Showing 10 of 613 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).
This paper presents cross sections for the production of a W boson in association with jets, measured in proton--proton collisions at $\sqrt{s}=7$ TeV with the ATLAS experiment at the Large Hadron Collider. With an integrated luminosity of $4.6 fb^{-1}$, this data set allows for an exploration of a large kinematic range, including jet production up to a transverse momentum of 1 TeV and multiplicities up to seven associated jets. The production cross sections for W bosons are measured in both the electron and muon decay channels. Differential cross sections for many observables are also presented including measurements of the jet observables such as the rapidities and the transverse momenta as well as measurements of event observables such as the scalar sums of the transverse momenta of the jets. The measurements are compared to numerous QCD predictions including next-to-leading-order perturbative calculations, resummation calculations and Monte Carlo generators.
Distribution of inclusive jet multiplicity.
Breakdown of systematic uncertainties in percent in inclusive jet multiplicity in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in inclusive jet multiplicity in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of exclusive jet multiplicity.
Breakdown of systematic uncertainties in percent in exclusive jet multiplicity in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in exclusive jet multiplicity in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (leading jet) [GeV] with at least one jet in the event.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (leading jet) [GeV] with exactly one jet in the event.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with exactly one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with exactly one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (leading jet) [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (leading jet) [GeV] with at least three jets in the event.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (2nd jet) [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in pT (2nd jet) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (2nd jet) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (3rd jet) [GeV] with at least three jets in the event.
Breakdown of systematic uncertainties in percent in pT (3rd jet) [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (3rd jet) [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (4th jet) [GeV] with at least four jets in the event.
Breakdown of systematic uncertainties in percent in pT (4th jet) [GeV] with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (4th jet) [GeV] with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of pT (5th jet) [GeV] with at least five jets in the event.
Breakdown of systematic uncertainties in percent in pT (5th jet) [GeV] with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in pT (5th jet) [GeV] with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of leading jet rapidity with at least one jet in the event.
Breakdown of systematic uncertainties in percent in leading jet rapidity with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in leading jet rapidity with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of 2nd jet rapidity with at least two jets in the event.
Breakdown of systematic uncertainties in percent in 2nd jet rapidity with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in 2nd jet rapidity with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with at least one jet in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with exactly one jet in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with exactly one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with exactly one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with exactly two jets in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with exactly two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with exactly two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with at least three jets in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with exactly three jets in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with exactly three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with exactly three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with at least four jets in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of HT [GeV] with at least five jets in the event.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in HT [GeV] with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of DPhi(jj) [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in DPhi(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in DPhi(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of Dy(jj) [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in Dy(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in Dy(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of DR(jj) [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in DR(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in DR(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of m(jj) [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in m(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in m(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of 3rd jet rapidity with at least three jets in the event.
Breakdown of systematic uncertainties in percent in 3rd jet rapidity with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in 3rd jet rapidity with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of 4th jet rapidity with at least four jets in the event.
Breakdown of systematic uncertainties in percent in 4th jet rapidity with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in 4th jet rapidity with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of 5th jet rapidity with at least five jets in the event.
Breakdown of systematic uncertainties in percent in 5th jet rapidity with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in 5th jet rapidity with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with at least one jet in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with at least two jets in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with exactly two jets in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with exactly two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with exactly two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with at least three jets in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with exactly three jets in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with exactly three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with exactly three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with at least four jets in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Distribution of ST [GeV] with at least five jets in the event.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
Breakdown of systematic uncertainties in percent in ST [GeV] with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.
This paper presents a measurement of the $W$ boson production cross section and the $W^{+}/W^{-}$ cross-section ratio, both in association with jets, in proton--proton collisions at $\sqrt{s}=8$ TeV with the ATLAS experiment at the Large Hadron Collider. The measurement is performed in final states containing one electron and missing transverse momentum using data corresponding to an integrated luminosity of 20.2 fb$^{-1}$. Differential cross sections for events with one or two jets are presented for a range of observables, including jet transverse momenta and rapidities, the scalar sum of transverse momenta of the visible particles and the missing transverse momentum in the event, and the transverse momentum of the $W$ boson. For a subset of the observables, the differential cross sections of positively and negatively charged $W$ bosons are measured separately. In the cross-section ratio of $W^{+}/W^{-}$ the dominant systematic uncertainties cancel out, improving the measurement precision by up to a factor of nine. The observables and ratios selected for this paper provide valuable input for the up quark, down quark, and gluon parton distribution functions of the proton.
Cross section for the production of W bosons for different inclusive jet multiplicities.
Statistical correlation between bins in data for the cross section for the production of W bosons for different inclusive jet multiplicities.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the inclusive jet multiplicity.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the inclusive jet multiplicity.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the inclusive jet multiplicity.
Differential cross section for the production of W bosons as a function of H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Differential cross section for the production of W bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Differential cross section for the production of W bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Differential cross section for the production of W bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Differential cross section for the production of W bosons as a function of second leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of second leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross section for the production of W bosons as a function of second leading jet rapidity for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of second leading jet rapidity for events with N<sub> jets</sub> ≥ 2.
Differential cross section for the production of W bosons as a function of Δ R<sub>jet1,jet2</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of Δ R<sub>jet1,jet2</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross section for the production of W bosons as a function of dijet invariant mass for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of dijet invariant mass for events with N<sub> jets</sub> ≥ 2.
Cross section for the production of W bosons as a function of exclusive jet multiplicity.
Statistical correlation between bins in data for the cross section for the production of W bosons as a function of exclusive jet multiplicity.
Differential cross section for the production of W bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross section for the production of W bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross section for the production of W bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Differential cross section for the production of W bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 0.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 0.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the electron η for events with N<sub> jets</sub> ≥ 0.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 0.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 0.
Differential cross section for the production of W bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 1.
Differential cross sections for the production of W<sup>+</sup> bosons, W<sup>-</sup> bosons and the W<sup>+</sup>/W<sup>-</sup> cross section ratio as a function of the electron η for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>+</sup> bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 1.
Statistical correlation between bins in data for the differential cross sections for the production of W<sup>-</sup> bosons as a function of the electron η for events with N<sub> jets</sub> ≥ 1.
List of experimentally considered systematic uncertainties for the W+jets cross section measurement
Non-perturbative corrections for the cross section for the production of W bosons for different inclusive jet multiplicities.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the inclusive jet multiplicity.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of second leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of second leading jet rapidity for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of Δ R<sub>jet1,jet2</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of dijet invariant mass for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the cross section for the production of W bosons as a function of exclusive jet multiplicity.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
Non-perturbative corrections for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 2.
NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the H<sub> T</sub> for events with N<sub> jets</sub> ≥ 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].
NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the W p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].
NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the leading jet p<sub>T</sub> for events with N<sub> jets</sub> ≥ 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].
NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W<sup>+</sup> bosons and W<sup>-</sup> bosons as a function of the leading jet rapidity for events with N<sub> jets</sub> ≥ 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].
Event-shape observables measured using charged particles in inclusive $Z$-boson events are presented, using the electron and muon decay modes of the $Z$ bosons. The measurements are based on an integrated luminosity of $1.1 {\rm fb}^{-1}$ of proton--proton collisions recorded by the ATLAS detector at the LHC at a centre-of-mass energy $\sqrt{s}=7$ TeV. Charged-particle distributions, excluding the lepton--antilepton pair from the $Z$-boson decay, are measured in different ranges of transverse momentum of the $Z$ boson. Distributions include multiplicity, scalar sum of transverse momenta, beam thrust, transverse thrust, spherocity, and $\mathcal{F}$-parameter, which are in particular sensitive to properties of the underlying event at small values of the $Z$-boson transverse momentum. The Sherpa event generator shows larger deviations from the measured observables than Pythia8 and Herwig7. Typically, all three Monte Carlo generators provide predictions that are in better agreement with the data at high $Z$-boson transverse momenta than at low $Z$-boson transverse momenta and for the observables that are less sensitive to the number of charged particles in the event.
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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.
Jet substructure observables have significantly extended the search program for physics beyond the Standard Model at the Large Hadron Collider. The state-of-the-art tools have been motivated by theoretical calculations, but there has never been a direct comparison between data and calculations of jet substructure observables that are accurate beyond leading-logarithm approximation. Such observables are significant not only for probing the collinear regime of QCD that is largely unexplored at a hadron collider, but also for improving the understanding of jet substructure properties that are used in many studies at the Large Hadron Collider. This Letter documents a measurement of the first jet substructure quantity at a hadron collider to be calculated at next-to-next-to-leading-logarithm accuracy. The normalized, differential cross-section is measured as a function of log$_{10}\rho^2$, where $\rho$ is the ratio of the soft-drop mass to the ungroomed jet transverse momentum. This quantity is measured in dijet events from 32.9 fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton-proton collisions recorded by the ATLAS detector. The data are unfolded to correct for detector effects and compared to precise QCD calculations and leading-logarithm particle-level Monte Carlo simulations.
Data from Fig 3a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 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 uncertainties from the calculations are shown on each one. 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 3b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 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 uncertainties from the calculations are shown on each one. 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 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 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 uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.
Data from FigAux 4 and FigAux 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 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 uncertainties from the calculations are shown on each one. 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 {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 4 and FigAux 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 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 uncertainties from the calculations are shown on each one. 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 {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 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 uncertainties from the calculations are shown on each one. 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 {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.
Data from FigAux 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.
Data from FigAux 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.
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.
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 $H_{\text{T}}^{\text{had}}$ 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.
Measurements of normalized differential cross-sections of top-quark pair production are presented as a function of the top-quark, $t\bar{t}$ system and event-level kinematic observables in proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=8$ TeV}. The observables have been chosen to emphasize the $t\bar{t}$ production process and to be sensitive to effects of initial- and final-state radiation, to the different parton distribution functions, and to non-resonant processes and higher-order corrections. The dataset corresponds to an integrated luminosity of 20.3 fb$^{-1}$, recorded in 2012 with the ATLAS detector at the CERN Large Hadron Collider. Events are selected in the lepton+jets channel, requiring exactly one charged lepton and at least four jets with at least two of the jets tagged as originating from a $b$-quark. The measured spectra are corrected for detector effects and are compared to several Monte Carlo simulations. The results are in fair agreement with the predictions over a wide kinematic range. Nevertheless, most generators predict a harder top-quark transverse momentum distribution at high values than what is observed in the data. Predictions beyond NLO accuracy improve the agreement with data at high top-quark transverse momenta. Using the current settings and parton distribution functions, the rapidity distributions are not well modelled by any generator under consideration. However, the level of agreement is improved when more recent sets of parton distribution functions are used.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system invariant mass $m^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system invariant mass $m^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system transverse momentum $p_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system transverse momentum $p_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t$}$ system absolute rapidity $|y^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t$}$ system absolute rapidity $|y^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the hadronic top-quark transverse momentum $p_{T}^{t}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the hadronic top-quark transverse momentum $p_{T}^{t}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the hadronic top-quark absolute rapidity $|y^{t}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the hadronic top-quark absolute rapidity $|y^{t}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system absolute out-of-plane momentum $|p_{out}^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system absolute out-of-plane momentum $|p_{out}^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system azimuthal angle $\Delta \phi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system azimuthal angle $\Delta \phi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the scalar sum of the hadronic and leptonic top-quark transverse momenta $H_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the scalar sum of the hadronic and leptonic top-quark transverse momenta $H_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for $y_{boost}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for $y_{boost}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for $\chi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for $\chi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for $R_{Wt}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Fiducial phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for $R_{Wt}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system invariant mass $m^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system invariant mass $m^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system transverse momentum $p_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system transverse momentum $p_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system absolute rapidity $|y^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system absolute rapidity $|y^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the top-quark transverse momentum $p_{T}^{t}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the top-quark transverse momentum $p_{T}^{t}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the top-quark absolute rapidity $|y^{t}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the top-quark absolute rapidity $|y^{t}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system absolute out-of-plane momentum $|p_{out}^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system absolute out-of-plane momentum $|p_{out}^{t\bar{t}}|$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system azimuthal angle $\Delta \phi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the $t\bar{t}$ system azimuthal angle $\Delta \phi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for the scalar sum of the hadronic and leptonic top-quark transverse momenta $H_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for the scalar sum of the hadronic and leptonic top-quark transverse momenta $H_{T}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for $y_{boost}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for $y_{boost}^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space absolute differential cross-sections after combining the e+jets and $\mu$+jets channels for $\chi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Full phase-space relative differential cross-sections after combining the e+jets and $\mu$+jets channels for $\chi^{t\bar{t}}$. All uncertainties are quoted as a percentage with respect to the cross-section values in each bin.
Searches for scalar leptoquarks pair-produced in proton-proton collisions at $\sqrt{s}=13$ TeV at the Large Hadron Collider are performed by the ATLAS experiment. A data set corresponding to an integrated luminosity of 36.1 fb$^{-1}$ is used. Final states containing two electrons or two muons and two or more jets are studied, as are states with one electron or muon, missing transverse momentum and two or more jets. No statistically significant excess above the Standard Model expectation is observed. The observed and expected lower limits on the leptoquark mass at 95% confidence level extend up to 1.29 TeV and 1.23 TeV for first- and second-generation leptoquarks, respectively, as postulated in the minimal Buchm\"uller-R\"uckl-Wyler model, assuming a branching ratio into a charged lepton and a quark of 50%. In addition, measurements of particle-level fiducial and differential cross sections are presented for the $Z\rightarrow ee$, $Z\rightarrow\mu\mu$ and $t\bar{t}$ processes in several regions related to the search control regions. Predictions from a range of generators are compared with the measurements, and good agreement is seen for many of the observables. However, the predictions for the $Z\rightarrow\ell\ell$ measurements in observables sensitive to jet energies disagree with the data.
Inclusive cross-section and uncertainty from each source, for the dominant process in the each measurement region.
Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $p_{T}^{ee}$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $p_{T}^{\mu\mu}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $p_{T}^{e\mu}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $p_{T}^{ee}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $p_{T}^{\mu\mu}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $p_{T}^{e\mu}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $ee jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $e\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $eejj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.
Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $e\mu jj$ measurement region.
Expected and observed 95% CL lower limits on first- and second-generation leptoquark masses for different values of $\beta$.
Event yields in the dimuon channel control regions with total uncertainties. The observed number of events is given in the first row. The background event numbers as obtained from the fit are shown together with the total uncertainties. The second row shows the total background expectation, the further rows show the breakdown into different background components.
Event yields in the dielectron channel control regions with total uncertainties. The observed number of events is given in the first row. The background event numbers as obtained from the fit are shown together with the total uncertainties. The second row shows the total background expectation, the further rows show the breakdown into different background components.
Distribution of $m_{LQ}^{min}$ in the training region for the BDT for the $ee jj$ and $\mu\mu jj$ channels. Data are shown together with predicted total background expectation.
Distribution of $m_{LQ}^{T}$ in the training region for the BDT for the $e\nu jj$ and $\mu\nu jj$ channels. Data are shown together with predicted total background expectation.
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