The $m_{\mathrm{eff}}$ distribution for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement in SR2. Distributions for data, the estimated SM backgrounds, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal region.

Expected 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}\tilde{\chi}_1^{0}$ masses in the context of the higgsino GGM scenario in SR0C. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}\tilde{\chi}_1^{0}$ masses in the context of the higgsino GGM scenario in SR0D. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/Z$ model for $\lambda_{12k} \neq 0$ RPV couplings. For the $\lambda_{12k} \neq 0$ case, the results from SR0B are adopted everywhere in the final exclusion limit contours, as is found to be the most powerful signal region among SR0A and SR0B in the majority of the signal grid points of this model.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/Z$ model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2. The results from the combination SR0B + SR1 + SR2 are finally adopted everywhere in the final exclusion limit contour since they provide the best expected limit.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/h$ model for $\lambda_{12k} \neq 0$ RPV couplings. For the $\lambda_{12k} \neq 0$ case, the results from SR0B are adopted everywhere in the final exclusion limit contours, as is found to be the most powerful signal region among SR0A and SR0B in the majority of the signal grid points of this model.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/h$ model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2. The results from the combination SR0B + SR1 + SR2 are finally adopted everywhere in the final exclusion limit contour since they provide the best expected limit.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the slepton/sneutrino model for $\lambda_{12k} \neq 0$ RPV couplings.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the slepton/sneutrino model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the gluino model for $\lambda_{12k} \neq 0$ RPV couplings.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the gluino model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2.

The best expected exclusion power between the overlapping SR0C and SR0D at each signal point as is adopted in the limit combination of the GGM higgsino model.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the GGM Higgsino models with BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=100% and BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=50% fulfilling the selection criteria of SR0C. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the GGM Higgsino models with BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=100% and BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=50% fulfilling the selection criteria of SR0D. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Cutflow event yields in regions SR0A and SR0B for RPV models with the $\lambda_{12k} \neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The last entries show the normalized number of events surviving the selection requirements of SR0A and SR0B.

Cutflow event yields in regions SR0C and SR0D for GGM Higgsino models with BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=100% or BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=50%, and $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^0\tilde{\chi}_1^0$ mass of 400 GeV. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The "Generator Filter" step is applied during the MC generation of the simulated events; the BR=100% sample has a generator filter of $\geq 4e/\mu$ leptons with $p_{\mathrm{T}}>4$ GeV and $|\eta|<2.8$, and the BR=50% sample has a generator filter of $\geq 4 e/\mu/\tau_{\mathrm{had-vis}}$ leptons with $p_{\mathrm{T}}(e,\mu)>4$ GeV, $p_{\mathrm{T}}(\tau_{\mathrm{had-vis}}^{\mathrm{visible}})>15$ GeV and $|\eta|<2.8$. The $ZZ$ selection cutflow step refers to the mass window cut for the leading and subleading $Z$ boson candidate between $81.2-101.2$ GeV and $61.2-101.2$ GeV, respectively. The last entries show the efficiency of events surviving the selection requirements defined in SR0C and SR0D.

Cutflow event yields in region SR1 for RPV models with the $\lambda_{i33} \neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The last entries show the normalized number of events surviving the selection requirements of SR1.

Cutflow event yields in region SR2 for RPV models with the $\lambda_{i33} \neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The last entries show the normalized number of events surviving the selection requirements of SR2.

Cross sections for the slepton/snuetrino model for different NLSP masses.

Expected 95% CL limit on cross section times acceptance times branching ratio for each width and mass of Gaussian signal shape tested, in the region defined by |y*|<0.3.

Observed 95% CL limit on cross section times acceptance times branching ratio for each width and mass of Gaussian signal shape tested, in the region defined by |y*|<0.6.

Expected 95% CL limit on cross section times acceptance times branching ratio for each width and mass of Gaussian signal shape tested, in the region defined by |y*|<0.6.

The test statistic q as a function of $\mu_{\mathrm{ttH}}$ for all decay modes at 7+8 TeV and at 13 TeV, separately, and for all decay modes at all CM energies. The expected SM result for the overall combination is also shown.

Expected and observed upper limits at the 95% CL on the pp --> X --> ZZ cross section as a function of $m_X$ with $\Gamma_X$=10 GeV in VBF production mode.

Expected and observed upper limits at the 95% CL on the pp --> X --> ZZ cross section as a function of $m_X$ with $\Gamma_X$=100 GeV with VBF fraction profiled.

Expected and observed upper limits at the 95% CL on the pp --> X --> ZZ cross section as a function of $m_X$ with $\Gamma_X$=100 GeV in VBF production mode.

Observed upper limits at the 95% CL on the pp --> X --> ZZ cross section as a function of $m_X$ and $\Gamma_X$ values when VBF fraction is profiled.

Observed upper limits at the 95% CL on the pp --> X --> ZZ cross section as a function of $m_X$ and $\Gamma_X$ values in VBF production mode.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 5, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 10, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 20, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 5, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 10, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 20, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the lepton specific, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For lepton specific only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the lepton specific, tanbeta 2, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For lepton specific only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the lepton specific, tanbeta 3, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For lepton specific only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 5, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 10, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 20, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 130 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 130 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 140 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 140 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 150 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 150 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 160 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 160 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 170 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 170 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 180 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 180 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 190 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 190 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 210 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 210 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 220 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 220 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 230 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 230 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 240 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 240 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 250 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 250 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 260 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 260 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 270 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 270 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 280 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 280 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 290 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 290 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 310 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 310 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 320 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 320 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 330 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 330 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 340 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 340 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 350 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 350 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 360 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 360 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 370 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 370 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 380 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 380 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 390 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 390 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 400 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 400 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 410 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 410 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 420 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 420 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 430 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 430 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 440 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 440 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 450 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 450 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 460 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 460 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 470 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 470 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 480 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 480 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 490 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 490 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 510 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 510 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 520 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 520 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 530 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 530 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 540 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 540 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 550 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 550 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 560 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 560 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 570 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 570 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 580 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 580 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 590 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 590 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 600 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 600 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 610 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 610 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 620 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 620 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 630 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 630 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 640 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 640 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 650 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 650 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 660 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 660 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 670 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 670 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 680 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 680 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 690 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 690 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 700 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 700 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 200 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 200 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 300 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 300 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 500 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 500 GeV for the nb >= 3 (3 tag) category.

Distributions of E_T^miss in the signal region of the had-had channel before the respective selection requirements, indicated by the vertical line and arrow, are applied. Here, &tau;₁ (&tau;₂) refers to the leading (subleading) &tau;_had. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty in the SM background. The error bars on the black data points represent the statistical uncertainty in the data yields. The dashed line shows the expected additional yields from a benchmark signal model. The rightmost bin includes the overflow.

<b>Exclusion contour (obs)</b> Expected (solid blue line) and observed (solid red line) exclusion-limit contours at 95% confidence level in the plane of top-squark and tau-slepton mass for the simplified model, obtained from the statistical combination of the lep-had and had-had channels, using full experimental and theory systematic uncertainties except the theoretical uncertainty in the signal cross section. The yellow band shows one-standard-deviation variations around the expected limit contour. The dotted red lines indicate how the observed limit moves when varying the signal cross section up or down by the corresponding uncertainty in the theoretical value. For comparison, the plot also shows the observed exclusion contour from the ATLAS Run-1 analysis as the area shaded in gray and the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments as a green band.

<b>Exclusion contour (exp)</b> Expected (solid blue line) and observed (solid red line) exclusion-limit contours at 95% confidence level in the plane of top-squark and tau-slepton mass for the simplified model, obtained from the statistical combination of the lep-had and had-had channels, using full experimental and theory systematic uncertainties except the theoretical uncertainty in the signal cross section. The yellow band shows one-standard-deviation variations around the expected limit contour. The dotted red lines indicate how the observed limit moves when varying the signal cross section up or down by the corresponding uncertainty in the theoretical value. For comparison, the plot also shows the observed exclusion contour from the ATLAS Run-1 analysis as the area shaded in gray and the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments as a green band.

Expected numbers of events from the SM background processes from the background fit and observed event yield in data for the signal regions in the lep-had and had-had channel, given for an integrated luminosity of 36.1 fb^-1. The expected yield for the signal model with m(t&#771;₁)=1100 GeV and m(&tau;&#771;₁)=590 GeV is shown for comparison. The uncertainties include both the statistical and systematic uncertainties and are truncated at zero. The total background from events with a fake tau lepton in the lep-had channel (fake &tau;_had + e /&mu;) is obtained from the fake-factor method.

Normalization factors obtained from the background-only fit. The normalization factor for tt&#772; events with fake tau leptons is only relevant for the had-had channel.

Left to right: observed 95% CL upper limits on the visible cross section (&lang; A &epsilon; &sigma; &rang;_obs⁹⁵) and on the number of signal events (S_obs⁹⁵ ). The third column (S_exp⁹⁵) shows the expected 95% CL upper limit on the number of signal events, given the expected number (and &plusmn; 1&sigma; excursions on the expectation) of background events. The last two columns indicate the CL_b value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)) and the corresponding significance (Z).

Signal acceptance A in percent for the signal region of the lep-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Signal acceptance A in percent for the signal region of the had-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Reconstruction efficiency &epsilon; in percent for the signal region of the lep-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Reconstruction efficiency &epsilon; in percent for the signal region of the had-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Upper exclusion limits at 95% confidence level on the observed cross section &sigma;_obs⁹⁵ for the simplified signal model as function of the top-squark mass m(t&#771;₁) and tau-slepton mass m(&tau;&#771;₁) in GeV.

Number of events passing each selection step of SR LH (lep-had channel) before (raw number) and after applying event-based weights (weighted) for signal point m(t&#771;₁) = 1100 GeV, m(&tau;&#771;₁) = 590 GeV. The preselection step here does not include the requirements on the tau p_T and the number of bjets.

Number of events passing each selection step of SR HH (had-had channel) before (raw number) and after applying event-based weights (weighted) for signal point m(t&#771;₁) = 1100 GeV, m(&tau;&#771;₁) = 590 GeV. The preselection step here does not include the requirements on the tau p_T and the number of bjets. Here, &tau;₁ (&tau;₂) refers to the leading (subleading) &tau;_had.

Distribution of $m_{jj}$ in data and simulation for events in the signal regions of the low-mass search. The points show the data while the filled histograms show the background contributions. The gray band shows the statistical and systematic uncertainties in the background, while the dashed vertical region ("Higgs") shows the expected SM Higgs boson mass range, which is excluded from this analysis. A 600 GeV bulk graviton signal prediction is represented by the black dashed histogram; for visibility, the signal cross-section is increased by a factor of 50. The background normalization is derived from the final fit to the $m_{VZ}$ observable in data.

The m$_{j}$ distributions of the events in data, compared to the expected background shape, for the high-mass analysis in the electron channel, for the high-purity category. The expected background shape is extracted from a fit to the data sidebands (Z+jets) or derived from simulation ("top quark" and "VV"). The dashed region around the background sum represents the uncertainty in the Z+jets distribution, while the dashed vertical region ("Higgs") shows the expected SM Higgs boson mass range, excluded from the analysis.

The m$_{j}$ distributions of the events in data, compared to the expected background shape, for the high-mass analysis in the electron channel, for the low-purity category. The expected background shape is extracted from a fit to the data sidebands (Z+jets) or derived from simulation ("top quark" and "VV"). The dashed region around the background sum represents the uncertainty in the Z+jets distribution, while the dashed vertical region ("Higgs") shows the expected SM Higgs boson mass range, excluded from the analysis.

The m$_{j}$ distributions of the events in data, compared to the expected background shape, for the high-mass analysis in the muon channel, for the high-purity category. The expected background shape is extracted from a fit to the data sidebands (Z+jets) or derived from simulation ("top quark" and "VV"). The dashed region around the background sum represents the uncertainty in the Z+jets distribution, while the dashed vertical region ("Higgs") shows the expected SM Higgs boson mass range, excluded from the analysis.

The m$_{j}$ distributions of the events in data, compared to the expected background shape, for the high-mass analysis in the muon channel, for the low-purity category. The expected background shape is extracted from a fit to the data sidebands (Z+jets) or derived from simulation ("top quark" and "VV"). The dashed region around the background sum represents the uncertainty in the Z+jets distribution, while the dashed vertical region ("Higgs") shows the expected SM Higgs boson mass range, excluded from the analysis.

Expected and observed distributions of the resonance candidate mass $m_{\text{VZ}}$ in the high-mass analysis, in the electron channel, high-purity category. The shaded area represents the post-fit uncertainty in the background. The expected contribution from W' signal candidates with mass $m_{\text{X}} = 2000$ GeV, normalized to a cross section of 100 fb$^{-1}$, is also shown.

Expected and observed distributions of the resonance candidate mass $m_{\text{VZ}}$ in the high-mass analysis, in the electron channel, low-purity category. The shaded area represents the post-fit uncertainty in the background. The expected contribution from W' signal candidates with mass $m_{\text{X}} = 2000$ GeV, normalized to a cross section of 100 fb$^{-1}$, is also shown.

Expected and observed distributions of the resonance candidate mass $m_{\text{VZ}}$ in the high-mass analysis, in the muon channel, high-purity category. The shaded area represents the post-fit uncertainty in the background. The expected contribution from W' signal candidates with mass $m_{\text{X}} = 2000$ GeV, normalized to a cross section of 100 fb$^{-1}$, is also shown.

Expected and observed distributions of the resonance candidate mass $m_{\text{VZ}}$ in the high-mass analysis, in the muon channel, low-purity category. The shaded area represents the post-fit uncertainty in the background. The expected contribution from W' signal candidates with mass $m_{\text{X}} = 2000$ GeV, normalized to a cross section of 100 fb$^{-1}$, is also shown.

Sideband $m_{ZV}$ distribution for the low-mass search in the merged V, untagged category, after fitting the sideband data alone. The points show the data while the filled histograms show the background contributions. Electron and muon categories are combined. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{ZV}$; the bin widths are indicated by the horizontal error bars.

Sideband $m_{ZV}$ distribution for the low-mass search in the merged V, tagged category, after fitting the sideband data alone. The points show the data while the filled histograms show the background contributions. Electron and muon categories are combined. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{ZV}$; the bin widths are indicated by the horizontal error bars.

Sideband $m_{ZV}$ distribution for the low-mass search in the resolved V, untagged category, after fitting the sideband data alone. The points show the data while the filled histograms show the background contributions. Electron and muon categories are combined. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{ZV}$; the bin widths are indicated by the horizontal error bars.

Sideband $m_{ZV}$ distribution for the low-mass search in the resolved V, tagged category, after fitting the sideband data alone. The points show the data while the filled histograms show the background contributions. Electron and muon categories are combined. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{ZV}$; the bin widths are indicated by the horizontal error bars.

The signal region $m_{VZ}$ distribution for the low-mass search, in the the merged V, untagged category, after fitting the signal and sideband regions. Electron and muon categories are combined. A 600 GeV bulk graviton signal prediction is represented by the black dashed histogram. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{VZ}$; the bin widths are indicated by the horizontal error bars.

The signal region $m_{VZ}$ distribution for the low-mass search, in the the merged V, tagged category, after fitting the signal and sideband regions. Electron and muon categories are combined. A 600 GeV bulk graviton signal prediction is represented by the black dashed histogram. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{VZ}$; the bin widths are indicated by the horizontal error bars.

The signal region $m_{VZ}$ distribution for the low-mass search, in the the resolved V, untagged category, after fitting the signal and sideband regions. Electron and muon categories are combined. A 600 GeV bulk graviton signal prediction is represented by the black dashed histogram. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{VZ}$; the bin widths are indicated by the horizontal error bars.

The signal region $m_{VZ}$ distribution for the low-mass search, in the the resolved V, tagged category, after fitting the signal and sideband regions. Electron and muon categories are combined. A 600 GeV bulk graviton signal prediction is represented by the black dashed histogram. The gray band indicates the statistical and post-fit systematic uncertainties in the normalization and shape of the background. Larger bin widths are used at higher values of $m_{VZ}$; the bin widths are indicated by the horizontal error bars.

Observed and expected 95% CL upper limit on $\sigma_{W'} Br(W' \to ZW)$ as a function of the resonance mass, taking into account all statistical and systematic uncertainties. The electron and muon channels and the various categories used in the analysis are combined together. The green (inner) and yellow (outer) bands represent the 68% and 95% coverage of the expected limit in the background-only hypothesis.

Observed and expected 95% CL upper limit on $\sigma_{G} Br(G \to ZZ)$ as a function of the resonance mass, taking into account all statistical and systematic uncertainties. The electron and muon channels and the various categories used in the analysis are combined together. The green (inner) and yellow (outer) bands represent the 68% and 95% coverage of the expected limit in the background-only hypothesis.

Observed local p-values for W' narrow resonances as a function of the resonance mass.

Observed local p-values for G narrow resonances as a function of the resonance mass.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of Additional jets.

Absolute cross section at particle level as a function of Additional jets.

Covariance matrix of absolute cross section at particle level as a function of Additional jets.

Covariance matrix of absolute cross section at particle level as a function of Additional jets.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at particle level as a function of $p_\text{T}(b_\text{l})$.

Absolute cross section at particle level as a function of $p_\text{T}(b_\text{l})$.

Absolute cross section at particle level as a function of $p_\text{T}(b_\text{h})$.

Absolute cross section at particle level as a function of $p_\text{T}(b_\text{h})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{1})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{1})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{2})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{2})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{3})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{3})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{4})$.

Absolute cross section at particle level as a function of $p_\text{T}(j_\text{4})$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.

Absolute cross section at particle level as a function of $|\eta(b_\text{l})|$.

Absolute cross section at particle level as a function of $|\eta(b_\text{l})|$.

Absolute cross section at particle level as a function of $|\eta(b_\text{h})|$.

Absolute cross section at particle level as a function of $|\eta(b_\text{h})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{W1})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{W1})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{W2})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{W2})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{1})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{1})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{2})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{2})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{3})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{3})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{4})|$.

Absolute cross section at particle level as a function of $|\eta(j_\text{4})|$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.

Absolute cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.

Absolute cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.

Covariance matrix of absolute cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{l})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{l})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of Additional jets.

Normalized cross section at particle level as a function of Additional jets.

Covariance matrix of normalized cross section at particle level as a function of Additional jets.

Covariance matrix of normalized cross section at particle level as a function of Additional jets.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of Additional jets vs. $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at particle level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at particle level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at particle level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at particle level as a function of $p_\text{T}(b_\text{l})$.

Normalized cross section at particle level as a function of $p_\text{T}(b_\text{l})$.

Normalized cross section at particle level as a function of $p_\text{T}(b_\text{h})$.

Normalized cross section at particle level as a function of $p_\text{T}(b_\text{h})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W1})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{W2})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{1})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{1})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{2})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{2})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{3})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{3})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{4})$.

Normalized cross section at particle level as a function of $p_\text{T}(j_\text{4})$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $p_\text{T}(\mathrm{jet})$.

Normalized cross section at particle level as a function of $|\eta(b_\text{l})|$.

Normalized cross section at particle level as a function of $|\eta(b_\text{l})|$.

Normalized cross section at particle level as a function of $|\eta(b_\text{h})|$.

Normalized cross section at particle level as a function of $|\eta(b_\text{h})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{W1})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{W1})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{W2})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{W2})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{1})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{1})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{2})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{2})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{3})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{3})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{4})|$.

Normalized cross section at particle level as a function of $|\eta(j_\text{4})|$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $|\eta(\text{jet})|$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{l})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(b_\text{h})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W1})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{W2})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{1})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{2})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{3})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.

Normalized cross section at particle level as a function of $\Delta R_{\text{j}_\text{t}}(j_\text{4})$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_{\text{j}_\text{t}}$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{l})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(b_\text{h})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W1})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{W2})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{1})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{2})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{3})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.

Normalized cross section at particle level as a function of $\Delta R_\text{t}(j_\text{4})$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.

Covariance matrix of normalized cross section at particle level as a function of Jet type vs. $\Delta R_\text{t}$.

gap fraction at particle level.

gap fraction at particle level.

Covariance matrix of gap fraction at particle level.

Covariance matrix of gap fraction at particle level.

gap fraction at particle level.

gap fraction at particle level.

Covariance matrix of gap fraction at particle level.

Covariance matrix of gap fraction at particle level.

jet multiplicities for $p_{T}(jet) > 30.0$ GeV.

jet multiplicities for $p_{T}(jet) > 30.0$ GeV.

jet multiplicities for $p_{T}(jet) > 50.0$ GeV.

jet multiplicities for $p_{T}(jet) > 50.0$ GeV.

jet multiplicities for $p_{T}(jet) > 75.0$ GeV.

jet multiplicities for $p_{T}(jet) > 75.0$ GeV.

jet multiplicities for $p_{T}(jet) > 100.0$ GeV.

jet multiplicities for $p_{T}(jet) > 100.0$ GeV.

Covariance matrix of jet multiplicities with different pT(jet) thresholds.

Covariance matrix of jet multiplicities with different pT(jet) thresholds.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of absolute cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of absolute cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of absolute cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{high})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{low})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{l})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{l})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Covariance matrix of normalized cross section at the parton level as a function of $|y(\text{t}_\text{h})|$ vs. $p_\text{T}(\text{t}_\text{h})$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Covariance matrix of normalized cross section at the parton level as a function of $M(\text{t}\bar{\text{t}})$ vs. $|y(\text{t}\bar{\text{t}})|$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Covariance matrix of normalized cross section at the parton level as a function of $p_\text{T}(\text{t}_\text{h})$ vs. $M(\text{t}\bar{\text{t}})$.

Normalized dijet angular distribution for events with 4.2 < dijet mass < 4.8 TeV.

Normalized dijet angular distribution for events with 3.6 < dijet mass < 4.2 TeV.

Normalized dijet angular distribution for events with 3.0 < dijet mass < 3.6 TeV.

Normalized dijet angular distribution for events with 2.4 < dijet mass < 3.0 TeV.

The 95% CL upper limits on the quark coupling gq, as a function of mass, for an axialvector or vector DM mediator with gDM = 1.0 and mDM = 1 GeV.

Electroweak correction factors for events with dijet mass > 6.0 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Electroweak correction factors for events with 5.4 < dijet mass < 6.0 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Electroweak correction factors for events with 4.8 < dijet mass < 5.4 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Electroweak correction factors for events with 4.2 < dijet mass < 4.8 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Electroweak correction factors for events with 3.6 < dijet mass < 4.2 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Electroweak correction factors for events with 3.0 < dijet mass < 3.6 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Electroweak correction factors for events with 2.4 < dijet mass < 3.0 TeV computed by the authors of JHEP 11 (2012) 095 (arXiv:1210.0438).

Covariance matrix for 1 < CHI < 2.

Covariance matrix for 2 < CHI < 3.

Covariance matrix for 3 < CHI < 4.

Covariance matrix for 4 < CHI < 5.

Covariance matrix for 5 < CHI < 6.

Covariance matrix for 6 < CHI < 7.

Covariance matrix for 7 < CHI < 8.

Covariance matrix for 8 < CHI < 9.

Covariance matrix for 9 < CHI < 10.

Covariance matrix for 10 < CHI < 12.

Covariance matrix for 12 < CHI < 14.

Covariance matrix for 14 < CHI < 16.

NLO QCD+EW prediction and benchmark signal predictions for dijet mass > 6.0 TeV.

NLO QCD+EW prediction and benchmark signal predictions for 5.4 < dijet mass < 6.0 TeV.

NLO QCD+EW prediction and benchmark signal predictions for 4.8 < dijet mass < 5.4 TeV.

NLO QCD+EW prediction and benchmark signal predictions for 4.2 < dijet mass < 4.8 TeV.

NLO QCD+EW prediction and benchmark signal predictions for 3.6 < dijet mass < 4.2 TeV.

NLO QCD+EW prediction and benchmark signal predictions for 3.0 < dijet mass < 3.6 TeV.

NLO QCD+EW prediction and benchmark signal predictions for 2.4 < dijet mass < 3.0 TeV.