The observed exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

The expected exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

The observed exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown for the region $m_{\tilde{t}} - m_{\tilde{\chi}^0_{1,2}, \tilde{\chi}^\pm_{1}} \geq m_\text{top}$ where $B(\tilde{t} \rightarrow b \chi^{+}_{1}) = B(\tilde{t} \rightarrow t \chi^{0}_{1,2}) = 0.5$.

The expected exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown for the region $m_{\tilde{t}} - m_{\tilde{\chi}^0_{1,2}, \tilde{\chi}^\pm_{1}} \geq m_\text{top}$ where $B(\tilde{t} \rightarrow b \chi^{+}_{1}) = B(\tilde{t} \rightarrow t \chi^{0}_{1,2}) = 0.5$.

Observed model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1})$ signal grid. Limits are shown for $B(\tilde{t} \rightarrow b \chi^{+}_{1})$ equal to unity.

Observed model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1} / \tilde{\chi}^{0}_{1,2})$ signal grid. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

Expected model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1})$ signal grid. Limits are shown for $B(\tilde{t} \rightarrow b \chi^{+}_{1})$ equal to unity.

Expected model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1} / \tilde{\chi}^{0}_{1,2})$ signal grid. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

Expected background and observed number of events in different jet and $b$-tag multiplicity bins.

Cut flow for a model of top-squark pair production with the top squark decaying to a $b$-quark and a chargino. The chargino decays through the non-zero RPV coupling $\lambda^{''}_{323}$ via a virtual top squark to $bbs$ quark triplets ($m_{\tilde{t}}$ = 800 GeV, $m_{\tilde{\chi}^{\pm}_{1}}$ = 750 GeV). The multijet trigger consists of four jets satisfying $p_{\text{T}}\geq(100)120$ GeV for the 2015-2016 (2017-2018) data period. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. The numbers in $N_{\text{weighted}}$ are normalized by the integrated luminosity of 139 fb$^{-1}$.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Post-fit yields for data and prediction in each of the single-bin signal regions of the analysis.

Observed 95% confidence level limit for the two-step signal grid.

Observed 95% confidence level limit for the two-step signal grid with the signal cross section increased by one sigma.

Observed 95% confidence level limit for the two-step signal grid with the signal cross section decreased by one sigma.

Expected 95% confidence level limit for the two-step signal grid.

Expected 95% confidence level limit for the two-step signal grid plus one sigma from experimental systematics.

Expected 95% confidence level limit for the two-step signal grid minus one sigma from experimental systematics.

Observed 95% confidence level limit for the Gtt signal grid.

Observed 95% confidence level limit for the Gtt signal grid with the signal cross section increased by one sigma.

Observed 95% confidence level limit for the Gtt signal grid with the signal cross section decreased by one sigma.

Expected 95% confidence level limit for the Gtt signal grid.

Expected 95% confidence level limit for the Gtt signal grid plus one sigma from experimental systematics.

Expected 95% confidence level limit for the Gtt signal grid minus one sigma from experimental systematics.

Observed 95% confidence level limit for the RPV signal grid.

Observed 95% confidence level limit for the RPV signal grid with the signal cross section increased by one sigma.

Observed 95% confidence level limit for the RPV signal grid with the signal cross section decreased by one sigma.

Expected 95% confidence level limit for the RPV signal grid.

Expected 95% confidence level limit for the RPV signal grid plus one sigma from experimental systematics.

Expected 95% confidence level limit for the RPV signal grid minus one sigma from experimental systematics.

Observed 95% confidence level limit for the two-step signal grid.

Expected 95% confidence level limit for the two-step signal grid.

Observed 95% confidence level limit for the Gtt signal grid.

Expected 95% confidence level limit for the Gtt signal grid.

Observed 95% confidence level limit for the RPV signal grid.

Expected 95% confidence level limit for the RPV signal grid.

$\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in the signal region SR-10ij50-0ib-MJ340. Two benchmark signal models are shown along with the background yields. These models, each representing a single mass point, are labelled 'RPV' with $(m_{\tilde{g}}, m_{\tilde{t}}) = (1600, 600) \, \mathrm{GeV}$ and 'two-step' with $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

$\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in the signal region SR-12ij50-2ib. Two benchmark signal models are shown along with the background yields. These models, each representing a single mass point, are labelled 'RPV' with $(m_{\tilde{g}}, m_{\tilde{t}}) = (1600, 600) \, \mathrm{GeV}$ and 'two-step' with $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

$\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in the signal region SR-9ij80-0ib. Two benchmark signal models are shown along with the background yields. These models, each representing a single mass point, are labelled 'RPV' with $(m_{\tilde{g}}, m_{\tilde{t}}) = (1600, 600) \, \mathrm{GeV}$ and 'two-step' with $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-8ij50-0ib-MJ500. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-9ij50-0ib-MJ340. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-10ij50-0ib-MJ340. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-10ij50-0ib-MJ500. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-10ij50-1ib-MJ500. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-11ij50-0ib. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-12ij50-2ib. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-9ij80-0ib. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.

Acceptance for the signal region SR-8ij50-0ib-MJ500 showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-8ij50-0ib-MJ500 showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-9ij50-0ib-MJ340 showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-9ij50-0ib-MJ340 showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-10ij50-0ib-MJ340 showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-10ij50-0ib-MJ340 showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-10ij50-0ib-MJ500 showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-10ij50-0ib-MJ500 showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-10ij50-1ib-MJ500 showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-10ij50-1ib-MJ500 showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-11ij50-0ib showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-11ij50-0ib showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-12ij50-2ib showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-12ij50-2ib showing the efficiency for the complete two-step signal grid.

Acceptance for the signal region SR-9ij80-0ib showing the acceptance for the complete two-step signal grid.

Efficiency for the signal region SR-9ij80-0ib showing the efficiency for the complete two-step signal grid.

The normalisation factors for the dominant backgrounds of the analysis in each of the multi-bin and single-bin regions.

Post-fit yields for data and prediction in each of the single-bin validation regions to test the $N_{\mathrm{jet}}$ extraction.

Post-fit yields for data and prediction in each of the single-bin validation regions to test the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ extrapolation.

Post-fit yields for data and prediction in each of the multi-bin validation regions to test the $N_{\mathrm{jet}}$ extraction.

Post-fit yields for data and prediction in each of the multi-bin validation regions to test the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ extrapolation.

The observed Cls from the best expected signal regions for the two-step decay.

The observed Cls from the best expected signal regions for the Gtt decay.

The observed Cls from the best expected signal regions for the RPV decay.

Number of events in each signal region broken down by background type and the number of observed data events.

From left to right; the $95\%$ CL upper limits on the visible cross section (${\langle \epsilon\sigma \rangle}^{95}_{obs}$) and on the number of signal events. Next is the $95\%$ CL upper limit on the number of signal events, given the expected number of background events. The last two columns show the confidence level for the background only hypothesis ($CL_{b}$) and the dicovery $p$-value along with the Gaussian significance (Z).

Visualisation of the highest jet multiplicity event selected in signal regions targeting long cascade decays of pair-produced gluinos. This event was recorded by ATLAS on 23 October 2016, and contains 16 jets, illustrated by cones. Yellow blocks represent the calorimeter energy measured in noise-suppressed clusters. Of the reconstructed jets, 13 (11) have transverse momenta above 50 GeV (80 GeV), with 3 (2) being b-tagged. The leading jet has a transverse momentum of 507 GeV, and the sum of jet transverse momenta $H_T=2.9$ TeV. A value of 343 GeV is observed for the $E_{T}^{miss}$, whose direction is shown by the dashed red line, producing a significance $S(E_{T}^{miss})=6.4$. The sum of the masses of large-radius jets is evaluated as $M_{J}^{\Sigma}=1070$ GeV.

Visualisation of the highest jet multiplicity event selected in a control region used to make predictions of the background from multijet production. This event was recorded by ATLAS on 18 July 2018, and contains 19 jets, illustrated by cones. Yellow blocks represent the calorimeter energy measured in in noise-suppressed clusters. Of the reconstructed jets, 16 (10) have transverse momenta above 50 GeV (80 GeV). No jets were b-tagged. The leading et has a transverse momentum of 371 GeV, and the sum of jet transverse momenta $H_T=2.2$ TeV. A value of 8 GeV is observed for the $E_{T}^{miss}$, whose direction is shown by the dashed red line, producing a significance $S(E_{T}^{miss})=0.2$. The sum of the masses of large-radius jets is evaluated as $M_{J}^{\Sigma}=767$ GeV.

The minus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The plus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The minus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The plus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The observed exclusion contour at 95% CL as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$. Points that are within the contours are excluded.

The expected exclusion contour at 95% CL as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$. Points that are within the contours are excluded.

The minus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

The plus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

The plus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

The minus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

Model dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{0}_{1})$ signal grid. The column titled 'Leading Region' stores information on which of the fit regions (SRA-B, SRC or SRD) is the dominant based on the expected CLs values.

Expected model dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{0}_{1})$ signal grid. The column titled 'Leading Region' stores information on which of the fit regions (SRA-B, SRC or SRD) is the dominant based on the expected CLs values.

Model dependent upper limit on the cross section for the $LQ_{3}^{u}$ signal grid with $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau))=0$ %. Only the SRA-B fit region is considered in this interpretation.

Expected model dependent upper limit on the cross section for the $LQ_{3}^{u}$ signal grid with $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau))=0$ %. Only the SRA-B fit region is considered in this interpretation.

The distributions of $S$ in SRA-TW. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $\it{m}^{\mathrm{R=1.2}}_{1}$ in SRB-TT. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of R$_{ISR}$ in SRC signal regions before R$_{ISR}$ cuts are applied. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $E^{miss}_{T}/\sqrt{H_{T}}$ in SRD0. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $E^{miss}_{T}/\sqrt{H_{T}}$ in SRD1. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $E^{miss}_{T}/\sqrt{H_{T}}$ in SRD2. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (1300,1)\ \mathrm{GeV} $ in SRA-TT. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 30000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (1300,1)\ \mathrm{GeV} $ in SRA-TW. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 30000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (1300,1)\ \mathrm{GeV} $ in SRA-T0. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 30000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (700,400)\ \mathrm{GeV} $ in signal regions SRB-TT, SRB-TW and SRB-T0. The regions differ by the last cut applied. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 60000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (500,327)\ \mathrm{GeV} $ in regions SRC-1, SRC-2, SRC-3, SRC-4 and SRC-5. The regions differ by the last cut applied. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 150000 raw MC events with filter efficiency of 0.384 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (550,500)\ \mathrm{GeV} $ in SRD0. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 90000 raw MC events with filter efficiency of 0.428 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (550,500)\ \mathrm{GeV} $ in SRD1. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 90000 raw MC events with filter efficiency of 0.428 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (550,500)\ \mathrm{GeV} $ in SRD2. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 90000 raw MC events with filter efficiency of 0.428 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Signal acceptance in SRA-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRA-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRA-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRA-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRA-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRA-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRB-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRB-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRB-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRB-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRB-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRB-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRC1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRC2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRC3 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC3 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRC4 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC4 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ plane showed in the paper plot.

Signal acceptance in SRC5 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ plane showed in the paper plot.

Signal efficiency in SRC5 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRD0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRD0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRD1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRD1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRD2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRD2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.

Distributions of $m_{T2}$ in VR-top-low for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-high for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-VZ for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-top-WW for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Breakdown of the dominant systematic uncertainties on background estimates in the inclusive SRs requiring $m_{T2}$>100 GeV after performing the profile likelihood fit. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty. The percentages show the size of the uncertainty relative to the total expected background. "Top theoretical uncertainties" refers to $t\bar t$ theoretical uncertainties and the uncertainty associated to $Wt-t\bar t$ interference added quadratically.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Distributions of $m_{T2}$ in SRSF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Observed exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Upper limits on signal cross-section (fb) for chargino-pair production with W -boson-mediated decays.

Upper limits on signal cross-section (fb) for chargino-pair production with slepton/sneutrino-mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed.

Upper limits on signal cross-section (fb) for slepton-pair production.

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[120,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[120,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[120,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[120,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[120,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[120,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[120,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[120,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,105).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,105).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,105).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,105).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,105).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,105).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,105).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,105).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[105,110).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[105,110).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[105,110).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[105,110).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[105,110).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[105,110).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[105,110).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[105,110).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[110,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[110,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[110,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[110,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[110,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[110,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[110,120).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[110,120).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[120,140).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[120,140).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[120,140).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[120,140).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[120,140).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[120,140).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[120,140).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[120,140).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[140,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[140,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[140,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[140,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[140,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[140,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[140,160).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[140,160).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,180).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,180).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,180).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,180).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,180).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,180).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[160,180).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[160,180).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[180,220).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[180,220).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[180,220).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[180,220).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[180,220).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[180,220).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[180,220).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[180,220).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[220,260).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[220,260).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[220,260).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[220,260).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[220,260).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[220,260).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[220,260).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[220,260).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[260,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[260,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[260,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[260,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[260,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[260,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[260,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[260,inf).

Cutflow for supersymmetric model where $\tilde{\chi}_1^{\pm}\tilde{\chi}_1^{\mp}$ decay via $W^{\pm}W^{\mp}$. The masses of the two charginos are 300 GeV, while the mass of $\tilde{\chi}_1^{0}$ is 50 GeV. The numbers are normalised to the luminosity of 139~fb$^{-1}$.

Cutflow for supersymmetric model where $\tilde{\chi}_1^{\pm}\tilde{\chi}_1^{\mp}$ decay via slepton-neutrino/sneutrino-lepton pair. The masses of the two charginos are 600 GeV, while the mass of $\tilde{\chi}_1^{0}$ is 1 GeV. The slepton/sneutrino masses are 300 GeV. The numbers are normalised to the luminosity of 139~fb$^{-1}$.

Cutflow for supersymmetric model where $\tilde\ell\tilde\ell$ are produced. Only $\tilde{e}$ and $\tilde{\mu}$ are considered in this model. The masses of the two sleptons are 400 GeV, while the mass of $\tilde{\chi}_1^{0}$ is 200 GeV. The numbers are normalised to the luminosity of 139~fb$^{-1}$.

Distributions of MaxMin alternative algorithm $m(h_{\mathrm{cand1}},h_{\mathrm{cand2}})_{\mathrm{avg}}$ after the background-only fit. The backgrounds which contribute only a small amount (diboson, W+jets and ttbar+W/Z/h) are grouped and labelled as `Other'.

Distributions of Leading jet $p_T$ after the background-only fit. The backgrounds which contribute only a small amount (diboson, W+jets and ttbar+W/Z/h) are grouped and labelled as `Other'.

Distributions of MaxMin algorithm $m_{hcand}$ after the background-only fit. The backgrounds which contribute only a small amount (diboson, W+jets and ttbar+W/Z/h) are grouped and labelled as `Other'.

Signal efficiency in SRA_M_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRC_28 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRC_26 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRC_24 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_M_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRB for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_L_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRC_incl for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_L_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_incl_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_incl_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_H_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRA_L_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRB for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRC_22 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_H_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_24 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_26 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRA_H_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_incl_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_22 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRA_M_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRC_28 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRA_H_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_incl_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRA_L_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRA_M_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_incl for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Observed 95% CLs exclusion limit for the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid for the best combined signal regions.

Expected 95% CLs exclusion limit for the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid for the best combined signal regions.

Observed 95% CLs exclusion limit for the $M(\widetilde{\chi}_{1}^{0})$=60GeV signal grid for the best combined signal regions.

Expected 95% CLs exclusion limit for the $M(\widetilde{\chi}_{1}^{0})$=60GeV signal grid for the best combined signal regions.

Model dependent upper limit on the best combined signal regions considered in the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid

Model dependet upper limits on the best combined signal regions considered in the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid

Result of background only fit applied to signal regions. Event yields from the signal regions compared with SM MC predictions for the 3 highest contributing backgrounds separately and combined minor backgrounds.

Expected background event yields and dominant systematic uncertainties on background estimates in the A-type (inclusive), B-type and C-type (inclusive) regions.

Background-only fit results for the A- and B-type regions performed using 139$fb^{-1}$ of data. The quoted uncertainties on the fitted SM background include both the statistical and systematic uncertainties.

Background-only fit results for the C-type region performed using 139$fb^{-1}$ of data. The quoted uncertainties on the fitted SM background include both the statistical and systematic uncertainties.

Observed 95% CL upper limits on the visible cross sections σvis, the observed (S95obs) and expected (S95exp) 95% CL upper limits on the number of signal events with ± 1 σ excursions of the expectation, the CL of the background-only hypothesis, CLB, the discovery p-value (p0), truncated at 0.5, and the associated significance.

Cutflow of the MC events scaled to 139 $fb^{-1}$ for the SRA selections, with a scalar bottom signal of m$(\widetilde{b}_{1},\widetilde{\chi}_2^0,\widetilde{\chi}_1^0) = (1100, 330, 200)$ GeV, considered.

Cutflow of the MC events scaled to 139 $fb^{-1}$ for the SRB selections, with a scalar bottom signal of m$(\widetilde{b}_{1},\widetilde{\chi}_2^0,\widetilde{\chi}_1^0) = (700, 680, 550)$ GeV, considered.

Cutflow of the MC events scaled to 139 $fb^{-1}$ for the SRC selections, with a scalar bottom signal of m$(\widetilde{b}_{1},\widetilde{\chi}_2^0,\widetilde{\chi}_1^0) = (1200, 1150, 60)$ GeV, considered.

Dependence of the overall signal efficiency on the pT of the long-lived Z' for Z' -> ee. The error bars indicate the total uncertainties.

Dependence of the overall signal efficiency on the transverse decay radius Rxy of the long-lived Z' for Z' -> emu. The error bars indicate the total uncertainties.

Dependence of the overall signal efficiency on the pT of the long-lived Z' for Z' -> emu. The error bars indicate the total uncertainties.

Dependence of the overall signal efficiency on the transverse decay radius Rxy of the long-lived Z' for Z' -> mumu. The error bars indicate the total uncertainties.

Dependence of the overall signal efficiency on the pT of the long-lived Z' for Z' -> mumu. The error bars indicate the total uncertainties.

Overall signal efficiency as a function of the mean proper lifetime (ctau) of the neutralino for the lambda121 scenario of the RPV SUSY model. The error bars indicate the total uncertainties.

Overall signal efficiency as a function of the mean proper lifetime (ctau) of the neutralino for the lambda122 scenario of the RPV SUSY model. The error bars indicate the total uncertainties.

95% CL upper limits on the squark-antisquark production cross-section as a function of the mean proper lifetime (ctau) of the neutralino for the lambda121 scenario of the RPV SUSY model and a 700 GeV squark. The uncertainties of the expected limit indicate the +-1sigma variations.

95% CL upper limits on the squark-antisquark production cross-section as a function of the mean proper lifetime (ctau) of the neutralino for the lambda122 scenario of the RPV SUSY model and a 700 GeV squark. The uncertainties of the expected limit indicate the +-1sigma variations.

95% CL upper limits on the squark-antisquark production cross-section as a function of the mean proper lifetime (ctau) of the neutralino for the lambda121 scenario of the RPV SUSY model and a 1600 GeV squark. The uncertainties of the expected limit indicate the +-1sigma variations.

95% CL upper limits on the squark-antisquark production cross-section as a function of the mean proper lifetime (ctau) of the neutralino for the lambda122 scenario of the RPV SUSY model and a 1600 GeV squark. The uncertainties of the expected limit indicate the +-1sigma variations.

Fraction of detector volume covered by the material veto as a function of z and Rxy of the displaced dilepton vertex.

Fraction of detector volume covered by the disabled pixel modules veto veto as a function of z and Rxy of the displaced dilepton vertex.

Observed Rxy distribution of vertices composed of two non-leptonic tracks in a control sample in the data and the predicted distribution obtained from the event mixing. The error bars indicate the statistical uncertainties.

Rxy distributions of Kshort vertices from the large radius tracking in the data and background MC samples. Data has been normalised such that the total number of Kshort from the standard tracking in the data agrees with the total number of Kshort from the standard tracking in the MC. The error bars indicate the statistical uncertainties.

Acceptance per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> eenu. The error bars indicate the statistical uncertainties.

Acceptance per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> emunu. The error bars indicate the statistical uncertainties.

Acceptance per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> mumunu. The error bars indicate the statistical uncertainties.

Detection efficiency per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> eenu. The error bars indicate the total uncertainties.

Detection efficiency per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> emunu. The error bars indicate the total uncertainties.

Detection efficiency per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> mumunu. The error bars indicate the total uncertainties.

Overall signal efficiency (acceptance times efficiency) per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> eenu. The error bars indicate the total uncertainties.

Overall signal efficiency (acceptance times efficiency) per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> emunu. The error bars indicate the total uncertainties.

Overall signal efficiency (acceptance times efficiency) per decay as a function of the mean proper lifetime (ctau) of the neutralino for neutralino -> mumunu. The error bars indicate the total uncertainties.

Detection efficiency per decay for Rxy < 22 mm as a function of the invariant mass and pT of the electron pair in LLP -> eeX.

Detection efficiency per decay for 22 <= Rxy < 38 mm as a function of the invariant mass and pT of the electron pair in LLP -> eeX.

Detection efficiency per decay for 38 <= Rxy < 73 mm as a function of the invariant mass and pT of the electron pair in LLP -> eeX.

Detection efficiency per decay for 73 <= Rxy < 111 mm as a function of the invariant mass and pT of the electron pair in LLP -> eeX.

Detection efficiency per decay for 111 <= Rxy < 145 mm as a function of the invariant mass and pT of the electron pair in LLP -> eeX.

Detection efficiency per decay for 145 <= Rxy < 300 mm as a function of the invariant mass and pT of the electron pair in LLP -> eeX.

Detection efficiency per decay for Rxy < 22 mm as a function of the invariant mass and pT of the electron and muon pair in LLP -> emuX.

Detection efficiency per decay for 22 <= Rxy < 38 mm as a function of the invariant mass and pT of the electron and muon pair in LLP -> emuX.

Detection efficiency per decay for 38 <= Rxy < 73 mm as a function of the invariant mass and pT of the electron and muon pair in LLP -> emuX.

Detection efficiency per decay for 73 <= Rxy < 111 mm as a function of the invariant mass and pT of the electron and muon pair in LLP -> emuX.

Detection efficiency per decay for 111 <= Rxy < 145 mm as a function of the invariant mass and pT of the electron and muon pair in LLP -> emuX.

Detection efficiency per decay for 145 <= Rxy < 300 mm as a function of the invariant mass and pT of the electron and muon pair in LLP -> emuX.

Detection efficiency per decay for Rxy < 22 mm as a function of the invariant mass and pT of the muon pair in LLP -> mumuX.

Detection efficiency per decay for 22 <= Rxy < 38 mm as a function of the invariant mass and pT of the muon pair in LLP -> mumuX.

Detection efficiency per decay for 38 <= Rxy < 73 mm as a function of the invariant mass and pT of the muon pair in LLP -> mumuX.

Detection efficiency per decay for 73 <= Rxy < 111 mm as a function of the invariant mass and pT of the muon pair in LLP -> mumuX.

Detection efficiency per decay for 111 <= Rxy < 145 mm as a function of the invariant mass and pT of the muon pair in LLP -> mumuX.

Detection efficiency per decay for 145 <= Rxy < 300 mm as a function of the invariant mass and pT of the muon pair in LLP -> mumuX.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 100 GeV and LLP -> ee.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 100 GeV and LLP -> emu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 100 GeV and LLP -> mumu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 250 GeV and LLP -> ee.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 250 GeV and LLP -> emu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 250 GeV and LLP -> mumu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 500 GeV and LLP -> ee.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 500 GeV and LLP -> emu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 500 GeV and LLP -> mumu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 750 GeV and LLP -> ee.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 750 GeV and LLP -> emu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 750 GeV and LLP -> mumu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 1000 GeV and LLP -> ee.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 1000 GeV and LLP -> emu.

Detection efficiency per decay as a function of the transverse decay radius Rxy and the dilepton pT for a LLP mass of 1000 GeV and LLP -> mumu.

Acceptance, detection efficiency, and overall signal efficiency in the Z' toy model for mZ' = 100 GeV, three mean proper lifetimes (ctau) and the three decay modes of the Z'.

Acceptance, detection efficiency, and overall signal efficiency in the Z' toy model for mZ' = 250 GeV, three mean proper lifetimes (ctau) and the three decay modes of the Z'.

Acceptance, detection efficiency, and overall signal efficiency in the Z' toy model for mZ' = 500 GeV, three mean proper lifetimes (ctau) and the three decay modes of the Z'.

Acceptance, detection efficiency, and overall signal efficiency in the Z' toy model for mZ' = 750 GeV, three mean proper lifetimes (ctau) and the three decay modes of the Z'.

Acceptance, detection efficiency, and overall signal efficiency in the Z' toy model for mZ' = 1000 GeV, three mean proper lifetimes (ctau) and the three decay modes of the Z'.

The observed and expected 95% CL upper limits on model-independent visible cross-sections, along with the observed $p0$ values, for the stable signal region, as a function of different mass windows, for which the lower bound is shown. The upper boundary on the mass window is 5 TeV for all windows.

The observed and expected 95% CL upper limits on model-independent visible cross-sections, along with the observed $p0$ values, for the metastable signal region, as a function of different mass windows, for which the lower bound is shown. The upper boundary on the mass window is 5 TeV for all windows.

For each gluino lifetime and mass in the signal samples, the lower boundary of the mass window in which at least $70\%$ of the reconstructed signal appears. The upper boundary for all mass windows is 5 TeV.

Acceptance and efficiency for a representative set of pair-produced gluino signal samples. The mass of the gluino ($m(\tilde{g})$), its lifetime ($\tau(\tilde{g})$) and the mass of the neutralino ($m(\tilde{\chi}^{0})$) are given in the first three columns. The Pythia 6.4.27 signal samples shown in this table are not reweighted to match the transverse momentum of the gluino-gluino system as simulated by MadGraph5_aMC@NLO. The acceptance is defined as the fraction of events passing a loose set of fiducial requirements. The full simulation efficiency (Full sim. $\epsilon$) is defined as the ratio of the number of reconstructed events, as expected by the full ATLAS simulation, and the number of events passing the fiducial requirements. The parameterised simulation efficiency (Param. sim. $\epsilon$) is defined as the ratio of the number of events estimated using a set of parametrised efficiencies (see auxiliary Figures 9,10,11,12) and the number of events passing the fiducial requirements alone.

The reconstructed candidate track mass distributions for observed data, predicted background, and the expected contribution from two signal models in the metastable R-hadron signal region. The yellow band around the background estimation includes both the statistical and systematic uncertainties.

The reconstructed candidate track mass distributions for observed data, predicted background, and the expected contribution from two signal models in the stable R-hadron signal region. The yellow band around the background estimation includes both the statistical and systematic uncertainties.

The 95% CL upper limit on the cross-section as a function of mass for gluinos with lifetime $\tau = 10$ ns decaying into $q\bar{q}$ and a 100 GeV neutralino, with the observed limit shown as a solid black line. The predicted production cross-section values are shown in purple along with their uncertainty. The expected upper limit in the case of only background is shown by the dashed black line, with a green $\pm 1\sigma$ and a yellow $\pm 2\sigma$ band.

The 95% CL upper limit on the cross-section as a function of mass for stable gluino $R$-hadrons, with the observed limit shown as a solid black line. The predicted production cross-section values are shown in purple along with their uncertainty. The expected upper limit in the case of only background is shown by the dashed black line, with a green $\pm 1\sigma$ and a yellow $\pm 2\sigma$ band.

Observed 95% lower limits on the gluino mass in the gluino lifetime--mass plane. The excluded area is to the left of the curves.

Expected 95% lower limits on the gluino mass in the gluino lifetime--mass plane. The excluded area is to the left of the curves.

The 95% CL upper limit on the cross-section as a function of mass for gluinos with lifetime $\tau = 1$ ns decaying into $q\bar{q}$ and a 100 GeV neutralino, with the observed limit shown as a solid black line. The predicted production cross-section values are shown in purple along with their uncertainty. The expected upper limit in the case of only background is shown by the dashed black line, with a green $\pm 1\sigma$ and a yellow $\pm 2\sigma$ band.

The 95% CL upper limit on the cross-section as a function of mass for gluinos with lifetime $\tau = 3$ ns decaying into $q\bar{q}$ and a 100 GeV neutralino, with the observed limit shown as a solid black line. The predicted production cross-section values are shown in purple along with their uncertainty. The expected upper limit in the case of only background is shown by the dashed black line, with a green $\pm 1\sigma$ and a yellow $\pm 2\sigma$ band.

The 95% CL upper limit on the cross-section as a function of mass for gluinos with lifetime $\tau = 30$ ns decaying into $q\bar{q}$ and a 100 GeV neutralino, with the observed limit shown as a solid black line. The predicted production cross-section values are shown in purple along with their uncertainty. The expected upper limit in the case of only background is shown by the dashed black line, with a green $\pm 1\sigma$ and a yellow $\pm 2\sigma$ band.

The 95% CL upper limit on the cross-section as a function of mass for gluinos with lifetime $\tau = 50$ ns decaying into $q\bar{q}$ and a 100 GeV neutralino, with the observed limit shown as a solid black line. The predicted production cross-section values are shown in purple along with their uncertainty. The expected upper limit in the case of only background is shown by the dashed black line, with a green $\pm 1\sigma$ and a yellow $\pm 2\sigma$ band.

The relationship between generated and reconstructed mass for gluino $R$-hadrons. Above 1500 GeV, the reconstructed mass falls below the generated mass due to bias in the reconstructed momentum. The uncertainty on the reconstructed mass is dominated by momentum uncertainty. The black dots represent the reconstructed mass computed as the most probable value of a Gaussian fit function, with the error bars showing its statistical uncertainty, while the orange band is the full-width at half maximum of the reconstructed mass distribution.

The parameterised efficiency for events to pass metastable event selections (including trigger, E$_{T}^{miss}$, and event cleaning requirements) as a function of the true E$_{T}^{miss}$ in the system, which is calculated at generator level. Event-level efficiencies are evaluated for events which have at least true E$_{T}^{miss} > 50$ GeV. The metastable event efficiencies are evaluated for different radial regions depending on the smallest radial distance, R, at which an R-hadron decays in the detector.

The parameterised efficiency for events to pass metastable event selections (including trigger, E$_{T}^{miss}$, and event cleaning requirements) as a function of the true E$_{T}^{miss}$ in the system, which is calculated at generator level. Event-level efficiencies are evaluated for events which have at least true E$_{T}^{miss} > 50$ GeV. The stable event efficiencies are evaluated for samples in which no R-hadron decays within the detector.

The parameterised efficiency for particles to pass full track selections in the metastable signal region, as function of the particle’s $\beta$, in different bins of transverse momentum, $p_{T}$, and for different radial decay positions of the particle. The efficiency is evaluated for particles which pass a loose set of fiducial requirements at generator level.

The parameterised efficiency for particles to pass full track selections in the metastable signal region, as function of the particle’s $\beta$, in different bins of transverse momentum, $p_{T}$, and for different radial decay positions of the particle. The efficiency is evaluated for particles which pass a loose set of fiducial requirements at generator level.

The parameterised efficiency for particles to pass full track selections in the metastable signal region, as function of the particle’s $\beta$, in different bins of transverse momentum, $p_{T}$, and for different radial decay positions of the particle. The efficiency is evaluated for particles which pass a loose set of fiducial requirements at generator level.

The parameterised efficiency for particles to pass full track selections in the metastable signal region, as function of the particle’s $\beta$, in different bins of transverse momentum, $p_{T}$, and for different radial decay positions of the particle. The efficiency is evaluated for particles which pass a loose set of fiducial requirements at generator level.

The parameterised efficiency for particles to pass full track selections in the metastable signal region, as function of the particle’s $\beta$, in different bins of transverse momentum, $p_{T}$, and for different radial decay positions of the particle. The efficiency is evaluated for particles which pass a loose set of fiducial requirements at generator level.

The parameterised efficiency for particles to pass full track selections in the metastable signal region, as function of the particle’s $\beta$, in different bins of transverse momentum, $p_{T}$. The stable efficiency is evaluated for samples which do not decay within the detector. The efficiency is evaluated for particles which pass a loose set of fiducial requirements at generator level.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for the ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,100) GeV and ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,800) GeV models. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma\gamma}_{W-L}$ and $\mathrm{SR}^{\gamma\gamma}_{W-H}$. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of combined statistical and systematic uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for the ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,100) GeV and ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,800) GeV models. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma\gamma}_{W-L}$ and $\mathrm{SR}^{\gamma\gamma}_{W-H}$. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of combined statistical and systematic uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Comparisons between expected and observed content of the validation and signal regions for the photon+jets analysis. The uncertainties in the expected numbers of events are the combined statistical and systematic uncertainties. The lower panel shows the pull (difference between observed and expected event counts normalized by the uncertainty) for each region.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma j}_{H}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for points in the $m_{\tilde{g}}-m_{\tilde{\chi}^{0}_{1}}$ parameter space of the GGM model relevant to the photon+jets analysis (mass values in GeV). The value of the gluino mass arises from the choice $M_{3}$ = 1900 GeV. The $\tilde{\chi}^{0}_{1}$ mass values of 1868, 1920, 442 and 652 GeV arise from the choices $\mu$ = 1810, 1868, 400 and 600 GeV, respectively, combined with the constraint that the branching fraction of $\tilde{\chi}^{0}_{1}$ $\to \gamma \tilde{G}$ be 50%. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma j}_{H}$ and $\mathrm{SR}^{\gamma j}_{L}$ for $\mathrm{SR}^{\gamma j}_{L200}$, the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement is 200 GeV rather than 300 GeV. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of statistical uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma j}_{L}$ or $\mathrm{SR}^{\gamma j}_{L200}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for points in the $m_{\tilde{g}}-m_{\tilde{\chi}^{0}_{1}}$ parameter space of the GGM model relevant to the photon+jets analysis (mass values in GeV). The value of the gluino mass arises from the choice $M_{3}$ = 1900 GeV. The $\tilde{\chi}^{0}_{1}$ mass values of 1868, 1920, 442 and 652 GeV arise from the choices $\mu$ = 1810, 1868, 400 and 600 GeV, respectively, combined with the constraint that the branching fraction of $\tilde{\chi}^{0}_{1} \to \gamma \tilde{G}$ be 50%. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma j}_{H}$ and $\mathrm{SR}^{\gamma j}_{L}$ for $\mathrm{SR}^{\gamma j}_{L200}$, the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement is 200 GeV rather than 300 GeV. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of statistical uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Expected exclusion limits in the gluino-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 1600$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 1600$ GeV.

Observed exclusion limits in the gluino--bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 1600$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 1600$ GeV.

Expected exclusion limit in the squark-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 900$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 900$ GeV.

Observed exclusion limit in the squark--bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 900$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 900$ GeV.

Expected exclusion limit in the wino-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 400$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}}$ < 400$ GeV.

Observed exclusion limit in the wino-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 400$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}}$ < 400$ GeV.

Expected exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis.

Observed exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis.

Distribution of the transverse momentum $p_{\mathrm{T}} (\ell\gamma\gamma)$ of events in the $\ell\gamma\gamma$ control region (except without a cut on $p_{\mathrm{T}} (\ell\gamma\gamma)$). Also shown is the expected contribution from various SM sources, including $W(\to\ell\nu) + \gamma\gamma$ production itself. The displayed uncertainties are a combination of those from all SM sources except $W(\to\ell\nu) + \gamma\gamma$ production, and include statistical and systematic uncertainties.

Distribution of $E_{\mathrm{T}}^{\mathrm{miss}}$ for diphoton events in a validation region defined by a requirement of $H_{\mathrm{T}} > 1750$ GeV. Also shown is the expected contribution from various SM sources, as well as their combined statistical and systematic uncertainties.

Distribution of $H_{\mathrm{T}}$ for diphoton events in a validation region defined by requirement of $E_{\mathrm{T}}^{\mathrm{miss}} > 100$ GeV. Also shown is the expected contribution from various SM sources, as well as their combined statistical and systematic uncertainties.

Distribution of $m_{\mathrm{eff}}$ for events satisfying all requirements $\mathrm{SR}^{\gamma j}_{H}$ save the $m_{\mathrm{eff}}$ requirement itself. Also shown is the expected contribution from various SM sources, and their combined statistical uncertainties.

Distribution of $m_{\mathrm{eff}}$ for events satisfying all requirements $\mathrm{SR}^{\gamma j}_{L}$ save the $m_{\mathrm{eff}}$ requirement itself. Also shown is the expected contribution from various SM sources, and their combined statistical uncertainties.

Derived exclusion limits for the gluino-bino GGM model explored by the diphoton analysis. For each point in the gluino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the squark-bino GGM model explored by the diphoton analysis. For each point in the squark-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the wino-bino GGM model explored by the diphoton analysis. For each point in the wino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{W-L}$ or $\mathrm{SR}^{\gamma\gamma}_{W-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis. For each point in the higgsino-bino parameter space, the SR ($\mathrm{SR}^{\gamma j}_{L}$ or $\mathrm{SR}^{\gamma j}_{H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the gluino-bino GGM model explored by the diphoton analysis. For each point in the gluino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where SL and SH mean $\mathrm{SR}^{\gamma\gamma}_{S-L}$ and $\mathrm{SR}^{\gamma\gamma}_{S-H}$, respectively.

Derived exclusion limits for the squark-bino GGM model explored by the diphoton analysis. For each point in the squark-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where SL and SH mean $\mathrm{SR}^{\gamma\gamma}_{S-L}$ and $\mathrm{SR}^{\gamma\gamma}_{S-H}$, respectively.

Derived exclusion limits for the wino--bino GGM model explored by the diphoton analysis. For each point in the wino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{W-L}$ or $\mathrm{SR}^{\gamma\gamma}_{W-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where WL and WH mean $\mathrm{SR}^{\gamma\gamma}_{W-L}$ and $\mathrm{SR}^{\gamma\gamma}_{W-H}$, respectively.

Derived exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis. For each point in the higgsino-bino parameter space, the SR ($\mathrm{SR}^{\gamma j}_{L}$ or $\mathrm{SR}^{\gamma j}_{H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where L and H mean $\mathrm{SR}^{\gamma j}_{L}$ and $\mathrm{SR}^{\gamma j}_{H}$, respectively.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-L}$ for the signal models of the gluino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-H}$ for the signal models of the gluino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-L}$ for the signal models of the squark-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-H}$ for the signal models of the squark-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{W-L}$ for the signal models of the wino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{W-H}$ for the signal models of the wino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma j}_{L}$ for the signal models of the photon+jets GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma j}_{H}$ for the signal models of the photon+jets GGM grid.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ selection for one relevant signal point in the gluino-bino model, where the gluinos have mass of 1900 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 300 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ selection for one relevant signal point in the gluino-bino model, where the gluinos have mass of 1900 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 1700 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ selection for one relevant signal point in the squark-bino model, where the squarks have mass of 1700 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 200 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ selection for one relevant signal point in the squark-bino model, where the squarks have mass of 1700 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 1600 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ selection for one relevant signal point in the wino-bino model, where the winos have mass of 1000 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 200 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ selection for one relevant signal point in the wino-bino model, where the winos have mass of 1000 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 800 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma j}_{L}$ selection, for two relevant signal points in the higgsino-bino model, where the gluinos have mass of 1974 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 442 GeV (10000 generated events), and 652 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma j}_{H}$ selection, for two relevant signal points in the higgsino-bino model, where the gluinos have mass of 1974 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 1868 GeV (10000 generated events), and 1920 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.