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.

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.

Contributions from different categories of uncertainties relative to the expected background yields in the defined SRs, as obtained after performing the likelihood ratio test discussed in Section 9 in the paper. The uncertainties are shown for the combination of the individual channels of the $2\ell^{sc}$, $3\ell$ and $4\ell$ SRs. The SRs are indicated along the horizontal axis. In the HEPData entry, the x-axis is simplified for easier visualisation. The first number indicates the sub channel (2:$2\ell^{sc}$, 3:$3\ell$, 4:$4\ell$), while the second number indicates the mass point (2:200, 3:300, 4:400, 5:500).

Data event yields compared with the expected contributions from relevant background sources, for the combination of the individual channels of the $2\ell^{sc}$, $3\ell$ and $4\ell$ SRs. The total uncertainties in the expected event yields are shown as the hatched bands. The SRs are indicated along the horizontal axis. In the HEPData entry, the x-axis is simplified for easier visualisation. The first number indicates the sub channel (2:$2\ell^{sc}$, 3:$3\ell$, 4:$4\ell$), while the second number indicates the mass point (2:200, 3:300, 4:400, 5:500).

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.

Observed and expected upper limits of the $H^{\pm\pm}$ pair production cross section times branching fraction at 95% CL obtained from the combination of 2$\ell^{sc}$, 3$\ell$ and 4$\ell$ channels. The region above the observed limit is excluded by the measurement. The bands represent the expected exclusion curves within one and two standard deviations.

The theoretical prediction of Figure 9(a) in the paper.

Observed and expected upper limits of the $H^{\pm\pm}$ and $H^{\pm}$ production cross section times branching fraction at 95% CL obtained from the combination of 2$\ell^{sc}$, 3$\ell$ and 4$\ell$ channels. The region above the observed limit is excluded by the measurement. The bands represent the expected exclusion curves within one and two standard deviations.

The theoretical prediction of Figure 9(b) in the paper.

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 200~GeV$ or $m_{H^{\pm\pm}} = 220~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 300~GeV$ or $m_{H^{\pm\pm}} = 350~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 400~GeV$ or $m_{H^{\pm\pm}} = 450~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 500~GeV$ or $m_{H^{\pm\pm}} = 550~GeV$ or $m_{H^{\pm\pm}} = 600~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Summary of the observed and expected 95% CL upper limits on the cross section for $\mathrm{LQ}_{3}^{\mathrm{d}}$ pair production as a function of $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$ under the assumptions of B=1.

Summary of the observed and expected 95% CL upper limits on B as a function of $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$.

Summary of the observed and expected 95% CL upper limits on B as a function of $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$.

Cutflow of the preselection requirements (see Section 5) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the preselection requirements (see Section 5) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the signal region requirements in 1$\ell+\geq 1\tau$ channel (see Table 3) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. Events that satisfy the preselection requirements are considered. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the signal region requirements in 1$\ell+\geq 1\tau$ channel (see Table 3) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. Events that satisfy the preselection requirements are considered. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the signal region requirements in the 2$\ell$OS+$\geq 1\tau$ channel (see Table 4) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. Events that satisfy the preselection requirements are considered. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the signal region requirements in the 2$\ell$OS+$\geq 1\tau$ channel (see Table 4) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. Events that satisfy the preselection requirements are considered. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the signal region requirements in the $2\ell$SS/$3\ell+\geq 1\tau$ channel (see Table 5) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. Events that satisfy the preselection requirements are considered. In this channel, two signal regions (SR-L and SR-H) are defined based on $p_{\mathrm{T}, 1}^{\tau}$, with SR-L and SR-H requiring $125< p_{\mathrm{T}, 1}^{\tau} < 225$ GeV and $p_{\mathrm{T}, 1}^{\tau}>225$ GeV, respectively. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

Cutflow of the signal region requirements in the $2\ell$SS/$3\ell+\geq 1\tau$ channel (see Table 5) for $\mathrm{LQ}_{3}^{\mathrm{d}}$ signals with $m_{\mathrm{LQ}_{3}^{\mathrm{d}}}$=0.9, 1.1, and 1.3 TeV, assuming B=1. Events that satisfy the preselection requirements are considered. In this channel, two signal regions (SR-L and SR-H) are defined based on $p_{\mathrm{T}, 1}^{\tau}$, with SR-L and SR-H requiring $125< p_{\mathrm{T}, 1}^{\tau} < 225$ GeV and $p_{\mathrm{T}, 1}^{\tau}>225$ GeV, respectively. The yields correspond to an integrated luminosity of 139 fb$^{-1}$.

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)

Observed and expected limits on $\tan\beta$ as a function of $m_{H^+}$ in the $M_h^{125}(\tilde{\chi})$ scenario. Limits are shown for $\tan\beta$ values in the range of 0.5-60 due to the availability of the model prediction. The bands surrounding the expected limits show the 68% and 95% confidence intervals. Uncertainties in the predicted $H^+$ cross-sections or branching ratios are not considered.

Observed and expected limits on $\tan\beta$ as a function of $m_{H^+}$ in the $M_h^{125}(\tilde{\tau})$ scenario. Limits are shown for $\tan\beta$ values in the range of 0.5-60 due to the availability of the model prediction. The bands surrounding the expected limits show the 68% and 95% confidence intervals. Uncertainties in the predicted $H^+$ cross-sections or branching ratios are not considered.

Observed and expected limits on $\tan\beta$ as a function of $m_{H^+}$ in the $M_h^{125}$(alignment) scenario. Limits are shown for $\tan\beta$ values in the range of 0.5-60 due to the availability of the model prediction. The bands surrounding the expected limits show the 68% and 95% confidence intervals. Uncertainties in the predicted $H^+$ cross-sections or branching ratios are not considered.

Observed and expected limits on $\tan\beta$ as a function of $m_{H^+}$ in the $M_{h_1}^{125}$(CPV) scenario. Limits are shown for $\tan\beta$ values in the range of 0.5-60 due to the availability of the model prediction. The bands surrounding the expected limits show the 68% and 95% confidence intervals. Uncertainties in the predicted $H^+$ cross-sections or branching ratios are not considered.

Event yields of the SM background processes and the 800 GeV $H^{+}$ sample in the four analysis regions before the fit to the data. Uncertainties include both statistical and systematic uncertainties. The yields of the $H^{+}$ signal sample correspond to a cross-section times branching fraction of 10 pb.

Event acceptance for the different $H^+$ mass signal samples.

Measured normalised differential fiducial cross sections of $\gamma\gamma \rightarrow \gamma\gamma$ production in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for diphoton $|cos(\theta*)|$ are shown as points with error bars giving the statistical uncertainty and grey bands indicating the size of the total uncertainty. The results are compared with the prediction from the SuperChic v3.0 MC generator (solid line).

Measured normalised differential fiducial cross sections of $\gamma\gamma \rightarrow \gamma\gamma$ production in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for average photon transverse momentum are shown as points with error bars giving the statistical uncertainty and grey bands indicating the size of the total uncertainty. The results are compared with the prediction from the SuperChic v3.0 MC generator (solid line) with bands denoting the theoretical uncertainty.

Measured normalised differential fiducial cross sections of $\gamma\gamma \rightarrow \gamma\gamma$ production in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for average photon transverse momentum are shown as points with error bars giving the statistical uncertainty and grey bands indicating the size of the total uncertainty. The results are compared with the prediction from the SuperChic v3.0 MC generator (solid line).

Measured differential fiducial cross sections of $\gamma\gamma \rightarrow \gamma\gamma$ production in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for diphoton rapidity are shown as points with error bars giving the statistical uncertainty and grey bands indicating the size of the total uncertainty. The results are compared with the prediction from the SuperChic v3.0 MC generator (solid line) with bands denoting the theoretical uncertainty.

Measured normalised differential fiducial cross sections of $\gamma\gamma \rightarrow \gamma\gamma$ production in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for diphoton rapidity are shown as points with error bars giving the statistical uncertainty and grey bands indicating the size of the total uncertainty. The results are compared with the prediction from the SuperChic v3.0 MC generator (solid line).

The 95% CL upper limit on the ALP cross section $\sigma_{\gamma\gamma\rightarrow a \rightarrow\gamma\gamma}$ for the $\gamma\gamma\rightarrow a \rightarrow \gamma\gamma$ process as a function of ALP mass m$_{a}$. The observed upper limit is shown as a solid black line and the expected upper limit is shown by the dashed black line with its $\pm1$ and $\pm2$ standard deviation bands. The discontinuity at $m_{a}=70 GeV$ is caused by the increase of the mass-bin width which brings an increase in signal acceptance.

The 95% CL upper limit on the ALP coupling $1/\Lambda_{a}$ for the $\gamma\gamma\rightarrow a \rightarrow \gamma\gamma$ process as a function of ALP mass m$_{a}$. The observed upper limit is shown as a solid black line and the expected upper limit is shown by the dashed black line with its $\pm1$ and $\pm2$ standard deviation bands. The discontinuity at $m_{a} = 70 GeV$ is caused by the increase of the mass-bin width which brings an increase in signal acceptance.

Fiducial cross section for light-by-light scattering

The +1 $\sigma$ band of the observed exclusion contor for the $Z^{\prime}_{B}$ model in the $m_{\chi}$-$m_{Z^{\prime}_{B}}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the $Z^{\prime}_{B}$ model in the $m_{\chi}$-$m_{Z^{\prime}_{B}}$ plane.

A comparison of the inferred limits to the constraints from direct detection experiments on the spin-independent DM--nucleon cross section in the context of the $Z'_B$ simplified model with vector couplings. Limits are shown at 90% CL.

The observed exclusion contor for the $Z^{\prime}$-2HDM model in the $m_{A}$-$m_{Z^{\prime}}$ plane.

The expected exclusion contor for the $Z^{\prime}$-2HDM model in the $m_{A}$-$m_{Z^{\prime}}$ plane.

The +1 $\sigma$ band of the observed exclusion contor for the $m_{A}$-$Z^{\prime}$-2HDM model in the $m_{Z^{\prime}}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the $m_{A}$-$Z^{\prime}$-2HDM model in the $m_{Z^{\prime}}$ plane.

The observed exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The expected exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The +1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The observed exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The expected exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The +1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The exclusion limits at 95% CL for the 2HDM+a model as a function of $\sin \theta$ for $m_{A,H^{\pm},H}$= 600GeV, $m_a$ = 200GeV, $\tan \beta$ = 1.0$.

The exclusion limits at 95% CL for the 2HDM+a model as a function of $\sin \theta$ for $m_{A,H^{\pm},H}$= 1000GeV, $m_a$ = 350GeV, $\tan \beta$ = 1.0$.

Breakdown of the dominant systematic uncertainties.

Event yields in the range of 120 $<m_{\gamma\gamma}<$ 130 GeV for data, signal models, the SM Higgs boson background and non-resonant background in each analysis category, for an integrated luminosity of $139$fb$^{-1}$.

Detailed background contributions from the SM Higgs boson and continuum background for each cut

Detailed contributions from the signals for each cut.

Acceptance times efficiency for several signals in each category.

IRing2 for HT2>=500 GeV, NJets>=5

IRing2 for HT2>=1000 GeV, NJets>=2

IRing2 for HT2>=1000 GeV, NJets>=3

IRing2 for HT2>=1000 GeV, NJets>=4

IRing2 for HT2>=1000 GeV, NJets>=5

IRing2 for HT2>=1500 GeV, NJets>=2

IRing2 for HT2>=1500 GeV, NJets>=3

IRing2 for HT2>=1500 GeV, NJets>=4

IRing2 for HT2>=1500 GeV, NJets>=5

IRing128 for HT2>=500 GeV, NJets>=2

IRing128 for HT2>=500 GeV, NJets>=3

IRing128 for HT2>=500 GeV, NJets>=4

IRing128 for HT2>=500 GeV, NJets>=5

IRing128 for HT2>=1000 GeV, NJets>=2

IRing128 for HT2>=1000 GeV, NJets>=3

IRing128 for HT2>=1000 GeV, NJets>=4

IRing128 for HT2>=1000 GeV, NJets>=5

IRing128 for HT2>=1500 GeV, NJets>=2

IRing128 for HT2>=1500 GeV, NJets>=3

IRing128 for HT2>=1500 GeV, NJets>=4

IRing128 for HT2>=1500 GeV, NJets>=5

ICyl16 for HT2>=500 GeV, NJets>=2

ICyl16 for HT2>=500 GeV, NJets>=3

ICyl16 for HT2>=500 GeV, NJets>=4

ICyl16 for HT2>=500 GeV, NJets>=5

ICyl16 for HT2>=1000 GeV, NJets>=2

ICyl16 for HT2>=1000 GeV, NJets>=3

ICyl16 for HT2>=1000 GeV, NJets>=4

ICyl16 for HT2>=1000 GeV, NJets>=5

ICyl16 for HT2>=1500 GeV, NJets>=2

ICyl16 for HT2>=1500 GeV, NJets>=3

ICyl16 for HT2>=1500 GeV, NJets>=4

ICyl16 for HT2>=1500 GeV, NJets>=5

IRing2 covariance for HT2>=500 GeV, NJets>=2 (Table 1)

IRing2 covariance for HT2>=500 GeV, NJets>=3 (Table 2)

IRing2 covariance for HT2>=500 GeV, NJets>=4 (Table 3)

IRing2 covariance for HT2>=500 GeV, NJets>=5 (Table 4)

IRing2 covariance for HT2>=1000 GeV, NJets>=2 (Table 5)

IRing2 covariance for HT2>=1000 GeV, NJets>=3 (Table 6)

IRing2 covariance for HT2>=1000 GeV, NJets>=4 (Table 7)

IRing2 covariance for HT2>=1000 GeV, NJets>=5 (Table 8)

IRing2 covariance for HT2>=1500 GeV, NJets>=2 (Table 9)

IRing2 covariance for HT2>=1500 GeV, NJets>=3 (Table 10)

IRing2 covariance for HT2>=1500 GeV, NJets>=4 (Table 11)

IRing2 covariance for HT2>=1500 GeV, NJets>=5 (Table 12)

IRing128 covariance for HT2>=500 GeV, NJets>=2 (Table 13)

IRing128 covariance for HT2>=500 GeV, NJets>=3 (Table 14)

IRing128 covariance for HT2>=500 GeV, NJets>=4 (Table 15)

IRing128 covariance for HT2>=500 GeV, NJets>=5 (Table 16)

IRing128 covariance for HT2>=1000 GeV, NJets>=2 (Table 17)

IRing128 covariance for HT2>=1000 GeV, NJets>=3 (Table 18)

IRing128 covariance for HT2>=1000 GeV, NJets>=4 (Table 19)

IRing128 covariance for HT2>=1000 GeV, NJets>=5 (Table 20)

IRing128 covariance for HT2>=1500 GeV, NJets>=2 (Table 21)

IRing128 covariance for HT2>=1500 GeV, NJets>=3 (Table 22)

IRing128 covariance for HT2>=1500 GeV, NJets>=4 (Table 23)

IRing128 covariance for HT2>=1500 GeV, NJets>=5 (Table 24)

ICyl16 covariance for HT2>=500 GeV, NJets>=2 (Table 25)

ICyl16 covariance for HT2>=500 GeV, NJets>=3 (Table 26)

ICyl16 covariance for HT2>=500 GeV, NJets>=4 (Table 27)

ICyl16 covariance for HT2>=500 GeV, NJets>=5 (Table 28)

ICyl16 covariance for HT2>=1000 GeV, NJets>=2 (Table 29)

ICyl16 covariance for HT2>=1000 GeV, NJets>=3 (Table 30)

ICyl16 covariance for HT2>=1000 GeV, NJets>=4 (Table 31)

ICyl16 covariance for HT2>=1000 GeV, NJets>=5 (Table 32)

ICyl16 covariance for HT2>=1500 GeV, NJets>=2 (Table 33)

ICyl16 covariance for HT2>=1500 GeV, NJets>=3 (Table 34)

ICyl16 covariance for HT2>=1500 GeV, NJets>=4 (Table 35)

ICyl16 covariance for HT2>=1500 GeV, NJets>=5 (Table 36)

IRing2 covariance, complete

1-IRing128 covariance, complete

1-ICyl16 covariance, complete

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of single plus non-resonant plus pair vector LQ production from the combination of the high b-jet $p_{T}$ signal region for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of single plus non-resonant plus pair vector LQ production from the combination of the high b-jet $p_{T}$ signal region for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of single plus non-resonant plus pair vector LQ production from the combination of the high b-jet $p_{T}$ signal region for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 2.5]

The two-dimensional 95% CL exclusion limits in the $\lambda$-$m_{LQ}$ plane for singly plus non-resonant produced vector LQ (green lines) and for the sum, referred as Total, of single plus non-resonant plus pair vector LQ production (blue lines). Regions to the left of the lines are excluded. The dotted area shows the preferred region where the chosen LQ model can explain observed B anomalies. [$\kappa$ = 0]

The two-dimensional 95% CL exclusion limits in the $\lambda$-$m_{LQ}$ plane for singly plus non-resonant produced vector LQ (green lines) and for the sum, referred as Total, of single plus non-resonant plus pair vector LQ production (blue lines). Regions to the left of the lines are excluded. The dotted area shows the preferred region where the chosen LQ model can explain observed B anomalies. [$\kappa$ = 1]

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.0 on the cross-section of single plus non-resonant plus pair $\widetilde{S_{1}}$ production hypotheses from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.7 on the cross-section of single plus non-resonant plus pair $\widetilde{S_{1}}$ production hypotheses from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 2.5 on the cross-section of single plus non-resonant plus pair $\widetilde{S_{1}}$ production hypotheses from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

The two-dimensional 95% CL exclusion limits in the $\lambda$-$m_{LQ}$ plane for singly plus non-resonant produced $\widetilde{S_{1}}$ (green lines) and for the sum, referred as Total, of single plus non-resonant plus pair vector LQ production (blue lines). Regions to the left of the lines are excluded.

Observed (solid line) and expected (dashed line) 95% upper limits on the visible cross-section, $\sigma_{\mathrm{vis}}$, obtained from model-independent search by a signal-plus-background fit in the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly plus non-resonant produced vector LQ signals from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly plus non-resonant produced vector LQ signals from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly plus non-resonant produced vector LQ signals from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 2.5]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly plus non-resonant produced vector LQ signals from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly plus non-resonant produced vector LQ signals from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly plus non-resonant produced vector LQ signals from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 2.5]

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.0 on the cross-section of singly produced $\widetilde{S_{1}}$ signal hypotheses from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.7 on the cross-section of singly produced $\widetilde{S_{1}}$ signal hypotheses from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 2.5 on the cross-section of singly produced $\widetilde{S_{1}}$ signal hypotheses from the combination of the high b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels.

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $\widetilde{S_{1}}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 1.0]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $\widetilde{S_{1}}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 1.7]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $\widetilde{S_{1}}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 2.5]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $U_1^{MIN}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 1.0]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $U_1^{MIN}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 1.7]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $U_1^{MIN}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 2.5]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $U_1^{YM}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 1.0]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $U_1^{YM}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 1.7]

The signal acceptance times efficiency for single plus non-resonant produced LQ in the $U_1^{YM}$ model from the combination of the high and low b-jet $p_{T}$ signal regions for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels. [$\lambda$ = 2.5]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly produced vector LQ signals from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly produced vector LQ signals from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly produced vector LQ signals from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 2.5]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly produced vector LQ signals from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly produced vector LQ signals from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of singly produced vector LQ signals from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 2.5]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of the single plus non-resonant plus pair production of vector LQ from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of the single plus non-resonant plus pair production of vector LQ from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of the single plus non-resonant plus pair production of vector LQ from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{YM}$ model ($\kappa$ = 0) with $\lambda$ = 2.5]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of the single plus non-resonant plus pair production of vector LQ from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.0]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of the single plus non-resonant plus pair production of vector LQ from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 1.7]

Observed (solid line) and expected (dashed line) 95% CL upper limits on the cross-section of the single plus non-resonant plus pair production of vector LQ from the combination of the high and low b-jet $p_{T}$ category for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$U_1^{MIN}$ model ($\kappa$ = 1) with $\lambda$ = 2.5]

The two-dimensional 95% CL exclusion limits in the $\lambda$-$m_{LQ}$ plane for singly plus non-resonant produced vector LQ (green lines) and for the sum, referred as Total, of single plus non-resonant plus pair vector LQ production (blue lines).Regions to the left of the lines are excluded. The dotted area shows the preferred region where the chosen LQ model can explain observed B anomalies. Results are extracted from the combination of the high and low b-jet $p_{T}$ signal region for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$\kappa$ = 0]

The two-dimensional 95% CL exclusion limits in the $\lambda$-$m_{LQ}$ plane for singly plus non-resonant produced vector LQ (green lines) and for the sum, referred as Total, of single plus non-resonant plus pair vector LQ production (blue lines).Regions to the left of the lines are excluded. The dotted area shows the preferred region where the chosen LQ model can explain observed B anomalies. Results are extracted from the combination of the high and low b-jet $p_{T}$ signal region for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region. [$\kappa$ = 1]

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.0 on the cross-section of singly-produced $\widetilde{S_{1}}$ signals from the combination of the high and low b-jet $p_{T}$ categories for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.7 on the cross-section of singly-produced $\widetilde{S_{1}}$ signals from the combination of the high and low b-jet $p_{T}$ categories for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 2.5 on the cross-section of singly-produced $\widetilde{S_{1}}$ signals from the combination of the high and low b-jet $p_{T}$ categories for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.0 on the cross-section of single plus non-resonant plus pair $\widetilde{S_{1}}$ production signal from the combination of the high and low b-jet $p_{T}$ categories for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 1.7 on the cross-section of single plus non-resonant plus pair $\widetilde{S_{1}}$ production signal from the combination of the high and low b-jet $p_{T}$ categories for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.

Observed (solid line) and expected (dashed line) 95% CL upper limits for $\lambda$ = 2.5 on the cross-section of single plus non-resonant plus pair $\widetilde{S_{1}}$ production signal from the combination of the high and low b-jet $p_{T}$ categories for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.

The two-dimensional 95% CL exclusion limits in the $\lambda$-$m_{LQ}$ plane for singly plus non-resonant produced $\widetilde{S_{1}}$ (green lines) and for the sum, referred as Total, of single plus non-resonant plus pair $\widetilde{S_{1}}$ production (blue lines). Regions to the left of the lines are excluded. Results are extracted from the combination of the high and low b-jet $p_{T}$ signal region for the $\tau_\text{lep}\tau_\text{had}$ and $\tau_\text{had}\tau_\text{had}$ channels, neglecting the interference between the LQ non-resonant production processes and the SM processes. However, the interference between the non-resonant LQ production and SM diagrams, such as those from Z+jets, could be non-negligible for events in the low b-jet $p_{T}$ signal region.