21 A. Vallier,5 J. A. Valls Ferrer,171 T. R. Van Daalen,14 H. Van der Graaf,118 P. Van Gemmeren,6 I. Van Vulpen,118 M. Vanadia,71a,71b W. Vandelli,35 A. Vaniachine,163 P. Vankov,118 R. Vari,70a E. W. Varnes,7 C. Varni,53b,53a T. Varol,41 D. Varouchas,129 K. E. Varvell,154 G. A. Vasquez,144b J. G. Vasquez,180 F. Vazeille,37 D. Vazquez Furelos,14 T. Vazquez Schroeder,35 J. Veatch,51 V. Vecchio,72a,72b L. M. Veloce,164 F. Veloso,137a,137c S. Veneziano,70a A. Ventura,65a,65b N. Venturi,35 A. Verbytskyi,113 V. Vercesi,68a M. Verducci,72a,72b C. M. Vergel Infante,76 C. Vergis,24 W. Verkerke,118 A. T. Vermeulen,118 J. C. Vermeulen,118 M. C. Vetterli,149,av N. Viaux Maira,144b M. Vicente Barreto Pinto,52 I. Vichou,170,† T. Vickey,146 O. E. Vickey Boeriu,146 G. H. A. Viehhauser,132 S. Viel,18 L. Vigani,132 M. Villa,23b,23a M. Villaplana Perez,66a,66b E. Vilucchi,49 M. G. Vincter,33 V. B. Vinogradov,77 A. Vishwakarma,44 C. Vittori,23b,23a I. Vivarelli,153 S. Vlachos,10 M. Vogel,179 P. Vokac,139 G. Volpi,14 S. E. von Buddenbrock,32c E. Von Toerne,24 V. Vorobel,140 K. Vorobev,110 M. Vos,171 J. H. Vossebeld,88 N. Vranjes,16 M. Vranjes Milosavljevic,16 V. Vrba,139 M. Vreeswijk,118 T. Šfiligoj,89 R. Vuillermet,35 I. Vukotic,36 T. Ženiš,28a L. Živković,16 P. Wagner,24 W. Wagner,179 J. Wagner-Kuhr,112 H. Wahlberg,86 S. Wahrmund,46 K. Wakamiya,80 V. M. Walbrecht,113 J. Walder,87 R. Walker,112 S. D. Walker,91 W. Walkowiak,148 V. Wallangen,43a,43b A. M. Wang,57 C. Wang,58b F. Wang,178 H. Wang,18 H. Wang,3 J. Wang,154 J. Wang,59b P. Wang,41 Q. Wang,125 R.-J. Wang,133 R. Wang,58a R. Wang,6 S. M. Wang,155 W. T. Wang,58a W. Wang,15c,ad W. X. Wang,58a,ad Y. Wang,58a,al Z. Wang,58c C. Wanotayaroj,44 A. Warburton,101 C. P. Ward,31 D. R. Wardrope,92 A. Washbrook,48 A. T. Watson,21 M. F. Watson,21 G. Watts,145 S. Watts,98 B. M. Waugh,92 A. F. Webb,11 S. Webb,97 C. Weber,180 M. S. Weber,20 S. A. Weber,33 S. M. Weber,59a A. R. Weidberg,132 J. Weingarten,45 M. Weirich,97 C. Weiser,50 P. S. Wells,35 T. Wenaus,29 T. Wengler,35 S. Wenig,35 N. Wermes,24 M. D. Werner,76 P. Werner,35 M. Wessels,59a T. D. Weston,20 K. Whalen,128 N. L. Whallon,145 A. M. Wharton,87 A. S. White,103 A. White,8 M. J. White,1 R. White,144b D. Whiteson,168 B. W. Whitmore,87 F. J. Wickens,141 W. Wiedenmann,178 M. Wielers,141 C. Wiglesworth,39 L. A. M. Wiik-Fuchs,50 F. Wilk,98 H. G. Wilkens,35 L. J. Wilkins,91 H. H. Williams,134 S. Williams,31 C. Willis,104 S. Willocq,100 J. A. Wilson,21 I. Wingerter-Seez,5 E. Winkels,153 F. Winklmeier,128 O. J. Winston,153 B. T. Winter,50 M. Wittgen,150 M. Wobisch,93 A. Wolf,97 T. M. H. Wolf,118 R. Wolff,99 J. Wollrath,50 M. W. Wolter,82 H. Wolters,137a,137c V. W. S. Wong,172 N. L. Woods,143 S. D. Worm,21 B. K. Wosiek,82 K. W. Woźniak,82 K. Wraight,55 S. L. Wu,178 X. Wu,52 Y. Wu,58a T. R. Wyatt,98 B. M. Wynne,48 S. Xella,39 Z. Xi,103 L. Xia,175 D. Xu,15a H. Xu,58a,e L. Xu,29 T. Xu,142 W. Xu,103 Z. Xu,150 B. Yabsley,154 S. Yacoob,32a K. Yajima,130 D. P. Yallup,92 D. Yamaguchi,162 Y. Yamaguchi,162 A. Yamamoto,79 T. Yamanaka,160 F. Yamane,80 M. Yamatani,160 T. Yamazaki,160 Y. Yamazaki,80 Z. Yan,25 H. J. Yang,58c,58d H. T. Yang,18 S. Yang,75 Y. Yang,160 Z. Yang,17 W-M. Yao,18 Y. C. Yap,44 Y. Yasu,79 E. Yatsenko,58c,58d J. Ye,41 S. Ye,29 I. Yeletskikh,77 E. Yigitbasi,25 E. Yildirim,97 K. Yorita,176 K. Yoshihara,134 C. J. S. Young,35 C. Young,150 J. Yu,76 X. Yue,59a S. P. Y. Yuen,24 B. Zabinski,82 052004-20 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) G. Zacharis,10 E. Zaffaroni,52 R. Zaidan,14 A. M. Zaitsev,121,an T. Zakareishvili,156b N. Zakharchuk,33 S. Zambito,57 D. Zanzi,35 D. R. Zaripovas,55 S. V. Zeißner,45 C. Zeitnitz,179 G. Zemaityte,132 J. C. Zeng,170 O. Zenin,121 D. Zerwas,129 M. Zgubič,132 D. F. Zhang,15b F. Zhang,178 G. Zhang,58a G. Zhang,15b H. Zhang,15c J. Zhang,6 L. Zhang,15c L. Zhang,58a M. Zhang,170 R. Zhang,58a R. Zhang,24 X. Zhang,58b Y. Zhang,15d Z. Zhang,129 P. Zhao,47 Y. Zhao,58b Z. Zhao,58a A. Zhemchugov,77 Z. Zheng,103 D. Zhong,170 B. Zhou,103 C. Zhou,178 M. S. Zhou,15d M. Zhou,152 N. Zhou,58c Y. Zhou,7 C. G. Zhu,58b H. L. Zhu,58a H. Zhu,15a J. Zhu,103 Y. Zhu,58a X. Zhuang,15a K. Zhukov,108 V. Zhulanov,120b,120a A. Zibell,174 D. Zieminska,63 N. I. Zimine,77 S. Zimmermann,50 Z. Zinonos,113 M. Ziolkowski,148 G. Zobernig,178 A. Zoccoli,23b,23a K. Zoch,51 T. G. Zorbas,146 R. Zou,36 and L. Zwalinski35 (ATLAS Collaboration) 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany, New York, USA 3 Department of Physics, University of Alberta, Edmonton, Alberta, Canada 4a Department of Physics, Ankara University, Ankara, Turkey 4b Istanbul Aydin University, Istanbul, Turkey 4c Division of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5 LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 6 High Energy Physics Division, Argonne National Laboratory, Argonne, Illinois, USA 7 Department of Physics, University of Arizona, Tucson, Arizona, USA 8 Department of Physics, University of Texas at Arlington, Arlington, Texas, USA 9 Physics 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, Texas, USA 12a Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 12b Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 12c Department of Physics, Bogazici University, Istanbul, Turkey 12d Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15a Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China 15b Physics Department, Tsinghua University, Beijing, China 15c Department of Physics, Nanjing University, Nanjing, China 15d University of Chinese Academy of Science (UCAS), Beijing, China 16 Institute of Physics, University of Belgrade, Belgrade, Serbia 17 Department for Physics and Technology, University of Bergen, Bergen, Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley, California, USA 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 22 Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia 23a Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy 23b INFN Sezione di Bologna, Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston, Massachusetts, USA 26 Department of Physics, Brandeis University, Waltham, Massachusetts, USA 27a Transilvania University of Brasov, Brasov, Romania 27b Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 27c Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania 27d National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca, Romania 27e University Politehnica Bucharest, Bucharest, Romania 27f West University in Timisoara, Timisoara, Romania 28a Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic 052004-21 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) 28b Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton, New York, USA 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 32a Department of Physics, University of Cape Town, Cape Town, South Africa 32b Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa 32c School of Physics, University of the Witwatersrand, Johannesburg, South Africa 33 Department of Physics, Carleton University, Ottawa, Ontario, Canada 34a Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies—Université Hassan II, Casablanca, Morocco 34b Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat, Morocco 34c Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Morocco 34d Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco 34e Faculté des sciences, Université Mohammed V, Rabat, Morocco 35 CERN, Geneva, Switzerland 36 Enrico Fermi Institute, University of Chicago, Chicago, Illinois, USA 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 38 Nevis Laboratory, Columbia University, Irvington, New York, USA 39 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 40a Dipartimento di Fisica, Università della Calabria, Rende, Italy 40b INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Italy 41 Physics Department, Southern Methodist University, Dallas, Texas, USA 42 Physics Department, University of Texas at Dallas, Richardson, Texas, USA 43a Department of Physics, Stockholm University, Stockholm, Sweden 43b Oskar Klein Centre, Stockholm, Sweden 44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 45 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 46 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 47 Department of Physics, Duke University, Durham, North California, USA 48 SUPA—School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 49 INFN e Laboratori Nazionali di Frascati, Frascati, Italy 50 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 51 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 52 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland 53a Dipartimento di Fisica, Università di Genova, Genova, Italy 53b INFN Sezione di Genova, Genova, Italy 54 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 55 SUPA—School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, Massachusetts, USA 58a Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China 58b Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China 58c School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai, China 58d Tsung-Dao Lee Institute, Shanghai, China 59a Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 59b Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 61a Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China 61b Department of Physics, University of Hong Kong, Hong Kong, China 61c Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 62 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan 63 Department of Physics, Indiana University, Bloomington, Indiana, USA 64a INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy 64b ICTP, Trieste, Italy 052004-22 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) 64c Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy 65a INFN Sezione di Lecce, Lecce, Italy 65b Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 66a INFN Sezione di Milano, Milano, Italy 66b Dipartimento di Fisica, Università di Milano, Milano, Italy 67a INFN Sezione di Napoli, Napoli, Italy 67b Dipartimento di Fisica, Università di Napoli, Napoli, Italy 68a INFN Sezione di Pavia, Pavia, Italy 68b Dipartimento di Fisica, Università di Pavia, Pavia, Italy 69a INFN Sezione di Pisa, Pisa, Italy 69b Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 70a INFN Sezione di Roma, Roma, Italy 70b Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy 71a INFN Sezione di Roma Tor Vergata, Roma, Italy 71b Dipartimento di Fisica, Università di Roma Tor Vergata, Roma, Italy 72a INFN Sezione di Roma Tre, Roma, Italy 72b Dipartimento di Matematica e Fisica, Università Roma Tre, Roma, Italy 73a INFN-TIFPA, Trento, Italy 73b Università degli Studi di Trento, Trento, Italy 74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 75 University of Iowa, Iowa City, Iowa, USA 76 Department of Physics and Astronomy, Iowa State University, Ames, Iowa, USA 77 Joint Institute for Nuclear Research, Dubna, Russia 78a Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, Brazil 78b Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil 78c Universidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil 78d Instituto de Física, Universidade de São Paulo, São Paulo, Brazil 79 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 80 Graduate School of Science, Kobe University, Kobe, Japan 81a AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow, Poland 81b Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland 82 Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland 83 Faculty of Science, Kyoto University, Kyoto, Japan 84 Kyoto University of Education, Kyoto, Japan 85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka, Japan 86 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 87 Physics Department, Lancaster University, Lancaster, United Kingdom 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 89 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 90 School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 91 Department of Physics, Royal Holloway University of London, Egham, United Kingdom 92 Department of Physics and Astronomy, University College London, London, United Kingdom 93 Louisiana Tech University, Ruston, Louisiana, USA 94 Fysiska institutionen, Lunds universitet, Lund, Sweden 95 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, France 96 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 97 Institut für Physik, Universität Mainz, Mainz, Germany 98 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 100 Department of Physics, University of Massachusetts, Amherst, Massachusetts, USA 101 Department of Physics, McGill University, Montreal Quebec, Canada 102 School of Physics, University of Melbourne, Victoria, Australia 103 Department of Physics, University of Michigan, Ann Arbor, Michigan, USA 104 Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA 105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 052004-23 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) 106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 107 Group of Particle Physics, University of Montreal, Montreal Québec, Canada 108 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 109 Institute for Theoretical and Experimental Physics of the National Research Centre Kurchatov Institute, Moscow, Russia 110 National Research Nuclear University MEPhI, Moscow, Russia 111 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 112 Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany 113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany 114 Nagasaki Institute of Applied Science, Nagasaki, Japan 115 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 116 Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, USA 117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 118 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 119 Department of Physics, Northern Illinois University, DeKalb, Illinois, USA 120a Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk, Russia 120b Novosibirsk State University Novosibirsk, Novosibirsk, Russia 121 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia 122 Department of Physics, New York University, New York, New York, USA 123 The Ohio State University, Columbus, Ohio, USA 124 Faculty of Science, Okayama University, Okayama, Japan 125 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, Oklahoma, USA 126 Department of Physics, Oklahoma State University, Stillwater, Oklahoma, USA 127 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 128 Center for High Energy Physics, University of Oregon, Eugene, Oregon, USA 129 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 130 Graduate School of Science, Osaka University, Osaka, Japan 131 Department of Physics, University of Oslo, Oslo, Norway 132 Department of Physics, Oxford University, Oxford, United Kingdom 133 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France 134 Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania, USA 135 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 136 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA 137a Laboratório de Instrumentação e Física Experimental de Partículas—LIP, Lisboa, Spain 137b Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Spain 137c Departamento de Física, Universidade de Coimbra, Coimbra, Spain 137d Centro de Física Nuclear da Universidade de Lisboa, Lisboa, Spain 137e Departamento de Física, Universidade do Minho, Braga, Spain 137f Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada, Spain 137g Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 138 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 139 Czech Technical University in Prague, Prague, Czech Republic 140 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 141 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 142 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 143 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, California, USA 144a Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile 144b Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 145 Department of Physics, University of Washington, Seattle, Washington, USA 146 Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 147 Department of Physics, Shinshu University, Nagano, Japan 148 Department Physik, Universität Siegen, Siegen, Germany 149 Department of Physics, Simon Fraser University, Burnaby British Columbia, Canada 150 SLAC National Accelerator Laboratory, Stanford, California, USA 052004-24 PROPERTIES OF g → bb̄ AT … PHYS. REV. D 99, 052004 (2019) 151 Physics Department, Royal Institute of Technology, Stockholm, Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA 153 Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom 154 School of Physics, University of Sydney, Sydney, Australia 155 Institute of Physics, Academia Sinica, Taipei, Taiwan 156a E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia 156b High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 157 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 158 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 159 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 160 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 161 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 162 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 163 Tomsk State University, Tomsk, Russia 164 Department of Physics, University of Toronto, Toronto, Ontario, Canada 165a TRIUMF, Vancouver, British Columbia, Canada 165b Department of Physics and Astronomy, York University, Toronto, Ontario, Canada 166 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 167 Department of Physics and Astronomy, Tufts University, Medford, Massachusetts, USA 168 Department of Physics and Astronomy, University of California Irvine, Irvine, California, USA 169 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 170 Department of Physics, University of Illinois, Urbana, Illinois, USA 171 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia—CSIC, Valencia, Spain 172 Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada 173 Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, Canada 174 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 175 Department of Physics, University of Warwick, Coventry, United Kingdom 176 Waseda University, Tokyo, Japan 177 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 178 Department of Physics, University of Wisconsin, Madison, Wisconsin, USA 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 180 Department of Physics, Yale University, New Haven, Connecticut, USA 181 Yerevan Physics Institute, Yerevan, Armenia † Deceased. a Also at Borough of Manhattan Community College, City University of New York, New York, USA. b Also at California State University, East Bay, Hayward, California, USA. c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa. d Also at CERN, Geneva, Switzerland. e Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France. f Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland. g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain. h Also at Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain. i Also at Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. j Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates. k Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece. l Also at Department of Physics and Astronomy, University of Louisville, Louisville, Kentucky, USA. m Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom. n Also at Department of Physics, California State University, Fresno, California, USA. o Also at Department of Physics, California State University, Sacramento, California, USA. p Also at Department of Physics, King’s College London, London, United Kingdom. q Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia. r Also at Department of Physics, the Stanford University, USA. s Also at Department of Physics, University of Fribourg, Fribourg, Switzerland. t Also at Department of Physics, University of Michigan, Ann Arbor, Michigan, USA. u Also at Giresun University, Faculty of Engineering, Giresun, Turkey. v Also at Graduate School of Science, Osaka University, Osaka, Japan. 052004-25 M. AABOUD et al. PHYS. REV. D 99, 052004 (2019) w Also at Hellenic Open University, Patras, Greece. x Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania. y Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain. z Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany. aa Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands. ab Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary. ac Also at Institute of Particle Physics (IPP), Canada. ad Also at Institute of Physics, Academia Sinica, Taipei, Taiwan. ae Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan. af Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia. ag Also at Instituto de Física Teórica de la Universidad Autónoma de Madrid, Spain. ah Also at Istanbul University, Deptartment of Physics, Istanbul, Turkey. ai Also at Joint Institute for Nuclear Research, Dubna, Russia. aj Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France. ak Also at Louisiana Tech University, Ruston, Louisiana, USA. al Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France. am Also at Manhattan College, New York New York, USA. an Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. ao Also at National Research Nuclear University MEPhI, Moscow, Russia. ap Also at Physics Dept. University of South Africa, Pretoria, South Africa. aq Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. ar Also at School of Physics, Sun Yat-sen University, Guangzhou, China. as Also at The City College of New York, New York, New York, USA. at Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China. au Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. av Also at TRIUMF, Vancouver, British Columbia, Canada. aw Also at Universita di Napoli Parthenope, Napoli, Italy. 052004-26