The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the $\text{t}\bar{\text{t}}$ CR. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the low-MET bin for the WZ-enriched region. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the WZ-enriched region. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the SS CR. Uncertainties include both the statistical and systematic components.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the med-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the ultra-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 3$\ell$-Ewk search regions: the post-fit distribution of the $M^{\text{min}}_{\text{SFOS}}(\ell\ell)$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 3$\ell$-Ewk search regions: the post-fit distribution of the $M^{\text{min}}_{\text{SFOS}}(\ell\ell)$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the med-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the ultra-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the TChiWZ model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. Results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2} \widetilde{m}_{\tilde{\chi}^0_1} > 0$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the TChiWZ model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. Results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2} \widetilde{m}_{\tilde{\chi}^0_1} < 0$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the simplified higgsino model. The simplified model includes both neutralino pair and neutralino-chargino production modes. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the pMSSM higgsino model. The pMSSM one includes all possible production modes. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the T2bff$\tilde{\chi}^0_1$ simplified model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the T2bW simplified model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

Measured DPS effective cross section

pp -> J/psi J/psi J/psi X cross section

$pp \rightarrow J/\psi J/\psi J/\psi X~$ fiducial cross section

Distribution of the three leading leptons flavour in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

Efficiency, acceptance, and proportion of events with leptonic tau decays in WZ production

WZ fiducial cross section in the four flavour exclusive and the flavour inclusive channels

WZ total cross section extrapolated from the four flavour exclusive and the flavour inclusive channels

Distribution of the total lepton charge in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

W$^{+}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels

W$^{-}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels

WZ charge asymmetry ratio measured on each of the four flavour exclusive and the flavour inclusive channels

Distribution of the cosine of the W polarization angle times total lepton charge in the SR-WZ with uncertainties evaluated after the W polarization fit

Distribution of the cosine of the Z polarization angle in the SR-WZ with uncertainties evaluated after the Z polarization fit

Best fits to the W and Z polarization fractions

2D confidence regions at the 68, 95, and 99% CL in the $f_O^W$-$f_{L}^W-f_R^W$ plane

2D confidence regions at the 68, 95, and 99% CL in the $f_O^Z$-$f_{L}^Z-f_R^Z$ plane

Distribution of the invariant mass of the WZ system in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering both the SM interferences and purely BSM (order $\Lambda^{-2}$ and $\Lambda^{-4}$) terms

2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane

2D confidence regions at the 68, 95, and 99% CL in the $c_{w}$-$c_{b}$ plane

2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane

Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering only the SM-EFT interference (order $\Lambda^{-2}$) terms

Evolution of the best fit and expected and observed 95% CI for the $c_{w}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $c_{b}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $c_{www}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{www}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{w}$ parameter as a function of the cutoff scale

Differential cross section with respect to the transverse momentum of the Z boson

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the Z boson

Response matrix for the $p_{T}$ of the Z boson obtained with POWHEG

Differential cross section with respect to the transverse momentum of the leading jet

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the leading jet

Response matrix for the $p_{T}$ of the leading jet obtained with POWHEG

Differential cross section with respect to the jet multiplicity

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the jet multiplicity

Response matrix for the jet multiplicity obtained with POWHEG

Differential cross section with respect to the invariant mass of the WZ system

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the invariant mass of the WZ system

Response matrix for the invariant mass of the WZ system obtained with POWHEG

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson

Response matrix for the $p_{T}$ of the lepton associated to the W boson obtained with POWHEG

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{+}$Z only

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{-}$Z only

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge

Response matrix for the cosine of the W polarization angle times total lepton charge obtained with POWHEG

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only

Differential cross section with respect to the cosine of the Z polarization angle

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle

Response matrix for the cosine of the Z polarization angle obtained with POWHEG

Differential cross section with respect to the cosine of the Z polarization angle, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{+}$Z only

Differential cross section with respect to the cosine of the Z polarization angle, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{-}$Z only

$M_{p\eta}$ invariant mass distribution for $E_\gamma$ = 1400-1500 MeV.

$M_{p\eta}$ invariant mass distribution for $E_\gamma$ = 1500-1600 MeV.

$M_{p\eta}$ invariant mass distribution under the conditions: $E_\gamma$ = 1400-1500 MeV; 1120 MeV $\leq M_{p\pi^0} \leq$ 1220 MeV; $\theta_p^{lab} \leq$ 25°; 25° $\leq \theta_\gamma^{lab} \leq$ 155°.

$M_{p\eta}$ invariant mass distribution under the conditions: $E_\gamma$ = 1400-1500 MeV; 1120 MeV $\leq M_{p\pi^0} \leq$ 1220 MeV; $\theta_p^{lab} \leq$ 25°; 1° $\leq \theta_\gamma^{lab} \leq$ 155°.

$M_{p\eta}$ invariant mass distribution for $E_\gamma$ = 1400-1500 MeV and $M_{p\pi^0} \leq$ 1190 MeV, allowing for all proton and photon laboratory angles.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1170 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1180 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1200 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1210 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1220 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1400-1420 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1460 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1460-1500 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1500-1540 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1540-1600 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

$M_{p\eta}$ invariant mass distribution for the incident photon energy range $E_\gamma$ = 1420-1540 MeV and $M_{p\pi^0} \leq$ 1190 MeV.

Excess yield relative to the PWA distributions in the invariant mass distributions $M^2_{p\eta}$.

Excess yield relative to the PWA distributions in the invariant mass distributions $M^2_{\pi^0\eta}$.

Excess yield relative to the PWA distributions in the invariant mass distributions $M^2_{p\pi^0}$, with a cut on the structure in the $M^2_{p\eta}$ invariant mass distribution.

Excess yield relative to the PWA distributions in the $p-\eta$ opening angle distribution in the $\gamma p$ centre-of-mass system.

Excess yield relative to the PWA distributions in the $p-\eta$ opening angle distribution in the $\gamma p$ centre-of-mass system.

Excess yield relative to the PWA distributions in the $\pi^0-\eta$ opening angle distribution in the $\gamma p$ centre-of-mass system.

Excess yield relative to the PWA distributions in the $\pi^0-\eta$ opening angle distribution in the $\gamma p$ centre-of-mass system.

Differential cross section $d\sigma/dM_{\pi^0\eta}$ for $E_\gamma$ = 1540-1600 MeV.

$M_{p\eta}$ invariant mass distribution, taken as difference to the PWA calculations (s. Fig. 11).

$M_{\pi^0\eta}$ invariant mass distribution, taken as difference to the PWA calculations (s. Fig. 11).

$\theta_{p\eta}$ opening angle distribution, taken as difference to the PWA calculations (s. Fig. 12).

$\theta_{\pi^0\eta}$ opening angle distribution, taken as difference to the PWA calculations (s. Fig. 12).

Comparison of the pT spectrum for the fourth-leading from data to different PYTHIA8 (P8),HERWIG++ (H++),and HERWIG7 (H7) tunes.

Comparison of the eta spectrum for the leading jet from data to different PYTHIA8 (P8),HERWIG++ (H++),and HERWIG7 (H7) tunes.

Comparison of the eta spectrum for the sub-leading from data to different PYTHIA8 (P8),HERWIG++ (H++),and HERWIG7 (H7) tunes.

Comparison of the eta spectrum for the third-leading from data to different PYTHIA8 (P8),HERWIG++ (H++),and HERWIG7 (H7) tunes.

Comparison of the eta spectrum for the fourth-leading from data to different PYTHIA8 (P8),HERWIG++ (H++),and HERWIG7 (H7) tunes.

Comparison of the DeltaPhiSoft distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes. All distributions have been normalized to regions where a reduced DPS contribution is expected.

Comparison of the DeltaPhiMin distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes. All distributions have been normalized to regions where a reduced DPS contribution is expected.

Comparison of the DeltaY distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes. All distributions have been normalized to regions where a reduced DPS contribution is expected.

Comparison of the phi_ij distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes. All distributions have been normalized to regions where a reduced DPS contribution is expected.

Comparison of the Deltap_{T,Soft} distribution from data to different PYTHIA 8 (P8), HERWIG ++ (H++), and HERWIG 7 (H7) tunes. All distributions have been normalized to regions where a reduced DPS contribution is expected.

Comparison of the DeltaS distribution from data to different PYTHIA 8 (P8), HERWIG ++ (H++), and HERWIG 7 (H7) tunes. All distributions have been normalized to regions where a reduced DPS contribution is expected.

Comparison of the pT spectrum for the leading jet from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the pT spectrum for the sub-leading from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the pT spectrum for the third-leading from data to different PYTHIA8 (P8),HERWIG++ (H++),and HERWIG7 (H7) tunes.

Comparison of the pT spectrum for the fourth-leading from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the eta spectrum for the leading jet from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the eta spectrum for the sub-leading from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the eta spectrum for the third-leading from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the eta spectrum for the fourth-leading from data with different KATIE(KT), MADGRAPH5aMC@NLO(MG5), and POWHEG(PW) models

Comparison of the DeltaPhiSoft distribution from data to differentKATIE(KT), MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the DeltaPhiMin, distribution from data to differentKATIE(KT), MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the DeltaY and distribution from data to differentKATIE(KT), MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the phi_ij distribution from data to differentKATIE(KT), MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the Deltap_{T,Soft} distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations. All distributions havebeen normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the DeltaS distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations. All distributions havebeen normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the pT spectrum for the leading jet from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the pT spectrum for the sub-leading from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the pT spectrum for the third-leading from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the pT spectrum for the fourth-leading from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the eta spectrum for the leading jet from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the eta spectrum for the sub-leading from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the eta spectrum for the third-leading from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the eta spectrum for the fourth-leading from data with different SPS+DPS KATIE( KT) and PYTHIA8 (P8) models.

Comparison of the DeltaPhiSoft distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the DeltaPhiMin distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the DeltaY distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the phi_ij distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models. All distributions have been normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the Deltap_{T,Soft} distributions from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models. All distributions havebeen normalized to regions where a reduced DPS sensitivity is expected.

Comparison of the DeltaS distributions from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models. All distributions have been normalized to regions where a reduced DPS contribution is expected.

The DeltaS distribution obtained from the mixed data sample compared to predictions from the pure DPS sample in PYTHIA 8 (P8) and KATIE (KT). The distributions are normalized to unity.

The results of the template fit for the POWHEG (PW) NLO 2 -> 2 model without the hard MPI removed. As the DeltaS distribution obtained from the mixed data sample carries a statistical and systematic uncertainty, so does the total fitted sample.

Comparison of the DeltaPhiSoft distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes.

Comparison of the DeltaPhiMin distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes.

Comparison of the DeltaY distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes.

Comparison of the phi_ij distribution from data to different PYTHIA8 (P8),HERWIG++ (H++), and HERWIG7 (H7) tunes.

Comparison of the Deltap_{T,Soft} distribution from data to different PYTHIA 8 (P8), HERWIG ++ (H++), and HERWIG 7 (H7) tunes.

Comparison of the DeltaS distribution from data to different PYTHIA 8 (P8), HERWIG ++ (H++), and HERWIG 7 (H7) tunes.

Comparison of the DeltaPhiSoft distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations.

Comparison of the DeltaPhiMin distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations.

Comparison of the DeltaY distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations.

Comparison of the phi_ij distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations.

Comparison of the Deltap_{T,Soft} distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations.

Comparison of the DeltaS distribution from data to different KATIE(KT),MADGRAPH5aMC@NLO(MG5), andPOWHEG(PW) implementations.

Comparison of the DeltaPhiSoft distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models.

Comparison of the DeltaPhiMin distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models.

Comparison of the DeltaY distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models.

Comparison of the phi_ij distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models.

Comparison of the Deltap_{T,Soft} distribution from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models.

Comparison of the DeltaS distributions from data to different SPS+DPS KATIE (KT) and PYTHIA 8 (P8) models.

The distributions of the transfer factor ($\alpha$) versus $m_T$ in the HP VBF category is shown. The last bin corresponds to the value obtained by integrating events above the penultimate bin.

The distributions of the transfer factor ($\alpha$) versus $m_T$ in the HP ggF/DY category is shown. The last bin corresponds to the value obtained by integrating events above the penultimate bin.

The distributions of the transfer factor ($\alpha$) versus $m_T$ in the LP VBF category is shown. The last bin corresponds to the value obtained by integrating events above the penultimate bin.

The distributions of the transfer factor ($\alpha$) versus $m_T$ in the LP ggF/DY category is shown. The last bin corresponds to the value obtained by integrating events above the penultimate bin.

Distributions of mT for high-purity ggF/DY CR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for high-purity VBF CR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for low-purity ggF/DY CR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for low-purity VBF CR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for high-purity ggF/DY SR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for high-purity VBF SR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for low-purity ggF/DY SR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Distributions of mT for low-purity VBF SR events after performing background-only fits. The last bin corresponds to the yields integrated above the penultimate bin. Both the ggF and VBF-produced 1 TeV graviton signals (normalized to 10 fb) are shown in the plot. Nonresonant background corresponds to the Z+jets and W+jets events. Resonant background corresponds to the ttbar, single top, and di/tri-boson events.

Expected and observed 95% CL upper limits on the product of the radion (R) production (gluon-gluon fusion) cross section and the R -> ZZ branching fraction versus the radion mass.

Expected and observed 95% CL upper limits on the product of the radion (R) production (vector boson fusion) cross section and the R -> ZZ branching fraction versus the radion mass.

Expected and observed 95% CL upper limits on the product of the W' (W') production (Drell-Yan) cross section and the W' -> WZ branching fraction versus the W' mass.

Expected and observed 95% CL upper limits on the product of the W' (W') production (vector boson fusion) cross section and the W' -> WZ branching fraction versus the W' mass.

Expected and observed 95% CL upper limits on the product of the graviton (G) production (gluon-gluon fusion) cross section and the G -> ZZ branching fraction versus the graviton mass.

Expected and observed 95% CL upper limits on the product of the graviton (G) production (vector boson fusion) cross section and the G -> ZZ branching fraction versus the graviton mass.

Exclusion limits on the product of the production cross section and the branching fraction for a Radion produced by vector boson fusion and decaying to WW, as a function of the resonance mass hypothesis.

Exclusion limits on the product of the production cross section and the branching fraction for a Z' produced by quark annihilation and decaying to WW, as a function of the resonance mass hypothesis.

Exclusion limits on the product of the production cross section and the branching fraction for a Z' produced by vector boson fusion and decaying to WW, as a function of the resonance mass hypothesis.

Exclusion limits on the product of the production cross section and the branching fraction for a W' produced by quark annihilation and decaying to WZ, as a function of the resonance mass hypothesis.

Exclusion limits on the product of the production cross section and the branching fraction for a W' produced by vector boson fusion and decaying to WZ, as a function of the resonance mass hypothesis.

Exclusion limits on the product of the production cross section and the branching fraction for a W' produced by quark annihilation and decaying to WH, as a function of the resonance mass hypothesis.

Post-fit $m_\text{T}(\gamma, E_\text{T}^\text{miss})$ distribution in the inclusive signal region for the dark-photon search with the 125 GeV mass $\mathcal{B}(H \to \gamma \gamma_\text{d})$ signal normalization set to zero. A $H \to \gamma \gamma_\text{d}$ decay signal is shown for two different mass hypotheses, 125 GeV and 500 GeV, and scaled to a $\mathcal{B}(H \to \gamma \gamma_\text{d})$ of 2% and 1%, respectively. Events with $m_\text{T}(\gamma, E_\text{T}^\text{miss})$ larger than the rightmost bin boundary are added to that bin.

The 95% CL upper limit on the Higgs boson production cross-section times branching ratio to $\gamma \gamma_\text{d}$ is shown for different VBF-produced scalar-mediator-mass hypotheses in the NWA. The theoretically predicted cross-section of a Higgs boson produced via VBF and with the $\mathcal{B}(H \to \gamma \gamma_\text{d}) =$ 5% is superimposed on the $\pm 1\sigma$ and $\pm 2\sigma$ NNLO QCD + NLO EW uncertainty band of the expected production cross-section limit.

Post-fit $m_\text{jj}$ distribution in the inclusive signal region. The Higgs boson invisible decay signal is scaled to a $\mathcal{B}_\text{inv}$ of 37%. Events with $m_\text{jj}$ larger than the rightmost bin boundary are added to that bin.

Post-fit $m_\text{jj}$ distribution in the one-lepton control region $W_{\ell \nu}^\gamma$ CR. Events with $m_\text{jj}$ larger than the rightmost bin boundary are added to that bin.

Post-fit $m_\text{T}$ distribution in the one lepton control region. Events with $m_\text{T}$ larger than the rightmost bin boundary are added to that bin.

Post-fit photon centrality distribution in the zero lepton signal plus control region with the $\mathcal{B}_\text{inv}$ signal normalization set to zero in the fit.

Post-fit photon $E_\text{T}$ distribution in the zero lepton signal region with the $\mathcal{B}_\text{inv}$ signal normalization set to zero in the fit.

Post-fit photon centrality distribution in the zero lepton signal plus control region resulting from the fit to the $m_\text{jj}$ distribution for EW $Z \gamma + \text{jets}$. The post-fit uncertainties include statistical, experimental, and theory contributions.

Post-fit photon $E_\text{T}$ distribution in the zero lepton signal region resulting from the fit to the $m_\text{jj}$ distribution for EW $Z \gamma + \text{jets}$. The post-fit uncertainties include statistical, experimental, and theory contributions.

Post-fit DNN output score distribution in the one lepton control region.

Yields for the EW $Z \gamma + \text{jets}$ process are shown after each selection along with relative and absolute signal acceptance efficiencies.

Yields for the 125 GeV Higgs boson with $\mathcal{B}_\text{inv.} =$ 1 signal produced by the vector boson fusion process in association with a final state photon are shown after each selection along with relative and absolute signal acceptance efficiencies.

Yields for the 125 GeV Higgs boson with $\mathcal{B}(H \to \gamma \gamma_\text{d}) =$ 1 signal produced by the vector boson fusion process are shown after each selection along with relative and absolute signal acceptance efficiencies.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.