Fraction of events where both bosons are longitudinally polarized in the region with $p_T^Z>200$ GeV using two unconstrained parameters.

Numbers of observed and expected events in the 00-enhanced signal regions, before the fit. The total uncertainties are quoted.

Summary of the relative uncertainties in the measured longitudinal-longitudinal fractions $f_{00}$. The uncertainties are reported as percentages.

Numbers of observed and expected events in the 00-enhanced signal regions, after the fit. The total uncertainties are quoted.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of the TT state.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of the TT state.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of the TT state.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of of the sum of all polarization.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of of the sum of all polarization.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of of the sum of all polarization.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of the TT state.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of the TT state.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of the TT state.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of of the sum of all polarization.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of of the sum of all polarization.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of of the sum of all polarization.

The $\mathcal{D}$ value for the unfolded $|\Delta Y(l_{W}Z)|$ distributions of the TT polarization state as a function of the $p_T^{WZ}$ cut value.

The $\mathcal{D}$ value for the unfolded $|\Delta Y(WZ)|$ distributions of the TT polarization state as a function of the $p_T^{WZ}$ cut value.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Observed and expected 95% CL upper limits on the visible $\tau\nu$ production cross section, $\sigma_{\rm vis} = \sigma(pp \to \tau\nu +X) \cdot \mathcal{A} \cdot \varepsilon$, for $m_{\rm T}$ thresholds ranging from 200 to 2950 GeV. See HepData abstract for details on how to use this data for reinterpretation.

Observed 95% CL upper limits on the cross-section times branching ratio as a function of the W′ mass in several NUGIM models defined by the value of the parameter cot$\theta$.

Expected 95% CL upper limits on the cross-section times branching ratio as a function of the W′ mass in several NUGIM models defined by the value of the parameter cot$\theta$.

Acceptance for $W^{\prime}_{\rm SSM}$ as a function of the $W^{\prime}_{\rm SSM}$ mass, shown after successively applying selection at generator-level. The acceptance times efficiency is calculated with respect to all $W^{\prime}_{\rm SSM} \to \tau\nu$ events with a generated $\tau\nu$ mass above 25 GeV. The "selected tau" criteria include the requirement of a $\tau_{\rm had-vis}$ with $p_{\rm T}$ > 30 GeV and $|\eta|$ < 2.4.

Acceptance times efficiency for $W^{\prime}_{\rm SSM}$ as a function of the $W^{\prime}_{\rm SSM}$ mass, shown after successively applying selection at reconstruction-level. The acceptance times efficiency is calculated with respect to all $W^{\prime}_{\rm SSM} \to \tau\nu$ events with a generated $\tau\nu$ mass above 25 GeV. "Preselection" includes all criteria prior to those shown.

Distributions of $S_{\mathrm{ML}}$ for data, simulated background and signal events with $n_{\mathrm{track}}$ of 4. The distributions are shown for split-SUSY signals with a gluino mass of 2000 GeV and neutralino mass of 1800 GeV. Different gluino proper decay lengths are shown as $c\tau$ in the legend. All distributions are normalized to unity.

Distributions of $S_{\mathrm{ML}}$ for data, simulated background and signal events with $n_{\mathrm{track}}$ of $\geq$ 5. The distributions are shown for split-SUSY signals with a gluino mass of 2000 GeV and neutralino mass of 1900 GeV. Different gluino proper decay lengths are shown as $c\tau$ in the legend. All distributions are normalized to unity.

Distributions of $S_{\mathrm{ML}}$ for data, simulated background and signal events with $n_{\mathrm{track}}$ of $\geq$ 5. The distributions are shown for split-SUSY signals with a gluino mass of 2000 GeV and neutralino mass of 1800 GeV. Different gluino proper decay lengths are shown as $c\tau$ in the legend. All distributions are normalized to unity.

The distribution of $n_{\mathrm{track}}$ in different $S_{\mathrm{ML}}$ regions for simulated background events. Events with 0 $ < S_{\mathrm{ML}} < $ 0.2 (blue), 0.2 $ < S_{\mathrm{ML}} < $ 0.6 (red), and 0.6 $ < S_{\mathrm{ML}} < $ 1.0 (green) are compared. All distributions are normalized to unity. The similar $n_{\mathrm{track}}$ distributions demonstrate that $n_{\mathrm{track}}$ and $S_{\mathrm{ML}}$ are decorrelated.

The distribution of $d_{\mathrm{BV}}$ in $K_{\mathrm{S}}^{\mathrm{0}}$ vertices in data (black) and simulation (purple). The lower panel shows the ratio between data and simulation.

The vertex reconstruction efficiency for artificially displaced vertices in data (black) and simulation (red). In this example, the artificially displaced vertices are corrected to mimic split-SUSY signal events with gluino mass of 2000 GeV and neutralino mass of 1800 GeV.

The ML tagging efficiency for artificially displaced vertices in data (black) and simulation (red). In this example, the artificially displaced vertices are corrected to mimic split-SUSY signal events with gluino mass of 2000 GeV and neutralino mass of 1800 GeV.

Number of predicted and observed events in the control, validation, and search regions. Predictions are calculated using Eqs. (2) and (3) and fitting the data under the background-only hypothesis. Regions are organized by $S_{\mathrm{ML}}$ and $n_{\mathrm{track}}$ values, and region names corresponding with Fig. 7 are given in parentheses. The predicted number of events that pass the $S_{\mathrm{ML}}$ selection and the observed number of events that pass or fail the $S_{\mathrm{ML}}$ selection are shown in seperate rows.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY signal model with a mass splitting of 100 GeV, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY signal model with a mass splitting of 100 GeV, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY model with a $c\tau$ of 10 mm, shown as a function of gluino mass and mass splitting. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY model with a $c\tau$ of 10 mm, shown as a function of gluino mass and mass splitting. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the GMSB SUSY signal model, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the GMSB SUSY signal model, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

Efficiency of signal region A for all signal samples interperted in this search. The cross sections of all signal models are taken to be 1 fb in the table.

Summary of observed and expected background yields in the SR after the likelihood fit under the background-only hypothesis.

Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA to TMS association procedure, as a function of generated $L_{xy}$, in all HAHM signal samples combined.

Efficiencies of the Run 2 and Run 3 displaced dimuon triggers as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 50\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 1\ cm$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 10\ m$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 20\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 50\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 125\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 200\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 350\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHbH channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bZtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHbH channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bZtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 3-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 3-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 4-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 4-jet bZbZ channel.

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 0% $\mathcal{B}(B \to bH)$, 100% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 25% $\mathcal{B}(B \to bH)$, 25% $\mathcal{B}(B \to bH)$, and 50% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 50% $\mathcal{B}(B \to bH)$, 50% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 100% $\mathcal{B}(B \to bH)$, 0% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

Median expected exclusion limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}(B \to bH)$ and $\mathcal{B}(B \to tW)$, with $\mathcal{B}(B \to tW) = 1 - \mathcal{B}(B \to bH) - \mathcal{B}(B \to bZ)$. The grey area corresponds to the region where the exclusion limit is less than 1000 GeV.

Median observed exclusion limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}(B \to bH)$ and $\mathcal{B}(B \to tW)$, with $\mathcal{B}(B \to tW) = 1 - \mathcal{B}(B \to bH) - \mathcal{B}(B \to bZ)$. The grey area corresponds to the region where the exclusion limit is less than 1000 GeV.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

The fitted slopes of the E3C/E2C data distributions as a function of jet pt are used to illustrate the dependency of alphas on jet pt.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

The chi2 scan result using eq.3 for different alphas.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The energy weight of E2C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

The energy weight of E3C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

Observed upper limits at 95% CL on B($\text{H} \rightarrow \text{a}_{1}\text{a}_{1}$) in percent, obtained from the combination of the $\mu\mu$bb and $\tau\tau$bb channels. The results are obtained as functions of $m_{\text{a}_{1}}$ for 2HDM+S Type I (independent of tan$\beta$), Type II (tan$\beta$=2.0), Type III (tan$\beta$=2.0), and Type IV (tan$\beta$=0.6), respectively.

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Forward $\eta$

Inclusive $\lt N_{Lund} \gt$

Inclusive $\lt N_{Lund}^{Primary} \gt$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$ and a $-1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Expected exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

The $1\sigma$ variation of expected 95% CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

The $2\sigma$ variation of expected 95%CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Observed exclusion limits at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{ GeV}$ and a $+1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{ GeV}$ and a $-1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Expected exclusion limits at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

The $1\sigma$ variation of expected 95% CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

The $2\sigma$ variation of expected 95%CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Observed upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$

Expected upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$

Observed upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Expected upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Post-fit distribution of $N_{\mathrm{tops}}^{\mathrm{DNN}} (\mathrm{R=1.0})$ in the SRA signal region presented without the associated SRA applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T2}}(j^{b}_{R=1.0}, c)$ in the SRA signal region presented without the associated SRA applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $p_{\mathrm{T}}(c_{1})$ in the SRB signal region presented without the associated SRB applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T}}(j, \mathrm{E}_{\mathrm{T}}^{\mathrm{miss}})_{\mathrm{close}}$ in the SRB signal region presented without the associated SRB applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $\mathrm{E}_{\mathrm{T}}^{\mathrm{miss}} \textrm{Significance}$ in the SRC signal region presented without the associated SRC applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T}}(j, \mathrm{E}_{\mathrm{T}}^{\mathrm{miss}})_{\mathrm{close}}$ in the SRC signal region presented without the associated SRC applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of NN signal score in the SRD signal region presented without the associated SRD applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{eff}}$ in the SRD signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Pull plots showing the SRA, SRB and SRC post-fit data and SM agreement using the background-only fit configuration

Pull plots showing the SRD post-fit data and SM agreement using the background-only fit configuration

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region A. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region B. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region C. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 750. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1000. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1250. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1500. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1750. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 2000. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Signal acceptance in the SRA$^{[450,575]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRA$^{[450,575]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRA$^{\geq 575}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRA$^{\geq 575}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{[150,400]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{[150,400]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{\geq 400}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{\geq 400}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[150,300]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[150,300]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[300,500]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[300,500]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{\geq 500}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{\geq 500}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1250 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1250 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1500 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1500 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD2000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD2000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.