Transverse profile for 70 GeV < pT < 100 GeV.

Longitudinal profile for pT > 100 GeV.

Transverse profile for pT > 100 GeV.

Average longitudinal profile

Transverse profile for pT > 100 GeV.

Distribution of the four-lepton invariant mass in the four-lepton final state for the ggF-MVA-low category.

Distribution of the four-lepton invariant mass in the four-lepton final state for the VBF-MVA-enriched category.

Distribution of the transverse mass in the llvv final state for the ggF-enriched eevv category.

Distribution of the transverse mass in the llvv final state for the ggF-enriched mumuvv category.

Distribution of the transverse mass in the llvv final state for the VBF-enriched eevv category.

Distribution of the transverse mass in the llvv final state for the VBF-enriched mumuvv category.

The upper limits at 95% CL on the cross section times branching ratio as a function of the heavy resonance mass for the ggF production mode

The upper limits at 95% CL on the cross section times branching ratio as a function of the heavy resonance mass for the VBF production mode

The upper limits at 95% CL on the cross section for the ggF production mode times branching ratio as a function of the heavy resonance mass for an additional heavy scalar assuming a width of 1% of its mass

The upper limits at 95% CL on the cross section for the ggF production mode times branching ratio as a function of the heavy resonance mass for an additional heavy scalar assuming a width of 5% of its mass

The upper limits at 95% CL on the cross section for the ggF production mode times branching ratio as a function of the heavy resonance mass for an additional heavy scalar assuming a width of 10% of its mass

The upper limits at 95% CL on the cross section for the ggF production mode times branching ratio as a function of the heavy resonance mass for an additional heavy scalar assuming a width of 15% of its mass

The upper limits at 95% CL on the cross section times branching ratio for a KK graviton produced with k/M_{PI} = 1

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.

Upper limits on $\mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ at 95% CL including the BDT cut as a function of the signal mass.

Upper limits on $\mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ at 95% CL without the BDT cut as a function of the signal mass.

The number of events when applying each cut in the analysis for $m_a$ = 16 GeV. The first row shows the approximate total number of events scaled to the theoretical signal cross-section and luminosity of the full Run 2 dataset, assuming $ \mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ = 0.16%.

The number of events when applying each cut in the analysis for $m_a$ = 20 GeV. The first row shows the approximate total number of events scaled to the theoretical signal cross-section and luminosity of the full Run 2 dataset, assuming $ \mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ = 0.16%.

The number of events when applying each cut in the analysis for $m_a$ = 30 GeV. The first row shows the approximate total number of events scaled to the theoretical signal cross-section and luminosity of the full Run 2 dataset, assuming $ \mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ = 0.16%.

The number of events when applying each cut in the analysis for $m_a$ = 40 GeV. The first row shows the approximate total number of events scaled to the theoretical signal cross-section and luminosity of the full Run 2 dataset, assuming $ \mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ = 0.16%.

The number of events when applying each cut in the analysis for $m_a$ = 50 GeV. The first row shows the approximate total number of events scaled to the theoretical signal cross-section and luminosity of the full Run 2 dataset, assuming $ \mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ = 0.16%.

The number of events when applying each cut in the analysis for $m_a$ = 60 GeV. The first row shows the approximate total number of events scaled to the theoretical signal cross-section and luminosity of the full Run 2 dataset, assuming $ \mathcal{B}(H\rightarrow aa\rightarrow bb\mu\mu)$ = 0.16%.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

IRing2 covariance, complete

1-IRing128 covariance, complete

1-ICyl16 covariance, complete

Differential cross section as a function of $m_{\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $p_{T,\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $a_{T,\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $\phi_{\eta}*$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $\pi-\Delta\phi_{\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $|cos\theta*|^{(CS)}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

The contribution from different systematic uncertainties to the measured $t\bar{t}t\bar{t}$ production cross section, grouped in categories.

The results of the fitted signal strength $\mu$ in the 1L/2LOS and 2LSS/3L combined channel.

The results of fitted inclusive ${t\bar{t}t\bar{t}}$ cross-section in the 1L/2LOS and 2LSS/3L combined channel.

Comparison between data and prediction for the distribution of the sum of the pseudo-continuous b-tagging score over the six jets with the highest score in the 1L,$\geq$9j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of the sum of the pseudo-continuous b-tagging score over the six jets with the highest score in the 1L,$\geq$9j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of the sum of the pseudo-continuous b-tagging score over the six jets with the highest score in the 2LOS,$\geq$7j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of the sum of the pseudo-continuous b-tagging score over the six jets with the highest score in the 2LOS,$\geq$7j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,$\geq$8j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,$\geq$8j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,$\geq$6j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,$\geq$6j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of number of jets in the 1L,$\geq$8j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of number of jets in the 1L,$\geq$8j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of number of jets in the 2LOS,$\geq$6j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of number of jets in the 2LOS,$\geq$6j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of b-jets multiplicity in the 1L,$\geq$8j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of b-jets multiplicity in the 1L,$\geq$8j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of b-jets multiplicity in the 2LOS,$\geq$6j,$\geq$3b region before the fit.

Comparison between data and prediction for the distribution of b-jets multiplicity in the 2LOS,$\geq$6j,$\geq$3b region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,9j,4b signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,9j,4b signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,9j,$\geq$5b signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,9j,$\geq$5b signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,3bL signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,3bL signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,3bH signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,3bH signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,4b signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,4b signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,$\geq$5b signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,$\geq$5b signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,7j,$\geq$4b signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,7j,$\geq$4b signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,3bL signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,3bL signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,3bH signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,3bH signal region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,$\geq$4b signal region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,$\geq$4b signal region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,3bV validation region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,3bV validation region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,9j,3bV validation region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,9j,3bV validation region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,3bV validation region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 1L,$\geq$10j,3bV validation region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,3bV validation region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,3bV validation region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,7j,3bV validation region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,7j,3bV validation region after the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,3bV validation region before the fit.

Comparison between data and prediction for the distribution of the BDT score in the 2LOS,$\geq$8j,3bV validation region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,3bL control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,3bL control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,3bH control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,3bH control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,9j,3bL control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,9j,3bL control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,9j,3bH control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,9j,3bH control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,4b control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,4b control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,$\geq$5b control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 1L,8j,$\geq$5b control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,3bL control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,3bL control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,3bH control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,3bH control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,7j,3bL control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,7j,3bL control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,7j,3bH control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,7j,3bH control region after the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,$\geq$4b control region before the fit.

Comparison between data and prediction for the distribution of the scalar sum of all jet and lepton pT in the event in the 2LOS,6j,$\geq$4b control region after the fit.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.10. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

Expected and observed limits computed using asymptotic formulas as a function of the signal mass m_{X} and the relative width $\Gamma_{X}/m_{X}$ for the spin-0 resonance search.

Expected and observed limits computed using asymptotic formulas as a function of the signal mass m_{X} and the relative width $\Gamma_{X}/m_{X}$ for the spin-0 resonance search.

Expected and observed limits computed using asymptotic formulas as a function of the signal mass m_{G*} and k/Mpl for the spin-2 resonance search.

Expected and observed limits computed using asymptotic formulas as a function of the signal mass m_{G*} and k/Mpl for the spin-2 resonance search.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 2% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 2% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 6% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 6% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 10% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 10% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.01. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.01. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.05. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.05. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

Diphoton invariant mass in the signal region using a binning based on the invariant mass resolution.

Diphoton invariant mass in the signal region using a binning based on the invariant mass resolution.

Diphoton invariant mass in the signal region binned in 1 GeV bins..

Diphoton invariant mass in the signal region binned in 1 GeV bins..

Effect of event selections on a NWA spin-0 and a spin-2 ($k/Mpl=0.01$) MC samples generated for m_X = 1 TeV and on the data. For the MC samples, the event yield is shown after applying event weights; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts. The cross-section times branching ratio for the spin-0 sample is normalized to the MadGraph NLO prediction of 112~fb and the cross-section times branching ratio for the graviton corresponds to the Pythia8 prediction of 12~fb.

Effect of event selections on a NWA spin-0 and a spin-2 ($k/Mpl=0.01$) MC samples generated for m_X = 1 TeV and on the data. For the MC samples, the event yield is shown after applying event weights; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts. The cross-section times branching ratio for the spin-0 sample is normalized to the MadGraph NLO prediction of 112~fb and the cross-section times branching ratio for the graviton corresponds to the Pythia8 prediction of 12~fb.

Parameterization for the CX and AX factors as function of mX for a scalar particle, as obtained from the narrow-width MC signal samples.

Parameterization for the CX and AX factors as function of mX for a scalar particle, as obtained from the narrow-width MC signal samples.

Parameterization for the $C_{X}\cdot A_{X}$ factor as function of $m_{G^*}$ for the RS graviton, as obtained from k/MPl = 0.05 MC signal samples.

Parameterization for the $C_{X}\cdot A_{X}$ factor as function of $m_{G^*}$ for the RS graviton, as obtained from k/MPl = 0.05 MC signal samples.

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of m_{X} [GeV].

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of m_{X} [GeV].

Parameterization of the Double Sided Crystal Ball function parameters describing the graviton mass resolution model as a function of m_{X} [GeV].

Parameterization of the Double Sided Crystal Ball function parameters describing the graviton mass resolution model as a function of m_{X} [GeV].

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

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

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

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

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

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

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

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

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

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

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

Distribution of $m_{\mathrm{T},3l}$ in the JNLow SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.

Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q0 SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.

Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q2 SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.

Distribution of $m_{\mathrm{T},3l}$ in the ZL-CR after the background-only fit.

Distribution of $m_{\mathrm{T},3l}$ in the Fake-CR after the background-only fit.

Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q0 DB-CR after the background-only fit.

Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q0 RT-CR after the background-only fit.

Expected and observed exclusion limit for the combination of the two- (from Ref. EXOT-2018-33), three- and four-lepton channels, for the type-III seesaw process with the corresponding one- and two-standard-deviation uncertainty bands, showing the 95% CL upper limit on the cross-section.

Expected and observed 95% ( $CL_s$ ) exclusion limits in the three lepton channel for the type-III seesaw process with the corresponding one- and two-standard-deviation bands, showing the 95% CL upper limit on the cross-section.

Expected and observed 95% ( $CL_s$ ) exclusion limits in the four lepton channel for the type-III seesaw process with the corresponding one- and two-standard-deviation bands, showing the 95% CL upper limit on the cross-section.

Cross-sections of signal Monte Carlo samples for each mass point considered in this analysis. Leading order cross-sections ( $\sigma_{LO}$ ) are computed by the generator and then rescaled to next-to-leading cross-sections ( $\sigma_{NLO+NLL}$ ), with their corresponding uncertainties, using information taken from Refs. C1C1 and N2C1.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the ZL SR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the ZLVeto SR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the JNLow SR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q0 SR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q2 SR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the ZL CR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q0-DB CR. Preselection represents events with at least three leptons.

Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q0-RT CR. Preselection represents events with at least three leptons.

Expected background yields and observed data after the background-only fit, in the three-lepton CRs and VRs.

Expected background yields and observed data after the background-only fit, in the four-lepton CRs and VRs.

Expected and observed 95% ( $CL_s$ ) exclusion limits in the three and four lepton channels for the type-III seesaw process with the corresponding one- and two-standard-deviation bands, showing the 95% CL upper limit on the cross-section.

Distributions of $s_{\text{T}}$ in the single-tau one-bin SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Distributions of $m_{\text{T}}(\tau)$ in the single-tau one-bin SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Distributions of $\Sigma m_{\text{T}}(b_{1,2})$ in the single-tau $p_{\text{T}}(\tau)$-binned SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Distributions of $p_{\text{T}}(\tau)$ in the single-tau $p_{\text{T}}(\tau)$-binned SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.

Observed event yields in data ('Observed') and expected event yields for SM background processes obtained from the background-only fit ('Total bkg.' and rows below) in the signal regions of the di-tau and single-tau channels. The quoted uncertainties include both the statistical and systematic uncertainties and are truncated at zero yield. By construction, no $t\bar{t}$ (2 real $\tau$) events can pass the selections in the single-tau channel. As the individual uncertainties are correlated, they do not add in quadrature to equal the total background uncertainty.

From left to right: upper limits at the 95% confidence level (CL) on the visible cross section ($\sigma_\text{vis}$) and on the number of signal events ($S_{\text{obs}}^{95}$). The third column ($S_{\text{exp}}^{95}$) shows the upper limit at the 95% CL on the number of signal events, given the expected number (and $\pm 1\,\sigma$ excursions on the expectation) of background events. The last two columns indicate the confidence level observed for the background-only hypothesis ($\text{CL}_{b}$), the discovery $p$-value ($p(s=0)$) and the significance ($Z$). In the di-tau SR, where fewer events are observed than predicted by the fitted background estimate, the $p$-value is capped at 0.5.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for the minimal-coupling scenario. The limits are derived from the binned single-tau signal region.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for the minimal-coupling scenario. The limits are derived from the binned single-tau signal region.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for vector leptoquarks with additional gauge couplings. The limits are derived from the binned single-tau signal region.

Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for vector leptoquarks with additional gauge couplings. The limits are derived from the binned single-tau signal region.

Exclusion contours at the 95% confidence level for the stop-stau signal model as a function of the masses of the top squark $m(\tilde{t}_{1})$ and of the tau slepton $m(\tilde{\tau}_{1})$. Expected and observed limits are shown for the present search in comparison to observed limits from previous ATLAS analyses based on data from Run-1 of the LHC at $\sqrt{s} = 8$ TeV [Eur. Phys. J. C 76 (2016)] and on a partial dataset from Run 2 at $\sqrt{s} = 13$ TeV [Phys. Rev. D 98 (2018) 032008]. The green band indicates the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments.

Exclusion contours at the 95% confidence level for the stop-stau signal model as a function of the masses of the top squark $m(\tilde{t}_{1})$ and of the tau slepton $m(\tilde{\tau}_{1})$. Expected and observed limits are shown for the present search in comparison to observed limits from previous ATLAS analyses based on data from Run-1 of the LHC at $\sqrt{s} = 8$ TeV [Eur. Phys. J. C 76 (2016)] and on a partial dataset from Run 2 at $\sqrt{s} = 13$ TeV [Phys. Rev. D 98 (2018) 032008]. The green band indicates the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{u}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for up-type leptoquarks $\text{LQ}_{3}^{\text{u}})$ with charge $+2/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{u}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for up-type leptoquarks $\text{LQ}_{3}^{\text{u}})$ with charge $+2/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{d}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for down-type leptoquarks $\text{LQ}_{3}^{\text{d}})$ with charge $-1/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{d}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for down-type leptoquarks $\text{LQ}_{3}^{\text{d}})$ with charge $-1/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.

Upper limits on the signal cross section at the 95 % confidence level for the stop-stau signal model.

Upper limits on the signal cross section at the 95 % confidence level for the scalar third-generation leptoquark signal model with up-type leptoquarks.

Upper limits on the signal cross section at the 95 % confidence level for the scalar third-generation leptoquark signal model with down-type leptoquarks.

Upper limits on the signal cross section at the 95 % confidence level for the vector third-generation leptoquark signal model with minimal coupling (MC).

Upper limits on the signal cross section at the 95 % confidence level for the vector third-generation leptoquark signal model with additional gauge couplings (YM).

Acceptance of the one-bin signal region of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the one-bin signal region of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.

Efficiency of the signal region of the di-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the one-bin signal region of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.

Efficiency of the signal region of the di-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.

Efficiency of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.

Efficiency of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.

Acceptance of the one-bin signal region of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the one-bin signal region of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of top squarks with decays via tau sleptons.

Acceptance of the signal region of the di-tau channel for pair production of top squarks with decays via tau sleptons.

Efficiency of the signal region of the di-tau channel for pair production of top squarks with decays via tau sleptons.

Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the di-tau SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the single-tau one-bin SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the single-tau multi-bin SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the di-tau SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau one-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau multi-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the di-tau SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau one-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau multi-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the di-tau SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the single-tau one-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the single-tau multi-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the di-tau SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the single-tau one-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the single-tau multi-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.

Distribution of the missing transverse energy in CRTop. The Z+HF and $t\bar{t}$ scale factors, described in the text, have been applied to the simulated samples. The distribution labeled "Signal" is for the model with ($m_a$, $m_{\tilde{\chi}_{1}^{0}}$, $m_{\tilde{\chi}_{2}^{0}}$) = (45 GeV, 10 GeV, 80 GeV).

Distribution of the dijet invariant mass in the signal region, shown together with the parameterized background model (labelled "Bkg Model"). For reference, the MC prediction for the SM background is also shown (labelled "SM MC"). The Z+HF and $t\bar{t}$ scale factors, described in the text, have been applied to the simulated samples. The signal region is defined to have dijet invariant mass > 20 GeV. The distribution labeled "Signal" is for the model with ($m_a$, $m_{\tilde{\chi}_{1}^{0}}$, $m_{\tilde{\chi}_{2}^{0}}$) = (45 GeV, 10 GeV, 80 GeV).

Upper limits at 95% CL on the $pp \rightarrow ZH$ cross section times the branching ratio for $Z \rightarrow \ell^{+}\ell^{-}$ (where $\ell = e, \mu \;\mathrm{or}\; \tau$) and $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 65$ GeV in the NMSSM scenario described in the text. All branching ratios in the Higgs boson decay chain after the decay $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0}$ are set to 100%.

Upper limits at 95% CL on the $pp \rightarrow ZH$ cross section times the branching ratio for $Z \rightarrow \ell^{+}\ell^{-}$ (where $\ell = e, \mu \;\mathrm{or}\; \tau$) and $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 80$ GeV in the NMSSM scenario described in the text. All branching ratios in the Higgs boson decay chain after the decay $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0}$ are set to 100%.

Upper limits at 95% CL on the $pp \rightarrow ZH$ cross section times the branching ratio for $Z \rightarrow \ell^{+}\ell^{-}$ (where $\ell = e, \mu\; \mathrm{or}\; \tau$) and $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 95$ GeV in the NMSSM scenario described in the text. All branching ratios in the Higgs boson decay chain after the decay $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0}$ are set to 100%.

Upper limits at 95% CL on the $pp \rightarrow ZH$ cross section times the branching ratio for $Z \rightarrow \ell^{+}\ell^{-}$ (where $\ell = e, \mu\; \mathrm{or}\; \tau$) and $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 110$ GeV in the NMSSM scenario described in the text. All branching ratios in the Higgs boson decay chain after the decay $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0}$ are set to 100%.

Upper limits at 95% CL on the $pp \rightarrow ZH$ cross section times the branching ratio for $Z \rightarrow \ell^{+}\ell^{-}$ (where $\ell = e, \mu\; \mathrm{or}\; \tau$) and $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 20$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 80$ GeV in the NMSSM scenario described in the text. All branching ratios in the Higgs boson decay chain after the decay $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0}$ are set to 100%.

Upper limits at 95% CL on the $pp \rightarrow ZH$ cross section times the branching ratio for $Z \rightarrow \ell^{+}\ell^{-}$ (where $\ell = e, \mu \;\mathrm{or}\; \tau$) and $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 30$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 80$ GeV in the NMSSM scenario described in the text. All branching ratios in the Higgs boson decay chain after the decay $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0}$ are set to 100%.

Upper limits at 95% CL on the branching ratio $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 65$ GeV in the NMSSM scenario described in the text. The SM $ZH$ cross section is assumed.

Upper limits at 95% CL on the branching ratio $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 80$ GeV in the NMSSM scenario described in the text. The SM $ZH$ cross section is assumed.

Upper limits at 95% CL on the branching ratio $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 95$ GeV in the NMSSM scenario described in the text. The SM $ZH$ cross section is assumed.

Upper limits at 95% CL on the branching ratio $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 10$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 110$ GeV in the NMSSM scenario described in the text. The SM $ZH$ cross section is assumed.

Upper limits at 95% CL on the branching ratio $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 20$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 80$ GeV in the NMSSM scenario described in the text. The SM $ZH$ cross section is assumed.

Upper limits at 95% CL on the branching ratio $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ as a function of $m_{a}$ for $m_{\tilde{\chi}_{1}^{0}} = 30$ GeV and $m_{\tilde{\chi}_{2}^{0}} = 80$ GeV in the NMSSM scenario described in the text. The SM $ZH$ cross section is assumed.

Unweighted and weighted number of events after each stage of selection for the NMSSM scenario with $pp \rightarrow ZH$, $Z \rightarrow \ell^{+}\ell^{-}$, $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ where $(m_{a}, m_{\tilde{\chi}_{1}^{0}}, m_{\tilde{\chi}_{2}^{0}}) = (45,10,80)$ GeV and the Z boson decaying to $e^{+}e^{-}, \;\mu^{+}\mu^{-} \;\mathrm{or}\; \tau^{+}\tau^{-}$. All branching ratios in the Higgs boson decay chain are set to 100%. The weighted number of events corresponds to an integrated luminosity of $139 \;\mathrm{fb}^{-1}$. The "skimming selection" required either a single electron or muon with $p_{T} > 100\;$ GeV or a pair of electrons or muons each with $p_{T} > 20\;$ GeV. The preselection requirements include the trigger, absence of a bad jet or bad muon, and two leptons, where a lepton is either an electron or a muon.

Acceptance and efficiency of this analysis for the signal models considered in this paper. The signal is an NMSSM scenario with $pp \rightarrow ZH$, $Z \rightarrow \ell^{+}\ell^{-}$, $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ where the values of $(m_{a}, m_{\tilde{\chi}_{1}^{0}}, m_{\tilde{\chi}_{2}^{0}})$ are varied. The Z boson decays to $e^{+}e^{-}, \mu^{+}\mu^{-} \;\mathrm{or}\; \tau^{+}\tau^{-}$. All branching ratios in the Higgs boson decay chain are set to 100%. The product of acceptance times efficiency is defined by the fraction of simulated events that pass all the selection criteria of this analysis. The acceptance is defined by the fraction of simulated events that pass all the selection criteria as applied to Monte Carlo truth-level quantities. The efficiency is then defined as the acceptance times efficiency, divided by the acceptance.

Acceptance and efficiency of this analysis for the signal models considered in this paper. The signal is an NMSSM scenario with $pp \rightarrow ZH$, $Z \rightarrow \ell^{+}\ell^{-}$, $H \rightarrow \tilde{\chi}_{2}^{0}\tilde{\chi}_{1}^{0} \rightarrow a \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0} \rightarrow b\bar{b} \tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}$ where the values of $(m_{a}, m_{\tilde{\chi}_{1}^{0}}, m_{\tilde{\chi}_{2}^{0}})$ are varied. The Z boson decays to $e^{+}e^{-}, \mu^{+}\mu^{-} \;\mathrm{or}\; \tau^{+}\tau^{-}$. All branching ratios in the Higgs boson decay chain are set to 100%. The product of acceptance times efficiency is defined by the fraction of simulated events that pass all the selection criteria of this analysis. The acceptance is defined by the fraction of simulated events that pass all the selection criteria as applied to Monte Carlo truth-level quantities. The efficiency is then defined as the acceptance times efficiency, divided by the acceptance.

Expected +- 1 sigma 95% CL exclusion limit for the Zprime-2HDM model.

Expected +- 2 sigma 95% CL exclusion limit for the Zprime-2HDM model.

Observed 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected +- 1 sigma 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected +- 2 sigma 95% CL exclusion limit for the 2HDM+a model ggF production.

Observed 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected +- 1 sigma 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected +- 2 sigma 95% CL exclusion limit for the 2HDM+a model bbA production.

Observed 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected +- 1 sigma 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected +- 2 sigma 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected and observed upper limits at 95% CL on cross-section for Zprime-2HDM model.

Expected and observed upper limits at 95% CL on cross-section for ggF producton in the 2HDM+a model.

Expected and observed upper limits at 95% CL on cross-section for bbA producton in the 2HDM+a model.

Model-independent upper limits on the visible cross-section $σ_{vis, $h(\bar{b})+DM} ≡ σ_{h+DM} \times B(h \to b\bar{b}) \times \mathcal{A} \times \epsilon$ in the different signal regions.

Theory cross-section for Zprime-2HDM model.

Theory cross-section for bbA production in the 2HDM+a model.

Theory cross-section for ggF production in the 2HDM+a model.

Distribution of Higgs boson candidate mass in 2b region with MET=150-200 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=200-350 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=350-500 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=500-750 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET > 750 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=150-200 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=200-350 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=350-500 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET > 500 GeV.

Yields in 1-lepton control region.

Yields in 2-lepton control region.

MET distribution in 2b region of the 0-lepton channel.

MET distribution in 3b region of the 0-lepton channel.

Expected signal yields after certain selection cuts in 2b region with MET=150-200 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=200-350 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=350-500 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=500-750 GeV.

Expected signal yields after certain selection cuts in 2b region with MET > 750 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=150-200 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=200-350 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=350-500 GeV.

Expected signal yields after certain selection cuts in 3b region with MET > 500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET > 750 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET>500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET > 750 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET > 500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET > 750 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET > 500 GeV.

List of relative uncertainties of the measured inclusive $t\bar{t}\text{Z}$ cross section from the combined fit. The uncertainties are symmetrised for presentation and grouped into the categories described in the text. The quadratic sum of the individual uncertainties is not equal to the total uncertainty due to correlations introduced by the fit.

The definitions of the trilepton signal regions: for the inclusive measurement, a combination of the regions with pseudo-continuous $b$-tagging 3$\ell$-Z-1$b$4$j$-PCBT and 3$\ell$-Z-2$b$3$j$-PCBT is used, whereas for the differential measurement, only the region 3$\ell$-Z-2$b$3$j$, with a fixed $b$-tagging WP is employed.

The definitions of the trilepton signal regions: for the inclusive measurement, a combination of the regions with pseudo-continuous $b$-tagging 3$\ell$-Z-1$b$4$j$-PCBT and 3$\ell$-Z-2$b$3$j$-PCBT is used, whereas for the differential measurement, only the region 3$\ell$-Z-2$b$3$j$, with a fixed $b$-tagging WP is employed.

The definitions of the four tetralepton signal regions. The regions are defined to target different $b$-jet multiplicities and flavour combinations of the non-Z leptons.

The definitions of the four tetralepton signal regions. The regions are defined to target different $b$-jet multiplicities and flavour combinations of the non-Z leptons.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cutflow event yields in regions SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$, SR0$_{\mathrm{breq}}$, and SR5L for RPV models with the $\lambda_{12k}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR1$_{\mathrm{breq}}$ for RPV models with the $\lambda_{i33}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR2$_{\mathrm{breq}}$ for RPV models with the $\lambda_{i33}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR0-ZZ$^{\mathrm{loose}}$, SR0-ZZ$^{\mathrm{tight}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR5L the higgsino GGM RPC model with BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 50% and higgsino masses of 200 GeV, or BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 100% and higgsino masses of 300 GeV. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The generator filter is a selection of $\geq$4e/$\mu$/$\tau_{\mathrm{had-vis}}$ leptons with $p_{\mathrm{T}}(e,\mu)>4$GeV, $p_{\mathrm{T}}(\tau_{\mathrm{had-vis}})>15$GeV and $|\eta|<2.8$ and is applied during the MC generation of the simulated events. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} > 9$ GeV are required.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 7 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 8 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 9 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 10 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 11 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 12 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 13 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for 14 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $1\ell$ category for at least 15 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for 4 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for 5 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for 6 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for 7 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for 8 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for 9 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold regions defined for the EWK analysis in the $2\ell^{\mathrm{sc}}$ category for at least 10 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold region in the $1\ell$ category for at least 15 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 40 GeV jet $p_{\mathrm{T}}$ threshold region in the $1\ell$ category for at least 12 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 60 GeV jet $p_{\mathrm{T}}$ threshold region in the $1\ell$ category for at least 11 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 80 GeV jet $p_{\mathrm{T}}$ threshold region in the $1\ell$ category for at least 10 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 100 GeV jet $p_{\mathrm{T}}$ threshold region in the $1\ell$ category for at least 8 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 20 GeV jet $p_{\mathrm{T}}$ threshold region in the $2\ell^{\mathrm{sc}}$ category for at least 10 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 40 GeV jet $p_{\mathrm{T}}$ threshold region in the $2\ell^{\mathrm{sc}}$ category for at least 8 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 60 GeV jet $p_{\mathrm{T}}$ threshold region in the $2\ell^{\mathrm{sc}}$ category for at least 7 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 80 GeV jet $p_{\mathrm{T}}$ threshold region in the $2\ell^{\mathrm{sc}}$ category for at least 7 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

The observed data event yields and the corresponding estimates for the backgrounds in the different $b$-jet multiplicity bins for the 100 GeV jet $p_{\mathrm{T}}$ threshold region in the $2\ell^{\mathrm{sc}}$ category for at least 6 jets. The background is estimated by including all bins in the fit. All uncertainties, which may be correlated across the bins, are included in the total background uncertainty.

Data event yields compared with the expected contributions from relevant background sources, in the discovery signal regions defined for the $1\ell$ category, as well as the observed and expected 95% CL model-independent upper limits on product of cross-section, acceptance and efficiency (in fb). The parameters of the model are determined in a fit to a reduced set of bins, corresponding to the model-independent fit discussed in the text.

Data event yields compared with the expected contributions from relevant background sources, in the discovery signal regions defined for the $2\ell^{\mathrm{sc}}$ category, as well as the observed and expected 95% CL model-independent upper limits on product of cross-section, acceptance and efficiency (in fb). The parameters of the model are determined in a fit to a reduced set of bins, corresponding to the model-independent fit discussed in the text.

Expected 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model.

Observed 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model.

Expected 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model.

Observed 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model.

Expected 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with bino LSP.

Observed 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with bino LSP.

Expected 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with higgsino LSP.

Observed 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with higgsino LSP.

Expected 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with wino LSP.

Observed 95% CL exclusion contour for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with wino LSP.

Expected 95% CL exclusion contour for the stop pair production model with bino LSP.

Observed 95% CL exclusion contour for the stop pair production model with bino LSP.

Expected 95% CL exclusion contour for the stop pair production model with higgsino LSP.

Observed 95% CL exclusion contour for the stop pair production model with higgsino LSP.

Expected 95% CL exclusion contour for the stop pair production model with wino LSP.

Observed 95% CL exclusion contour for the stop pair production model with wino LSP.

Expected 95% CL excluded cross section for the RPV model with electroweakino prodiction with higgsino LSP hypothesis.

Observed 95% CL excluded cross section for the RPV model with electroweakino prodiction with higgsino LSP hypothesis.

Expected 95% CL excluded cross section for the RPV model with electroweakino prodiction with wino LSP hypothesis.

Observed 95% CL excluded cross section for the RPV model with electroweakino prodiction with wino LSP hypothesis.

Data event yields compared with the expected contributions from relevant background sources, in the discovery signal regions defined for the $1\ell$ category with 20 and 40 GeV jet $p_{\mathrm{T}}$ thresholds. The uncertainties across backgrounds can exhibit strong anticorrelations.

Data event yields compared with the expected contributions from relevant background sources, in the discovery signal regions defined for the $1\ell$ category with 60, 80 and 100 GeV jet $p_{\mathrm{T}}$ thresholds. The uncertainties across backgrounds can exhibit strong anticorrelations.

Data event yields compared with the expected contributions from relevant background sources, in the discovery signal regions defined for the $2\ell^{\mathrm{sc}}$ category with 20 and 40 GeV jet $p_{\mathrm{T}}$ thresholds. The uncertainties across backgrounds can exhibit strong anticorrelations.

Data event yields compared with the expected contributions from relevant background sources, in the discovery signal regions defined for the $2\ell^{\mathrm{sc}}$ category with 60, 80 and 100 GeV jet $p_{\mathrm{T}}$ thresholds. The uncertainties across backgrounds can exhibit strong anticorrelations.

Expected 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model.

Observed 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model.

Expected 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model.

Observed 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model.

Expected 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with bino LSP.

Observed 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with bino LSP.

Expected 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with wino LSP.

Observed 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with wino LSP.

Expected 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with higgsino LSP.

Observed 95% CL excluded cross section for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with higgsino LSP.

Expected 95% CL excluded cross section for the stop pair production model with bino LSP.

Observed 95% CL excluded cross section for the stop pair production model with bino LSP.

Expected 95% CL excluded cross section for the stop pair production model with wino LSP.

Observed 95% CL excluded cross section for the stop pair production model with wino LSP.

Expected 95% CL excluded cross section for the stop pair production model with higgsino LSP.

Observed 95% CL excluded cross section for the stop pair production model with higgsino LSP.

Acceptance and efficiency for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model in the $1\ell$ category.

Acceptance and efficiency for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model in the $2\ell^{\mathrm{sc}}$ category.

Acceptance and efficiency for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model in the $1\ell$ category.

Acceptance and efficiency for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model in the $2\ell^{\mathrm{sc}}$ category.

Acceptance and efficiency for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with higgsino LSP in the $1\ell$ category.

Acceptance and efficiency for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with higgsino LSP in the $2\ell^{\mathrm{sc}}$ category.

Acceptance and efficiency for the stop model with higgsino LSP in the $1\ell$ category.

Acceptance and efficiency for the stop model with higgsino LSP in the $2\ell^{\mathrm{sc}}$ category.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 4 jets, considering $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 5 jets, considering $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 6 jets, considering $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 7 jets, considering $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 8 jets, considering $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $2\ell^{\mathrm{sc}}$ category, considering $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 4 jets, considering $\tilde{\chi}^0_1 \tilde{\chi}^0_2$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 5 jets, considering $\tilde{\chi}^0_1 \tilde{\chi}^0_2$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 6 jets, considering $\tilde{\chi}^0_1 \tilde{\chi}^0_2$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 7 jets, considering $\tilde{\chi}^0_1 \tilde{\chi}^0_2$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $1\ell$ category with 8 jets, considering $\tilde{\chi}^0_1 \tilde{\chi}^0_2$ production.

Acceptance and efficiency for the or the electroweakino production model in the EWK analysis discovery SR for the $2\ell^{\mathrm{sc}}$ category, considering $\tilde{\chi}^0_1 \tilde{\chi}^0_2$ production.

Cut flow for the gluino $\tilde{g}\rightarrow q\bar{q}\tilde{\chi}^0_1\rightarrow q\bar{q}q\bar{q} \ell\nu$ model with $m_{\tilde{g}} = 2$ TeV and $m_{\tilde{\chi}^0_1} = 1$ TeV. The column labelled $\mathrm{N}_{\mathrm{raw}}$ shows the number of generated events, while $\mathrm{N}_{\mathrm{events}}$ shows the expected number of events with a luminosity of 139fb$^{−1}$. The last column shows the cut flow efficiency with respect to all weighted events. The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies $p_{\mathrm{T}} > 25$ GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. Selections that have not been evaluated in the analysis or are not applicable are denoted with a dash (--).

Cut flow for the gluino $\tilde{g}\rightarrow \bar{t}\tilde{t} \rightarrow \bar{t}\bar{b}\bar{s}$ model with with $m_{\tilde{g}} = 1.6$ TeV and $m_{\tilde{t}} = 1$ TeV. The column labelled $\mathrm{N}_{\mathrm{raw}}$ shows the number of generated events, while $\mathrm{N}_{\mathrm{events}}$ shows the expected number of events with a luminosity of 139fb$^{−1}$. The last column shows the cut flow efficiency with respect to all weighted events. The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies $p_{\mathrm{T}} > 25$ GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. Selections that have not been evaluated in the analysis or are not applicable are denoted with a dash (--).

Cut flow for the gluino $\tilde{g}\rightarrow t\bar{t}\tilde{\chi}^0_1 \rightarrow t\bar{t} tbs$ model with $m_{\tilde{g}} = 2.2$ TeV and $m_{\tilde{\chi}^0_1} = 1.05$ TeV. The column labelled $\mathrm{N}_{\mathrm{raw}}$ shows the number of generated events, while $\mathrm{N}_{\mathrm{events}}$ shows the expected number of events with a luminosity of 139fb$^{−1}$. The last column shows the cut flow efficiency with respect to all weighted events. The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies $p_{\mathrm{T}} > 25$ GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. Selections that have not been evaluated in the analysis or are not applicable are denoted with a dash (--).

Cut flow for the stop model with $m_{\tilde{t}} = 1.175$ TeV and $m_{\tilde{\chi}^0_1} = 0.7$ TeV. The column labelled $\mathrm{N}_{\mathrm{raw}}$ shows the number of generated events, while $\mathrm{N}_{\mathrm{events}}$ shows the expected number of events with a luminosity of 139fb$^{−1}$. The last column shows the cut flow efficiency with respect to all weighted events. The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies $p_{\mathrm{T}} > 25$ GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. Selections that have not been evaluated in the analysis or are not applicable are denoted with a dash (--).

Cut flow for the electroweakino production model, considering only the production of $\tilde{\chi}^{\pm}_1 \tilde{\chi}^0_1$, with $m(\tilde{\chi}^{\pm}_1,\tilde{\chi}^0_1)= 250$ GeV. The column labelled $\mathrm{N}_{\mathrm{raw}}$ shows the number of generated events, while $\mathrm{N}_{\mathrm{events}}$ shows the expected number of events with a luminosity of 139fb$^{−1}$. The last column shows the cut flow efficiency with respect to all weighted events. The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies $p_{\mathrm{T}} > 25$ GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. In the $2\ell^{\mathrm{sc}}$ category no events are expected, as only one lepton is expected to be produced in the decay.Selections that have not been evaluated in the analysis or are not applicable are denoted with a dash (--).

Cut flow for the electroweakino production model, considering only the production of $\tilde{\chi}^0_1 \tilde{\chi}^0_2$, with $m(\tilde{\chi}^0_1,\tilde{\chi}^0_2)= 250$ GeV. The column labelled $\mathrm{N}_{\mathrm{raw}}$ shows the number of generated events, while $\mathrm{N}_{\mathrm{events}}$ shows the expected number of events with a luminosity of 139fb$^{−1}$. The last column shows the cut flow efficiency with respect to all weighted events. The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies $p_{\mathrm{T}} > 25$ GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. Selections that have not been evaluated in the analysis or are not applicable are denoted with a dash (--).

The best-fit predicted and observed distributions of the combined NN output in the high-$p_\text{T}$-SR for the $\mu\tau_e$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the collinear mass $m_\text{coll}$ in the high-$p_\text{T}$-SR for the $e\tau_\mu$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the collinear mass $m_\text{coll}$ in the high-$p_\text{T}$-SR for the $\mu\tau_e$ channel. The first and last bin include underflow and overflow events, respectively.

Observed and expected upper limits on $\mathcal{B}(Z\rightarrow\ell\tau)$ with leptonically decaying $\tau$ at 95% confidence level.

Observed and expected upper limits on $\mathcal{B}(Z\rightarrow\ell\tau)$ at 95% confidence level, combination of hadronically and leptonically decaying $\tau$ channels.

The best-fit predicted and observed distributions of the collinear mass $m_\text{coll}$ in the CRZ$\tau\tau$ for the $e\tau_\mu$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the collinear mass $m_\text{coll}$ in the CRZ$\tau\tau$ for the $\mu\tau_e$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the combined NN output in the CRZ$\tau\tau$ for the $e\tau_\mu$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the combined NN output in the CRZ$\tau\tau$ for the $\mu\tau_e$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the leading lepton transverse momentum $p_\text{T}(e)$ in the high-$p_\text{T}$-SR for the $e\tau_\mu$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the leading lepton transverse momentum $p_\text{T}(\mu)$ in the high-$p_\text{T}$-SR for the $\mu\tau_e$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the missing transverse momentum $E^\text{miss}_\text{T}$ in the low-$p_\text{T}$-SR for the $e\tau_\mu$ channel. The first and last bin include underflow and overflow events, respectively.

The best-fit predicted and observed distributions of the missing transverse momentum $E^\text{miss}_\text{T}$ in the low-$p_\text{T}$-SR for the $\mu\tau_e$ channel. The first and last bin include underflow and overflow events, respectively.

Cumulative acceptance times efficiency as a function of resonance mass for each event selection step in the resolved channel for the spin-2 signal models. The local maximum at 251 GeV is a consequence of the near-threshold kinematics.

Corrected $m(HH)$ distribution in the resolved $4b$ validation region (dots), compared with the reweighted distribution in $2b$ validation region (teal histogram). The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The final bin includes overflow. The background uncertainty (gray band) is computed by adding all individual components in quadrature. The bottom panel shows the difference between the $4b$ and reweighted $2b$ distributions, relative to the $2b$ distribution.

Corrected $m(HH)$ distribution in the resolved $4b$ validation region (dots), compared with the reweighted distribution in $2b$ validation region (teal histogram). The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The final bin includes overflow. The background uncertainty (gray band) is computed by adding all individual components in quadrature. The bottom panel shows the difference between the $4b$ and reweighted $2b$ distributions, relative to the $2b$ distribution.

Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-0 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.

Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-0 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.

Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-2 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.

Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-2 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-0.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-0.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-2.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-2.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-0 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-0 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-2 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-2 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.

Comparison of the background model (stacked histograms) with data (dots) in the $2b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $2b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $3b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $3b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $4b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $4b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-0 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-0 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-2 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The theoretical prediction for the bulk RS model with $k/\bar{M}_{\text{Pl}} = 1$ (solid red line) is shown; the decrease below 350 GeV is due to a sharp reduction in the $G^{*}_{\text{KK}} \rightarrow HH$ branching ratio. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-2 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The theoretical prediction for the bulk RS model with $k/\bar{M}_{\text{Pl}} = 1$ (solid red line) is shown; the decrease below 350 GeV is due to a sharp reduction in the $G^{*}_{\text{KK}} \rightarrow HH$ branching ratio. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.

Expected 95% CL upper limits with 1-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 300 GeV.

Expected 95% CL upper limits with 2-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 300 GeV.

Observed 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 300 GeV.

Expected 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Expected 95% CL upper limits with 1-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Expected 95% CL upper limits with 2-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Observed 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Expected 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Expected 95% CL upper limits with 1-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Expected 95% CL upper limits with 2-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Observed 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Observed (black solid curve) and expected (black dashed curve) 95% CL upper limits on the production of a heavy Higgs boson as functions of its mass with ( fW, fWW) fixed at (0, 6200). The green (inner) and yellow (outer) bands represent 1 and 2 sigma uncertainty in the expected limits. Theoretical predictions (red solid curve) as functions of the heavy Higgs boson mass are overlaid.

Observed (black solid curve) and expected (black dashed curve) 95% CL upper limits on the production of a heavy Higgs boson as functions of its mass with ( fW, fWW) fixed at (1350, 0). The green (inner) and yellow (outer) bands represent 1 and 2 sigma uncertainty in the expected limits. Theoretical predictions (red solid curve) as functions of the heavy Higgs boson mass are overlaid.

Summary of the total uncertainty in the background prediction for each SR of the tt0L-low, tt0L-high, tt1L and tt2L analysis channels in the statistical combination. Their dominant contributions are indicated by individual lines. Individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

$E_{\text{T}}^{\text{miss}}$ distribution in SR0X for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

$E_{\text{T}}^{\text{miss}}$ distribution in SRWX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

$E_{\text{T}}^{\text{miss}}$ distribution in SRTX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Representative fit distribution in the different flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of selected reconstructed events divided by the acceptance.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of selected reconstructed events divided by the acceptance.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of selected reconstructed events divided by the acceptance.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Correlation matrix for the measurement of STXS parameters in each of the 28 regions considered.

Fitted values for kappa_g and kappa_y.

Correlation matrix for the measurement of kappa_g and kappa_y.

Summary of the 68% CL confidence intervals for individual measurements of SMEFT parameters observed in data. In each case, SMEFT parameters other than the one measured are fixed to 0.

Results of the EV parameter measurement in data, in the linear and linear+quadratic parameterizations of the SMEFT. All the EVs parameters are free to vary in the fits. The ranges correspond to 68% CL confidence intervals.

Observed linear correlation coefficients of the EVs parameters in the linear parameterization.

Observed linear correlation coefficients of the EVs parameters in the linear+quadratic parameterization.

Cross-sections times H->yy branching ratio for ggF +bbH, VBF, VH, ttH, and tH production. The values are obtained from a simultaneous fit to all categories. The theory uncertainties in the predictions include uncertainties due to missing higher-order terms in the perturbative QCD calculations and choices of parton distribution functions and value of alpha_s, as well as the H->yy branching ratio uncertainty.

Best-fit values and uncertainties for STXS parameters in each of the 28 regions considered. The values for the gg->H process also include the contributions from bbH production.

Correlation matrix for the measurement of STXS parameters in each of the 33 regions considered.

Post-fit event yields in the signal and control regions obtained from a scan over $\epsilon_{VV}$ exploiting both shape and rate information. The quoted uncertainties include the theoretical and experimental systematic sources and those due to sample statistics. The fit constrains the total expected yield to the observed yield. The diboson background is split into $W W$ and non-$W W$ contributions.

Best-fit values and their uncertainties as obtained from the shape-only and shape-plus-rate likelihood fits to the Asimov dataset and to ATLAS data. Results of both shape-only and shape+rate fits for $a_L$ and $a_T$ are shown. Results of fits to one parameter with the other one fixed or profiled are presented.

Best-fit values and their uncertainties as obtained from the shape-only and shape-plus-rate likelihood fits to the Asimov dataset and to ATLAS data. Results of both shape-only and shape+rate fits for $\epsilon_{VV}$ and $\kappa_{VV}$ are shown. Results of fits to one parameter with the other one fixed or profiled are presented.

The contributions of the leading individual systematic uncertainties together with the data statistical uncertainties, in the one dimensional fit for the pseudo-observables $\kappa_{VV}$ (a) and $\epsilon_{VV}$ (b) for electroweak-boson polarisation in the VBF $H\to WW$ channel. Both shape and rate informations are exploited in the fit. The theoretical and experimental uncertainties are subdivided further into categories.

The contributions of the leading individual systematic uncertainties together with the data statistical uncertainties, in the one dimensional fit for the pseudo-observables $\kappa_{VV}$ (a) and $\epsilon_{VV}$ (b) for electroweak-boson polarisation in the VBF $H\to WW$ channel. Both shape and rate informations are exploited in the fit. The theoretical and experimental uncertainties are subdivided further into categories.

Post-fit distribution of the BDT response observable presented in the four $|\Delta \eta jj|$ categories of the ggF +2 jets signal region, with signal and background yields fixed from the fit for $\mu^{\text{ggF+2jets}}$. The distributions of the ggF + 2 jets and VBF processes are overlaid with their respective contributions multiplied by 50.

The weighted $\Delta \Phi_{jj}$ post-fit distribution in the ggF +2 jets signal region, with signal and background yields fixed from the fit to $\tan \alpha$ using shape and rate information.

Expected and observed likelihood curves for scans over $\tan \alpha$ where only the shape is taken into account in the fit, $\mu_{VBF}$ is fixed.

Expected and observed likelihood curves for scans over $\tan \alpha$ where both shape and normalisation are taken into account in the fit, $\mu_{VBF}$ is fixed.

68% and 95% CL two-dimensional likelihood contours of the CP-even and CP-odd coupling parameters $K_{gg} \cos(\alpha)$ and $K_{gg} \sin(\alpha)$. The minima are represented by black stars, while the SM value is shown as a red star.

The weighted $\Delta \Phi jj$ distribution in the VBF signal region, with signal and background yields fixed from the fit for $\epsilon_{VV}$ using shape and rate information.

Likelihood scans over the transversally polarised couplings. The fit is using shape-only information. All relevant experimental and modelling systematic uncertainties are considered in the fit.

Likelihood scans over the transversally polarised couplings. The fit is using shape + rate information. All relevant experimental and modelling systematic uncertainties are considered in the fit.

Likelihood scans over the longitudinally polarised couplings. The fit is using shape + rate information. All relevant experimental and modelling systematic uncertainties are considered in the fit.

Likelihood scans over $\kappa_{VV}$ with the $\epsilon_{VV}$ profiled. The fit is performed using both shape and rate information. All relevant experimental and theoretical systematic uncertainties are considered in the fit.

Likelihood scans over $\epsilon_{VV}$ with the $\kappa_{VV}$ profiled. The fit is performed using both shape and rate information. All relevant experimental and theoretical systematic uncertainties are considered in the fit.

The contributions of the leading individual systematic uncertainties together with the data statistical uncertainties, in the one dimensional fit for electroweak-boson polarisation in the VBF $H\to WW$ channel, using (aL, aT) parametrisation. Both shape and rate informations are exploited in the fit. The theoretical and experimental uncertainties are subdivided further into categories.

The contributions of the leading individual systematic uncertainties together with the data statistical uncertainties, in the one dimensional fit for electroweak-boson polarisation in the VBF $H\to WW$ channel, using (aL, aT) parametrisation.. Both shape and rate informations are exploited in the fit. The theoretical and experimental uncertainties are subdivided further into categories.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the hadhad category. The efficiencies are calculated with respect to HH to bbtautau decays in which both tau-leptons decay hadronically. The ‘Pre-selection’ consists of basic requirements, including that at least two tau-had-vis pass loose kinematic requirements, at least one of the tau-had-vis candidate passes identification requirement, and that events do not contain an electron or muon. The ‘Object selections’ require exactly two tau-had-vis candidates, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Loose tau ID’ requires that both tau-had-vis candidates pass identification requirements. The ‘DTT offline jet cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad SLT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad LTT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Post-fit expected number of signal and background events and observed number of data events after applying the selection criteria and requiring exactly 2 b-tagged jets and assuming a background-only hypothesis.

Observed and expected limits at 95% CL on the cross-section of HH production, for the non-resonant ggF+VBF HH search, and the resonant HH search for four values of the resonance mass mX.

Acceptance times efficiency for the full analysis selections as a function of the resonance mass mX in the hadhad, lephad SLT and lephad LTT trigger categories, and the combined lephad. The acceptance times efficiency is evaluated for HH to bbtautau decays, with respect to the targeted tau-lepton decay modes (lephad or hadhad).

Post-fit distribution of mHH in the hadhad channel.

Post-fit distribution of mHH in the lephad SLT channel.

Post-fit distribution of mHH in the lephad LTT channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of of di-b-jet mass in the hadhad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

BDT for SM HH in the hadhad channel

NN for SM HH in the lephad SLT channel

NN for SM HH in the lephad LTT channel

PNN for mX = 500 GeV resonant HH in the hadhad channel