The measured $p_{\mathrm{T}}^{\mathrm{recoil}}$ distributions in the top control region, compared with the background predictions as estimated after the simultaneous, binned background-only fit to the data in the control regions. The last bin of the distribution contains overflows.

The measured $p_{\mathrm{T}}^{\mathrm{recoil}}$ distributions in the $Z\rightarrow\mu\mu$ control region, compared with the background predictions as estimated after the simultaneous, binned background-only fit to the data in the control regions. The last bin of the distribution contains overflows.

The measured $p_{\mathrm{T}}^{\mathrm{recoil}}$ distributions in the $Z\rightarrow ee$ control region, compared with the background predictions as estimated after the simultaneous, binned background-only fit to the data in the control regions. The last bin of the distribution contains overflows.

The measured $p_{\mathrm{T}}^{\mathrm{recoil}}$ distributions in the signal region, compared with the background predictions as estimated after the simultaneous, binned background-only fit to the data in the control regions. The last bin of the distribution contains overflows.

Observed exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

Expected exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

1 $\sigma$ up expected exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

1 $\sigma$ down expected exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

2 $\sigma$ up expected exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

2 $\sigma$ down expected exclusion contour at 95% CL in the $m_{Z_{A}}-m_\chi$ parameter plane for the axial-vector mediator model.

Observed exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

Expected exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

1 $\sigma$ up expected exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

1 $\sigma$ down expected exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

2 $\sigma$ up expected exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

2 $\sigma$ down expected exclusion contour at 95% CL in the $m_{Z_{P}}-m_\chi$ parameter plane for the pseudo-scalar mediator model.

A comparison of the inferred limits with the constraints from direct-detection experiments on the spin-dependent WIMP-neutron and WIMP-proton scattering cross section as a function of the WIMP mass, in the context of the simplified model with axial-vector couplings. Limits are shown at 90% CL.

Observed excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

Expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma$ up expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma$ down expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

2 $\sigma$ up expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

2 $\sigma$ down expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to c + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

Observed excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

Expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma$ up expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma$ down expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

2 $\sigma$ up expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

2 $\sigma$ down expected excluded regions at the 95% CL in the ($\tilde{t}_1,\tilde{\chi}^{0}_{1}$)mass plane for the decay channel $\tilde{t}_1 \to b + ff^{'} + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

Observed exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

Expected exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma$ up expected exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

1 $\sigma$ down expected exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

2 $\sigma$ up expected exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

2 $\sigma$ down expected exclusion plane at 95% CL as a function of sbottom and neutralino masses for the decay channel $\tilde{b}_1 \to b + \tilde{\chi}^{0}_{1}$ ($\mathcal{B}=100\%$)

Observed exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

Expected exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

1 $\sigma$ up expected exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

1 $\sigma$ down expected exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

2 $\sigma$ up expected exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

2 $\sigma$ down expected exclusion region at 95$\%$ CL as a function of squark mass and the squark--neutralino massdifference for $\tilde{q} \to q + \tilde{\chi}^{0}_{1}$ and $\tilde{q}_\mathrm{L} + \tilde{q}_\mathrm{R}$with ($\tilde{u},\tilde{d},\tilde{c},\tilde{s}$).

Observed and expected exclusions at $95\%$ CL on the Horndeski dark-energy model for $m_{\phi}=0.1$ GeV and $c_{i\neq 2}=0$, $c_{2}=1$, expressed in terms of the visible cross section as a function of the suppression scale $M_{2}$.

$95\%$ CL observed and expected lower limits on the fundamental Planck scale in $4+n$ dimensions, $M_D$, as a function of the number of extra dimensions $n$, considering nominal LO signal cross sections.

Observed and expected 95$\%$ CL upper limits on the coupling $c_{\stackrel{\sim}{\smash{G}}}$ as a function of the effective scale $f_a$ for ALP mass of 1 MeV. The bands indicate the $\pm 1\sigma$ theory uncertainties in the observed limit and the $\pm 1\sigma$and $\pm 2\sigma$ ranges of the expected limit in the absence of a signal. The 95$\%$ CL limits are computed with no suppression of the events with $\hat{s} > f_a^2$.

Observed exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ up observed exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

1 $\sigma_{\mathrm{theory}}^{\mathrm{PDF+scale}}$ down observed exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

Expected exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

1 $\sigma$ up expected exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

1 $\sigma$ down expected exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

2 $\sigma$ up expected exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

2 $\sigma$ down expected exclusion contour at 95% CL in the $m_{Z_{V}}-m_\chi$ parameter plane for the vector mediator model.

A comparison of the inferred limits with the constraints from direct-detection experiments on the spin-independent WIMP-nucleon scattering cross section as a function of the WIMP mass, in the context of the simplified model with vector couplings. Limits are shown at 90% CL.

Inferred 95% CL limits on the WIMP annihilation rate as a function of WIMP mass, for the pseudoscalar mediator model. The annihilation rate is defined as the product of cross section $\sigma$ and relative velocity $v_{rel}$, averaged over the DM velocity distribution ($\langle\sigma v_{rel}\rangle$). Both the $qq$ and $gg$ annihilation channels are considered in the calculation of the annihilation rate

95% CL upper limits on the cross-section $\sigma$ in the $m_{Z_A}-m_{\chi}$ parameter plane for the axial-vector mediator model.

95% CL upper limits on the cross-section $\sigma$ in the $m_{Z_P}-m_{\chi}$ parameter plane for the pseudo-scalar mediator model.

Contribution to the total SR background uncertainty in exclusive bins of the SR, as obtained from the CR-only fit. In the table, the contribution of each source of systematic is shown as the sum in quadrature of the individual contributions represented by the corresponding nuisance parameters.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM0.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM1.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM2.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM3.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM4.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM5.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM6.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM7.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM8.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM9.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM10.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM11.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the exclusive SR bin EM12.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM0.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM1.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM2.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM3.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM4.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM5.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM6.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM7.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM8.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM9.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM10.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM11.

Expected and observed backgrounds, before and after a binned likelihood fit in the control regions, in the inclusive SR IM12.

Cutflow of several signal benchmarks. A cut on the truth $E_\mathrm{T}^\mathrm{miss}$ at 150 GeV is applied.

Cutflow of several signal benchmarks. A cut on the truth $E_\mathrm{T}^\mathrm{miss}$ at 350 GeV is applied.

Data compared to the SM prediction in each region of the positive \lam{} analysis, after the fit to data. The shaded bands represent the total post-fit uncertainty on the prediction.

Fit results in the ($\kappa_{Z}$, $\kappa_{W}$) plane, using the negative-$\lambda_{WZ}$ analysis. The results are overlaid with the confidence regions (shown in blue) from a separate fit using a combination of the Higgs boson measurements collected in Nature 607 (2022) 52. This fit assumes that all Higgs boson couplings besides the ones represented are positive, and that only SM particles contribute to loop processes. Confidence regions are constructed from the log-likelihood ratio $\Lambda_{LR}=-2\ln(L/L_{max})$, where $L_{\max}$ is the likelihood for the best-fit point, which is shown as a black dot for the VBF $WH$ analysis or a blue dot for the Higgs combination. The $1\sigma$, $2\sigma$, and $5\sigma$ regions are defined by $\Lambda_{LR}$ values smaller than 2.30, 6.18, and 28.7, respectively. The SM value is marked with a star, while green triangles mark the points with $\kappa_{Z} = \pm1$, $\kappa_{W} = \mp1$.

Figure 8(left) of the article. Differential fiducial cross-section of the Z boson $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 8(right) of the article. Differential fiducial cross-section of the leading $b$-jet $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 9 of the article. Differential fiducial cross-section of the $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 10(left) of the article. Differential fiducial cross-section of the $\Delta \phi$ between the leading and sub-leading $b$-jets in events with $Z \left( \rightarrow \ell\ell \right) + 2 b$-jets. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 10(right) of the article. Differential fiducial cross-section of the invariant mass of the leading and sub-leading $b$-jets in events with $Z \left( \rightarrow \ell\ell \right) + 2 b$-jets. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 11(left) of the article. Differential fiducial cross-section of the Z boson $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 11(right) of the article. Differential fiducial cross-section of the leading $c$-jet $p_T$ in events with $Z \left( \rightarrow \ell\ell ight) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 12 of the article. Differential fiducial cross-section of the leading $c$-jet $x_F$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 18 & 24) for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 4.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 19 & 25) for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 9.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 20 & 26) for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 5.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 21 & 27) for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 10.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 22 & 28) for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 11.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper of the paper to hadron level with R 0.3 cone labelling (see Tables 23 & 29) for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 6.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level R 0.3 cone labelling for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 12.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 13.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 14.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 15.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 16.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 17.

Correction factors from parton level to hadron level for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors statistical uncertainty of Pythia8 simulations. See Table 12.

Correction factors from parton level to hadron level for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 13.

Correction factors from parton level to hadron level for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 14.

Correction factors from parton level to hadron level for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 15.

Correction factors from parton level to hadron level for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 16.

Correction factors from parton level to hadron level for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 17.

Post-fit distribution of the GNN score evaluated with $m_{H/A}$ = 400 GeV in the validation region in the 2LOS region with $\geq 8$ jets. These regions do not enter the fit. The post-fit background prediction is obtained using the post-fit nuisance parameters from the background-only fit in the control and signal regions.

Observed and expected 95% CL upper limits on the cross-section times branching ratio,$ \sigma(pp \rightarrow t\bar{t}H/A ) \times B(H/A \rightarrow t\bar{t})$, as a function of $m_{H/A}$, obtained using the 1L/2LOS final states.

Observed and expected 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario where both the scalar $H$ and pseudo-scalar $A$ contribute with $m_H = m_A$ is shown.

Observed and expected 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from scalar $H$ only is shown.

Observed and expected 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from pseudo-scalar $A$ only is shown.

Expected and observed 95% CL upper limits on the cross-section times branching ratio,$ \sigma(pp \rightarrow t\bar{t}H/A ) \times B(H/A \rightarrow t\bar{t})$, as a function of $m_{H/A}$, obtained from the combination of the 1L/2LOS and 2LSS/ML final states.

Expected and observed 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS and 2LSS/ML final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario where both the scalar $H$ and pseudo-scalar $A$ contribute with $m_H = m_A$ is shown.

Expected and observed 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS and 2LSS/ML final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from scalar $H$ only is shown.

Expected and observed 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS and 2LSS/ML final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from pseudo-scalar $A$ only is shown.

Expected and Observed 95% CL upper limits on the $ \sigma(pp \rightarrow t\bar{t})\times B(S_8\rightarrow t\bar{t})$ production cross-section as a function of $m_{S_8}$, obtained from the combination of the 1L/2LOS and 2LSS/ML final states. The expected limits from the individual 1L/2LOS and 2LSS/ML analyses are also shown.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}$ = 700 GeV in the 1L region with $\geq 10$ jets and four $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}$ = 700 GeV in the 2LOS region with $\geq 8$ jets and $\geq 4$ $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}=$1000 GeV,in the 1L region with $\geq 10$ jets four $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}=$1000 GeV in the 2LOS region with $\geq 8$ jets and $\geq 4$ $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the most important global features used in the GNN training, Sum of the pcb scores of the six jets with the highest scores ($\sum_{i\in[1,6]}\mathrm{pcb}_i$), in the region where the training is performed, i.e., the region with $\geq 9$ jets and $\geq3$ $b$-tagged jets in the 1L channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, Sum of the pcb scores of the six jets with the highest scores ($\sum_{i\in[1,6]}\mathrm{pcb}_i$), in the region where the training is performed i.e.the region with $\geq 7$ jets and $\geq3$ $b$-tagged jets in the 2LOS channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, $p_{\mathrm{T}}$ sum of all reconstructed leptons and jets ($\mathrm{H}_{\mathrm{T}}^{\mathrm{all}}$), in the region where the training is performed, i.e., the region with $\geq 9$ jets and $\geq3$ $b$-tagged jets in the 1L channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, $p_{\mathrm{T}}$ sum of all reconstructed leptons and jets ($\mathrm{H}_{\mathrm{T}}^{\mathrm{all}}$), in the region where the training is performed, i.e. the region with $\geq 7$ jets and $\geq3$ $b$-tagged jets in the 2LOS channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, Number of jets, in the region where the training is performed, i.e., the region with $\geq 9$ jets and $\geq3$ $b$-tagged jets in the 1L channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, Number of jets, in the region where the training is performed, i.e., the region with $\geq 7$ jets and $\geq3$ $b$-tagged jets in the 2LOS channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Dijet invariant mass distributions data compared to the fitted background estimates for the $j b b$ channel. The distributions are shown here with the $m_{jj}$ resolution binning.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Total invariant mass distribution $m_{4\ell}$ in Signal Region 2.

Selected events in the ($m_{4\ell}$, $\left\langle m_{\ell\ell}\right\rangle$) plane, SR1

Selected events in the ($m_{4\ell}$, $\left\langle m_{\ell\ell}\right\rangle$) plane, SR2

Expected background events in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR1

Expected signal shape for $(m_{S}, m_{Z_d}) = (110~\text{GeV},30~\text{GeV}$) in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR1. The signal distributions are normalized to unit volume.

Expected background events in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR2. Events that satisfy $|m_{ab,cd} - m_Z| < 8~\text{GeV}$ are removed by the Z-veto requirement, however not all events lying behind the mask in the plot, corresponding to $\langle m_{\ell\ell}\rangle$ values between 83 and 99 GeV, are deleted.

Expected signal shape for $(m_{S}, m_{Z_{d}}) = (750~\text{GeV},150~\text{GeV}$) in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR2. The signal distributions are normalized to unit volume. Events that satisfy $|m_{ab,cd} - m_Z| < 8~\text{GeV}$ are removed by the Z-veto requirement, however not all events lying behind the mask in the plot, corresponding to $\langle m_{\ell\ell}\rangle$ values between 83 and 99 GeV, are deleted.

Expected 95% CL limit on $\sigma \mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$, SR1

Observed 95% CL limit on $\sigma \mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$, SR1

Observed / Expected 95% CL limit ratio in SR1

Expected 95% CL limit on $\mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$ with Z-veto, SR2

Observed 95% CL limit on $\sigma \mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$ with Z-veto, SR2

Observed / Expected 95% CL limit ratio with Z-veto, SR2

Local $p_0$-values in the ($m_{S}, m_{Z_{d}}$) plane, SR1

Local $p_0$-values in the ($m_{S}, m_{Z_{d}}$) plane, SR2 with Z-veto applied

Observed data in Signal Region 1 in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane.

Observed data in Signal Region 2 in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane. Events that satisfy $|m_{ab,cd} - m_Z| < 8~\text{GeV}$ are removed by the Z-veto requirement, however not all events lying behind the mask in the plot, corresponding to <m_ll> values between 83 and 99 GeV, are deleted.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 1 in the $4e$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 1 in the $2e2\mu$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 1 in the $4\mu$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 2 in the $4e$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 2 in the $2e2\mu$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 2 in the $4\mu$ final state.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 1 in the $4e$ final state. The SR1 requirement $m_{4\ell} < 115~\text{GeV}$ is not applied here.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 1 in the $2e2\mu$ final state. The SR1 requirement $m_{4\ell} < 115~\text{GeV}$ is not applied here.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 1 in the $4\mu$ final state. The SR1 requirement $m_{4\ell} < 115~\text{GeV}$ is not applied here.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 2 in the $4e$ final state.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 2 in the $2e2\mu$ final state.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 2 in the $4\mu$ final state.

The observed 95&#37; CL exclusion limits as a function of &kappa;_2V (obtained using the signal strength &mu;_VBF as the POI) from the combined ggF and VBF signal regions, as shown by the solid black line. The value of &kappa;_&lambda; is fixed to 1. The blue and yellow bands show respectively the 1&sigma; and 2&sigma; bands around the expected exclusion limits, which are shown by the dashed black line. The expected exclusion limits are obtained using a fit to the data with the background-only hypothesis. The dark red line shows in the predicted VBF HH cross-section as a function of &kappa;_2V.

The observed 95&#37; CL exclusion limit obtained using the signal strength &mu;_ggF+VBF as the POI in the two-dimensional &kappa;_&lambda; vs. &kappa;_2V space, obtained from the combined ggF and VBF signal model, as shown by the solid black line. The blue and yellow bands show respectively the 1&sigma; and 2&sigma; bands around the expected exclusion limits, which are shown by the dashed black line. The star denotes the SM prediction (&kappa;_&lambda;=κ_2V=1). The displayed values are in units of $\mu_{ggF}$.

The expected and observed profile likelihood ratio scans for &kappa;_&lambda; coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of &kappa;_2V is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2&sigma; exclusion boundary.

The expected and observed profile likelihood ratio scans for the &kappa;_2V coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of &kappa;_&lambda; is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2&sigma; exclusion boundary.

The observed profile likelihood ratio exclusion limits for the two-dimensional &kappa;_&lambda; vs. &kappa;_2V modifier space, shown by the solid dark purple line at the 1&sigma; level and the dashed turquoise line at the 2&sigma; level. The black cross denotes the best-fit values of (&kappa;_&lambda;,&kappa;_2V). For both the expected and observed limit plots, the black star indicates the SM prediction (&kappa;_&lambda;=κ_2V=1).

The expected profile likelihood ratio exclusion limits for the two-dimensional &kappa;_&lambda; vs. &kappa;_2V modifier space, where the solid pink line denotes the 1&sigma;-level exclusion and the dashed orange line denotes the 2&sigma;-level exclusion. The black cross denotes the best-fit values of (&kappa;_&lambda;,&kappa;_2V). For both the expected and observed limit plots, the black star indicates the SM prediction (&kappa;_&lambda;=κ_2V=1).

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_{H G} vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_tG vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_{t H} vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_H&#9633; vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the two relevant HH HEFT coefficients: (a) c_tt&#772;HH and (b) c_ggHH, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the two relevant HH HEFT coefficients: (a) c_tt&#772;HH and (b) c_ggHH, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

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

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

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

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

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

Observed (points with error bars) and expected background (solid histograms) distributions for $E_{T}^{miss}$ in the signal region (a) SRL, (b) SRM and (c) SRH after the background-only fit applied to the CRs. The predicted signal distributions for the two models with a gluino mass of 2000 GeV and neutralino mass of 250 GeV (SRL), 1050 GeV (SRM) or 1950 GeV (SRH) are also shown for comparison. The uncertainties in the SM background are only statistical.

Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.

Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Cutflow for the SRL selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 250 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Cutflow for the SRM selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1050 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Cutflow for the SRH selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1950 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.