Showing 10 of 492 results
$Z$ boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from Standard Model predictions. All previous measurements of $Z$ boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins. In this analysis, a machine learning method called OmniFold is used to produce a simultaneous measurement of twenty-four $Z$+jets observables using $139$ fb$^{-1}$ of proton-proton collisions at $\sqrt{s}=13$ TeV collected with the ATLAS detector. Unlike any previous fiducial differential cross-section measurement, this result is presented unbinned as a dataset of particle-level events, allowing for flexible re-use in a variety of contexts and for new observables to be constructed from the twenty-four measured observables.
Differential cross-section in bins of dimuon $p_\text{T}$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of dimuon rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading muon $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading muon $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading muon $\eta$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading muon $\eta$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading muon $\phi$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading muon $\phi$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet azimuth. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet azimuth. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet mass. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet mass. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet constituent multiplicity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet constituent multiplicity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet $\tau_1$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet $\tau_1$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet $\tau_2$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet $\tau_2$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet $\tau_3$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of subleading charged particle jet $\tau_3$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of leading charged particle jet $\tau_{21}$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Differential cross-section in bins of $\Delta R$ between the leading charged particle jet and the dilepton system. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>
Jet substructure quantities are measured using jets groomed with the soft-drop grooming procedure in dijet events from 32.9 fb$^{-1}$ of $pp$ collisions collected with the ATLAS detector at $\sqrt{s} = 13$ TeV. These observables are sensitive to a wide range of QCD phenomena. Some observables, such as the jet mass and opening angle between the two subjets which pass the soft-drop condition, can be described by a high-order (resummed) series in the strong coupling constant $\alpha_S$. Other observables, such as the momentum sharing between the two subjets, are nearly independent of $\alpha_S$. These observables can be constructed using all interacting particles or using only charged particles reconstructed in the inner tracking detectors. Track-based versions of these observables are not collinear safe, but are measured more precisely, and universal non-perturbative functions can absorb the collinear singularities. The unfolded data are directly compared with QCD calculations and hadron-level Monte Carlo simulations. The measurements are performed in different pseudorapidity regions, which are then used to extract quark and gluon jet shapes using the predicted quark and gluon fractions in each region. All of the parton shower and analytical calculations provide an excellent description of the data in most regions of phase space.
Data from Fig 6a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6c. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6d. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6e. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6f. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 7a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7d. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7e. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7f. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 8a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8d. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8e. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8f. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 21b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5a. The unfolded $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5a. The unfolded $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 36-40a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 51-55a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 66-70a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 26-30a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30c. The unfolded $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 41-45a. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90a. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45b. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90b. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 56-60a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60b. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105b. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60c. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105c. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 31-35a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35c. The unfolded $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 46-50a. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95a. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50b. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95b. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 61-65a. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110a. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65b. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110b. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65c. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110c. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 6a. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15a. Theextracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6b. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15b. The extracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6c. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15c. The extracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 7a. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16a. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7b. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16b. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7c. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16c. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8a. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17a. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8b. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17b. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8c. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17c. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 6a. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15a. Theextracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6b. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15b. The extracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6c. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15c. The extracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 7a. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16a. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7b. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16b. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7c. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16c. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8a. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17a. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8b. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17b. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8c. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17c. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 99a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 101a. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102a. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101b. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102b. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101c. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102c. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103a. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104a. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103b. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104b. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103c. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104c. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 105a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 107a. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108a. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107b. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108b. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107c. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108c. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109a. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110a. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109b. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110b. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109c. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110c. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 111a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 113a. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114a. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113b. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114b. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113c. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114c. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115a. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116a. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115b. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116b. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115c. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116c. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 99d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 101d. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102d. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101e. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102e. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101f. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102f. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 103d. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104d. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103e. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104e. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103f. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104f. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 105d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 107d. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108d. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107e. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108e. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107f. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108f. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 109d. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110d. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109e. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110e. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109f. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110f. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 111d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 111f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 112f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 113d. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114d. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113e. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114e. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113f. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114f. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 115d. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116d. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115e. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116e. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115f. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116f. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
A measurement of novel event shapes quantifying the isotropy of collider events is performed in 140 fb$^{-1}$ of proton-proton collisions with $\sqrt s=13$ TeV centre-of-mass energy recorded with the ATLAS detector at CERN's Large Hadron Collider. These event shapes are defined as the Wasserstein distance between collider events and isotropic reference geometries. This distance is evaluated by solving optimal transport problems, using the 'Energy-Mover's Distance'. Isotropic references with cylindrical and circular symmetries are studied, to probe the symmetries of interest at hadron colliders. The novel event-shape observables defined in this way are infrared- and collinear-safe, have improved dynamic range and have greater sensitivity to isotropic radiation patterns than other event shapes. The measured event-shape variables are corrected for detector effects, and presented in inclusive bins of jet multiplicity and the scalar sum of the two leading jets' transverse momenta. The measured distributions are provided as inputs to future Monte Carlo tuning campaigns and other studies probing fundamental properties of QCD and the production of hadronic final states up to the TeV-scale.
IRing2 for HT2>=500 GeV, NJets>=2
IRing2 for HT2>=500 GeV, NJets>=3
IRing2 for HT2>=500 GeV, NJets>=4
IRing2 for HT2>=500 GeV, NJets>=5
IRing2 for HT2>=1000 GeV, NJets>=2
IRing2 for HT2>=1000 GeV, NJets>=3
IRing2 for HT2>=1000 GeV, NJets>=4
IRing2 for HT2>=1000 GeV, NJets>=5
IRing2 for HT2>=1500 GeV, NJets>=2
IRing2 for HT2>=1500 GeV, NJets>=3
IRing2 for HT2>=1500 GeV, NJets>=4
IRing2 for HT2>=1500 GeV, NJets>=5
IRing128 for HT2>=500 GeV, NJets>=2
IRing128 for HT2>=500 GeV, NJets>=3
IRing128 for HT2>=500 GeV, NJets>=4
IRing128 for HT2>=500 GeV, NJets>=5
IRing128 for HT2>=1000 GeV, NJets>=2
IRing128 for HT2>=1000 GeV, NJets>=3
IRing128 for HT2>=1000 GeV, NJets>=4
IRing128 for HT2>=1000 GeV, NJets>=5
IRing128 for HT2>=1500 GeV, NJets>=2
IRing128 for HT2>=1500 GeV, NJets>=3
IRing128 for HT2>=1500 GeV, NJets>=4
IRing128 for HT2>=1500 GeV, NJets>=5
ICyl16 for HT2>=500 GeV, NJets>=2
ICyl16 for HT2>=500 GeV, NJets>=3
ICyl16 for HT2>=500 GeV, NJets>=4
ICyl16 for HT2>=500 GeV, NJets>=5
ICyl16 for HT2>=1000 GeV, NJets>=2
ICyl16 for HT2>=1000 GeV, NJets>=3
ICyl16 for HT2>=1000 GeV, NJets>=4
ICyl16 for HT2>=1000 GeV, NJets>=5
ICyl16 for HT2>=1500 GeV, NJets>=2
ICyl16 for HT2>=1500 GeV, NJets>=3
ICyl16 for HT2>=1500 GeV, NJets>=4
ICyl16 for HT2>=1500 GeV, NJets>=5
IRing2 covariance for HT2>=500 GeV, NJets>=2 (Table 1)
IRing2 covariance for HT2>=500 GeV, NJets>=3 (Table 2)
IRing2 covariance for HT2>=500 GeV, NJets>=4 (Table 3)
IRing2 covariance for HT2>=500 GeV, NJets>=5 (Table 4)
IRing2 covariance for HT2>=1000 GeV, NJets>=2 (Table 5)
IRing2 covariance for HT2>=1000 GeV, NJets>=3 (Table 6)
IRing2 covariance for HT2>=1000 GeV, NJets>=4 (Table 7)
IRing2 covariance for HT2>=1000 GeV, NJets>=5 (Table 8)
IRing2 covariance for HT2>=1500 GeV, NJets>=2 (Table 9)
IRing2 covariance for HT2>=1500 GeV, NJets>=3 (Table 10)
IRing2 covariance for HT2>=1500 GeV, NJets>=4 (Table 11)
IRing2 covariance for HT2>=1500 GeV, NJets>=5 (Table 12)
IRing128 covariance for HT2>=500 GeV, NJets>=2 (Table 13)
IRing128 covariance for HT2>=500 GeV, NJets>=3 (Table 14)
IRing128 covariance for HT2>=500 GeV, NJets>=4 (Table 15)
IRing128 covariance for HT2>=500 GeV, NJets>=5 (Table 16)
IRing128 covariance for HT2>=1000 GeV, NJets>=2 (Table 17)
IRing128 covariance for HT2>=1000 GeV, NJets>=3 (Table 18)
IRing128 covariance for HT2>=1000 GeV, NJets>=4 (Table 19)
IRing128 covariance for HT2>=1000 GeV, NJets>=5 (Table 20)
IRing128 covariance for HT2>=1500 GeV, NJets>=2 (Table 21)
IRing128 covariance for HT2>=1500 GeV, NJets>=3 (Table 22)
IRing128 covariance for HT2>=1500 GeV, NJets>=4 (Table 23)
IRing128 covariance for HT2>=1500 GeV, NJets>=5 (Table 24)
ICyl16 covariance for HT2>=500 GeV, NJets>=2 (Table 25)
ICyl16 covariance for HT2>=500 GeV, NJets>=3 (Table 26)
ICyl16 covariance for HT2>=500 GeV, NJets>=4 (Table 27)
ICyl16 covariance for HT2>=500 GeV, NJets>=5 (Table 28)
ICyl16 covariance for HT2>=1000 GeV, NJets>=2 (Table 29)
ICyl16 covariance for HT2>=1000 GeV, NJets>=3 (Table 30)
ICyl16 covariance for HT2>=1000 GeV, NJets>=4 (Table 31)
ICyl16 covariance for HT2>=1000 GeV, NJets>=5 (Table 32)
ICyl16 covariance for HT2>=1500 GeV, NJets>=2 (Table 33)
ICyl16 covariance for HT2>=1500 GeV, NJets>=3 (Table 34)
ICyl16 covariance for HT2>=1500 GeV, NJets>=4 (Table 35)
ICyl16 covariance for HT2>=1500 GeV, NJets>=5 (Table 36)
IRing2 covariance, complete
1-IRing128 covariance, complete
1-ICyl16 covariance, complete
The measurement of charged-particle event shape variables is presented in inclusive inelastic pp collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC. The observables studied are the transverse thrust, thrust minor and transverse sphericity, each defined using the final-state charged particles' momentum components perpendicular to the beam direction. Events with at least six charged particles are selected by a minimum-bias trigger. In addition to the differential distributions, the evolution of each event shape variable as a function of the leading charged particle transverse momentum, charged particle multiplicity and summed transverse momentum is presented. Predictions from several Monte Carlo models show significant deviations from data.
Normalized distributions of Tranverse Thrust for 4 ranges of leading particle PT.
Normalized distributions of Tranverse Thrust for 5 lower limit values of leading particle PT.
Normalized distributions of Tranverse Thrust Minor for 4 ranges of leading particle PT.
Normalized distributions of Tranverse Thrust Minor for 5 lower limit values of leading particle PT.
Normalized distributions of Tranverse Sphericity for 4 ranges of leading particle PT.
Normalized distributions of Tranverse Sphericity for 5 lower limit values of leading particle PT.
Mean Values of Thrust, Thrust Minor and Sphericity verses Multiplicity.
Mean Values of Thrust, Thrust Minor and Sphericity verses charged particle PT scalar sum.
A measurement of observables sensitive to effects of colour reconnection in top-quark pair-production events is presented using 139 fb$^{-1}$ of 13$\,$TeV proton-proton collision data collected by the ATLAS detector at the LHC. Events are selected by requiring exactly one isolated electron and one isolated muon with opposite charge and two or three jets, where exactly two jets are required to be $b$-tagged. For the selected events, measurements are presented for the charged-particle multiplicity, the scalar sum of the transverse momenta of the charged particles, and the same scalar sum in bins of charged-particle multiplicity. These observables are unfolded to the stable-particle level, thereby correcting for migration effects due to finite detector resolution, acceptance and efficiency effects. The particle-level measurements are compared with different colour reconnection models in Monte Carlo generators. These measurements disfavour some of the colour reconnection models and provide inputs to future optimisation of the parameters in Monte Carlo generators.
Naming convention for the observables at different levels of the analysis. At the background-subtracted level the contributions of tracks from pile-up collisions and tracks from secondary vertices are subtracted. At the corrected level the tracking-efficiency correction (TEC) is applied. The observables at particle level are the analysis results.
The $\chi^2$ and NDF for measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$
Normalised differential cross-section as a function of $n_\text{ch}$.
Normalised differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.
Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.
Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.
Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.
Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.
Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.
The $\chi^2$ and NDF for measured absolute differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$
Absolute differential cross-section as a function of $n_\text{ch}$.
Absolute differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.
Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.
Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.
Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.
Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.
Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.
Measurements of jet cross-section ratios between inclusive bins of jet multiplicity are performed in 140 fb$^{-1}$ of proton--proton collisions with $\sqrt{s}=13$ TeV center-of-mass energy, recorded with the ATLAS detector at CERN's Large Hadron Collider. Observables that are sensitive the energy-scale and angular distribution of radiation due to the strong interaction in the final state are measured double-differentially, in bins of jet multiplicity, and are unfolded to account for acceptance and detector-related effects. Additionally, the scalar sum of the two leading jets' transverse momenta is measured triple-differentially, in bins of the third jet's transverse momentum as well as bins of jet multiplicity. The measured distributions are used to construct ratios of the inclusive jet-multiplicity bins, which have been shown to be sensitive to the strong coupling $\alpha_{\textrm S}$ while being less sensitive than other observables to systematic uncertainties and parton distribution functions. The measured distributions are compared with state-of-the-art QCD calculations, including next-to-next-to-leading-order predictions. Studies leading to reduced jet energy scale uncertainties significantly improve the precision of this work, and are documented herein.
R32 for $H_{T2}$, 60 GeV < $p_{T,3}$
R32 for $H_{T2}$, 0.05 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, pT,jet
R32 for delta y
R32 for delta y max
R32 for $m_{jj}$
R32 for $m_{jj, max}$
R43 for $H_{T2}$, 60 GeV < $p_{T,3}$
R43 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$
R43 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$
R43 for $H_{T2}$, pT,jet
R43 for $Delta y_{jj}$
R43 for $Delta y_{jj, max}$
R43 for $m_{jj}$
R43 for $m_{jj, max}$
R54 for $H_{T2}$, 60 GeV < $p_{T,3}$
R54 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$
R54 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$
R54 for $Delta y_{jj}$
R54 for $Delta y_{jj, max}$
R54 for $m_{jj}$
R54 for $m_{jj, max}$
R42 for $H_{T2}$, 60 GeV < $p_{T,3}$
R42 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$
R42 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$
R42, pT,jet
R42 for $Delta y_{jj}$
R42 for $Delta y_{jj, max}$
R42 for $m_{jj}$
R42 for $m_{jj}$
HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 2
HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=3
HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=4
HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 5
HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 2
HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=3
HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=4
HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 5
HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 2
HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=3
HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=4
HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 5
HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 2
HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=3
HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=4
HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 5
HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 2
HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=3
HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=4
HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 5
pTnincl
pTnincl
pTnincl
mjj max, $N_{jet}$ >= 2
mjj max, $N_{jet}$ >= 3
mjj max, $N_{jet}$ >= 4
mjj max, $N_{jet}$ >= 5
$m_{jj}$ $N_{jet}$ >= 2
$m_{jj}$ $N_{jet}$ >= 3
$m_{jj}$ $N_{jet}$ >= 4
$m_{jj}$ $N_{jet}$ >= 5
$Delta y_{jj}$ $N_{jet}$ >= 2
$Delta y_{jj}$ $N_{jet}$ >= 3
$Delta y_{jj}$ $N_{jet}$ >= 4
$Delta y_{jj}$ $N_{jet}$ >= 5
$Delta y_{jj, max}$, $N_{jet}$ >= 2
$Delta y_{jj, max}$, $N_{jet}$ >= 3
$Delta y_{jj, max}$, $N_{jet}$ >= 4
$Delta y_{jj, max}$, $N_{jet}$ >= 5
R32 for $H_{T2}$, 60 GeV < $p_{T,3}$
R32 for $H_{T2}$, 0.05 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$
R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$
Distributions sensitive to the underlying event are studied in events containing one or more charged-particle jets produced in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector at the Large Hadron Collider (LHC). These measurements reflect 800 inverse microbarns of data taken during 2010. Jets are reconstructed using the antikt algorithm with radius parameter R varying between 0.2 and 1.0. Distributions of the charged-particle multiplicity, the scalar sum of the transverse momentum of charged particles, and the average charged-particle pT are measured as functions of pT^JET in regions transverse to and opposite the leading jet for 4 GeV < pT^JET < 100 GeV. In addition, the R-dependence of the mean values of these observables is studied. In the transverse region, both the multiplicity and the scalar sum of the transverse momentum at fixed pT^JET vary significantly with R, while the average charged-particle transverse momentum has a minimal dependence on R. Predictions from several Monte Carlo tunes have been compared to the data; the predictions from Pythia 6, based on tunes that have been determined using LHC data, show reasonable agreement with the data, including the dependence on R. Comparisons with other generators indicate that additional tuning of soft-QCD parameters is necessary for these generators. The measurements presented here provide a testing ground for further development of the Monte Carlo models.
Mean value of N(C=CHARGED) v jet PT for R=0.2.
Mean value of N(C=CHARGED) v jet PT for R=0.4.
Mean value of N(C=CHARGED) v jet PT for R=0.6.
Mean value of N(C=CHARGED) v jet PT for R=0.8.
Mean value of N(C=CHARGED) v jet PT for R=1.0.
Mean value of PT(C=AVERAGE) v jet PT for R=0.2.
Mean value of PT(C=AVERAGE) v jet PT for R=0.4.
Mean value of PT(C=AVERAGE) v jet PT for R=0.6.
Mean value of PT(C=AVERAGE) v jet PT for R=0.8.
Mean value of PT(C=AVERAGE) v jet PT for R=1.0.
Mean value of SUM(PT) v jet PT for R=0.2.
Mean value of SUM(PT) v jet PT for R=0.4.
Mean value of SUM(PT) v jet PT for R=0.6.
Mean value of SUM(PT) v jet PT for R=0.8.
Mean value of SUM(PT) v jet PT for R=1.0.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 4 to 5 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 5 to 6 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 6 to 8 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 8 to 11 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 11 to 14 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 14 to 19 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 19 to 24 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 24 to 31 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 31 to 50 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 50 to 100 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 4 to 5 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 5 to 6 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 6 to 8 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 8 to 11 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 11 to 14 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 14 to 19 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 19 to 24 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 24 to 31 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 31 to 50 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 50 to 100 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 4 to 5 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 5 to 6 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 6 to 8 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 8 to 11 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 11 to 14 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 14 to 19 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 19 to 24 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 24 to 31 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 31 to 50 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 50 to 100 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 4 to 5 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 5 to 6 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 6 to 8 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 8 to 11 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 11 to 14 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 14 to 19 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 19 to 24 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 24 to 31 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 31 to 50 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 50 to 100 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 4 to 5 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 5 to 6 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 6 to 8 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 8 to 11 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 11 to 14 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 14 to 19 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 19 to 24 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 24 to 31 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 31 to 50 GeV.
Distribution of the variable N(C=CHARGED) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 50 to 100 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 4 to 5 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 5 to 6 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 6 to 8 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 8 to 11 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 11 to 14 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 14 to 19 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 19 to 24 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 24 to 31 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 31 to 50 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 50 to 100 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 4 to 5 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 5 to 6 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 6 to 8 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 8 to 11 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 11 to 14 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 14 to 19 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 19 to 24 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 24 to 31 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 31 to 50 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 50 to 100 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 4 to 5 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 5 to 6 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 6 to 8 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 8 to 11 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 11 to 14 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 14 to 19 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 19 to 24 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 24 to 31 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 31 to 50 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 50 to 100 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 4 to 5 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 5 to 6 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 6 to 8 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 8 to 11 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 11 to 14 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 14 to 19 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 19 to 24 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 24 to 31 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 31 to 50 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 50 to 100 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 4 to 5 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 5 to 6 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 6 to 8 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 8 to 11 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 11 to 14 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 14 to 19 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 19 to 24 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 24 to 31 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 31 to 50 GeV.
Distribution of the variable PT(C=AVERAGE) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 50 to 100 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 4 to 5 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 5 to 6 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 6 to 8 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 8 to 11 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 11 to 14 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 14 to 19 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 19 to 24 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 24 to 31 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 31 to 50 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.2 and jet PT in the range 50 to 100 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 4 to 5 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 5 to 6 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 6 to 8 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 8 to 11 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 11 to 14 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 14 to 19 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 19 to 24 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 24 to 31 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 31 to 50 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.4 and jet PT in the range 50 to 100 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 4 to 5 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 5 to 6 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 6 to 8 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 8 to 11 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 11 to 14 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 14 to 19 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 19 to 24 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 24 to 31 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 31 to 50 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.6 and jet PT in the range 50 to 100 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 4 to 5 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 5 to 6 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 6 to 8 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 8 to 11 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 11 to 14 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 14 to 19 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 19 to 24 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 24 to 31 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 31 to 50 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=0.8 and jet PT in the range 50 to 100 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 4 to 5 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 5 to 6 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 6 to 8 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 8 to 11 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 11 to 14 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 14 to 19 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 19 to 24 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 24 to 31 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 31 to 50 GeV.
Distribution of the variable SUM(PT) in the AWAY and TRANSVERSE regions for R=1.0 and jet PT in the range 50 to 100 GeV.
Differential cross-section measurements are presented for the electroweak production of two jets in association with a $Z$ boson. These measurements are sensitive to the vector-boson fusion production mechanism and provide a fundamental test of the gauge structure of the Standard Model. The analysis is performed using proton-proton collision data collected by ATLAS at $\sqrt{s}$=13 TeV and with an integrated luminosity of 139 fb$^{-1}$. The differential cross-sections are measured in the $Z\rightarrow \ell^+\ell^-$ decay channel ($\ell=e,\mu$) as a function of four observables: the dijet invariant mass, the rapidity interval spanned by the two jets, the signed azimuthal angle between the two jets, and the transverse momentum of the dilepton pair. The data are corrected for the effects of detector inefficiency and resolution and are sufficiently precise to distinguish between different state-of-the-art theoretical predictions calculated using Powheg+Pythia8, Herwig7+Vbfnlo and Sherpa 2.2. The differential cross-sections are used to search for anomalous weak-boson self-interactions using a dimension-six effective field theory. The differential cross-section as a function of the signed azimuthal angle between the two jets is found to be particularly sensitive to the interference between the Standard Model and dimension-six scattering amplitudes and provides a direct test of charge-conjugation and parity invariance in the weak-boson self-interactions.
Differential cross-sections for EW $Zjj$ production as a function of $m_{jj}$ with breakdown of associated uncertainties. The statistical uncertainty is correlated across bins according to the statistical cross correlation matrix presented in Table 21.
Differential cross-sections for EW $Zjj$ production as a function of $|\Delta y_{jj}|$ with breakdown of associated uncertainties. The statistical uncertainty is correlated across bins according to the statistical cross correlation matrix presented in Table 21.
Differential cross-sections for EW $Zjj$ production as a function of $p_{\mathrm{T},\ell\ell}$ with breakdown of associated uncertainties. The statistical uncertainty is correlated across bins according to the statistical cross correlation matrix presented in Table 21.
Differential cross-sections for EW $Zjj$ production as a function of $\Delta\phi_{jj}$ with breakdown of associated uncertainties. The statistical uncertainty is correlated across bins according to the statistical cross correlation matrix presented in Table 21.
Differential cross-sections for inclusive $Zjj$ production in the EW $Zjj$ signal region ($N^\mathrm{gap}_\mathrm{jets}=0$, $\xi_Z < 0.5$) as a function of $m_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in the EW $Zjj$ signal region ($N^\mathrm{gap}_\mathrm{jets}=0$, $\xi_Z < 0.5$) as a function of $\Delta y_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in the EW $Zjj$ signal region ($N^\mathrm{gap}_\mathrm{jets}=0$, $\xi_Z < 0.5$) as a function of $p_{\mathrm{T},\ell\ell}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in the EW $Zjj$ signal region ($N^\mathrm{gap}_\mathrm{jets}=0$, $\xi_Z < 0.5$) as a function of $\Delta\phi_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRa ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z < 0.5$) as a function of $m_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRa ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z < 0.5$) as a function of $\Delta y_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRa ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z < 0.5$) as a function of $p_{\mathrm{T},\ell\ell}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRa ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z < 0.5$) as a function of $\Delta\phi_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRb ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z > 0.5$) as a function of $m_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRb ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z > 0.5$) as a function of $\Delta y_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRb ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z > 0.5$) as a function of $p_{\mathrm{T},\ell\ell}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRb ($N^\mathrm{gap}_\mathrm{jets} \geq 1$, $\xi_Z > 0.5$) as a function of $\Delta\phi_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRc ($N^\mathrm{gap}_\mathrm{jets} = 0$, $\xi_Z > 0.5$) as a function of $m_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRc ($N^\mathrm{gap}_\mathrm{jets} = 0$, $\xi_Z > 0.5$) as a function of $\Delta y_{jj}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRc ($N^\mathrm{gap}_\mathrm{jets} = 0$, $\xi_Z > 0.5$) as a function of $p_{\mathrm{T},\ell\ell}$ with breakdown of associated uncertainties.
Differential cross-sections for inclusive $Zjj$ production in control region CRc ($N^\mathrm{gap}_\mathrm{jets} = 0$, $\xi_Z > 0.5$) as a function of $\Delta\phi_{jj}$ with breakdown of associated uncertainties.
Statistical correlation between bins of the differential cross-section measurements of EW $Zjj$ production in the signal region ($N^\mathrm{gap}_\mathrm{jets} = 0$, $\xi_Z < 0.5$). The associated measured central values, statistical uncertainty magnitude and bin ranges are presented in Tables 1-4. The bins are presented in order such that for example mjj_bin_1 spans $m_{jj} \in (1000,1500)$ GeV (see Table 1), while pT_ll_bin7 spans $p_\mathrm{T}^{ll} \in (200,275)$ GeV (see Table 3).
Fiducial and differential measurements of $W^+W^-$ production in events with at least one hadronic jet are presented. These cross-section measurements are sensitive to the properties of electroweak-boson self-interactions and provide a test of perturbative quantum chromodynamics and the electroweak theory. The analysis is performed using proton$-$proton collision data collected at $\sqrt{s}=13~$TeV with the ATLAS experiment, corresponding to an integrated luminosity of 139$~$fb$^{-1}$. Events are selected with exactly one oppositely charged electron$-$muon pair and at least one hadronic jet with a transverse momentum of $p_{\mathrm{T}}>30~$GeV and a pseudorapidity of $|\eta|<4.5$. After subtracting the background contributions and correcting for detector effects, the jet-inclusive $W^+W^-+\ge 1~$jet fiducial cross-section and $W^+W^-+$ jets differential cross-sections with respect to several kinematic variables are measured, thus probing a previously unexplored event topology at the LHC. These measurements include leptonic quantities, such as the lepton transverse momenta and the transverse mass of the $W^+W^-$ system, as well as jet-related observables such as the leading jet transverse momentum and the jet multiplicity. Limits on anomalous triple-gauge-boson couplings are obtained in a phase space where interference between the Standard Model amplitude and the anomalous amplitude is enhanced.
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $p_{\mathrm{T}}^{\mathrm{lead.~lep.}}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 1168 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{lead.~lep.}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{lead.~lep.}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 609 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $p_{\mathrm{T}}^{\mathrm{lead.~jet}}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 1485 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{lead.~jet}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{lead.~jet}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $H_{\mathrm{T}}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 2969 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $H_{\mathrm{T}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $H_{\mathrm{T}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $S_{\mathrm{T}}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 3296 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $S_{\mathrm{T}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $S_{\mathrm{T}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $m_{\mathrm{T},e\mu}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 4130 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $m_{\mathrm{T},e\mu}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $m_{\mathrm{T},e\mu}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $m_{e\mu}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 3519 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $m_{e\mu}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $m_{e\mu}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $p_{\mathrm{T},e\mu}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 1067 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $\Delta\phi(e,\mu)$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $\Delta\phi(e,\mu)$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $\Delta\phi(e,\mu)$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $y_{e\mu}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $y_{e\mu}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $y_{e\mu}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $\cos\theta^*$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $\cos\theta^*$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $\cos\theta^*$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $n_{\mathrm{jet}}$. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $n_{\mathrm{jet}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $n_{\mathrm{jet}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $m_{e\mu}$ for $p_{\mathrm{T}}^{\mathrm{lead.~jet}} > 200$ GeV. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. Overflow events are included in the last bin. The largest observed value is 3519 GeV.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $m_{e\mu}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $m_{e\mu}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $\Delta\phi(e,\mu)$ for $p_{\mathrm{T}}^{\mathrm{lead.~jet}} > 200$ GeV. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $\Delta\phi(e,\mu)$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $\Delta\phi(e,\mu)$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $\Delta\phi(\mathrm{sub-lead.~lep.}, \mathrm{lead.~jet})$ for $p_{\mathrm{T}}^{\mathrm{lead.~lep.}} > 200$ GeV. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $\Delta\phi(\mathrm{sub-lead.~lep.}, \mathrm{lead.~jet})$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $\Delta\phi(\mathrm{sub-lead.~lep.}, \mathrm{lead.~jet})$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $\Delta R(\mathrm{sub-lead.~lep.}, \mathrm{lead.~jet})$ for $p_{\mathrm{T}}^{\mathrm{lead.~lep.}} > 200$ GeV. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $\Delta R(\mathrm{sub-lead.~lep.}, \mathrm{lead.~jet})$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $\Delta R(\mathrm{sub-lead.~lep.}, \mathrm{lead.~jet})$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}} / p_{\mathrm{T}}^{\mathrm{lead.~lep.}}$ for $p_{\mathrm{T}}^{\mathrm{lead.~lep.}} > 200$ GeV. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}} / p_{\mathrm{T}}^{\mathrm{lead.~lep.}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}} / p_{\mathrm{T}}^{\mathrm{lead.~lep.}}$
Measured fiducial cross section for $pp\rightarrow W^+W^-$+jets production for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}} / p_{\mathrm{T}}^{\mathrm{lead.~jet}}$ for $p_{\mathrm{T}}^{\mathrm{lead.~lep.}} > 200$ GeV. The second column contains the results obtained with a fiducial particle phase space that includes a veto on $b$-jets. This alternative result is obtained from the nominal result by the application of bin-wise correction that is calculated as the ratio of the predicted differential cross-section in the nominal analysis phase space and the predicted cross-section for a phase space that includes a veto on events with $b$-jets with $p_{\mathrm{T}} > 20$ GeV. Also shown are the Standard Model predictions for $q\bar{q} \rightarrow WW$, obtained from Sherpa 2.2.2, MadGraph 2.3.3 + Pythia 8.212 using FxFx merging, and Powheg MiNLO + Pythia 8.244. These predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of $gg\rightarrow WW$. Finally, the prediction from MATRIX is given, which includes nNLO QCD and NLO EW corrections to $WW$+jet production. The largest observed value is 19.6.
Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}} / p_{\mathrm{T}}^{\mathrm{lead.~jet}}$
Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.~lep.}} / p_{\mathrm{T}}^{\mathrm{lead.~jet}}$
This paper presents measurements of top-antitop quark pair ($t\bar{t}$) production in association with additional $b$-jets. The analysis utilises 140 fb$^{-1}$ of proton-proton collision data collected with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 13 TeV. Fiducial cross-sections are extracted in a final state featuring one electron and one muon, with at least three or four $b$-jets. Results are presented at the particle level for both integrated cross-sections and normalised differential cross-sections, as functions of global event properties, jet kinematics, and $b$-jet pair properties. Observable quantities characterising $b$-jets originating from the top quark decay and additional $b$-jets are also measured at the particle level, after correcting for detector effects. The measured integrated fiducial cross-sections are consistent with $t\bar{t}b\bar{b}$ predictions from various next-to-leading-order matrix element calculations matched to a parton shower within the uncertainties of the predictions. State-of-the-art theoretical predictions are compared with the differential measurements; none of them simultaneously describes all observables. Differences between any two predictions are smaller than the measurement uncertainties for most observables.
Measured and predicted fiducial cross-section results for additional b-jet production in four phase-space regions. The dashes (–) indicate that the predictions are not available. The differences between the various MC generator predictions are smaller than the size of theoretical uncertainties (20%–50%, not presented here) in the predictions.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least two $b$-jets as a function of the number of $b$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $b$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{4})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\text{min}\Delta R(bb)$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at leastfour $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{4})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.
The measured normalised differential cross-section as a function of $N_{b-\text{jets}}$ in the $e\mu+\geq2b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{4})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{4})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $m(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\text{min}\Delta R(bb)$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least two b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{4})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{4})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\text{min}\Delta R(bb)$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.
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