Table with post-fit yields in the four regions used in the measurement. The correlations of the nuisance parameters, as obtained by the fit, are considered for the calculation of the uncertainties.

Ratio of the $t\bar{t}$ to the $Z$-boson cross section compared to the predictions for several sets of parton distribution functions. The total cross section for $t\bar{t}$ production and the fiducial cross section for $Z$-boson production are used. The uncertainty on the prediction represents the PDF+$\alpha_{s}$ variations.

Nuisance parameter ranking plot for the $t\bar{t}$ signal-strength. Only the 10 most important nuisance parameters are shown. The impact of each nuisance parameter on the measured cross section, is defined as the shift induced in measured cross-section as the nuisance parameter is shifted by its pre-fit and post-fit uncertainties from its nominal best-fit value and then fixed while all other nuisance parameters profiled.

Nuisance parameter ranking plot for the ratio of the $t\bar{t}$ and $Z$-boson signal-strengths. Only the 10 most important nuisance parameters are shown. The impact of each nuisance parameter on the measured ratio, is defined as the shift induced in measured ratio as the nuisance parameter is shifted by its pre-fit and post-fit uncertainties from its nominal best-fit value and then fixed while all other nuisance parameters profiled.

Nuisance parameter ranking plot for the $Z$-boson signal-strength. Only the 10 most important nuisance parameters are shown. The impact of each nuisance parameter on the measured cross section, is defined as the shift induced in measured cross-section as the nuisance parameter is shifted by its pre-fit and post-fit uncertainties from its nominal best-fit value and then fixed while all other nuisance parameters profiled.

Antiparticle-to-particle yield ratio, 30-50% centrality

Antiparticle-to-particle yield ratio, 50-90% centrality

Electric-charge and baryon chemical potentials, $\mu_Q$ and $\mu_B$, extracted with the Thermal-FIST code, in the 0-5%, 5-10%, 10-30%, 30-50%, and 50-90% centrality classes

Covariance matrix of the centrality-uncorrelated uncertainties on $\mu_Q$ and $\mu_B$, extracted with the Thermal-FIST code, in the 0-5% centrality class

Covariance matrix of the centrality-uncorrelated uncertainties on $\mu_Q$ and $\mu_B$, extracted with the Thermal-FIST code, in the 5-10% centrality class

Covariance matrix of the centrality-uncorrelated uncertainties on $\mu_Q$ and $\mu_B$, extracted with the Thermal-FIST code, in the 10-30% centrality class

Covariance matrix of the centrality-uncorrelated uncertainties on $\mu_Q$ and $\mu_B$, extracted with the Thermal-FIST code, in the 30-50% centrality class

Covariance matrix of the centrality-uncorrelated uncertainties on $\mu_Q$ and $\mu_B$, extracted with the Thermal-FIST code, in the 50-90% centrality class

Baryon chemical potentials, $\mu_B$, extracted with the Thermal-FIST code, in the 0-90% centrality interval

$p_{\mathrm{T}}$-differential $\pi^{-}/\pi^{+}$ antiparticle-to-particle yield ratio, 0-5% centrality

$p_{\mathrm{T}}$-differential $\pi^{-}/\pi^{+}$ antiparticle-to-particle yield ratio, 5-10% centrality

$p_{\mathrm{T}}$-differential $\pi^{-}/\pi^{+}$ antiparticle-to-particle yield ratio, 10-30% centrality

$p_{\mathrm{T}}$-differential $\pi^{-}/\pi^{+}$ antiparticle-to-particle yield ratio, 30-50% centrality

$p_{\mathrm{T}}$-differential $\pi^{-}/\pi^{+}$ antiparticle-to-particle yield ratio, 50-90% centrality

$p_{\mathrm{T}}$-differential $\overline{\mathrm{p}}/\mathrm{p}$ antiparticle-to-particle yield ratio, 0-5% centrality

$p_{\mathrm{T}}$-differential $\overline{\mathrm{p}}/\mathrm{p}$ antiparticle-to-particle yield ratio, 5-10% centrality

$p_{\mathrm{T}}$-differential $\overline{\mathrm{p}}/\mathrm{p}$ antiparticle-to-particle yield ratio, 10-30% centrality

$p_{\mathrm{T}}$-differential $\overline{\mathrm{p}}/\mathrm{p}$ antiparticle-to-particle yield ratio, 30-50% centrality

$p_{\mathrm{T}}$-differential $\overline{\mathrm{p}}/\mathrm{p}$ antiparticle-to-particle yield ratio, 50-90% centrality

$c\mathrm{t}$-differential $\overline{ \Omega}^{+} / \Omega^-$ antiparticle-to-particle yield ratio, 0-5% centrality

$c\mathrm{t}$-differential $\overline{ \Omega}^{+} / \Omega^-$ antiparticle-to-particle yield ratio, 5-10% centrality

$c\mathrm{t}$-differential $\overline{ \Omega}^{+} / \Omega^-$ antiparticle-to-particle yield ratio, 10-30% centrality

$c\mathrm{t}$-differential $\overline{ \Omega}^{+} / \Omega^-$ antiparticle-to-particle yield ratio, 30-50% centrality

$c\mathrm{t}$-differential $\overline{ \Omega}^{+} / \Omega^-$ antiparticle-to-particle yield ratio, 50-90% centrality

$c\mathrm{t}$-differential ${}_{\overline{\Lambda}}^{3}\overline{\mathrm{H}} / ^{3}_{\Lambda}\mathrm{H}$ antiparticle-to-particle yield ratio, 0-5% centrality

$c\mathrm{t}$-differential ${}_{\overline{\Lambda}}^{3}\overline{\mathrm{H}} / ^{3}_{\Lambda}\mathrm{H}$ antiparticle-to-particle yield ratio, 5-10% centrality

$c\mathrm{t}$-differential ${}_{\overline{\Lambda}}^{3}\overline{\mathrm{H}} / ^{3}_{\Lambda}\mathrm{H}$ antiparticle-to-particle yield ratio, 10-30% centrality

$c\mathrm{t}$-differential ${}_{\overline{\Lambda}}^{3}\overline{\mathrm{H}} / ^{3}_{\Lambda}\mathrm{H}$ antiparticle-to-particle yield ratio, 30-50% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{H}} / ^{3}\mathrm{H}$ antiparticle-to-particle yield ratio, 0-5% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{H}} / ^{3}\mathrm{H}$ antiparticle-to-particle yield ratio, 5-10% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{H}} / ^{3}\mathrm{H}$ antiparticle-to-particle yield ratio, 10-30% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{H}} / ^{3}\mathrm{H}$ antiparticle-to-particle yield ratio, 30-50% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{H}} / ^{3}\mathrm{H}$ antiparticle-to-particle yield ratio, 50-90% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{He}} / ^{3}\mathrm{He}$ antiparticle-to-particle yield ratio, 0-5% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{He}} / ^{3}\mathrm{He}$ antiparticle-to-particle yield ratio, 5-10% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{He}} / ^{3}\mathrm{He}$ antiparticle-to-particle yield ratio, 10-30% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{He}} / ^{3}\mathrm{He}$ antiparticle-to-particle yield ratio, 30-50% centrality

$p_{\mathrm{T}}$-differential $^{3}\overline{\mathrm{He}} / ^{3}\mathrm{He}$ antiparticle-to-particle yield ratio, 50-90% centrality

Total covariance matrix of the baryon chemical potential, $\mu_B$, extracted with the Thermal-FIST code, in the 0-5%, 5-10%, 10-30%, 30-50%, and 50-90% centrality classes

Total covariance matrix of the electric-charge chemical potential, $\mu_Q$, extracted with the Thermal-FIST code, in the 0-5%, 5-10%, 10-30%, 30-50%, and 50-90% centrality classes

$\chi^{2}$ of the centrality-averaged baryon chemical potential, $\mu_B$, extracted with the Thermal-FIST code

$\chi^{2}$ of the centrality-averaged electric-charge chemical potential, $\mu_Q$, extracted with the Thermal-FIST code

$\chi^{2}$ of the centrality-averaged baryon chemical potential, $\mu_Q$, extracted with the Thermal-FIST code

Fiducial differential cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $N_{\mathrm{gap}\,\mathrm{jets}}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 14.

Fiducial differential cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $\xi_{\mathrm{j}3}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 15.

Fiducial differential cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $m_{\ell\ell}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 16.

Fiducial differential cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $m_{\mathrm{T}}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 17.

Fiducial differential cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $m_{\mathrm{jj}}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 18.

Fiducial differential cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $N_{\mathrm{gap}\,\mathrm{jets}}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 19.

Fiducial differential cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $\xi_{\mathrm{j}3}$. The correlation of uncertainties of the measured cross section across bins is presented in Table 20.

Observed correlations between the bins of the LH-unfolded cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $m_{\ell\ell}$. See Table 1.

Observed correlations between the bins of the LH-unfolded cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $m_{\mathrm{T}}$. See Table 2.

Observed correlations between the bins of the LH-unfolded cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $m_{\mathrm{jj}}$. See Table 3.

Observed correlations between the bins of the LH-unfolded cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $N_{\mathrm{gap}\,\mathrm{jets}}$. See Table 4.

Observed correlations between the bins of the LH-unfolded cross section of the electroweak $W^\pm W^\pm jj$ production as a function of $\xi_{\mathrm{j}3}$. See Table 5.

Observed correlations between the bins of the LH-unfolded cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $m_{\ell\ell}$. See Table 6.

Observed correlations between the bins of the LH-unfolded cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $m_{\mathrm{T}}$. See Table 7.

Observed correlations between the bins of the LH-unfolded cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $m_{\mathrm{jj}}$. See Table 8.

Observed correlations between the bins of the LH-unfolded cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $N_{\mathrm{gap}\,\mathrm{jets}}$. See Table 9.

Observed correlations between the bins of the LH-unfolded cross section of the inclusive $W^\pm W^\pm jj$ production as a function of $\xi_{\mathrm{j}3}$. See Table 10.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label M0 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label M1 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label M7 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient. The limits on M7 were obtained without taking into account the SM-EFT interference for the EW W$^\pm$Zjj final state.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label S02 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label S1 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label T0 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label T1 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Evolution of the one-dimensional expected and observed limits at 95% CL on the parameters corresponding to the quartic operators with label T2 as a function of the cut-off scale. The unitarity bounds as a function of the cut-off scale are defined for one non-zero Wilson coefficient.

Expected and observed exclusion limits at 95% CL for $\sigma_{\mathrm{VBF}}(\mathrm{H}^{\pm\pm}_5) \times \mathcal{B}(\mathrm{H}^{\pm\pm}_5 \rightarrow W^{\pm}W^{\pm})$ as a function of the doubly-charged Higgs mass.

Expected and observed exclusion limits at 95% CL for $\sin\theta_{\mathrm{H}}$ as a function of the doubly-charged Higgs mass in the Georgi-Machacek model.

The mass planes of the reconstructed Higgs boson candidates for the 2Pass selections of the analysis, shown for the VBF $\kappa_{2V} = 0$ HH samples.

The mass planes of the reconstructed Higgs boson candidates for the 2Pass selections of the analysis, shown for the $m_{X} = 1$ TeV spin-0 narrow-width resonance HH samples.

Observed values of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the resolved (dotted green) and boosted (dashed blue) analyses, and their combination (solid black), with all other coupling modifiers fixed to their SM predictions.

Expected values of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the resolved (dotted green) and boosted (dashed blue) analyses, and their combination (solid black), with all other coupling modifiers fixed to their SM predictions.

Observed $-2\ln\Lambda$ values of boosted-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Observed $-2\ln\Lambda$ values of resolved-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Observed $-2\ln\Lambda$ values of combination result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected $-2\ln\Lambda$ values of boosted-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected $-2\ln\Lambda$ values of resolved-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected $-2\ln\Lambda$ values of combination result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of spin-0 heavy resonances with narrow width assumptions. The SM H → bb branching ratio is assumed in both cases. The ±1$\sigma$ and ±2$\sigma$ uncertainty ranges for the expected limits are shown as coloured bands.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of spin-0 heavy resonances with broad width assumptions. The SM H → bb branching ratio is assumed in both cases. The ±1$\sigma$ and ±2$\sigma$ uncertainty ranges for the expected limits are shown as coloured bands.

Observed and expected 95% CL exclusion limits on the production cross-sections of the combined VBF HH process as a function of $\kappa_{2V}$ . The expected exclusion limits assume no HH production and are obtained using a fit to the data with the background-only hypothesis. The red line shows the theory prediction for the VBF HH cross-section as a function of $\kappa_{2V}$ where all other parameters and couplings are set to their SM values.

Observed and expected 95% CL exclusion limits on the production cross-sections of the combined HH process as a function of $\kappa_{\lambda}$ . The expected exclusion limits assume no HH production and are obtained using a fit to the data with the background-only hypothesis. The red line shows the theory prediction for the HH cross-section as a function of $\kappa_{\lambda}$ where all other parameters and couplings are set to their SM values.

Observed −2 ln Λ as a function of $\kappa_{\lambda}$ with the $\kappa_{2V}$ and $\kappa_{V}$ modifiers fixed to unity.

Expected −2 ln Λ as a function of $\kappa_{\lambda}$ with the $\kappa_{2V}$ and $\kappa_{V}$ modifiers fixed to unity.

Cumulative acceptance times efficiency as a function of resonance mass for each event selection step for the narrow-width signal

Cumulative acceptance times efficiency as a function of resonance mass for each event selection step for the broad-width signal

Cumulative acceptance times efficiency as a function of $\kappa_{2V}$ for each event selection step for the nonresonant signal.

The distributions of the invariant mass of the HH system for various values of $\kappa_{2V}$ .

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.

The double ratio of particle yield of f0((980) to K*(892)0

Transverse-momentum-differential particle yield ratio of f$_{0}$(980) to charged pions and its double ratio

Transverse-momentum-differential particle yield ratio of f$_{0}$(980) to K$^{*}(892)$^{0}$ and its double ratio

Nuclear modification factor of f0(980) in p-Pb collisions at 5.02 TeV

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

Summary of observed and predicted yields in the four signal region categories. The background prediction is shown after the combined likelihood fit to data under the background-only hypothesis across all control region and signal region categories. The expected signal yields that are obtained by using their theoretical cross sections are also shown with their pre-fit uncertainties, assuming B=1 and $\mu$=1. The "Other" contribution is dominated by $t\bar{t}t\bar{t}$ and $t\bar{t}WW$ in the $3\ell$ SRs, whereas it is dominated by $tWZ$ and $t\bar{t}WW$ in the $4\ell$ SRs. Dashes refer to components that are negligible or not applicable.

Summary of the data, background and signal yields in the four signal region categories. The background prediction is shown before the combined likelihood fit to data. Dashes (--) refer to components that are negligible or not applicable.

Summary of the data and background yields in the seven control region categories. The background prediction is shown before the combined likelihood fit to data. Dashes (--) refer to components that are negligible or not applicable.

Summary of the data and background yields in the seven control region categories. The background prediction is shown after the combined likelihood fit to data under the background-only hypothesis across all control region categories. Dashes (--) refer to components that are negligible or not applicable.