Post-fit agreement between data and MC prediction for the $SR_{\mathrm{loose}}^{2\ell3j}$ signal region, which uses the invariant mass of the c-tagged jet and the geometrically closest b-tagged jet, $m_{\mathit{cb}}^{\mathrm{min}\Delta R}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{loose}}^{1\ell6ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{tight}}^{1\ell6ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{loose}}^{2\ell4ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{tight}}^{2\ell4ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Jet multiplicity and tagging criteria for the SRs and CRs in the single-lepton and dilepton channels. All single-lepton regions are defined in the 5-jet-exclusive and 6-jet-inclusive jet selections, all dilepton regions in the 3-jet-exclusive and 4-jet-inclusive jet selections. By construction, $\mathrm{SR}^{2\ell}_{\mathrm{tight}}$ only exists for the 4-jet-inclusive selection. Identical requirements on inclusive and exclusive working points, e.g., one c@22% and one c@11% in the $\mathrm{CR}_1^{1\ell}$, indicate a veto of additional tags at the inclusive working point.

Breakdown of the fiducial $t\bar{t}+{\geq}2c$ and $t\bar{t}+1c$ cross-section uncertainties. Systematic uncertainties are grouped into signal and background modeling, instrumental, and MC statistics categories. The table lists the fractional uncertainty in the measured cross-sections in percent and is estimated from the covariance matrix of the profile likelihood fit, following EPJC 84 (2024) 593. Individual groups of uncertainties can be added in quadrature to obtain the total uncertainty. JES and JER denote the jet energy scale and resolution uncertainties, respectively. The fractional uncertainties in the fitted $t\bar{t}+{\geq}1c$ and $t\bar{t}+\text{light}$ normalization factors are included in the data statistics category. For presentation purposes, all uncertainties are symmetrized.

Measured fiducial cross-section values in comparison with various NLO+PS predictions. The uncertainties in the predictions include independent and simultaneous variations of the $\mu_R$ and $\mu_F$ scales and uncertainties in the choice of the PDF set, but no uncertainties in the parton shower or hadronization. The uncertainties in the measured values include all statistical and systematic uncertainties. For presentation purposes, the quoted measurement and prediction uncertainties are symmetrized.

Measured and predicted values for the $t\bar{t}+{\geq}1b$, $t\bar{t}+{\geq}2c$, and $t\bar{t}+1c$ cross-sections and for the cross-section ratios of these processes to total $t\bar{t}+\text{jets}$ production. The quoted uncertainties in the measurements include statistical and systematic uncertainties. The predictions are taken from the nominal setup using inclusive $t\bar{t}$ Powheg+Pythia8 predictions for the $t\bar{t}+{\geq}2c$, $t\bar{t}+1c$, and $t\bar{t}+\text{light}$ processes, and $t\bar{t}+b\bar{b}$ Powheg+Pythia8 predictions for the $t\bar{t}+{\geq}1b$ process. The first use the 5FS, the latter the 4FS, where the $b$-quarks are treated as massive particles and are included in the matrix element calculation. The uncertainties in the predictions include independent and simultaneous variations of the $\mu_R$ and $\mu_F$ scales and uncertainties in the choice of the PDF set.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+{\geq}2c$ cross-section measurement. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+1c$ cross-section measurement. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+{\geq}2c$ ratio to total $t\bar{t}+\text{jets}$ production. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+1c$ ratio to total $t\bar{t}+\text{jets}$ production. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Efficiencies and rejection rates for the $b$- and $c$-jet tagging working points of the $b/c$-tagger, as well as the selection cuts on the discriminants, $\mathcal{D}_b'$ and $\mathcal{D}_c'$ that define the tagging bins. The primes indicate that a standard logistic function is applied to the discriminants. The b@70% and c@22% working points are inclusive of their respective tighter working points. The values were estimated from single-lepton and dilepton $t\bar{t}$ events simulated with Powheg + Pythia 8.

Efficiencies and rejection rates for the $b$- and $c$-jet tagging working points of the $b/c$-tagger, as well as the selection cuts on the discriminants, $\mathcal{D}_b'$ and $\mathcal{D}_c'$ that define the tagging bins. The primes indicate that a standard logistic function is applied to the discriminants. The b@70% and c@22% working points are inclusive of their respective tighter working points. The values were estimated from single-lepton and dilepton $t\bar{t}$ events simulated with Powheg + Pythia 8.

Pre-fit event yields in the twelve single-lepton and dilepton control regions (CRs) of the analysis. The quoted uncertainties include both statistical and systematic components, but all normalization factors are fixed to their pre-fit values with no uncertainties. All uncertainties are assumed to be uncorrelated. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Pre-fit event yields in the seven signal regions (SRs) of the analysis. The quoted uncertainties include both statistical and systematic components, but all normalization factors are fixed to their pre-fit values with no uncertainties. All uncertainties are assumed to be uncorrelated. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Post-fit event yields in the twelve single-lepton and dilepton control regions (CRs) of the analysis. The quoted uncertainties include both statistical and systematic components, and post-fit correlations between uncertainties are taken into account. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Post-fit event yields in the seven signal regions (SRs) of the analysis. The quoted uncertainties include both statistical and systematic components, and post-fit correlations between uncertainties are taken into account. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Expected (background-only) and observed 95% CL upper limits on the VLQ coupling κ as functions of the VLQ mass m_Q, for T-singlet (a) and Y-triplet (b) vector-like quarks. The green and yellow bands indicate the systematic uncertainties which are profiled in the fit for the observed upper limits. For the T-singlet model, the result of the latest ATLAS combination of searches for single vector-like top-quarks  is overlaid; this includes the decay modes T→H t and T→Z t. These interpretations are limited to parameter combinations for which the model corrections are valid, by restricting the VLQ relative width to Γ_Q/m_Q < 0.5, as indicated by the grey dashed line.

Expected (background-only) and observed 95% CL upper limits on the VLQ coupling κ as functions of the VLQ mass m_Q, for T-singlet (a) and Y-triplet (b) vector-like quarks. The green and yellow bands indicate the systematic uncertainties which are profiled in the fit for the observed upper limits. For the T-singlet model, the result of the latest ATLAS combination of searches for single vector-like top-quarks  is overlaid; this includes the decay modes T→H t and T→Z t. These interpretations are limited to parameter combinations for which the model corrections are valid, by restricting the VLQ relative width to Γ_Q/m_Q < 0.5, as indicated by the grey dashed line.

Exclusion limit (at 95% CL) for (a) T-singlet and (b) Y-triplet, expressed in terms of an upper limit on the VLQ relative width Γ_Q/m_Q, within its validity range for the modelling used, as a function of the VLQ mass m_Q. This is an alternative presentation of the upper limit set in terms of the coupling κ in Figure 6. As can be seen by the fact the interference-included and signal-only exclusion curves overlap almost entirely, the effect of neglecting the interference is negligible for the vector-like Y-quark.

Exclusion limit (at 95% CL) for (a) T-singlet and (b) Y-triplet, expressed in terms of an upper limit on the VLQ relative width Γ_Q/m_Q, within its validity range for the modelling used, as a function of the VLQ mass m_Q. This is an alternative presentation of the upper limit set in terms of the coupling κ in Figure 6. As can be seen by the fact the interference-included and signal-only exclusion curves overlap almost entirely, the effect of neglecting the interference is negligible for the vector-like Y-quark.

Observed profile likelihood scans of $\kappa_\lambda$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Expected profile likelihood scans of $\kappa_\lambda$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Observed profile likelihood scans of $\kappa_{2V}$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Expected profile likelihood scans of $\kappa_{2V}$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Confidence level contours at 68% (solid line) and 95% (dashed line) in the $(\kappa_\lambda, \kappa_{2V})$ parameter space, when all other coupling modifiers are fixed to their SM predictions. The corresponding expected contours are shown by the inner and outer shaded regions The SM prediction is indicated by the star, while the best-fit value is denoted by the cross.

Confidence level contours at 68% (solid line) and 95% (dashed line) in the $(\kappa_\lambda, \kappa_{2V})$ parameter space, when all other coupling modifiers are fixed to their SM predictions. The corresponding expected contours are shown by the inner and outer shaded regions The SM prediction is indicated by the star, while the best-fit value is denoted by the cross.

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 2. 3he lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

The expected number of events (estimated by using simulation) from the SM HH signals and single Higgs boson production, and the expected number of events from the continuum background, evaluated in the 120 GeV < $m_{\gamma\gamma}$ < 130 GeV window in Run 3 for the different low-mass (LM) and high-mass (HM) categories. For comparison, the number of data events is also shown. The uncertainties in the HH signals and single Higgs boson backgrounds include the systematic uncertainties discussed in Section 6. Asymmetric uncertainties arise primarily from the theory calculation of the SM ggF HH cross section and the large uncertainty in the yield of single Higgs bosons produced in ggF events in association with heavy-flavour jets. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious signal uncertainty.

Data from Figure 2, panel d, upper panel, $v_{0}(p_{T})$ for $\eta_{gap}$ = 1, 0-5% Centrality

Data from Figure 2, panel d, lower panel, $v_{0}(p_{T})$ for $\eta_{gap}$ = 1, 50-60% Centrality

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=0

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=1

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=2

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=3

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=0

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=1

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=2

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=3

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 5-10% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 10-20% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 20-30% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 30-40% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 40-50% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 50-60% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 60-70% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 5-10% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 10-20% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 20-30% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 30-40% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 40-50% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 50-60% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 60-70% Centrality

Data from Figure 5, panel a, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Figure 5, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Appendix, Figure 6, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 0-5% Centrality

Data from Appendix, Figure 6, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 10-20% Centrality

Data from Appendix, Figure 6, panel c, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 30-40% Centrality

Data from Appendix, Figure 6, panel d, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 60-70% Centrality

Data from Appendix, Figure 7, Zero Crossing Point of $v_{0}(p_{T})$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 7, $\left\langle [p_{T}]\right\rangle$ for $p_{T}$ range of 0.5-10 GeV, $\eta_{gap}$ = 1

Data from Appendix, Figure 8, panel a, Closure of Sum Rule 1 for $\eta_{gap}$ = 1

Data from Appendix, Figure 8, panel b, Closure of Sum Rule 2 for $\eta_{gap}$ = 1

Data from Appendix, Figure 9, panel a, $v_{0}$ vs $N_{ch}$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 9, panel b, $v_{0} / v^{5\%}_{0}$ vs $N_{ch}$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 10, panel a, $v_{0}\sqrt{N_{ch}}$ vs $N_{ch}$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 10, panel b, $v_{0}\sqrt{N_{ch}}$ vs Centrality for $\eta_{gap}$ = 1

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 0-5% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 5-10% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 10-20% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 20-30% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 30-40% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 40-50% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 50-60% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 60-70% Centrality

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 0-5% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 5-10% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 10-20% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 20-30% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 30-40% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 40-50% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 50-60% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 60-70% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 0-5% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 5-10% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 10-20% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 20-30% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 30-40% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 40-50% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 50-60% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 60-70% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 0-5% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 5-10% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 10-20% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 20-30% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 30-40% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 40-50% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 50-60% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 60-70% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 0-5% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 5-10% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 10-20% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 20-30% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 30-40% Centrality

Data from Auxiliary Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 40-50% Centrality

Data from Auxiliary Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 50-60% Centrality

Data from Auxiliary Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 60-70% Centrality

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-ej and (c, d) SR-2L-ej of the e+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_de) = (2.0&nbsp;TeV, 1.0) and S&#771;₁ (m, y_de) = (3.0&nbsp;TeV, 1.0), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Exclusion limits for minimal models of S&#771;₁ production with only y_de being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the e+light-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_de plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13]. Constraints from weak charge measurements of protons and nuclei on y_de couplings derived by Ref. [10] are shown as light magenta line.

Exclusion limits for minimal models of S&#771;₁ production with only y_s&mu; being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the &mu;+light-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_s&mu; plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13].

Exclusion limits for minimal models of S&#771;₁ production with only y_be being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the e+b-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_be plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13].

Exclusion limits for minimal models of S&#771;₁ production with only y_b&mu; being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the &mu;+b-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_b&mu; plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13].

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Event selection cutflows of SR-1L-$eb$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{be} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$eb$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{be} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-1L-$ej$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{de} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$ej$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{de} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-1L-$\mu b$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{b\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$\mu b$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{b\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-1L-$\mu j$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{s\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$\mu j$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{s\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Observed 95% $\text{CL}_\text{S}$ upper limits on the production cross-section times acceptance times branching ratio to jets, $\sigma \cdot A \cdot \text{BR}$, of Gaussian-shaped signals of 5%, 10%, and 15% width relative to their peak mass, $m_G$. Also included are the corresponding expected upper limits predicted for the case the $m_{jj}$ distribution is observed to be identical to the background prediction in each bin and the $1\sigma$ and $2\sigma$ envelopes of outcomes expected for Poisson fluctuations around the background expectation. Limits are derived from the J100 signal region.

Observed 95% $\text{CL}_\text{S}$ upper limits on the the value of the coupling to quarks, $g_q$, of the $Z^\prime$ model as a function of the resonance mass $m_{Z^\prime}$. Also included are the corresponding expected upper limits predicted for the case the $m_{jj}$ distribution is observed to be identical to the background prediction in each bin and the $1\sigma$ and $2\sigma$ envelopes of outcomes expected for Poisson fluctuations around the background expectation. Limits are derived from the J50 signal region.

Observed 95% $\text{CL}_\text{S}$ upper limits on the the value of the coupling to quarks, $g_q$, of the $Z^\prime$ model as a function of the resonance mass $m_{Z^\prime}$. Also included are the corresponding expected upper limits predicted for the case the $m_{jj}$ distribution is observed to be identical to the background prediction in each bin and the $1\sigma$ and $2\sigma$ envelopes of outcomes expected for Poisson fluctuations around the background expectation. Limits are derived from the J100 signal region.

Acceptance of simulated $Z^\prime$ signal events given the analysis event selection as a function of $m_{Z^\prime}$. The solid black line corresponds to the acceptance without a lower $m_{jj}$ requirement applied, the solid blue line includes the $m_{jj} > 344$ GeV requirement of the J50 signal region, and the solid red line includes the $m_{jj} > 481$ GeV requirement of the J100 signal region.

Numbers of events in each of the two considered datasets after consecutively applying the analysis selections.