Missing transverse momentum distribution after SR3b selection, beside the $E_\mathrm{T}^\mathrm{miss}$ requirement. The results in the signal region correspond to the last inclusive bin. The systematic uncertainties include theory uncertainties for the backgrounds with prompt SS/3L and the full systematic uncertainties for data-driven backgrounds. For illustration the distribution for a benchmark SUSY scenario ($pp\to \tilde g\tilde g$, $\tilde g\to t\bar t\tilde\chi_1^0$, $m_{\tilde g}=1.2$ TeV, $m_{\tilde\chi_1^0}=0.7$ TeV) is also shown.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar q(\tilde\ell\ell/\tilde\nu\nu)$ decay: signal acceptance (in %) in the signal region SR0b3j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar q(\tilde\ell\ell/\tilde\nu\nu)$ decay: reconstruction efficiency (in %) in the signal region SR0b3j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar qWZ\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR0b5j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar qWZ\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR0b5j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde b_1\tilde b_1^*$ production and $\tilde b_1\to tW\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR1b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde b_1\tilde b_1^*$ production and $\tilde b_1\to tW\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR1b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to t\bar t\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR3b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to t\bar t\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR3b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

Expected 95% CL exclusion contour for the Gbb signal.

Observed 95% CL exclusion contour for the Gbb signal.

Expected 95% CL exclusion contour for the Gtt combination.

Observed 95% CL exclusion contour for the Gtt combination.

Acceptances for the Gbb model in SR-Gbb-A. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gbb model in SR-Gbb-B. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gbb model in SR-Gbb-C. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-0L-A. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-0L-B. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-0L-C. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-1L-A. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-1L-B. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptance times efficiency for the Gbb model in SR-Gbb-A.

Acceptance times efficiency for the Gbb model in SR-Gbb-B.

Acceptance times efficiency for the Gbb model in SR-Gbb-C.

Acceptance times efficiency for the Gtt model in SR-Gtt-0L-A.

Acceptance times efficiency for the Gtt model in SR-Gtt-0L-B.

Acceptance times efficiency for the Gtt model in SR-Gtt-0L-C.

Acceptance times efficiency for the Gtt model in SR-Gtt-1L-A.

Acceptance times efficiency for the Gtt model in SR-Gtt-1L-B.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gbb model in SR-Gbb-A.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gbb model in SR-Gbb-B.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gbb model in SR-Gbb-C.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-0L-A.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-0L-B.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-0L-C.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-1L-A.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-1L-B.

Signal region yielding the best expected sensitivity for each point of the parameter space in the Gbb model.

Signal region yielding the best expected sensitivity for each point of the parameter space in the Gtt model for the 0-lepton channel.

Signal region yielding the best expected sensitivity for each point of the parameter space in the Gtt model for the 1-lepton channel.

Combination of two 0-lepton and 1-lepton signal regions yielding the best expected sensitivity for each point of the parameter space in the Gtt model.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j50 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j50 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 9j50 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 9j50 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 9j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 7j80 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 7j80 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 7j80 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

Degree of multijet closure for signal and vaidation regions with at no b-jet requirement. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The bins labelled in bold are signal regions, while the others are validation regions. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions with at least 1 b-jet. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The bins labelled in bold are signal regions, while the others are validation regions. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions with at least 2 b-jets. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The bins labelled in bold are signal regions, while the others are validation regions. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Summary of all 15 signal regions (post-fit).

Signal region yielding the best-expected CLs value, the best expected CLs value, and the corresponding observed CLs value for the 2Step grid.

Signal region yielding the best-expected CLs value, the best expected CLs value, and the corresponding observed CLs value for the pMSSM grid.

95% CLs observed upper limit on model cross-section for 2-step signal points for the best-expected signal region.

Performance of the 8j50-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Comparison of data with estimated backgrounds in the $\tilde{E}_\text{T}^\text{miss}$ distribution with the TZCR1 event selection except for the requirement on $\tilde{E}_\text{T}^\text{miss}$. The variables $\tilde{E}_\text{T}^\text{miss}$ and $\tilde{m}_\text{T}$ are constructed in the same way as $E_\text{T}^\text{miss}$ and $m_\text{T}$ but treating the leading photon transverse momentum as invisible. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

Comparison of data with estimated backgrounds in the $\tilde{m}_\text{T}$ distribution with the TZCR1 event selection except for the requirement on $\tilde{m}_\text{T}$. The variables $\tilde{E}_\text{T}^\text{miss}$ and $\tilde{m}_\text{T}$ are constructed in the same way as $E_\text{T}^\text{miss}$ and $m_\text{T}$ but treating the leading photon transverse momentum as invisible. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

Comparison of the observed data ($n_\text{obs}$) with the predicted background ($n_\text{exp}$) in the validation and signal regions. The background predictions are obtained using the background-only fit configuration. The bottom panel shows the significance of the difference between data and predicted background, where the significance is based on the total uncertainty ($\sigma_\text{tot}$).

Jet multiplicity distributions for events where exactly two signal leptons are selected. No correction factors are included in the background normalizations. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

Jet multiplicity distributions for events where exactly one lepton plus one $\tau$ candidate are selected. No correction factors are included in the background normalizations. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

The $E_\text{T}^\text{miss}$ distribution in SR1. In the plot, the full event selection in the corresponding signal region is applied, except for the requirement on $E_\text{T}^\text{miss}$. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin contains the overflow. Benchmark signal models are overlaid for comparison. The benchmark models are specified by the gluino and stop masses, given in TeV in the table.

The $m_\text{T}$ distribution in SR1. In the plot, the full event selection in the corresponding signal region is applied, except for the requirement on $m_\text{T}$. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin contains the overflow. Benchmark signal models are overlaid for comparison. The benchmark models are specified by the gluino and stop masses, given in TeV in the table.

Expected (black dashed) 95% excluded regions in the plane of $m_{\tilde{g}}$ versus $m_{\tilde{t}_1}$ for gluino-mediated stop production.

Observed (red solid) 95% excluded regions in the plane of $m_{\tilde{g}}$ versus $m_{\tilde{t}_1}$ for gluino-mediated stop production.

Expected (black dashed) 95% excluded regions in the plane of $m_{\tilde{t}_1}$ versus $m_{\tilde{\chi}_1^0}$ for direct stop production.

Observed (red solid) 95% excluded regions in the plane of $m_{\tilde{t}_1}$ versus $m_{\tilde{\chi}_1^0}$ for direct stop production.

The expected upper limits on $T$ quark pair production times the squared branching ratio for $T \rightarrow tZ$ as a function of the $T$ quark mass.

The observed upper limits on $T$ quark pair production times the squared branching ratio for $T \rightarrow tZ$ as a function of the $T$ quark mass.

The expected limits on $T$ quarks as a function of the branching ratios $B\left(T \rightarrow bW\right)$ and $B\left(T \rightarrow tH\right)$ for a $T$ quark with a mass of 800 GeV. The $T$ is assumed to decay in three possible ways: $T \to tZ$, $T \to tH$, and $T \to bW$.

The observed limits on $T$ quarks as a function of the branching ratios $B\left(T \rightarrow bW\right)$ and $B\left(T \rightarrow tH\right)$ for a $T$ quark with a mass of 800 GeV. The $T$ is assumed to decay in three possible ways: $T \to tZ$, $T \to tH$, and $T \to bW$.

The $m_\text{T}$ distribution in the WVR2-tail validation region which has the same preselection and jet $p_\text{T}$ requirements as SR2.

The $am_\text{T2}$ distribution in the WVR2-tail validation region which has the same preselection and jet $p_\text{T}$ requirements as SR2.

Large-radius jet mass ($R=1.2$), decomposed into the number of small-radius jet constituents. The lower panel shows the ratio of the total data to the total prediction (summed over all jet multiplicities). Events are required to have one lepton, four jets with $p_\text{T}>80,50,40,40$ GeV, at least one $b$-tagged jet, $E_\text{T}^\text{miss}>200$ GeV, and $m_\text{T}>30$ GeV.

Distribution of $m_\text{T2}^\tau$ in data for a selection enriched in $t\bar{t}$ events with one hadronically decaying $\tau$. Events that have no hadronic $\tau$ candidate (that passes the Loose identification criteria, as well as other requirements) are not shown in the plot.

Upper limits on the model cross-section in units of pb for the gluino-mediated stop models.

Upper limits on the model cross-section in units of pb for the models with direct stop pair production.

Illustration of the best expected signal region per signal grid point for the gluino-mediated stop models. This mapping is used for the final combined exclusion limits.

Illustration of the best expected signal region per signal grid point for models with direct stop pair production. This mapping is used for the final combined exclusion limits.

Expected $CL_s$ values for the gluino-mediated stop models.

Observed $CL_s$ values for the gluino-mediated stop models.

Expected $CL_s$ values for the direct stop pair production models.

Observed $CL_s$ values for the direct stop pair production models.

Expected limit using SR1 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Expected limit using SR1 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Expected limit using SR1 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Observed limit using SR1 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Observed limit using SR1 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Observed limit using SR1 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Expected limit using SR2 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Expected limit using SR2 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Expected limit using SR2 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Observed limit using SR2 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Observed limit using SR2 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Observed limit using SR2 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Expected limit using SR1+SR2 (best expected) for models with direct stop pair production and an unpolarized stop (and bino LSP).

Expected limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Expected limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Observed limit using SR1+SR2 (best expected) for models with direct stop pair production and an unpolarized stop (and bino LSP).

Observed limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Observed limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Acceptance for SR1 in the gluino-mediated stop models. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR1 in the direct stop pair production. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR2 in the gluino-mediated stop models. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR2 in the direct stop pair production. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR3 in the gluino-mediated stop models. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR3 in the direct stop pair production. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Efficiency for SR1 in the gluino-mediated stop models. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR1 in the direct stop pair production. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR2 in the gluino-mediated stop models. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR2 in the direct stop pair production. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR3 in the gluino-mediated stop models. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR3 in the direct stop pair production. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Observed and expected numbers of events with exactly three leptons with the scalar sum of jet transverse momentum values HT < 200 GeV. "On-Z" refers to events with at least one E+ E- or MU+ MU- (OSSF) pair with dilepton mass between 75 and 105 GeV, while "Off-Z" refers to events with one or two OSSF pairs, none of which fall in this mass range. The OSSFN designation refers to the number of E+ E- and MU+ MU- pairs in the event. Search channels binned in ET have been combined into coarse ET bins for the purposes of presentation.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z GRAVITINO) = 0.5 for strong plus electroweak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0 for strong plus electroweak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0 for strong plus electroweak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z0 GRAVITINO) = 0.5. The chargino-Higgsino mass is fixed at 150 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z0 GRAVITINO) = 0.5. The chargino-Higgsino mass is fixed at 350 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 150 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 350 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 150 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 350 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 150 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 250 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0 and strong plus weak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 350 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0 and strong plus weak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the slepton co-NLSP model in the gluino-chargino (wino-like chargino) mass plane.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the stau-NLSP and stau-NNLSP scenario in the degenerate smuon- and selectron- stau mass plane.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the T1tttt model in the gluino-neutralino mass plane.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the T6ttWW model in the sbottom-chargino mass plane.

Observed 95% C.L. exclusion contour for the T1tttt scenario.

Observed - 1sig(theoretical) 95% C.L. exclusion contour for the T1tttt scenario.

Observed + 1sig(theoretical) 95% C.L. exclusion contour for the T1tttt scenario.

Expected 95% C.L. exclusion contour for the T1tttt scenario.

Expected - 1sig 95% C.L. exclusion contour for the T1tttt scenario.

Expected + 1sig 95% C.L. exclusion contour for the T1tttt scenario.

Observed 95% C.L. exclusion contour for the T6ttWW scenario.

Observed - 1sig(theoretical) 95% C.L. exclusion contour for the T6ttWW scenario.

Observed + 1sig(theoretical) 95% C.L. exclusion contour for the T6ttWW scenario.

Expected 95% C.L. exclusion contour for the T6ttWW scenario.

Expected - 1sig 95% C.L. exclusion contour for the T6ttWW scenario.

Expected + 1sig 95% C.L. exclusion contour for the T6ttWW scenario.

Observed and expected 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z GRAVITINO) = 0.5 for strong plus electroweak production.

Observed and expected 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0 for strong plus electroweak production. Successive instances of SIG(OBS) = SIG(EXP) =0 have been rebinned over M(STOP) to reduce the size of the table.

Observed and expected 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> Z0 GLUINO) = 1.0 for strong plus electroweak production.

Observed 95% C.L. limits on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 150 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

Observed 95% C.L. limits on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 250 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

Observed 95% C.L. limits on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 350 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

Observed and expected 95% C.L. limits for the slepton co-NLSP model in the gluino-chargino (wino-like chargino) mass plane are shown.

Observed and expected 95% C.L. limits for the stau-NLSP and stau-NNLSP scenario in the degenerate smuon- and selectron- stau mass plane are shown. Successive instances of SIG(OBS) = SIG(EXP) =0 have been rebinned over M(SELECTRON) to reduce the size of the table.

Observed and expected 95% C.L. limits for the T1tttt model in the gluino-neutralino mass plane are shown.

Observed and expected 95% C.L. limits for the T6ttWW model in the sbottom-chargino mass plane are given.