Pre-fit MC statistical uncertainties per bin. These values are used to quantify the MC statistical uncertainty contributions to the total uncertainty in $m_t$ based on elements from the covariance matrix obtained in the profile likelihood fit. Each MC statistical contribution is estimated by dividing the relevant covariance matrix entry (see Table 2) by the corresponding pre-fit MC statistical uncertainty for that bin.

The contribution of each source of systematic uncertainty to the total uncertainty in $m_t$ is provided. Each source's contribution is taken directly from the relevant element of the covariance matrix for the profile likelihood fit to $\overline{m_J}$, $m_{jj}$, and $m_{tj}$ using data. The sign of the effect of each uncertainty is included.

The fitted $m_t$ is provided for each replica generated using bootstrapping. Each replica is a 3D histogram of $m_{J}$, $m_{jj}$, and $m_{tj}$, where $m_{J}$ has 50 bins between 145.0 GeV and 205.0 GeV. This method uses the BootstrapGenerator class in ROOT to initialise a pseudo-random number generator for each event, which is seeded by the value of the eventNumber and runNumber variables for a given event in the input dataset. The pseudo-random numbers are drawn from a Poisson distribution with rate parameter equal to 1. These are used to fluctuate the amount by which each replica histogram is filled. There are 1000 replicas, numbered from 0 to 999.

The observed and expected ($\pm2\sigma$) upper limits at 95% CL on the branching ratio for $H\rightarrow aa\rightarrow \gamma\gamma\tau\tau$ as a function of the resonance mass hypothesis $m_{a}$.

The polynomial parameterisation of the total signal selection efficiency as a function of $m_{a}$, defined as the fraction of the reconstructed and selected events out of the total number of events in the signal simulated samples.

Product of acceptance and efficiency for pp->tbH(->Wh) as function of the charged Higgs boson mass for the resolved qqbb signal region.

Product of acceptance and efficiency for pp->tbH(->Wh) as function of the charged Higgs boson mass for the resolved lvbb signal region.

Product of acceptance and efficiency for pp->tbH(->Wh) as function of the charged Higgs boson mass for the merged lvbb signal region

Product of acceptance and efficiency for pp->tbH(->Wh) as function of the charged Higgs boson mass for the merged qqbb signal region

Distributions of the mWh observable in the low-purity signal regions of the resolved qqbb 5jex3bex event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the low-purity signal regions of the resolved qqbb 5jex4bin event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the low-purity signal regions of the resolved qqbb 6jin3bex event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the low-purity signal regions of the resolved qqbb 6jin4bin event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the high-purity signal regions of the resolved qqbb 5jex3bex event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the high-purity signal regions of the resolved qqbb 5jex4bin event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the high-purity signal regions of the resolved qqbb 6jin3bex event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the high-purity signal regions of the resolved qqbb 6jin4bin event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the signal regions of the resolved lvbb 5jex3bex event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the signal regions of the resolved lvbb 5jex4bin event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the signal regions of the resolved lvbb 6jin3bex event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

Distributions of the mWh observable in the signal regions of the resolved lvbb 6jin4bin event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The distributions are presented after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contribution assuming $m_{H^{\pm}}$ = 700 GeV, normalised to the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) of 0.064 pb, is shown as a dashed histogram.

The mWh distributions in the Low-NN-score SRs of the merged qqbb 0b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the Low-NN-score SRs of the merged qqbb 1b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the High-NN-score SRs of the merged qqbb 0b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the High-NN-score SRs of the merged qqbb 1b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the Low-NN-score SRs of the merged lvbb 0b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the Low-NN-score SRs of the merged lvbb 1b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the Medium-NN-score SRs of the merged lvbb 0b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the Medium-NN-score SRs of the merged lvbb 1b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the High-NN-score SRs of the merged lvbb 0b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

The mWh distributions in the High-NN-score SRs of the merged lvbb 1b event categories. The term ‘Others’ summarises events from tHjb, tWh, tttt, and SM Vh production. The background prediction is shown after a background-only maximum-likelihood fit to data. The individual background uncertainty does not take into account the possible correlations between the nuisance parameter. The expected signal contributions assuming $m_{H^{\pm}}$ = 900 GeV and $m_{H^{\pm}}$ = 2000 GeV, normalised the expected limit of the cross-section times branching ratio ($\sigma_{sig}$ × B) values of 0.036 pb and 2.7 fb respectively, are shown as dashed histograms.

Cutflow table for a few selected signal samples after applying the selection requirements of the resolved analysis. At the initial cut stage, all events from the tbH ± → W ± h(→ b b) production process (including those from the fully hadronic decay channel) are considered.

Cutflow table for a few selected signal samples after applying the selection requirements of the merged analysis. At the initial cut stage, all events from the tbH ± → W ± h(→ bb) production process (including those from the fully hadronic decay channel) are considered.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM1.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM1.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM1.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM1.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM1.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM1.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM2.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM2.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM2.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM2.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM2.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM2.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM3.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM3.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM3.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM3.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM3.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-HM3.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp1.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp1.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp1.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp1.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp1.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp1.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp2.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp2.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp2.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp2.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp2.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp2.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp3.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp3.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp3.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp3.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp3.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp3.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp-1c.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to charm quarks and neutralinos when only considering SR-Comp-1c.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to top or charm quarks and 1 GeV neutralinos for different branching fraction assumptions.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 1 GeV neutralinos for different branching fraction assumptions.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 1 GeV neutralinos for different branching fraction assumptions.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to top or charm quarks and 1 GeV neutralinos for different branching fraction assumptions.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 1 GeV neutralinos for different branching fraction assumptions.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 1 GeV neutralinos for different branching fraction assumptions.

Expected exclusion limit at 95% CL for pair production of top squarks decaying to top or charm quarks and 200 GeV neutralinos for different branching fraction assumptions.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 200 GeV neutralinos for different branching fraction assumptions.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 200 GeV neutralinos for different branching fraction assumptions.

Observed exclusion limit at 95% CL for pair production of top squarks decaying to top or charm quarks and 200 GeV neutralinos for different branching fraction assumptions.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 200 GeV neutralinos for different branching fraction assumptions.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of top squarks decaying to top or charm quarks and 200 GeV neutralinos for different branching fraction assumptions.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Expected exclusion limit at 95% CL for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Observed exclusion limit at 95% CL for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Expected exclusion limit $(-1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Expected exclusion limit $(+1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Expected exclusion limit at 95% CL for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Observed exclusion limit $(+1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Observed exclusion limit $(-1\sigma)$ at 95% CL for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Observed exclusion limit at 95% CL for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Observed upper limit on the cross-section at 95% CL for pair production of top squarks decaying into charm quarks and neutralinos for the fit with the best expected CL$_\mathrm{S}$.

Observed upper limit on the cross-section for pair production of up-type scalar LQs coupled to first generation leptons and second generation quarks.

Observed upper limit on the cross-section for pair production of up-type scalar LQs coupled to second generation leptons and quarks.

Expected upper limit on the cross-section $(-1\sigma)$ for $U(1)$ vector LQ models in the minimal coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,min}_{23}\rightarrow c\nu) = 0.5$.

Expected upper limit on the cross-section $(+1\sigma)$ for $U(1)$ vector LQ models in the minimal coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,min}_{23}\rightarrow c\nu) = 0.5$.

Expected upper limit on the cross-section for $U(1)$ vector LQ models in the minimal coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,min}_{23}\rightarrow c\nu) = 0.5$.

Observed upper limit on the cross-section for $U(1)$ vector LQ models in the minimal coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,min}_{23}\rightarrow c\nu) = 0.5$.

Expected upper limit on the cross-section $(-1\sigma)$ for $U(1)$ vector LQ models in the Yang-Mills coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,YM}_{23}\rightarrow c\nu) = 0.5$.

Expected upper limit on the cross-section $(+1\sigma)$ for $U(1)$ vector LQ models in the Yang-Mills coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,YM}_{23}\rightarrow c\nu) = 0.5$.

Expected upper limit on the cross-section for $U(1)$ vector LQ models in the Yang-Mills coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,YM}_{23}\rightarrow c\nu) = 0.5$.

Observed upper limit on the cross-section for $U(1)$ vector LQ models in the Yang-Mills coupling scenario with $\beta_{23}=1$, corresponding to a branching fraction $\mathcal{B}(\mathrm{vLQ}^\mathrm{u,YM}_{23}\rightarrow c\nu) = 0.5$.

Distribution of $R_{\mathrm{ISR}}$ in SR-Comp-1c showing the data and the expected backgrounds, after simultaneously fitting all the control regions. The pre-fit yields for a representative $m(\tilde{t}_1,\tilde{\chi}_1^0)=(450,430)$ GeV signal is included. Processes with top quarks and multiple vector bosons are included in "Other". The right-most bin does not include the overflow entry but the $x$-axis range is chosen to include all observed data.

Distribution of $E_\mathrm{T}^\mathrm{miss}\mathrm{ Sig.}$ in SR-HM1 showing the data and the expected backgrounds, after simultaneously fitting all the control regions. The pre-fit yields for a representative $m(\tilde{t}_1,\tilde{\chi}_1^0)=(1000,1)$ GeV signal is included. Processes with top quarks and multiple vector bosons are included in "Other". The right-most bin does not include the overflow entry but the $x$-axis range is chosen to include all observed data.

Distribution of $m_{\mathrm{T}}^{\mathrm{min}}(c)$ in SR-HM2 showing the data and the expected backgrounds, after simultaneously fitting all the control regions. The pre-fit yields for a representative $m(\tilde{t}_1,\tilde{\chi}_1^0)=(750,450)$ GeV signal is included. Processes with top quarks and multiple vector bosons are included in "Other". The right-most bin does not include the overflow entry but the $x$-axis range is chosen to include all observed data.

Distribution of $R_{\mathrm{ISR}}$ in SR-Comp1 showing the data and the expected backgrounds, after simultaneously fitting all the control regions. The pre-fit yields for a representative $m(\tilde{t}_1,\tilde{\chi}_1^0)=(600,550)$ GeV signal is included. Processes with top quarks and multiple vector bosons are included in "Other". The right-most bin does not include the overflow entry but the $x$-axis range is chosen to include all observed data.

Distribution of $R_{\mathrm{ISR}}$ in SR-Comp2 showing the data and the expected backgrounds, after simultaneously fitting all the control regions. The pre-fit yields for a representative $m(\tilde{t}_1,\tilde{\chi}_1^0)=(550,470)$ GeV signal is included. Processes with top quarks and multiple vector bosons are included in "Other". The right-most bin does not include the overflow entry but the $x$-axis range is chosen to include all observed data.

Distribution of $R_{\mathrm{ISR}}$ in SR-Comp3 showing the data and the expected backgrounds, after simultaneously fitting all the control regions. The pre-fit yields for a representative $m(\tilde{t}_1,\tilde{\chi}_1^0)=(550,375)$ GeV signal is included. Processes with top quarks and multiple vector bosons are included in "Other". The right-most bin does not include the overflow entry but the $x$-axis range is chosen to include all observed data.

Acceptance across the SUSY signal grid for SR-Comp-1c.

Acceptance across the SUSY signal grid for SR-HM1.

Acceptance across the SUSY signal grid for SR-HM2.

Acceptance across the SUSY signal grid for SR-HM3.

Acceptance across the SUSY signal grid for SR-HM-Disc.

Acceptance across the SUSY signal grid for SR-Comp1.

Acceptance across the SUSY signal grid for SR-Comp2.

Acceptance across the SUSY signal grid for SR-Comp3.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM1.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM2.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM3.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM-Disc.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM1.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM2.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM3.

Acceptance across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM-Disc.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM1.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM2.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM3.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM-Disc.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM1.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM2.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM3.

Acceptance for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM-Disc.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM1. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM2. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM3. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM-Disc. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM1. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM2. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM3. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM-Disc. Due to the limited statistical power of the signal samples, efficiencies can fluctuate and can drop to zero.

Acceptance times efficiency across the SUSY signal grid for SR-Comp-1c.

Acceptance times efficiency across the SUSY signal grid for SR-HM1.

Acceptance times efficiency across the SUSY signal grid for SR-HM2.

Acceptance times efficiency across the SUSY signal grid for SR-HM3.

Acceptance times efficiency across the SUSY signal grid for SR-HM-Disc.

Acceptance times efficiency across the SUSY signal grid for SR-Comp1.

Acceptance times efficiency across the SUSY signal grid for SR-Comp2.

Acceptance times efficiency across the SUSY signal grid for SR-Comp3.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM1.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM2.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM3.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{21}$ signal grid for SR-HM-Disc.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM1.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM2.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM3.

Acceptance times efficiency across the $\mathrm{LQ}^\mathrm{u}_{22}$ signal grid for SR-HM-Disc.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM1.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM2.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM3.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,min}_{23}$ signal model in SR-HM-Disc.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM1.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM2.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM3.

Acceptance times efficiency for the $\mathrm{vLQ}^\mathrm{u,YM}_{23}$ signal model in SR-HM-Disc.

Cutflow table for SR-Comp-1c using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (450,430)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-Comp1 using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (600,550)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-Comp2 using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (550,470)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-Comp3 using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (550,375)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM1 using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (1000,1)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM2 using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (750,450)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM3 using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (750,450)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM-Disc using the $m(\tilde{t}_{1},\tilde{\chi}_{1}^{0}) = (750,450)$ GeV signal point as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM1 using the $m(\mathrm{LQ}_{21}^\mathrm{u}) = 600$ GeV signal point, with a 50% branching fraction to $c\nu$, as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM2 using the $m(\mathrm{LQ}_{21}^\mathrm{u}) = 600$ GeV signal point, with a 50% branching fraction to $c\nu$, as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM3 using the $m(\mathrm{LQ}_{21}^\mathrm{u}) = 600$ GeV signal point, with a 50% branching fraction to $c\nu$, as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Cutflow table for SR-HM-Disc using the $m(\mathrm{LQ}_{21}^\mathrm{u}) = 600$ GeV signal point, with a 50% branching fraction to $c\nu$, as the benchmark. All jet-related variables use jets with $p_\mathrm{T}>40$ GeV, however, the $E_\mathrm{T}^\mathrm{miss}$ is calculated with jets with $p_\mathrm{T}>20$ GeV. The "Preselection" requirement includes: $E_\mathrm{T}^\mathrm{miss}>250$ GeV; events containing electrons or muons are vetoed; at least two jets must be present of which at least one must be identified as a $c$-tagged jet; events with jets that have been identified as $b$-tagged are discarded; the minimum azimuthal angular ($\phi$) distance between the (up to) four leading jets and the $\vec{p}_\mathrm{T}^\mathrm{miss}$ is required to be greater than 0.4.

Post-fit $m_\mathrm{MMC}$ distribution in the high-mass SR for the $m_A = 90\,\mathrm{GeV}$ signal mass hypothesis. $m_\mathrm{MMC}$ is the mass reconstructed by the Missing Mass Calculator. Processes contributing to the background Others are $Z/\gamma^* \rightarrow ee/\mu\mu$ and SM Higgs. The subscript on the $A\to\tau\tau$ process indicates the mass of the $A$ boson. otal includes all backgrounds and the signal process. The high-mass Signal Region is defined as: - 1 electron and 1 muon with opposite charge - $p_\mathrm{T}$ requirements of the leptons are a combination of the following: - $p_\mathrm{T}^e > 18\,\mathrm{GeV}$ and $p_\mathrm{T}^\mu > 15\,\mathrm{GeV}$ or - $p_\mathrm{T}^e > 10\,\mathrm{GeV}$ and $p_\mathrm{T}^\mu > 25\,\mathrm{GeV}$ or - $p_\mathrm{T}^e > 27\,\mathrm{GeV}$ and $p_\mathrm{T}^\mu > 10\,\mathrm{GeV}$ - $\vert \eta_e \vert < 2.47$, excluding $1.37 < \vert \eta_e \vert < 1.52$ - $\vert \eta_\mu \vert < 2.7$ - no jets with $b$-quarks - $\Delta R_{\ell\ell} < 1.0$ - $E_\mathrm{T}^\mathrm{miss} > 30\,\mathrm{GeV}$ - $m_\mathrm{T}^\mathrm{tot} = \sqrt{\left(p_\mathrm{T}^e+p_\mathrm{T}^\mu+E_\mathrm{T}^\mathrm{miss}\right)^2-\left(\vec{p}_\mathrm{T}^{\,e}+\vec{p}_\mathrm{T}^{\,\mu}+\vec{E}_\mathrm{T}^{\,\mathrm{miss}}\right)^2} < 65\,\mathrm{GeV}$ - $35\,\mathrm{GeV} < m_\mathrm{MMC} < 130\,\mathrm{GeV}$

Expected and observed $95\%$ CL limits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses ranging from $20$ to $90\,\mathrm{GeV}$.

Expected and observed $95\%$ CL limits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses ranging from $20$ to $90\,\mathrm{GeV}$.

Expected and observed $95\%$ CL limits on the absolute value of the up-type quark coupling parameter $|\zeta_\mathrm{u}|$ for masses of the $A$ boson ranging from $20$ to $90\,\mathrm{GeV}$.

Expected and observed $95\%$ CL limits on the absolute value of the up-type quark coupling parameter $|\zeta_\mathrm{u}|$ for masses of the $A$ boson ranging from $20$ to $90\,\mathrm{GeV}$.

Observed $p_0$ values for the fits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses from $20$ to $90\,\mathrm{GeV}$. For the fit on the up-type quark coupling parameter the $p_0$ values agree up to a precision of $O(10^{-5})$.

Observed $p_0$ values for the fits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses from $20$ to $90\,\mathrm{GeV}$. For the fit on the up-type quark coupling parameter the $p_0$ values agree up to a precision of $O(10^{-5})$.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the compressed SUSY simplified model with a bino-like LSP and wino-like NLSPs being considered. These are shown with $\pm1\sigma_\text{exp}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm1\sigma^{\text{SUSY}}_{\text{theory}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the ATLAS searches using the soft lepton signature is illustrated by the blue region while the limit imposed by the LEP experiments is shown in grey.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the compressed SUSY simplified model with a bino-like LSP and wino-like NLSPs being considered. These are shown with $\pm1\sigma_ ext{exp}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm1\sigma^{ ext{SUSY}}_{ ext{theory}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the ATLAS searches using the soft lepton signature is illustrated by the blue region while the limit imposed by the LEP experiments is shown in grey.

The expected upper limits on the cross-sections at 95% CL for the simplified SUSY model featuring the production of mass-degenerate pairs of wino-like $\widetilde{\chi}_{2}^{0}$ and $\widetilde{\chi}_{1}^{\pm}$ particles in association with at least two jets. The production modes considered are $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$.

The observed upper limits on the cross-sections at 95% CL for the simplified SUSY model featuring the production of mass-degenerate pairs of wino-like $\widetilde{\chi}_{2}^{0}$ and $\widetilde{\chi}_{1}^{\pm}$ particles in association with at least two jets. The production modes considered are $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$.

Truth-level signal acceptances in $\text{SR}_\text{2j}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. 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. The generator-level selections are the same as the ones at reconstruction level.

Truth-level signal acceptances in $\text{SR}_{\geq\text{3j}}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. 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. The generator-level selections are the same as the ones at reconstruction level.

Signal efficiencies in $\text{SR}_\text{2j}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. The efficiency is defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level. The generator-level selections are the same as the ones at reconstruction level.

Signal efficiencies in $\text{SR}_{\geq\text{3j}}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. The efficiency is defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level. The generator-level selections are the same as the ones at reconstruction level.

Event selection cutflows for signal samples with $m(\tilde\chi^0_2) =100~\text{GeV}$ and $\Delta m(\tilde\chi^0_2 / \tilde\chi^\pm_1,\tilde\chi^0_1) = 0.2~\text{GeV}, 1.0~\text{GeV},$ and $5.0~\text{GeV}$. The first number in the column for each mass point 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. All of the production modes are included. The total cross-section used to obtain the initial number of events ($\sigma$) includes the cuts applied at parton level as described in the main text.

Distributions of the $\mathrm{NN^{SvsB}}$ output for data and the expected background after the likelihood fit in the $SR_{cQu --}$ signal region. The post-fit background expectations are shown as filled histograms, the combined pre-fit background expectations are shown as dashed lines. The signal distribution using the Wilson coefficient values $c_{tu}^{(1)}=0.04$, $c_{Qu}^{(1)}=0.1$, $c_{Qu}^{(8)}=0.1$ is shown with a dotted line, normalized to the same number of events as the background.

Summary of the observed and predicted number of events in the five 2$\ell$ control regions. The background prediction is shown after the combined likelihood fit to data under the signal-plus-background hypothesis across all control regions and signal regions. The uncertainties in the total yields are smaller than the sum in quadrature of the uncertainties in the individual contributions due to anti-correlations resulting from the likelihood fit. Int Conv refers to events with photon conversions in the inner detector, while Mat Conv refers to events with photon conversions in the detector material. QMisID refers to events with charge-misidentified lepton.

Summary of the observed and predicted number of events in the four 3$\ell$ control regions. The background prediction is shown after the combined likelihood fit to data under the signal-plus-background hypothesis across all control regions and signal regions. The uncertainties in the total yields are smaller than the sum in quadrature of the uncertainties in the individual contributions due to anti-correlations resulting from the likelihood fit. Int Conv refers to events with photon conversions in the inner detector, while Mat Conv refers to events with photon conversions in the detector material. QMisID refers to events with charge-misidentified lepton.

Comparison of the observed limits on the three WCs to the limit obtained by the ATLAS Run~1 analysis of events with $b$-tagged jets and a same-sign lepton pair [JHEP10(2015)150]. The cross-section limits from that analysis are converted to the limits on the WCs by using the fitted EFT parametrization. The bounds on the WCs are shown at the 68$\%$ CL (solid) and/or 95$\%$ CL (dashed) levels. For the ATLAS Run~1 analysis only the bounds at 95$\%$ CL are available. The vertical bar represents the SM prediction. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$.

Observed lower limits at 95$\%$ confidence level on the scale of new physics $\Lambda$ for Wilson coefficient values of 0.01, 1 and $4\pi^2$. The limits are compared with the limits obtained by the ATLAS Run~1 analysis of events with $b$-tagged jets and a same-sign lepton pair.

Observed and expected limits at 95$\%$ confidence level on the cross-section of $\sigma(pp \rightarrow tt)$.

2D likelihood scans of the Wilson coefficients $c_{tu}^{(1)}$ versus $c_{Qu}^{(1)}$. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient pair.

2D likelihood scans of the Wilson coefficients $c_{tu}^{(1)}$ versus $c_{Qu}^{(8)}$. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient pair.

2D likelihood scans of the Wilson coefficients $c_{Qu}^{(1)}$ versus $c_{Qu}^{(8)}$. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient pair.

1D-likelihood scan for $c_{tu}^{(1)}$. The observed and expected limits at 68\% and 95\% confidence level (CL) can be read off by finding the intersection of the likelihood scan curve with the 1$\sigma$ and 2$\sigma$ lines. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient value.

1D-likelihood scan for $c_{Qu}^{(1)}$. The observed and expected limits at 68\% and 95\% confidence level (CL) can be read off by finding the intersection of the likelihood scan curve with the 1$\sigma$ and 2$\sigma$ lines. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient value.

1D-likelihood scan for $c_{Qu}^{(8)}$. The observed and expected limits at 68\% and 95\% confidence level (CL) can be read off by finding the intersection of the likelihood scan curve with the 1$\sigma$ and 2$\sigma$ lines. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient value.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $b$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{4})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\text{min}\Delta R(bb)$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at leastfour $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{4})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

The measured normalised differential cross-section as a function of $N_{b-\text{jets}}$ in the $e\mu+\geq2b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{4})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{4})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\text{min}\Delta R(bb)$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least two b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{4})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{4})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\text{min}\Delta R(bb)$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a selection of models targeted in the CalR+W channel for (a) WALP models and (b) WHS models with a SM Higgs boson mediator.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a selection of models targeted in the CalR+W channel for (a) WALP models and (b) WHS models with a SM Higgs boson mediator.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a variety of models probed by the CalR+Z channel for (a) ZALP models, (b) ZHS models with the SM Higgs boson as a mediator, and (c) HZZ_d models.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a variety of models probed by the CalR+Z channel for (a) ZALP models, (b) ZHS models with the SM Higgs boson as a mediator, and (c) HZZ_d models.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a variety of models probed by the CalR+Z channel for (a) ZALP models, (b) ZHS models with the SM Higgs boson as a mediator, and (c) HZZ_d models.

Summary of the observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on C_G as a function of C_W for a variety of ALP mass hypotheses probed by the CalR+R and Cal+Z channels for (a) WALP and (b) ZALP models.

Summary of the observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on C_G as a function of C_W for a variety of ALP mass hypotheses probed by the CalR+R and Cal+Z channels for (a) WALP and (b) ZALP models.

Regions in the ALP mass versus its coupling to gluons scaled by the ALP energy scale g_agg=4C_G/f_a excluded at 95&percnt; CL, for C_W=1, C_W=0.5, C_W=0.3 and C_W=0.15, in the (a) WALP and (b) ZALP models. The contours are obtained by interpolating between the exclusions for different LLP masses considered in the analysis, taking the logical union of the regions excluded for each mass.

Regions in the ALP mass versus its coupling to gluons scaled by the ALP energy scale g_agg=4C_G/f_a excluded at 95&percnt; CL, for C_W=1, C_W=0.5, C_W=0.3 and C_W=0.15, in the (a) WALP and (b) ZALP models. The contours are obtained by interpolating between the exclusions for different LLP masses considered in the analysis, taking the logical union of the regions excluded for each mass.

Signal efficiencies as a function of c&tau; for HS signals in the CalR+2J channel for samples with mediator masses (a) below 400 GeV and (b) equal to or above 400 GeV. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for HS signals in the CalR+2J channel for samples with mediator masses (a) below 400 GeV and (b) equal to or above 400 GeV. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for WALP and WHS signals in the CalR+W channel for (a) the WALP selection, (b) the CalR+W low-E_T selection and (c) the CalR+W high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for WALP and WHS signals in the CalR+W channel for (a) the WALP selection, (b) the CalR+W low-E_T selection and (c) the CalR+W high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for WALP and WHS signals in the CalR+W channel for (a) the WALP selection, (b) the CalR+W low-E_T selection and (c) the CalR+W high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (125, 16), (b) (600, 150) GeV, and the WALP selection for WALP models with (c) m_ALP = 1 GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (125, 16), (b) (600, 150) GeV, and the WALP selection for WALP models with (c) m_ALP = 1 GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (125, 16), (b) (600, 150) GeV, and the WALP selection for WALP models with (c) m_ALP = 1 GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

Distribution of the BDT output score in the the 3l channel signal region.

Distribution of the BDT output score in the 2lSC channel signal region.

Distribution of the BDT output score in the 2lSC+$\tau_{had}$ channel signal region.

Distribution of the BDT output score in the the 2l+2$\tau_{had}$ channel signal region.

Distribution of the BDT output score in the l+2$\tau_{had}$ channel signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+2(l,$\tau_{had}$) channel signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$e/\mu$ channel in the loose BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$e/\mu$ channel in the medium BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$e/\mu$ channel in the tight BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$\tau$ channel in the loose BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$\tau$ channel in the medium BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$\tau$ channel in the tight BDT signal region.

Observed and expected 95% CL upper limits on the signal strength for the ML channels and the $\gamma\gamma$+ML channels.

Expected values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.