The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 40 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 45 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 50 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 55 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 60 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 65 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 70 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 75 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 80 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 85 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 90 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 95 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

The observed 95% CL upper limits on the product of the cross section, and branching fraction for the production of X$\to\phi\phi\to$4b, with $m_\phi$ = 100 GeV. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to various extended higgs sector model cross sections as a function of the parameter $m_XN/f$.

Data event yields and predicted signal and background yields (including subdominant $t\bar{t}$) in bins of dijet and average jet mass.

Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for scalar portal samples with $m_\varPhi=125$~\GeV\ for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, ``MDT 1/2'' represent the first/second stations of MDT chambers and ``L/S'' indicate whether they are in Large or Small sectors.

Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Efficiency for the Muon RoI Cluster trigger in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).

Efficiency for the Muon RoI Cluster trigger in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).

Efficiency for the Muon RoI Cluster trigger in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).

Efficiency for the Muon RoI Cluster trigger in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.

Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of transverse decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of longitudinal decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of transverse decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of longitudinal decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.

Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=60$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=60$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=200$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=400$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.

Helicity-dependent differential cross section on deuteron at Egamma= 196. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 213. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 230. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 247. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 264. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 281. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 299. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 316. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 333. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 351. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 368. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 386. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 403. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 421. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 438. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 456. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 473. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 491. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 508. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 526. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 543. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 561. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 578. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 596. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 613. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 631. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 648. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 666. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 683. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 700. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 718. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 735. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 752. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 769. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 786. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 803. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 820. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 837. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 853. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 870. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 887. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 903. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 919. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 936. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 952. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 968. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 983. MeV

Helicity-dependent differential cross section on deuteron at Egamma= 999. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1015. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1030. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1046. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1061. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1076. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1091. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1105. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1120. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1134. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1148. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1162. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1176. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1190. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1203. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1217. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1230. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1243. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1256. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1268. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1281. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1293. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1305. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1317. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1328. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1340. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1351. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1362. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1374. MeV

Helicity-dependent differential cross section on deuteron at Egamma=1387. MeV

Helicity asymmetry E for quasi-free proton at W=1165. MeV

Helicity asymmetry E for quasi-free proton at W=1195. MeV

Helicity asymmetry E for quasi-free proton at W=1225. MeV

Helicity asymmetry E for quasi-free proton at W=1255. MeV

Helicity asymmetry E for quasi-free proton at W=1285. MeV

Helicity asymmetry E for quasi-free proton at W=1315. MeV

Helicity asymmetry E for quasi-free proton at W=1339. MeV

Helicity asymmetry E for quasi-free proton at W=1357. MeV

Helicity asymmetry E for quasi-free proton at W=1375. MeV

Helicity asymmetry E for quasi-free proton at W=1393. MeV

Helicity asymmetry E for quasi-free proton at W=1411. MeV

Helicity asymmetry E for quasi-free proton at W=1429. MeV

Helicity asymmetry E for quasi-free proton at W=1447. MeV

Helicity asymmetry E for quasi-free proton at W=1465. MeV

Helicity asymmetry E for quasi-free proton at W=1483. MeV

Helicity asymmetry E for quasi-free proton at W=1501. MeV

Helicity asymmetry E for quasi-free proton at W=1519. MeV

Helicity asymmetry E for quasi-free proton at W=1537. MeV

Helicity asymmetry E for quasi-free proton at W=1555. MeV

Helicity asymmetry E for quasi-free proton at W=1573. MeV

Helicity asymmetry E for quasi-free proton at W=1591. MeV

Helicity asymmetry E for quasi-free proton at W=1609. MeV

Helicity asymmetry E for quasi-free proton at W=1627. MeV

Helicity asymmetry E for quasi-free proton at W=1645. MeV

Helicity asymmetry E for quasi-free proton at W=1663. MeV

Helicity asymmetry E for quasi-free proton at W=1681. MeV

Helicity asymmetry E for quasi-free proton at W=1699. MeV

Helicity asymmetry E for quasi-free proton at W=1717. MeV

Helicity asymmetry E for quasi-free proton at W=1735. MeV

Helicity asymmetry E for quasi-free proton at W=1753. MeV

Helicity asymmetry E for quasi-free proton at W=1771. MeV

Helicity asymmetry E for quasi-free proton at W=1789. MeV

Helicity asymmetry E for quasi-free proton at W=1807. MeV

Helicity asymmetry E for quasi-free proton at W=1825. MeV

Helicity asymmetry E for quasi-free proton at W=1843. MeV

Helicity asymmetry E for quasi-free proton at W=1861. MeV

Helicity asymmetry E for quasi-free proton at W=1879. MeV

Helicity asymmetry E for quasi-free proton at W=1897. MeV

Helicity asymmetry E for quasi-free proton at W=1915. MeV

Helicity asymmetry E for quasi-free neutron at W=1165. MeV

Helicity asymmetry E for quasi-free neutron at W=1195. MeV

Helicity asymmetry E for quasi-free neutron at W=1225. MeV

Helicity asymmetry E for quasi-free neutron at W=1255. MeV

Helicity asymmetry E for quasi-free neutron at W=1285. MeV

Helicity asymmetry E for quasi-free neutron at W=1315. MeV

Helicity asymmetry E for quasi-free neutron at W=1339. MeV

Helicity asymmetry E for quasi-free neutron at W=1357. MeV

Helicity asymmetry E for quasi-free neutron at W=1375. MeV

Helicity asymmetry E for quasi-free neutron at W=1393. MeV

Helicity asymmetry E for quasi-free neutron at W=1411. MeV

Helicity asymmetry E for quasi-free neutron at W=1429. MeV

Helicity asymmetry E for quasi-free neutron at W=1447. MeV

Helicity asymmetry E for quasi-free neutron at W=1465. MeV

Helicity asymmetry E for quasi-free neutron at W=1483. MeV

Helicity asymmetry E for quasi-free neutron at W=1501. MeV

Helicity asymmetry E for quasi-free neutron at W=1519. MeV

Helicity asymmetry E for quasi-free neutron at W=1537. MeV

Helicity asymmetry E for quasi-free neutron at W=1555. MeV

Helicity asymmetry E for quasi-free neutron at W=1573. MeV

Helicity asymmetry E for quasi-free neutron at W=1591. MeV

Helicity asymmetry E for quasi-free neutron at W=1609. MeV

Helicity asymmetry E for quasi-free neutron at W=1627. MeV

Helicity asymmetry E for quasi-free neutron at W=1645. MeV

Helicity asymmetry E for quasi-free neutron at W=1663. MeV

Helicity asymmetry E for quasi-free neutron at W=1681. MeV

Helicity asymmetry E for quasi-free neutron at W=1699. MeV

Helicity asymmetry E for quasi-free neutron at W=1717. MeV

Helicity asymmetry E for quasi-free neutron at W=1735. MeV

Helicity asymmetry E for quasi-free neutron at W=1753. MeV

Helicity asymmetry E for quasi-free neutron at W=1771. MeV

Helicity asymmetry E for quasi-free neutron at W=1789. MeV

Helicity asymmetry E for quasi-free neutron at W=1807. MeV

Helicity asymmetry E for quasi-free neutron at W=1825. MeV

Helicity asymmetry E for quasi-free neutron at W=1843. MeV

Helicity asymmetry E for quasi-free neutron at W=1861. MeV

Helicity asymmetry E for quasi-free neutron at W=1879. MeV

Helicity asymmetry E for quasi-free neutron at W=1897. MeV

Helicity asymmetry E for quasi-free neutron at W=1915. MeV

The W' boson 95% CL limits on the product of cross section and branching fraction. The expected and observed limits are shown for the high VLQ mass range.

The W' boson 95% CL limits on the product of cross section and branching fraction. The expected and observed limits are shown for the low VLQ mass range.

Expected 95% CL limits for generalized hypotheses varying the fraction of tB (F(VLQ=B)) and bT (F(VLQ=T)) from the W′ decay

Expected 95% CL limits for generalized hypotheses varying the VLQ branching fraction to qH and qZ

Observed 95% CL limits for generalized hypotheses varying the fraction of tB (F(VLQ=B)) and bT (F(VLQ=T)) from the W′ decay

Observed 95% CL limits for generalized hypotheses varying the VLQ branching fraction to qH and qZ

The fraction of photon-induced background as compared with the total amount of DY signal plus photon-induced events ($N_{\gamma\gamma}/(N_{\gamma\gamma} + N_\mathrm{DY})$) in different dilepton mass bins. These numbers are averaged across the different years and channels.

Exclusion limits at 95% CL on the coupling K_L for a Z' in the sequential standard model as a function of the Z' mass.

Covariance matrix of the Absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $p_T^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $p_T^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $p_T^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $m^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $m^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $y^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $y^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $y^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $y^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $y^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $y^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $H_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $H_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $p_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $p_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $N^j$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $N^j$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $N^j$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $p_T^{j,1}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $p_T^{j,1}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $m(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $m(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute differential cross-section as a function of $p_T^{j,2}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the Absolute differential cross-section as function of $p_T^{j,2}$ at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative differential cross-section as a function of $p_T^{j,2}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.

Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.

Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 at particle level in the boosted topology, accounting for the statistical uncertainties.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t,h}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t,l}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t,l}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology.

Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology.

Differential cross section times dimuon branching fraction of Y(1S) as a function of $y^{Y}_{CM}$ in pPb collisions. The global uncertainty arises from the integrated luminosity uncertainty in pPb collisions.

Differential cross section times dimuon branching fraction of Y(2S) as a function of $y^{Y}_{CM}$ in pPb collisions. The global uncertainty arises from the integrated luminosity uncertainty in pPb collisions.

Differential cross section times dimuon branching fraction of Y(3S) as a function of $y^{Y}_{CM}$ in pPb collisions. The global uncertainty arises from the integrated luminosity uncertainty in pPb collisions.

Differential cross section times dimuon branching fraction of Y(1S) as a function of pT in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.

Differential cross section times dimuon branching fraction of Y(2S) as a function of pT in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.

Differential cross section times dimuon branching fraction of Y(3S) as a function of pT in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.

Differential cross section times dimuon branching fraction of Y(1S) as a function of $|y^{Y}_{CM}|$ in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.

Differential cross section times dimuon branching fraction of Y(2S) as a function of $|y^{Y}_{CM}|$ in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.

Differential cross section times dimuon branching fraction of Y(3S) as a function of $|y^{Y}_{CM}|$ in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.

Nuclear modification factor of Y(1S) as a function of pT. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(2S) as a function of pT. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(3S) as a function of pT. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(1S) as a function of $y^{Y}_{CM}$. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(2S) as a function of $y^{Y}_{CM}$. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(3S) as a function of $y^{Y}_{CM}$. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(1S) at forward and backward $y^{Y}_{CM}$ for pT < 6 GeV/c. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(2S) at forward and backward $y^{Y}_{CM}$ for pT < 6 GeV/c. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(3S) at forward and backward $y^{Y}_{CM}$ for pT < 6 GeV/c. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(1S) at forward and backward $y^{Y}_{CM}$ for 6 < pT < 30 GeV/c. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(2S) at forward and backward $y^{Y}_{CM}$ for 6 < pT < 30 GeV/c. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

Nuclear modification factor of Y(3S) at forward and backward $y^{Y}_{CM}$ for 6 < pT < 30 GeV/c. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

RFB of Y(1S) versus $N^{|\eta_{lab}|<2.4}_{tracks}$.

RFB of Y(2S) versus $N^{|\eta_{lab}|<2.4}_{tracks}$.

RFB of Y(3S) versus $N^{|\eta_{lab}|<2.4}_{tracks}$.

RFB of Y(1S) versus $E^{|\eta_{lab}|>4}_{T}$.

RFB of Y(2S) versus $E^{|\eta_{lab}|>4}_{T}$.

RFB of Y(3S) versus $E^{|\eta_{lab}|>4}_{T}$.

Nuclear modification factor of Y(1S), Y(2S), and Y(3S) integrated over pT and $y^{Y}_{CM}$. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.

The distribution of $\mathrm{p_{T}^{miss}}$ (GeV) in 2L1T MisID CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The distribution of $\mathrm{M_{T}}$ (GeV) in 3L OnZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The distribution of $\mathrm{H_{T}}$ (GeV) in 3L ttZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

Distribution of BDT score from the SS-M ($\mathrm{B_{e}=B_{\mu}=B_{\tau}}$) BDT for the 3L+2L1T CR events for the combined 2016-2018 data set. The 3L+2L1T CR consists of the 3L OnZ, 3L Z$\mathrm{\gamma}$, and 2L1T MisID CRs. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The distribution of visible diboson $\mathrm{p_{T}}$ (GeV) in 4L ZZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

Distribution of BDT score from the SS-M ($\mathrm{B_{e}=B_{\mu}=B_{\tau}}$) BDT for the 4L ZZ CR events for the combined 2016-2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The distribution of $\mathrm{L_{T}}$ in all seven multilepton channels for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton of $\mathrm{m_{\tau'}}$ = 1 TeV in the doublet scenario, before the fit, is also overlaid.

The distribution of $\mathrm{p_{T}^{miss}}$ in all seven multilepton channels for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermion of $\mathrm{m_{\Sigma}}$ = 1 TeV in the flavor-democratic scenario, before the fit, is also overlaid.

The distribution of $\mathrm{H_{T}}$ in all seven multilepton channels for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the scalar leptoquark of $mathrm{m_{S}}$ = 1 TeV coupled to a top quark and a $\tau$ lepton, before the fit, is also overlaid.

The distribution of $\mathrm{M_{OSSF}}$ in channels with at least one light lepton pair (4L, 3L1T, 3L, 2L2T, and 2L1T) for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermion of $\mathrm{m_{\Sigma}}$ = 1 TeV in the flavor-democratic scenario, before the fit, is also overlaid.

The $\mathrm{N_{b}}$ distribution in 4L, 3L1T, 3L, 2L2T, 2L1T, 1L3T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The invariant mass distribution of the opposite-sign same-flavor ($\mathrm{M_{OSSF}}$) tau lepton pair distribution in 2L2T, 1L3T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The $\mathrm{M_{T}^{12}}$ distribution in 4L, 3L1T, 3L, 2L2T, 2L1T, 1L3T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.

The $\mathrm{N_{b}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{L_{T}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{p_{T}^{miss}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{H_{T}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{M_{OSSF}}$ distribution in 3L, and 2L1T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The invariant mass distribution of the opposite-sign different-flavor ($\mathrm{M_{OSDF}}$) light lepton pair distribution in 3L, and 2L1T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The invariant mass distribution of the opposite-sign same-flavor ($\mathrm{M_{OSSF}}$) tau lepton pair distribution in 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The invariant mass distribution of the opposite-sign different-flavor ($\mathrm{M_{OSDF}}$) light lepton and tau lepton pair distribution in 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{M_{T}^{1}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{M_{T}^{12}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The model independent fundamental table categories for the combined 2016-2018 data set, as defined in Table 1. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The $\mathrm{N_{b}}$ distribution in 4L, 3L1T, 2L2T, and 1L3T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.

The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 1L2T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. An example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid. For this category, the signal yield is negligible and is not visible in the figure.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 1L2T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. An example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid. For this category, the signal yield is negligible and is not visible in the figure.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 1B/2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L1T, 2L2T, and 1L3T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 1B/2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L1T, 2L2T, and 1L3T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The VLL-L BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.

The VLL-L BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.

The VLL-L BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.

The VLL-M BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.

The VLL-M BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.

The VLL-M BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.

The VLL-H BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.

The VLL-H BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.

The VLL-H BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.

The VLL-L BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.

The VLL-L BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.

The VLL-L BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.

The VLL-M BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.

The VLL-M BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.

The VLL-M BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.

The VLL-H BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.

The VLL-H BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.

The VLL-H BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.

The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.

Observed and expected upper limits at 95%% CL on the production cross section for the type-III seesaw fermions in the flavor-democratic scenario using the table schemes and the BDT regions of the SS-M and the SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDTs. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.

Observed and expected upper limits at 95%% CL on the production cross section for the vector-like $\mathrm{\tau}$ leptons: doublet model. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks: $\mathrm{B_{\tau}=1}$ and $\mathrm{\beta=1}$. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks: $\mathrm{B_{e}=1}$ and $\mathrm{\beta=1}$. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks: $\mathrm{B_{\mu}=1}$ and $\mathrm{\beta=1}$. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\mathrm{\tau}$ leptons: singlet model. The limit is shown from the advanced $\mathrm{S_{T}}$ table for all masses.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the BDT regions.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the singlet scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the singlet scenario using the Fundamental $\mathrm{S_{T}}$ table.

Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the singlet scenario using the Advanced $\mathrm{S_{T}}$ table.

Observed lower limits at 95% CL on the mass of the type-III seesaw fermions in the plane defined by $\mathrm{B_{e}}$ and $\mathrm{B_{\tau}}$, with the constraint that $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$. These limits arise from the SS-H $\mathrm{B_{\tau}=1}$ BDT when $\mathrm{B_{\tau}\geq0.9}$, and by the SS-H $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$ BDT for the other decay branching fraction combinations.

Median Expected lower limits at 95% CL on the mass of the type-III seesaw fermions in the plane defined by $\mathrm{B_{e}}$ and $\mathrm{B_{\tau}}$, with the constraint that $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$. These limits arise from the SS-H $\mathrm{B_{\tau}=1}$ BDT when $\mathrm{B_{\tau}\geq0.9}$, and by the SS-H $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$ BDT for the other decay branching fraction combinations.

Acceptance times efficiency values for the major SM backgrounds WZ, ZZ, and ttZ in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample. The statistical uncertainty on the acceptance times efficiency values is insignificant with respect to the quoted precision.

Acceptance times efficiency values with statistical uncertainty for the vector-like $\mathrm{\tau}$ lepton model in the doublet scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the vector-like $\mathrm{\tau}$ lepton model in the singlet scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{(B_{e}=B_{\mu}=B_{\tau})}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{(B_{e}=1)}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{(B_{\mu}=1)}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1)}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Acceptance times efficiency values with statistical uncertainty for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.

The SR distributions of the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the Fundamental $\mathrm{S_{T}}$ table for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.

The SR distributions of the Advanced $\mathrm{S_{T}}$ table for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.