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

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Pre-fit background composition of the SR$3\ell$ Prod. The table shows the event yields as opposed to just the percentages of the relevant background processes.

Post-fit plot of $H_\text{T}(\text{jets})$ in the SR$2\ell$ Dec from a signal-blinded background-only fit.

Post-fit plot of $m(t_\text{SM}, b\text{-jet}_0)$ in the SR$2\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Dec from a signal-blinded background-only fit.

Post-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Prod from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Prod from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$e$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$\mu$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ $t\bar{t}V$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$e$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$\mu$ from the full fit to data.

Post-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}W$ from the full fit to data.

Post-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}Z$ from the full fit to data.

Observed and expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hu)$ for different analyses and their statistical combination.

Observed and expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hc)$ for different analyses and their statistical combination.

Post-fit normalisation factors of free-floating background processes and the signal normalisation.

Post-fit predicted and observed yields in all $2\ell$SS signal and control regions. Pre-fit signal contributions for a signal cross section equivalent to $\mathcal{B}(t\to Hq)=0.1\,\%$ are given as well.

Post-fit predicted and observed yields in all $3\ell$ signal and control regions. Pre-fit signal contributions for a signal cross section equivalent to $\mathcal{B}(t\to Hq)=0.1\,\%$ are given as well.

Expected upper limits on $\mathcal{B}(t\to Hq)$ for the nominal fit and alternative fit configurations. One contains the full phase space but only considers statistical uncertainties. Two other configurations consider the full set of systematic uncertainties, but only encompass one final state.

Expected and observed upper limits on $\mathcal{B}(t\to Hq)$ and $|C_{u\phi}^{qt,tq}|$ for the full fit containing all systematic uncertainties.

Pre-fit plot of $H_\text{T}(\text{jets})$ in the SR$2\ell$ Dec from a signal-blinded background-only fit.

Pre-fit plot of $m(t_\text{SM}, b\text{-jet}_0)$ in the SR$2\ell$ Prod from a signal-blinded background-only fit.

Pre-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Dec from a signal-blinded background-only fit.

Pre-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Prod from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$e$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$\mu$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ $t\bar{t}V$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$e$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$\mu$ from the full fit to data.

Pre-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}W$ from the full fit to data.

Pre-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}Z$ from the full fit to data.

Ranking of fit nuisance parameters according to their impact on the post-fit $tHu$ signal normalisation when fixed to $\pm1\sigma$

Ranking of fit nuisance parameters according to their impact on the post-fit $tHc$ signal normalisation when fixed to $\pm1\sigma$

Expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hu)$ for each individual final state and the full analysis.

Expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hc)$ for each individual final state and the full analysis.

Measured and expected differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ in bin of 50$< p_{T}^{4l} <$100 GeV

Measured and expected differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ in bin of 100$< p_{T}^{4l} <$600 GeV

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ in bin of 0.0$< y_{4l} <$0.4

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ in bin of 0.4$< y_{4l} <$0.8

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ in bin of 0.8$< y_{4l} <$1.2

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ in bin of 1.2$< y_{4l} <$2.5

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ for MELA$<$1.4 bin

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ for MELA$>$1.4 bin

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ for $\mu\mu\mu\mu$ events

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ for $eeee$ events

Measured and expected double differential cross-section $\text{d}\sigma / \text{d} m_{4l}$ as a function of $m_{4l}$ for $ee\mu\mu$ events

Systematic covariance matrix for the differential $m_{4l}$ distribution.

Statistical covariance matrix for the differential $m_{4l}$ distribution.

Background covariance matrix for the differential $m_{4l}$ distribution.

Systematic covariance matrix for the differential $m_{4l}$-$p_{T}^{4l}$ distribution.<br><br> Bins labelled 1-9 correspond to the 0$< p_{T}^{4l} < $20 GeV bin with $m_{4l}$ values as listed in Table 2.<br> Bins labelled 10-16 correspond to the 20$< p_{T}^{4l} <$50 GeV bin with $m_{4l}$ values as listed in Table 3.<br> Bins labelled 17-23 correspond to the 50$< p_{T}^{4l} <$100 GeV bin with $m_{4l}$ values as listed in Table 4.<br> Bins labelled 24-30 correspond to the 100$< p_{T}^{4l} <$600 GeV bin with $m_{4l}$ values as listed in Table 5.

Statistical covariance matrix for the differential $m_{4l}$-$p_{T}^{4l}$ distribution. <br><br> Bins labelled 1-9 correspond to the 0$< p_{T}^{4l} < $20 GeV bin with $m_{4l}$ values as listed in Table 2.<br> Bins labelled 10-16 correspond to the 20$< p_{T}^{4l} <$50 GeV bin with $m_{4l}$ values as listed in Table 3.<br> Bins labelled 17-23 correspond to the 50$< p_{T}^{4l} <$100 GeV bin with $m_{4l}$ values as listed in Table 4.<br> Bins labelled 24-30 correspond to the 100$< p_{T}^{4l} <$600 GeV bin with $m_{4l}$ values as listed in Table 5.

Background covariance matrix for the differential $m_{4l}$-$p_{T}^{4l}$ distribution. <br><br> Bins labelled 1-9 correspond to the 0$< p_{T}^{4l} < $20 GeV bin with $m_{4l}$ values as listed in Table 2.<br> Bins labelled 10-16 correspond to the 20$< p_{T}^{4l} <$50 GeV bin with $m_{4l}$ values as listed in Table 3.<br> Bins labelled 17-23 correspond to the 50$< p_{T}^{4l} <$100 GeV bin with $m_{4l}$ values as listed in Table 4.<br> Bins labelled 24-30 correspond to the 100$< p_{T}^{4l} <$600 GeV bin with $m_{4l}$ values as listed in Table 5.

Systematic covariance matrix for the differential $m_{4l}$-$y_{4l}$ distribution. <br><br> Bins labelled 1-9 correspond to the 0.0$< y_{4l} < $0.4 bin with $m_{4l}$ values as listed in Table 6.<br> Bins labelled 10-18 correspond to the 0.4$< y_{4l} <$0.8 bin with $m_{4l}$ values as listed in Table 7.<br> Bins labelled 19-26 correspond to the 0.8$< y_{4l} <$1.2 bin with $m_{4l}$ values as listed in Table 8.<br> Bins labelled 27-34 correspond to the 1.2$< y_{4l} <$2.5 bin with $m_{4l}$ values as listed in Table 9.

Statistical covariance matrix for the differential $m_{4l}$-$y_{4l}$ distribution. <br><br> Bins labelled 1-9 correspond to the 0.0$< y_{4l} < $0.4 bin with $m_{4l}$ values as listed in Table 6.<br> Bins labelled 10-18 correspond to the 0.4$< y_{4l} <$0.8 bin with $m_{4l}$ values as listed in Table 7.<br> Bins labelled 19-26 correspond to the 0.8$< y_{4l} <$1.2 bin with $m_{4l}$ values as listed in Table 8.<br> Bins labelled 27-34 correspond to the 1.2$< y_{4l} <$2.5 bin with $m_{4l}$ values as listed in Table 9.

Background covariance matrix for the differential $m_{4l}$-$y_{4l}$ distribution. <br><br> Bins labelled 1-9 correspond to the 0.0$< y_{4l} < $0.4 bin with $m_{4l}$ values as listed in Table 6.<br> Bins labelled 10-18 correspond to the 0.4$< y_{4l} <$0.8 bin with $m_{4l}$ values as listed in Table 7.<br> Bins labelled 19-26 correspond to the 0.8$< y_{4l} <$1.2 bin with $m_{4l}$ values as listed in Table 8.<br> Bins labelled 27-34 correspond to the 1.2$< y_{4l} <$2.5 bin with $m_{4l}$ values as listed in Table 9.

Systematic covariance matrix for the differential $m_{4l}$-MELA distribution. <br><br> Bins labelled 1-7 correspond to the MELA$<$ 0.4 bin with $m_{4l}$ values as listed in Table 10.<br> Bins labelled 8-12 correspond to the MELA$>$ 0.4 bin with $m_{4l}$ values as listed in Table 11.

Statistical covariance matrix for the differential $m_{4l}$-MELA distribution. <br><br> Bins labelled 1-7 correspond to the MELA$<$ 0.4 bin with $m_{4l}$ values as listed in Table 10.<br> Bins labelled 8-12 correspond to the MELA$>$ 0.4 bin with $m_{4l}$ values as listed in Table 11.

Background covariance matrix for the differential $m_{4l}$-MELA distribution. <br><br> Bins labelled 1-7 correspond to the MELA$<$ 0.4 bin with $m_{4l}$ values as listed in Table 10.<br> Bins labelled 8-12 correspond to the MELA$>$ 0.4 bin with $m_{4l}$ values as listed in Table 11.

Systematic covariance matrix for the differential $m_{4l}$-lepton flavour distribution. <br><br> Bins labelled 1-9 correspond to $\mu\mu\mu\mu$ events with $m_{4l}$ values as listed in Table 12.<br> Bins labelled 10-18 correspond to $eeee$ events with $m_{4l}$ values as listed in Table 13.<br> Bins labelled 19-27 correspond to $\mu\mu\mu\mu$ events with $m_{4l}$ values as listed in Table 14.<br>

Statistical covariance matrix for the differential $m_{4l}$-lepton flavour distribution. <br><br> Bins labelled 1-9 correspond to $\mu\mu\mu\mu$ events with $m_{4l}$ values as listed in Table 12.<br> Bins labelled 10-18 correspond to $eeee$ events with $m_{4l}$ values as listed in Table 13.<br> Bins labelled 19-27 correspond to $\mu\mu\mu\mu$ events with $m_{4l}$ values as listed in Table 14.<br>

Background covariance matrix for the differential $m_{4l}$-lepton flavour distribution. <br><br> Bins labelled 1-9 correspond to $\mu\mu\mu\mu$ events with $m_{4l}$ values as listed in Table 12.<br> Bins labelled 10-18 correspond to $eeee$ events with $m_{4l}$ values as listed in Table 13.<br> Bins labelled 19-27 correspond to $\mu\mu\mu\mu$ events with $m_{4l}$ values as listed in Table 14.<br>

The 95% CL upper limits of the cross section times branching fraction of $\Phi \to \mu^+\mu^-$ for b-associated production

The observed/expected 95% CL upper limits of the cross section times branching fraction of $\Phi \to \mu^+\mu^-$ as a function of $\Phi$ mass and the cross section fraction of the b-associated production.

Signal acceptance for different masses in bTag and bVeto signal regions.

Normalized differential cross section, $(1/\sigma_\text{fid})d\sigma_\text{fid}/d\log(m_{bb}/p_\text{T})$, as a function of $\log(m_{bb}/p_\text{T})$ for $m_{bb}$ the invariant mass of the two b-jets.

The nuclear modification factor R_pPb for prompt, isolated photons with rapidity in (1.09,1.90).

The nuclear modification factor R_pPb for prompt, isolated photons with rapidity in (−1.84,0.91).

The nuclear modification factor R_pPb for prompt, isolated photons with rapidity in (−2.83,−2.02).

The ratio of R_{pPb} from rapidity (1.09,1.90) to that of rapidity (−2.83,−2.02).