Expected +- 1 sigma 95% CL exclusion limit for the Zprime-2HDM model.

Expected +- 2 sigma 95% CL exclusion limit for the Zprime-2HDM model.

Observed 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected +- 1 sigma 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected +- 2 sigma 95% CL exclusion limit for the 2HDM+a model ggF production.

Observed 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected +- 1 sigma 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected +- 2 sigma 95% CL exclusion limit for the 2HDM+a model bbA production.

Observed 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected +- 1 sigma 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected +- 2 sigma 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected and observed upper limits at 95% CL on cross-section for Zprime-2HDM model.

Expected and observed upper limits at 95% CL on cross-section for ggF producton in the 2HDM+a model.

Expected and observed upper limits at 95% CL on cross-section for bbA producton in the 2HDM+a model.

Model-independent upper limits on the visible cross-section $σ_{vis, $h(\bar{b})+DM} ≡ σ_{h+DM} \times B(h \to b\bar{b}) \times \mathcal{A} \times \epsilon$ in the different signal regions.

Theory cross-section for Zprime-2HDM model.

Theory cross-section for bbA production in the 2HDM+a model.

Theory cross-section for ggF production in the 2HDM+a model.

Distribution of Higgs boson candidate mass in 2b region with MET=150-200 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=200-350 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=350-500 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=500-750 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET > 750 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=150-200 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=200-350 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=350-500 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET > 500 GeV.

Yields in 1-lepton control region.

Yields in 2-lepton control region.

MET distribution in 2b region of the 0-lepton channel.

MET distribution in 3b region of the 0-lepton channel.

Expected signal yields after certain selection cuts in 2b region with MET=150-200 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=200-350 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=350-500 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=500-750 GeV.

Expected signal yields after certain selection cuts in 2b region with MET > 750 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=150-200 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=200-350 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=350-500 GeV.

Expected signal yields after certain selection cuts in 3b region with MET > 500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET > 750 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET>500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET > 750 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET > 500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET > 750 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET > 500 GeV.

Transverse profile for 70 GeV < pT < 100 GeV.

Longitudinal profile for pT > 100 GeV.

Transverse profile for pT > 100 GeV.

Average longitudinal profile

Transverse profile for pT > 100 GeV.

Difference between the measured masses of top quarks and antiquarks.

Ratio of the measured masses of top anitiquarks to quarks.

Comparison of measurements of the top quark mass by ATLAS and CMS Collaborations in various event topologies and center-of-mass energies.

Comparison of the mass difference between top quarks and antiquarks measured by ATLAS and CMS colaborations in various event topologies and center-of-mass energies.

Event yields corresponding to positively and negatively charged muons in the final states obtained from simulated signal and background processes, as well as in data for the 2J1T event category.

Event yields corresponding to positively and negatively charged electron in the final states obtained from simulated signal and background processes, as well as in data for the 2J1T event category.

Summary of systematic uncertainties in GeV for each final-state lepton charge configuration. With the exception of the flavor-dependent JES sources, the total systematic uncertainty is obtained from the sum in quadrature of the individual systematic sources. The statistical uncertainties in the systematic shifts are quoted within parentheses whenever alternative simulated samples with systematic variations have been used.

Correlations in % among the BDT input variables used for the muon final state in signal events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in signal events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in background events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in background events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in signal events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in signal events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in background events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables in background events for the elecron final state in the 2J1T category after decorrelation.

Correlations in % among the parameter of interest and the nuisance parameters corresponding to the fit to data for the $\ell^{\pm}$ final state in the 2J1T event category.

Fitted event-level b-tagging selection efficiency as a function of the dijet invariant mass. The uncertainties are evaluated by propagating the uncertainties on the fit parameters to the fitted efficiency.

Distributions of $s_{\text{T}}$ in the single-tau one-bin SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Distributions of $m_{\text{T}}(\tau)$ in the single-tau one-bin SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Distributions of $\Sigma m_{\text{T}}(b_{1,2})$ in the single-tau $p_{\text{T}}(\tau)$-binned SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Distributions of $p_{\text{T}}(\tau)$ in the single-tau $p_{\text{T}}(\tau)$-binned SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Observed event yields in data ('Observed') and expected event yields for SM background processes obtained from the background-only fit ('Total bkg.' and rows below) in the signal regions of the di-tau and single-tau channels. The quoted uncertainties include both the statistical and systematic uncertainties and are truncated at zero yield. By construction, no $t\bar{t}$ (2 real $\tau$) events can pass the selections in the single-tau channel. As the individual uncertainties are correlated, they do not add in quadrature to equal the total background uncertainty.

From left to right: upper limits at the 95% confidence level (CL) on the visible cross section ($\sigma_\text{vis}$) and on the number of signal events ($S_{\text{obs}}^{95}$). The third column ($S_{\text{exp}}^{95}$) shows the upper limit at the 95% CL on the number of signal events, given the expected number (and $\pm 1\,\sigma$ excursions on the expectation) of background events. The last two columns indicate the confidence level observed for the background-only hypothesis ($\text{CL}_{b}$), the discovery $p$-value ($p(s=0)$) and the significance ($Z$). In the di-tau SR, where fewer events are observed than predicted by the fitted background estimate, the $p$-value is capped at 0.5.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for the minimal-coupling scenario. The limits are derived from the binned single-tau signal region.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for the minimal-coupling scenario. The limits are derived from the binned single-tau signal region.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for vector leptoquarks with additional gauge couplings. The limits are derived from the binned single-tau signal region.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for vector leptoquarks with additional gauge couplings. The limits are derived from the binned single-tau signal region.

Exclusion contours at the 95% confidence level for the stop-stau signal model as a function of the masses of the top squark $m(\tilde{t}_{1})$ and of the tau slepton $m(\tilde{\tau}_{1})$. Expected and observed limits are shown for the present search in comparison to observed limits from previous ATLAS analyses based on data from Run-1 of the LHC at $\sqrt{s} = 8$ TeV [Eur. Phys. J. C 76 (2016)] and on a partial dataset from Run 2 at $\sqrt{s} = 13$ TeV [Phys. Rev. D 98 (2018) 032008]. The green band indicates the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments.

Exclusion contours at the 95% confidence level for the stop-stau signal model as a function of the masses of the top squark $m(\tilde{t}_{1})$ and of the tau slepton $m(\tilde{\tau}_{1})$. Expected and observed limits are shown for the present search in comparison to observed limits from previous ATLAS analyses based on data from Run-1 of the LHC at $\sqrt{s} = 8$ TeV [Eur. Phys. J. C 76 (2016)] and on a partial dataset from Run 2 at $\sqrt{s} = 13$ TeV [Phys. Rev. D 98 (2018) 032008]. The green band indicates the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{u}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for up-type leptoquarks $\text{LQ}_{3}^{\text{u}})$ with charge $+2/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{u}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for up-type leptoquarks $\text{LQ}_{3}^{\text{u}})$ with charge $+2/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{d}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for down-type leptoquarks $\text{LQ}_{3}^{\text{d}})$ with charge $-1/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{d}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for down-type leptoquarks $\text{LQ}_{3}^{\text{d}})$ with charge $-1/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Upper limits on the signal cross section at the 95 % confidence level for the stop-stau signal model.

Upper limits on the signal cross section at the 95 % confidence level for the scalar third-generation leptoquark signal model with up-type leptoquarks.

Upper limits on the signal cross section at the 95 % confidence level for the scalar third-generation leptoquark signal model with down-type leptoquarks.

Upper limits on the signal cross section at the 95 % confidence level for the vector third-generation leptoquark signal model with minimal coupling (MC).

Upper limits on the signal cross section at the 95 % confidence level for the vector third-generation leptoquark signal model with additional gauge couplings (YM).

Acceptance of the one-bin signal region of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the one-bin signal region of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the signal region of the di-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the one-bin signal region of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the signal region of the di-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the one-bin signal region of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the signal region of the di-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the signal region of the di-tau channel for pair production of top squarks with decays via tau sleptons.

Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the di-tau SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the single-tau one-bin SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the single-tau multi-bin SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the di-tau SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau one-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau multi-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the di-tau SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau one-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau multi-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the di-tau SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the single-tau one-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the single-tau multi-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the di-tau SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the single-tau one-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the single-tau multi-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

The rejection factor (inverse of the efficiency) for jets that have the other origins (background jets) are shown. The background jet rejection factor is calculated using pre-selected large-$R$ jets in the sample of simulated $Z\rightarrow\nu\nu$ + jets events, dominated by initial state radiation (ISR) jets. The efficiency correction factors are applied on the background rejection for the $W_{qq}$/$Z_{qq}$-tagging. The systematic uncertainty is represented by the hashed bands.

The boson-tagging efficiency for jets arising from $Z/h$ bosons decaying into $b\bar{b}$ (signal jets). The signal jet efficiency of $Z_{bb}$/$h_{bb}$-tagging is evaluated using a sample of pre-selected large-$R$ jets ($p_{\textrm{T}}>200~\textrm{GeV}, |\eta|<2.0, m_{J} > 40~\textrm{GeV}$) in the simulated $(\tilde{W},\tilde{B})$ simplified model signal events with $\Delta m(\tilde{\chi}_{\textrm{heavy}},~\tilde{\chi}_{\textrm{light}}) \ge 400~\textrm{GeV}$. The jets are matched with generator-level $Z/h$-bosons by $\Delta R<1.0$ which decay into $b\bar{b}$. The systematic uncertainty is represented by the hashed bands.

The rejection factor (inverse of the efficiency) for jets that have the other origins (background jets) are shown. The background jet rejection factor is calculated using pre-selected large-$R$ jets in the sample of simulated $Z\rightarrow\nu\nu$ + jets events, dominated by initial state radiation (ISR) jets. As for the $Z_{bb}$/$h_{bb}$-tagging, the rejection is shown as the function of number of $b$- or $c$-quarks contained in the large-$R$ jet within $\Delta R<1.0$. The systematic uncertainty is represented by the hashed bands.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The total post-fit uncertainty in each of the SRs and VRs.

Summary of the observed data and predicted SM background in all SRs. The background prediction in SR-4Q (SR-2B2Q) is obtained by a background-only fit to CR0L-4Q (CR0L-2B2Q). The total systematic uncertainty on the background prediction is shown by the hatched area. Distributions of a few representative signals are overlaid. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\textrm{GeV}$. The bottom panel shows the statistical significance of the discrepancy between the observed number of events and the SM expectation.

Number of observed data events and the SM backgrounds in the SRs and the CR0L bins. The SM backgrounds are predicted by the background-only fits. Note that the relative uncertainty on the expected yield in the CRs between the reducible backgrounds are identical, given that a common normalization factor is assigned for all of them in the fit. Yields for negligible backgrounds are indicated by "0.0000001±0.0000001" in the table.

Number of observed data events and the post-fit SM background prediction in the VR1L(1Y) bins and the corresponding CR1L(1Y) bins. "-" indicates negligibly small contribution. Note that the relative uncertainty on the expected yield in the CRs between the reducible backgrounds are identical, given that a common normalization factor is assigned for all of them in the fit. Yields for negligible backgrounds are indicated by "0.0000001±0.0000001" in the table.

$m_{\textrm{eff}}$ distribution in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{1})$ in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $D_2(J_{1})$ in SR-4Q-VV. For $D_2$, the cut value applied for $V_{qq}$-tagging ($D_{2,\text{cut}}$) is subtracted as the off-set so that $D_2-D_{2,\text{cut}}(J)<0$ represents $J$ passing the $D_2$ selection. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{2})$ in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $D_2(J_{2})$ in SR-4Q-VV. For $D_2$, the cut value applied for $V_{qq}$-tagging ($D_{2,\text{cut}}$) is subtracted as the off-set so that $D_2-D_{2,\text{cut}}(J)<0$ represents $J$ passing the $D_2$ selection. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

$m_{\textrm{T2}}$ distributions in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{bb})$ in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m_{\textrm{eff}} $ in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

$m_{\textrm{T2}}$ distributions in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{bb})$ in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m_{\textrm{eff}} $ in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=75$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=75$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=50$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=50$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=25$% as no mass point on the plane can be excluded.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1C1-WW). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-WZ). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-Wh). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{H},\tilde{G})$ models. The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1C1-WW). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-WZ). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-Wh). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{H},\tilde{G})$ models. The black numbers represents the observed cross-section upper-limits.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1C1-WW) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-Wh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-Zh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-4Q-VV, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-2B2Q-VZ, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-2B2Q-Vh, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1C1-WW) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-Wh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-Zh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-4Q-VV. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-2B2Q-VZ. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-2B2Q-Vh. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

differential cross sections.

Correlation between the Wilson coefficients (in %), after the 5D fit to data.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

The correlation matrix for the Higgs pT measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The correlation matrix for the jet multiplicity measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The correlation matrix for the leading jet pT measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The fiducial integrated signal strength and cross section.