Measured and predicted fiducial cross-sections in both the lllljj and ll$\nu\nu$jj channels for the inclusive ZZjj processes. Uncertainties due to different sources are presented.

Measured and predicted fiducial cross-sections in both the lllljj and ll$\nu\nu$jj channels for the inclusive ZZjj processes. Uncertainties due to different sources are presented.

Observed and expected multivariate discriminant distribution in the $\ell\ell\ell\ell jj$ QCD CR.

Observed and expected multivariate discriminant distribution in the $\ell\ell\ell\ell jj$ QCD CR.

Observed and expected multivariate discriminant distribution in the $\ell\ell\ell\ell jj$ SR.

Observed and expected multivariate discriminant distribution in the $\ell\ell\ell\ell jj$ SR.

Observed and expected multivariate discriminant distribution in the $\ell\ell\nu\nu jj$ SR.

Observed and expected multivariate discriminant distribution in the $\ell\ell\nu\nu jj$ SR.

Pseudorapidity distribution of inclusive photons measured within $2.3<\eta_{\rm lab}<3.9$ in pp collisions at $\sqrt{s} = 5020~\mathrm{GeV}$.

Pseudorapidity distribution of inclusive photons measured within $2.3<\eta_{\rm lab}<3.9$ in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Pseudorapidity distribution of inclusive photons measured within $2.3<\eta_{\rm lab}<3.9$ in Pb-p collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Pseudorapidity distribution of inclusive photons measured within $2.3<\eta_{\rm lab}<3.9$ in pp collisions at various LHC energies.

$\langle N_{\rm \gamma} \rangle$ measured within $2.3<\eta_{\rm lab}<3.9$ as a function of collision energy in pp collisions.

Centrality-dependent pseudorapidity distribution of inclusive photons measured within $2.3<\eta_{\rm lab}<3.9$ in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$ (Centrality estimator - CL1).

Centrality-dependent pseudorapidity distribution of inclusive photons measured within $2.3<\eta_{\rm lab}<3.9$ in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$ (Centrality estimator - ZNA).

$\langle N_{\rm \gamma} \rangle$ measured within $2.3<\eta_{\rm lab}<3.9$ as a function of $\langle N_{\rm part} \rangle$ for CL1 centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

$\langle N_{\rm \gamma} \rangle$ measured within $2.3<\eta_{\rm lab}<3.9$ as a function of $\langle N_{\rm part} \rangle$ for ZNA (high-$p_{\rm T}$) centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

$\langle N_{\rm \gamma} \rangle$ measured within $2.3<\eta_{\rm lab}<3.9$ as a function of $\langle N_{\rm part} \rangle$ for ZNA (Pb-side) centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Values of $\langle N_{\rm \gamma} \rangle$/$\langle N_{\rm part} \rangle$ as a function of $\langle N_{\rm part} \rangle$ for CL1 centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Values of $\langle N_{\rm \gamma} \rangle$/$\langle N_{\rm part} \rangle$ as a function of $\langle N_{\rm part} \rangle$ for ZNA (high-$p_{\rm T}$) centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Values of $\langle N_{\rm \gamma} \rangle$/$\langle N_{\rm part} \rangle$ as a function of $\langle N_{\rm part} \rangle$ for ZNA (Pb-side) centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Values of $\langle N_{\rm \gamma} \rangle$/$\langle N_{\rm part} \rangle$ as a function of $\langle N_{\rm \gamma} \rangle$ for CL1 centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Values of $\langle N_{\rm \gamma} \rangle$/$\langle N_{\rm part} \rangle$ as a function of $\langle N_{\rm \gamma} \rangle$ for ZNA (high-$p_{\rm T}$) centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Values of $\langle N_{\rm \gamma} \rangle$/$\langle N_{\rm part} \rangle$ as a function of $\langle N_{\rm \gamma} \rangle$ for ZNA (Pb-side) centrality estimator in p-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5020~\mathrm{GeV}$.

Centrality dependence of $\langle \cos[6(\Psi_{6}-\Psi_{2})]\rangle_{\mathrm{GE}}$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV.

Centrality dependence of $\langle \cos[2\Psi_{2}+3\Psi_{3}-5\Psi_{5}]\rangle_{\mathrm{GE}}$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV.

Centrality dependence of $\langle \cos[2\Psi_{2}+4\Psi_{4}-6\Psi_{6}]\rangle_{\mathrm{GE}}$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV.

Centrality dependence of $\langle \cos[8\Psi_{2}-3\Psi_{3}-5\Psi_{5}]\rangle_{\mathrm{GE}}$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV.

Centrality dependence of $\langle \cos[2\Psi_{2}-6\Psi_{3}+4\Psi_{4}]\rangle_{\mathrm{GE}}$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV.

Centrality dependence of $\langle \cos[2\Psi_{2}-3\Psi_{3}-4\Psi_{4}+5\Psi_{5}]\rangle_{\mathrm{GE}}$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV.

Differential cross section of J/psi mesons from b hadrons as a function of dimuon pT in pp and PbPb collisions. The PbPb cross sections are normalised by TAA for direct comparison. Global uncertainties arise from the integrated luminosity uncertainty in pp collisions, and the number of minimum bias events and TAA uncertainties for PbPb collisions.

Differential cross section of prompt J/psi mesons as a function of dimuon rapidty in pp and PbPb collisions. The PbPb cross sections are normalised by TAA for direct comparison. Global uncertainties arise from the integrated luminosity uncertainty in pp collisions, and the number of minimum bias events and TAA uncertainties for PbPb collisions.

Differential cross section of J/psi mesons from b hadrons as a function of dimuon rapidty in pp and PbPb collisions. The PbPb cross sections are normalised by TAA for direct comparison. Global uncertainties arise from the integrated luminosity uncertainty in pp collisions, and the number of minimum bias events and TAA uncertainties for PbPb collisions.

Nuclear modification factor of prompt J/psi mesons as a function of dimuon rapidity. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of collision centrality. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of dimuon pT. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of dimuon pT in the mid-rapidity interval. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of dimuon pT in the most forward rapidity interval. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of collision centrality in the mid- and most forward rapidity intervals. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of pT in three centrality bins. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of collision centrality in the most forward rapidity range at moderate and high pT. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of collision centrality in the mid-rapidity range. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt psi(2S) mesons as a function of collision centrality in the mid-rapidity range. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

95% CL intervals on the nuclear modification factor of prompt psi(2S) mesons as a function of collision centrality in the mid-rapidity range. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt J/psi and psi(2S) mesons as a function of dimuon pT in the mid-rapidity range. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of collision centrality in the forward rapidity range. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt psi(2S) mesons as a function of collision centrality in the forward rapidity range. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

95% CL intervals on the nuclear modification factor of prompt psi(2S) mesons as a function of collision centrality in the forward rapidity range. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of prompt J/psi mesons as a function of dimuon pT in the forward rapidity range. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of prompt psi(2S) mesons as a function of dimuon pT in the forward rapidity range. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

95% CL intervals on the nuclear modification factor of prompt psi(2S) mesons as a function of dimuon pT in the forward rapidity range. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of dimuon rapidity. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of collision centrality. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of dimuon pT. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of dimuon pT in the mid-rapidity interval. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of dimuon pT in the most forward rapidity interval. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of collision centrality in the mid- and most forward rapidity intervals. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of pT in three centrality bins. Global uncertainties arise from the integrated luminosity uncertainty of the pp data, and the number of minimum bias events and TAA uncertainties of the PbPb data.

Nuclear modification factor of nonprompt J/psi mesons as a function of collision centrality in the most forward rapidity range at moderate and high pT. Global uncertainties arise from the full pp statistical and systematic uncertainties (including integrated luminosity uncertainty), and the number of minimum bias events uncertainty of the PbPb data.

Distribution of $S$, which is one of the discriminating variables used to define the $2\ell^{sc}$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $E_{T}^{miss}$, which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $\Delta R_{\ell^{\pm}\ell^{\pm}}$, which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $m_{x\ell}$ ($x$=3), which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $p_{T}^{leading jet}$, which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $E_{T}^{miss}$, which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $\Delta R_{\ell^{\pm}\ell^{\pm}}^{min}$, which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $m_{x\ell}$ ($x$=4), which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $p_{T}^{\ell_{1}}$, which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

The $E_{T}^{miss}$ distribution for the SRs of the $m_{H^{\pm\pm}} = 300~GeV$ signal mass hypothesis, where the selection requirement on $E_{T}^{miss}$ has been removed. In the attached plot, the signals are stacked on top of the backgrounds while individuals contributions of the $2\ell^{sc}$ channel are shown in HEPData. The last bin, isolated by a vertical red dashed line, is inclusive and corresponds to the SR.

The $E_{T}^{miss}$ distribution for the SRs of the $m_{H^{\pm\pm}} = 300~GeV$ signal mass hypothesis, where the selection requirement on $E_{T}^{miss}$ has been removed. In the attached plot, the signals are stacked on top of the backgrounds while individuals contributions of the $3\ell$ channel are shown in HEPData. The last bin, isolated by a vertical red dashed line, is inclusive and corresponds to the SR.

The $E_{T}^{miss}$ distribution for the SRs of the $m_{H^{\pm\pm}} = 300~GeV$ signal mass hypothesis, where the selection requirement on $E_{T}^{miss}$ has been removed. In the attached plot, the signals are stacked on top of the backgrounds while individuals contributions of the $4\ell$ channel are shown in HEPData. The last bin, isolated by a vertical red dashed line, is inclusive and corresponds to the SR.

The $m_{\mathrm{eff}}$ distribution in SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. 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 regions.

The $m_{\mathrm{eff}}$ distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. 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 regions.

The $m_{\mathrm{eff}}$ distribution in SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. 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 regions.

The $m_{\mathrm{eff}}$ distribution in SR0$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. 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 regions.

The $m_{\mathrm{eff}}$ distribution in SR1$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. 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 regions.

The $m_{\mathrm{eff}}$ distribution in SR2$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. 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 regions.

Expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ bserved 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed upper limit on the signal cross section in fb for the wino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the wino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the gluino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the gluino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the higgsino GGM models. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Best expected SR for the wino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the wino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the gluino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the gluino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the higgsino GGM models. A value of 6 corresponds to SR0-ZZ$^{\mathrm{loose}}$, 7 corresponds to SR0-ZZ$^{\mathrm{tight}}$, 8 corresponds to SR0-ZZ$^{\mathrm{loose}}_{\mathrm{bveto}}$, and 9 corresponds to SR0-ZZ$^{\mathrm{tight}}_{\mathrm{bveto}}$.

Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$^{\mathrm{tight}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR5L. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the taus leptons in distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light taus in distribution in SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with four light leptons and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with four light leptons and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with four light leptons and two Z candidates. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with exactly five light leptons. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with three light leptons and one tau and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with three light leptons and one tau and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with two light leptons and two taus and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with two light leptons and two taus and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

Cutflow event yields in regions SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$, SR0$_{\mathrm{breq}}$, and SR5L 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 = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR1$_{\mathrm{breq}}$ 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 = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR2$_{\mathrm{breq}}$ 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 = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR0-ZZ$^{\mathrm{loose}}$, SR0-ZZ$^{\mathrm{tight}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR5L the higgsino GGM RPC model with BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 50% and higgsino masses of 200 GeV, or BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 100% and higgsino masses of 300 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 = 139\,\mbox{fb\(^{-1}\)}$. The generator filter is a selection of $\geq$4e/$\mu$/$\tau_{\mathrm{had-vis}}$ leptons with $p_{\mathrm{T}}(e,\mu)>4$GeV, $p_{\mathrm{T}}(\tau_{\mathrm{had-vis}})>15$GeV and $|\eta|<2.8$ and is applied during the MC generation of the simulated events. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} > 9$ GeV are required.

Inclusive differential cross section for two electrons and two muons (Max = 1710~GeV).

Mass of the first dilepton pair in the $Z$ mass region (Max = 88.1~GeV).

Mass of the first dilepton pair in the $H$ mass region (Max = 107~GeV).

Mass of the first dilepton pair in the offshell region (Max = 146~GeV).

Mass of the first dilepton pair in the $ZZ$ mass region (Max = 232~GeV).

Mass of the second dilepton pair in the $Z$ mass region (Max = 44.2~GeV).

Mass of the second dilepton pair in the $H$ mass region (Max = 59.6~GeV).

Mass of the second dilepton pair in the offhell region (Max = 88.3~GeV).

Mass of the second dilepton pair in the $ZZ$ mass region (Max = 1000~GeV).

$p_T$ of the first dilepton pair in the $Z$ mass region (Max = 291~GeV).

$p_T$ of the first dilepton pair in the $H$ mass region (Max = 245~GeV).

$p_T$ of the first dilepton pair in the offshell region (Max = 216~GeV).

$p_T$ of the first dilepton pair in the $ZZ$ mass region (Max = 555~GeV).

$p_T$ of the second dilepton pair in the $Z$ mass region (Max = 117~GeV).

$p_T$ of the second dilepton pair in the $H$ mass region (Max = 124~GeV).

$p_T$ of the second dilepton pair in the offhell region (Max = 192~GeV).

$p_T$ of the second dilepton pair in the $ZZ$ mass region (Max = 555~GeV).

$\cos\theta^*$ between first dilepton pair in the $Z$ mass region.

$\cos\theta^*$ between first dilepton pair in the offshell region.

$\cos\theta^*$ between first dilepton pair in the $H$ mass region.

$\cos\theta^*$ between first dilepton pair in the $ZZ$ mass region.

$\cos\theta^*$ between second dilepton pair in the $Z$ mass region.

$\cos\theta^*$ between second dilepton pair in the $H$ mass region.

$\cos\theta^*$ between second dilepton pair in the offshell mass region.

$\cos\theta^*$ between second dilepton pair in the $ZZ$ mass region.

$\Delta y$ between the dilepton pairs in the $Z$ mass region.

$\Delta y$ between the dilepton pairs in the $H$ mass region.

$\Delta y$ between the dilepton pairs in the offshell region.

$\Delta y$ between the dilepton pairs in the $ZZ$ mass region.

$\Delta \phi$ between the dilepton pairs in the $Z$ mass region.

$\Delta \phi$ between the dilepton pairs in the $H$ mass region.

$\Delta \phi$ between the dilepton pairs in the offshell region.

$\Delta \phi$ between the dilepton pairs in the $ZZ$ mass region.

$\Delta \phi$ between the leading lepton pairs in the $Z$ mass region.

$\Delta \phi$ between the leading lepton pairs in the $H$ mass region.

$\Delta \phi$ between the leding lepton pairs in the offshell region.

$\Delta \phi$ between the leading lepton pairs in the $ZZ$ mass region.

Four lepton mass in first $p_T^{4l}$ slice (Max = 726~GeV)

Four lepton mass in second $p_T^{4l}$ slice (Max = 1079~GeV)

Four lepton mass in third $p_T^{4l}$ slice (Max = 1153~GeV)

Four lepton mass in fourth $p_T^{4l}$ slice (Max = 1320~GeV)

Four lepton mass in fifth $p_T^{4l}$ slice (Max = 1413~GeV)

Four lepton mass in first $y^{4l}$ slice (Max = 1710~GeV)

Four lepton mass in second $y^{4l}$ slice (Max = 1116~GeV)

Four lepton mass in third $y^{4l}$ slice (Max = 1413~GeV)

Four lepton mass in fourth $y^{4l}$ slice (Max = 1044~GeV)

Four lepton mass in fifth $y^{4l}$ slice (Max = 918~GeV)

Covariance matrix for M4l

Covariance matrix for M4lvChannel

Covariance matrix for m12_vs_M4l

Covariance matrix for m34_vs_M4l

Covariance matrix for pt12_vs_M4l

Covariance matrix for pt34_vs_M4l

Covariance matrix for CTS12_vs_M4l

Covariance matrix for CTS34_vs_M4l

Covariance matrix for dYpairs_vs_M4l

Covariance matrix for dPhiPairs_vs_M4l

Covariance matrix for dPhiLeadLep_vs_M4l

Covariance matrix for M4lvPt4lbin

Covariance matrix for M4lvRapiditybin

EFT results (linear)

EFT results (full)

K$^+$p (K$^+$p $\oplus$ K$^-\overline{\mathrm p}$) correlation function in p-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (40-100%).

K$^+$p (K$^+$p $\oplus$ K$^-\overline{\mathrm p}$) correlation function in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (60-70%).

K$^+$p (K$^+$p $\oplus$ K$^-\overline{\mathrm p}$) correlation function in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (70-80%).

K$^+$p (K$^+$p $\oplus$ K$^-\overline{\mathrm p}$) correlation function in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (80-90%).

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in pp collisions at $\sqrt{s}=13$ TeV.

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in p-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (0-20%).

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in p-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (20-40%).

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in p-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (40-100%).

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (60-70%).

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (70-80%).

K$^-$p (K$^-$p $\oplus$ K$^+\overline{\mathrm p}$) correlation function in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV (80-90%).

Scaling factor ($\alpha_j $) for $\mathrm{\overline{K}^0 n}$ extracted from the different fits of the K$^-$ p correlation function as a function of the core radius $r_{\mathrm{core}}$.

Scaling factor ($\alpha_j $) for $\pi \Sigma$ extracted from the different fits of the K$^-$ p correlation function as a function of the core radius $r_{\mathrm{core}}$.

This table shows the correlation coefficient between the $\alpha_{\mathrm{\overline{K}^0 n}}$ and $\alpha_{\pi \Sigma}$ parameters extracted from the different fits to the K$^-$ p correlation function with free weights. The results are shown as a function of the core radius $r_{\mathrm{core}}$ shown in Table 7. The error on the correlation coefficient, shown as statistical errors in the table, are obteined by using the Pearson's formula to evaluate the probable error of correlation coefficient.