The second harmonic coefficients as a functino of charged-particle multiplicity in pp collisions.

The third harmonic coefficients as a functino of charged-particle multiplicity in pp collisions.

The second and third harmonic coefficients as a functino of charged-particle multiplicity in p-Pb collisions.

The second harmonic coefficients as a functino of minimum leading particle transverse momentum.

The third harmonic coefficients as a functino of minimum leading particle transverse momentum.

The second harmonic coefficients as a functino of minimum jet transverse momentum.

The third harmonic coefficients as a functino of minimum jet transverse momentum.

Fully-corrected $\Delta_{\mathrm{recoil}} (\Delta\phi)$ distributions for $p_{\mathrm{T, jet}}^{\mathrm{ch}} \in (40, 60)$ GeV/$c$ measured in MB and HM-selected events in pp collisions at $\sqrt{s}= 13$ TeV.

Ratio of $\Delta_{\mathrm{recoil}} (p_{\mathrm{T, jet}}^{\mathrm{ch}})$ distributions measured in MB and HM-selected events in pp collisions at $\sqrt{s}= 13$ TeV.

Ratio of $\Delta_{\mathrm{recoil}} (\Delta\phi)$ distributions for jets with $p_{\mathrm{T, jet}}^{\mathrm{ch}} \in (20, 40)$ GeV/$c$ measured in MB and HM-selected events in pp collisions at $\sqrt{s}= 13$ TeV.

Ratio of $\Delta_{\mathrm{recoil}} (\Delta\phi)$ distributions for jets with $p_{\mathrm{T, jet}}^{\mathrm{ch}} \in (40, 60)$ GeV/$c$ measured in MB and HM-selected events in pp collisions at $\sqrt{s}= 13$ TeV.

Distribution of probability per trigger hadron of the number of jets with $p_{\mathrm{T, jet}}^{\mathrm{ch}} > 15$ GeV/$c$ and $|\eta_{\mathrm{jet}}| < 0.5$ recoiling from a high-$p_{\mathrm{T}}$ hadron (TT$\{20, 30\}$) for MB and HM-selected events in pp collisions at $\sqrt{s} = 13$ TeV.

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

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

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.