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.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of inclusive jet multiplicity, $N_{\text{jets}}^{\text{min}}$.

Measured cross section for Z+jets as a function of the transverse momementum of the Z boson, $p_{\text{T}}(\text{Z})$, for events with at least one jet and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momementum of the Z boson, $p_{\text{T}}(\text{Z})$, for events with at least one jet.

Measured cross section for Z+jets as a function of the transverse momenum of the first jet, $p_{\text{T}}(\text{j}_1)$, and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momenum of the first jet, $p_{\text{T}}(\text{j}_1)$.

Measured cross section for Z+jets as a function of the transverse momenum of the second jet, $p_{\text{T}}(\text{j}_2)$, and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momenum of the second jet, $p_{\text{T}}(\text{j}_2)$.

Measured cross section for Z+jets as a function of the transverse momenum of the third jet, $p_{\text{T}}(\text{j}_3)$, and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momenum of the third jet, $p_{\text{T}}(\text{j}_3)$.

Measured cross section for Z+jets as a function of the absolute rapidity of the first jet, $|y(\text{j}_1)|$, and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the absolute rapidity of the first jet, $|y(\text{j}_1)|$.

Measured cross section for Z+jets as a function of the absolute rapidity of the second jet, $|y(\text{j}_2)|$, and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the absolute rapidity of the second jet, $|y(\text{j}_2)|$.

Measured cross section for Z+jets as a function of the absolute rapidity of the third jet, $|y(\text{j}_3)|$, and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the absolute rapidity of the third jet, $|y(\text{j}_3)|$.

Measured cross section for Z+jets as a function of the $H_{\text{T}}$ observable for event with at least one jet and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the $H_{\text{T}}$ observable for event with at least one jet.

Measured cross section for Z+jets as a function of the $H_{\text{T}}$ observable for event with at least two jets and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the $H_{\text{T}}$ observable for event with at least two jets.

Measured cross section for Z+jets as a function of the $H_{\text{T}}$ observable for event with at least three jets and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the $H_{\text{T}}$ observable for event with at least three jets.

Measured cross section for Z+jets as a function of the transverse momentum balance between the Z boson and the accompanying jets, $p_{\text{T}}^{\text{bal}}$, for events with at least one jet and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momentum balance between the Z boson and the accompanying jets, $p_{\text{T}}^{\text{bal}}$, for events with at least one jet.

Measured cross section for Z+jets as a function of the transverse momentum balance between the Z boson and the accompanying jets, $p_{\text{T}}^{\text{bal}}$, for events with at least two jets and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momentum balance between the Z boson and the accompanying jets, $p_{\text{T}}^{\text{bal}}$, for events with at least two jets.

Measured cross section for Z+jets as a function of the transverse momentum balance between the Z boson and the accompanying jets, $p_{\text{T}}^{\text{bal}}$, for events with at least three jets and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the transverse momentum balance between the Z boson and the accompanying jets, $p_{\text{T}}^{\text{bal}}$, for events with at least three jets.

Measured cross section for Z+jets as a function of the JZB variable with no restriction on $p_{\text{T}}(\text{Z})$ and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the JZB variable with no restriction on $p_{\text{T}}(\text{Z})$.

Measured cross section for Z+jets as a function of the JZB variable for $p_{\text{T}}(\text{Z})\leq50\,$GeV) and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the JZB variable for $p_{\text{T}}(\text{Z})\leq50\,$GeV).

Measured cross section for Z+jets as a function of the JZB variable for $p_{\text{T}}(\text{Z})>50\,$GeV and breakdown of the relative uncertainty.

Bin-to-bin correlation in the measured cross section for Z+jets as a function of the JZB variable for $p_{\text{T}}(\text{Z})>50\,$GeV.

The observed and expected 95% CL upper limits on the production cross section times branching ratio for SM non-resonant Higgs pair production.

The expected and observed constraints on chargino lifetime and mass. The ratio of the vacuum expectation values of the two Higgs doublets, $\tan \beta$, is fixed to 5 with $\mu > 0$, where $\mu$ is the higgsino mass parameter. The direct chargino production cross section includes both $\widetilde{\chi}^{0}_{1}\widetilde{\chi}^\pm_{1}$ and $\widetilde{\chi}^\pm_{1}\widetilde{\chi}^\mp_{1}$ production in roughly a 2:1 ratio for all chargino masses considered, and the branching fraction of $\widetilde{\chi}^\pm_{1} \rightarrow \widetilde{\chi}^{0}_{1}\mathrm{\pi^{\pm}}$ is set to 100%. Chargino masses that are less than those on the curve are excluded at 95% CL.

The observed 95% CL upper limits on the product of the cross section for direct production of charginos and their branching fraction to $\widetilde{\chi}^{0}_{1}\mathrm{\pi^{\pm}}$ as a function of chargino mass and lifetime. The ratio of the vacuum expectation values of the two Higgs doublets, $\tan \beta$, is fixed to 5 with $\mu > 0$, where $\mu$ is the higgsino mass parameter. The direct chargino production cross section includes both $\widetilde{\chi}^{0}_{1}\widetilde{\chi}^\pm_{1}$ and $\widetilde{\chi}^\pm_{1}\widetilde{\chi}^\mp_{1}$ production in roughly a 2:1 ratio for all chargino masses considered, and the branching fraction of $\widetilde{\chi}^\pm_{1} \rightarrow \widetilde{\chi}^{0}_{1}\mathrm{\pi^{\pm}}$ is set to 100%.

Signal acceptance for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, with no generator-level filtering, while the numerator is the number of those events that are selected by the disappearing track signal selection, after being processed with the same reconstruction software used on the data. The acceptance corresponds to the 2015 data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, with no generator-level filtering, while the numerator is the number of those events that are selected by the disappearing track signal selection, after being processed with the same reconstruction software used on the data. The acceptance corresponds to the 2016A data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, with no generator-level filtering, while the numerator is the number of those events that are selected by the disappearing track signal selection, after being processed with the same reconstruction software used on the data. The acceptance corresponds to the 2016B data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events generated, with no generator-level filtering, while the numerator is the number of those events that are selected by the disappearing track signal selection, after being processed with the same reconstruction software used on the data. The acceptance corresponds to the 2015 data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events generated, with no generator-level filtering, while the numerator is the number of those events that are selected by the disappearing track signal selection, after being processed with the same reconstruction software used on the data. The acceptance corresponds to the 2016A data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events generated, with no generator-level filtering, while the numerator is the number of those events that are selected by the disappearing track signal selection, after being processed with the same reconstruction software used on the data. The acceptance corresponds to the 2016B data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2015 data-taking period, corresponding to $2.7\,\text{fb}^{-1}$, after the application of each of the disappearing track selections for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$. The selections listed are cumulative, i.e., only the events and objects passing a given selection are considered in subsequent selections. The uncertainties shown include only the statistical uncertainty resulting from the limited sizes of the generated samples.

Predicted signal yields for the 2016A data-taking period, corresponding to $8.3\,\text{fb}^{-1}$, after the application of each of the disappearing track selections for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$. The selections listed are cumulative, i.e., only the events and objects passing a given selection are considered in subsequent selections. The uncertainties shown include only the statistical uncertainty resulting from the limited sizes of the generated samples.

Predicted signal yields for the 2016B data-taking period, corresponding to $27.4\,\text{fb}^{-1}$, after the application of each of the disappearing track selections for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$. The selections listed are cumulative, i.e., only the events and objects passing a given selection are considered in subsequent selections. The uncertainties shown include only the statistical uncertainty resulting from the limited sizes of the generated samples.

(a) Centrality dependence of $v^{even}_{1}$ for $0.4 \lt p_{T} \lt 0.7$ GeV/c for Au+Au collisions at $\sqrt{s_{_{NN}}} = 200, 39$ and $19.6$ GeV; (b) $K$ vs. $\langle N_{ch} \rangle^{-1}$ for the $v^{even}_{1}$ values shown in (a). The $\langle N_{ch} \rangle$ values correspond to the centrality intervals indicated in panel (a).

Comparison of the $\sqrt{s_{_{NN}}}$ dependence of $v^{even}_{1}$ and $v_3$ for $0.4 \lt p_{T} \lt 0.7$ GeV/c in 0-10 central Au+Au collisions.

The elliptic flow, $v_{2}$, for $\Omega^{-}$ as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow, $v_{2}$, for $D^{0}$ mesons as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $K^{0}_{S}$ as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $\Lambda$ as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $\Xi^{-}$ as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $\Omega^{-}$ as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $D^{0}$ mesons as a function of $p_{T}$ in pPb collision at 8.16 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $K^{0}_{S}$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in pPb collision at 8.16 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $\Lambda$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in pPb collision at 8.16 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $\Xi^{-}$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in pPb collision at 8.16 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $\Omega^{-}$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in pPb collision at 8.16 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $D^{0}$ meson as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in pPb collision at 8.16 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $K^{0}_{S}$ as a function of $p_{T}$ in PbPb collision at 5.02 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $\Lambda$ as a function of $p_{T}$ in PbPb collision at 5.02 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $\Xi^{-}$ as a function of $p_{T}$ in PbPb collision at 5.02 TeV.

The elliptic flow after correcting back-to-back jet contribution, $v_{2}^{sub}$, for $\Omega^{-}$ as a function of $p_{T}$ in PbPb collision at 5.02 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $K^{0}_{S}$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in PbPb collision at 5.02 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $\Lambda$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in PbPb collision at 5.02 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $\Xi^{-}$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in PbPb collision at 5.02 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $\Omega^{-}$ as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in PbPb collision at 5.02 TeV.

The elliptic flow per constituent quark after correcting back-to-back jet contribution, $v_{2}^{sub}/n_{q}$, for $D^{0}$ meson as a function of transverse kinetic energy per constituent quark $KE_{T}/n_{q}$ in PbPb collision at 5.02 TeV.

Acceptance for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Acceptance for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Acceptance for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Detector efficiency for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Detector efficiency for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Detector efficiency for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Detector efficiency for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Detector efficiency for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Detector efficiency for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR1 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR1 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR2 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR2 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR3 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR3 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR4 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR4 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR5 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

SR5 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Upper limits on signal cross sections and exclusion limits at 95% CL for SR1 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Upper limits on signal cross sections and exclusion limits at 95% CL for SR2 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Upper limits on signal cross sections and exclusion limits at 95% CL for SR3 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Upper limits on signal cross sections and exclusion limits at 95% CL for SR4 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Upper limits on signal cross sections and exclusion limits at 95% CL for SR5 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Upper limits on signal cross sections and exclusion limits at 95% CL for the best expected SR in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

Minimum branching ratio excluded at 95% CL, assuming no sensitivity for other decay possibilities, in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.

The signal region with the best expected CLS value for each signal in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.

Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.

Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.

Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR1. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.

Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR2. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.

Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR3. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.

Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR4. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.

Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR5. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.

Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (450,425)$ GeV signal point for signal region SR1.

Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,420)$ GeV signal point for signal region SR2.

Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,350)$ GeV signal point for signal region SR3.

Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (600,350)$ GeV signal point for signal region SR4.

Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (900,1)$ GeV signal point for signal region SR5.

Efficiencies for qqbar->X(J=1)->Wgamma signal events to pass the category selections as a function of the resonance mass.

Efficiencies for qqbar->X(J=1)->Hgamma signal events to pass the category selections as a function of the resonance mass.

Distribution of the reconstructed J+gamma invariant mass in Z+gamma search BTAG category.

Distribution of the reconstructed J+gamma invariant mass in Z+gamma search D2 category.

Distribution of the reconstructed J+gamma invariant mass in Z+gamma search VMASS category.

Distribution of the reconstructed J+gamma invariant mass in Z+gamma search ELSE category.

Distribution of the reconstructed J+gamma invariant mass in W+gamma search D2 category.

Distribution of the reconstructed J+gamma invariant mass in W+gamma search VMASS category.

Distribution of the reconstructed J+gamma invariant mass in W+gamma search ELSE category.

Distribution of the reconstructed J+gamma invariant mass in H+gamma search BTAG category.

The 95% CL upper limits on cross section times branching ratio for spin-0 resonance decaying to Zgamma.

The 95% CL upper limits on cross section times branching ratio for gluon-gluon fusion production spin-2 resonance decaying to Zgamma.

The 95% CL upper limits on cross section times branching ratio for quark-antiquark annihilation production spin-2 resonance decaying to Zgamma.

The 95% CL upper limits on cross section times branching ratio for spin-1 resonance decaying to Wgamma.

The 95% CL upper limits on cross section times branching ratio for spin-1 resonance decaying to Hgamma.