Normalisation factors for the major background processes and ttW ($\Delta \eta < 0$) from the fit using only statistical uncertainties (fixing Nuisance Parameters (NP) to their fitted values) to data in all analysis regions.

Normalisation factors for the major background processes and ttW ($\Delta \eta < 0$) from the fit using using both statistical and systematic uncertainties to data in all analysis regions.

Correlation matrix between the Normalisation Factors in the fit using only statistical uncertainties (fixing Nuisance Parameters (NP) to their fitted values) to data in all analysis regions.

The most relevant systematic uncertainties ranked by their impact on the leptonic charge asymmetry ($A_c^{\ell}$) parameter at reconstructed level. The impact of the uncertainties is shown before and after the combined profile-likelihood fit to data in the signal and control regions. Pulls introduced by the fitting procedure are also shown. The entries shown in bold are the uncertainties of the freely floating background normalisations. ME stands for "matrix element", PS for "parton shower" and JER for "jet energy resolution". The gamma-uncertainties refer to the MC statistical uncertainties in a specific region and bin.

The most relevant systematic uncertainties ranked by their impact on the ttW Normalisation Factor ($\Delta \eta < 0$) parameter at reconstructed level. The impact of the uncertainties is shown before and after the combined profile-likelihood fit to data in the signal and control regions. Pulls introduced by the fitting procedure are also shown. ME stands for "matrix element", PS for "parton shower" and JER for "jet energy resolution". The gamma-uncertainties refer to the MC statistical uncertainties in a specific region and bin.

The predicted and observed numbers of events in the control and signal regions. The predictions are shown before the fit to data. The indicated uncertainties consider statistical as well as all experimental and theoretical systematic uncertainties. Background categories with event yields with a value of 1.0e-6 have a negligible contribution to the region and are manually set to that value.

The predicted and observed numbers of events in the control and signal regions. The predictions are shown after the fit to data. The indicated uncertainties consider statistical as well as all experimental and theoretical systematic uncertainties. Background categories with event yields with a value of 1.0e-6 have a negligible contribution to the region and are manually set to that value.

Measured differential cross section as a function of observable $ \Delta R (l,l)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ N_{jets}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{jj}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ H_{T}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \Delta \phi (Jet,\gamma)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \phi_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \cos \theta_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$ (Fig. 8 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ H_{T}$ (Fig. 8 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta \phi (Jet,\gamma)$ (Fig. 8 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta R (l,l)$ (Fig. 5 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma}$ (Fig. 5 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} < 200 GeV$ (Fig. 11 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } 200 GeV < p_{T}^{ll} + p_{T}^{\gamma} < 300 GeV$ (Fig. 11 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} > 300 GeV$ (Fig. 11 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{jj}$ (Fig. 7 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{ll\gamma j}$ (Fig. 7 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ N_{jets}$ (Fig. 6 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet1}$ (Fig. 6 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}$ (Fig. 6 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$ (Fig. 6 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll}$ (Fig. 5 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j}$ (Fig. 8 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} < 50 GeV$ (Fig. 12 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } 50 GeV < p_{T}^{ll\gamma} < 75 GeV$ (Fig. 12 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} > 75 GeV$ (Fig. 12 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 125 GeV < m_{ll\gamma} < 200 GeV$ (Fig. 10 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 200 GeV < m_{ll\gamma} < 300 GeV$ (Fig. 10 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } m_{ll\gamma} > 300 GeV$ (Fig. 10 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} + p_{T}^{\gamma}$ (Fig. 5 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \cos \theta_{CS}$ (Fig. 9 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \phi_{CS}$ (Fig. 9 (a))

$p_{\rm T}$-differential density of $\Xi^{\pm}$ in jets and the underlying event in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential density of inclusive $\Omega^{\pm}$ in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential density of $\Omega^{\pm}$ in jets and the underlying event in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential $(\Lambda + \overline{\Lambda})/2{\rm K}_{\rm S}^{0}$ ratio of inclusive particles in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential $(\Lambda + \overline{\Lambda})/2{\rm K}_{\rm S}^{0}$ ratio in jets and the underlying event in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ and $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratios of inclusive particles in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ and $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratios in jets and the underlying event in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential $(\Omega^{-} + \Omega^{+}) / 2{\rm K}_{\rm S}^{0}$, $(\Omega^{-} + \Omega^{+}) / (\Lambda + \overline{\Lambda})$ and $(\Omega^{-} + \Omega^{+}) / (\Xi^{-} + \Xi^{+})$ ratios of inclusive particles in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential $(\Omega^{-} + \Omega^{+}) / 2{\rm K}_{\rm S}^{0}$, $(\Omega^{-} + \Omega^{+}) / (\Lambda + \overline{\Lambda})$ and $(\Omega^{-} + \Omega^{+}) / (\Xi^{-} + \Xi^{+})$ ratios in jets and the underlying event in pp collisions at $\sqrt{s} = 13$ TeV.

$p_{\rm T}$-differential density of inclusive ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential densities of ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) in jets and the underlying event in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive $\Xi^{\pm}$ in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of $\Xi^{\pm}$ in jets and the underlying event in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive $\Omega^{\pm}$ in p-Pb collisions at $\sqrt{s} = 5.02$ TeV.

$p_{\rm T}$-differential density of $\Omega^{\pm}$ in jets and the underlying event in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Lambda + \overline{\Lambda})/2{\rm K}_{\rm S}^{0}$ ratio in jets for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ and $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratios in jets for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Omega^{-} + \Omega^{+}) / 2{\rm K}_{\rm S}^{0}$, $(\Omega^{-} + \Omega^{+}) / (\Lambda + \overline{\Lambda})$ and $(\Omega^{-} + \Omega^{+}) / (\Xi^{-} + \Xi^{+})$ ratios in jets in MB p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) for $0$-$10\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential densities of ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) in jets and the underlying event for $0$-$10\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive $\Xi^{\pm}$ for $0$-$10\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of $\Xi^{\pm}$ in jets and the underlying event for $0$-$10\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) for $10$-$40\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential densities of ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) in jets and the underlying event for $10$-$40\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive $\Xi^{\pm}$ for $10$-$40\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of $\Xi^{\pm}$ in jets and the underlying event for $10$-$40\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) for $40$-$100\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential densities of ${\rm K}_{\rm S}^{0}$ and $\Lambda$ ($\overline{\Lambda}$) in jets and the underlying event for $40$-$100\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of inclusive $\Xi^{\pm}$ for $40$-$100\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential density of $\Xi^{\pm}$ in jets and the underlying event for $40$-$100\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Lambda + \overline{\Lambda})/2{\rm K}_{\rm S}^{0}$ ratio of inclusive particles for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Lambda + \overline{\Lambda})/2{\rm K}_{\rm S}^{0}$ ratio in the underlying event for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Lambda + \overline{\Lambda})/2{\rm K}_{\rm S}^{0}$ ratio in jets for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ ratio of inclusive particles for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ ratio in the underlying event for $0$-$10\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ ratio in the underlying event for $10$-$40\%$ and $40$-$100\%$ event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / 2{\rm K}_{\rm S}^{0}$ ratio in jets for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratio of inclusive particles for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratio in the underlying event for $0$-$10\%$ event activity class in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratio in the underlying event for $10$-$40\%$ and $40$-$100\%$ event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

$p_{\rm T}$-differential $(\Xi^{-} + \Xi^{+}) / (\Lambda + \overline{\Lambda})$ ratio in jets for various event activity classes in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

Probability of finding at least one TAR jet, where the p_T-leading TAR jet passes the m_Wcand and D₂^&beta;=1 requirements, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=500 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1000 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1700 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied that do not pass the requirements of the merged category.

Observed exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected+1&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected-1&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected+2&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected-2&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=0.5 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=0.5 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=1 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=1.7 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=1.7 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=2.1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=2.1 TeV, shown in dashed blue.

Data overlaid on SM background yields stacked in each SR and CR category after the fit to data ('Post-fit'). The yields in the SR are broken down into their contributions to the individual bins. The maximum-likelihood estimators are set to the conditional values of the CR-only fit, and propagated to SR and CRs.

Dominant sources of uncertainty for three dark Higgs scenarios after the fit to data. The uncertainties are quantified in terms of their contribution to the fitted signal uncertainty that is expressed relative to the theory prediction. Three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01 and m_&chi;=200 GeV are considered, which are indicated using the (m_Z', m_s) format in units of GeV in the table columns.

Cumulative efficiencies in the merged category for three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01, m_Z' = 1 TeV, and m_&chi;=200 GeV considering s&rarr;W(&ell;&nu;)W(qq) decays only.

Cumulative efficiencies in the resolved category for three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01, m_Z' = 1 TeV, and m_&chi;=200 GeV considering s&rarr;W(&ell;&nu;)W(qq) decays only.

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={300, 350, 400, 500, 750} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={1000, 1700} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={2100, 2500, 2900, 3300} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Parametrization of the $A_{X}$ factor, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level, as function of $m_{X}$ obtained from the narrow width simulated signal samples produced in gluon fusion.

The correction factor, $C_{X}$, defined as the ratio between the number of reconstructed signal events passing the analysis cuts and the number of signal events at the particle level generated within the fiducial volume, and acceptance correction factor, $A_{X}$, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level. Both are computed for NWA spin-0 models as a function of $m_{X}$.

Effect of event selections on a scalar MC signal sample generated for $m_{X}$ = 15 GeV and on the data. For the MC sample, the efficiencies are shown after applying event weights and a truth level filter that requires two photons with $p^{\gamma\gamma}_{T}>40$ GeV; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts.

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of $m_{X}$ [GeV].

Pre-fit comparison between data and background in the baseline SR for two of the variables used as input for the BSM pBDT: HT.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR HF e.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR HF mu.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR Conv.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR ttW.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR lowBDT.

Data and post-fit background comparison obtained with the background-only fit in the validation region defined for ttW+jets events: number of jets.

Data and post-fit background comparison obtained with the background-only fit in the validation region defined for ttW+jets events: SM BDT.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 400 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 1000 GeV.

Observed (black solid line) and expected (black dashed line) 95% CL upper limits on the ttH/A cross-section times branching fraction of H/A->tt as a function of mH/A.

Observed (red line) and expected (black dashed line) exclusion regions at 95% CL in the tan(beta) versus mass plane assuming that both, a heavy scalar H and pseudo-scalar A, contribute to the tttt final state and have the same mass mH=mA.

Observed (red line) and expected (black dashed line) exclusion regions at 95% CL in the tan(beta) versus mass plane assuming that only the scalar H contributes to the tttt final state.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 500 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 600 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 700 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 800 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 900 GeV.

Observed (red line) and expected (black dashed line) exclusion regions at 95% CL in the tan(beta) versus mass plane assuming that only the pseudo-scalar A contributes to the tttt final state.

The product of acceptance and efficiency (A×ε), defined as the number of signal events satisfying the full set of selection criteria of the BSM SR, divided by the total number of generated signal events, for the tt̄H(→ tt̄ ) process in a type-II 2HDM for tanβ = 1 for the different heavy Higgs mass hypotheses.

Observed 95% CL limits on the production cross-section for scalar up-type LQs decaying into a top quark and a neutrino or a bottom quark and an electron.

Expected 95% CL limits on the production cross-section for scalar up-type LQs decaying into a top quark and a neutrino or a bottom quark and an electron.

Observed 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and a muon.

Expected 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and a muon.

Observed 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and an electron.

Expected 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and an electron.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Product of signal acceptance and efficiency in the training region for scalar up-type LQs decaying into top quarks and neutrinos or bottom quarks and muons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^u\rightarrow b\mu)$.

Product of signal acceptance and efficiency in the training region for scalar up-type LQs decaying into top quarks and neutrinos or bottom quarks and electrons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^u\rightarrow be)$.

Product of signal acceptance and efficiency in the training region for scalar down-type LQs decaying into top quarks and muons or bottom quarks and neutrinos depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^d\rightarrow t\mu)$.

Product of signal acceptance and efficiency in the training region for scalar down-type LQs decaying into top quarks and electrons or bottom quarks and neutrinos depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^d\rightarrow te)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the Yang-Mills coupling scenario decaying into top quarks and neutrinos or bottom quarks and muons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{YM}\rightarrow b\mu)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the Yang-Mills coupling scenario decaying into top quarks and neutrinos or bottom quarks and electrons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{YM}\rightarrow be)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the minimal coupling scenario decaying into top quarks and neutrinos or bottom quarks and muons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{min}\rightarrow b\mu)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the minimal coupling scenario decaying into top quarks and neutrinos or bottom quarks and electrons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{min}\rightarrow be)$.

Cut-flow for the four models containing scalar LQs at a mass of $m_{LQ}=1300$ GeV and $\mathcal{B}(LQ_{mix}\rightarrow q_3\ell)=0.5$.

Cut-flow for the four models containing vector LQs at a mass of $m_{LQ}=1300$ GeV and $\mathcal{B}(LQ_{mix}\rightarrow q_3\ell)=0.5$.

Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading small-R jet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading bjet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading large-R jet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from the initial step

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading small-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading bjet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading photon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading electron category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading muon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading small-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading bjet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading photon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading electron category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading muon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the inclusive category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the inclusive category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading small-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading bjet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading large-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading photon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading electron category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading muon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading small-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading bjet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading large-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading photon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading electron category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading muon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the inclusive category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the inclusive category

Acceptance times efficiency in an HVT model as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times the branching fraction($W^{\prime} \to ZW$) for the HVT signal as functions of mass($m_{ZX}$) in the leading large-R jet category.The full(dashed) red curves correspond to the theoretical predictions of HVT model A(B)

Comparison of the identification efficiencies using standard and merged-ee reconstruction as a function of true PT(Z)

Background rejection factor as a function of signal efficiency. The red curve shows the BDT performance whereas the blue curve corresponds to that of a cut-based analysis relying only on the jet energyfraction deposited in the EM calorimeter

BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped signal with a relative width value of 3%. The fractions of the pseudo-experiments that have the correctly identified interval and BH p-values below the threshold of 0.01 are indicated. The background is derived by the background-only fit in the full fitting range.

BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped signal with a relative width value of 3%. The fractions of the pseudo-experiments that have the correctly identified interval and BH p-values below the threshold of 0.01 are indicated. The background is derived by the background-only fit in the range excluding the BH interval.

Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is below 0.01 as a function of mass in the leading small-R jet category for Gaussian-shaped signal with a relative width of 3%.

Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is below 0.01 as a function of mass in the leading small-R jet category for Gaussian-shaped signal with a relative width of 3%.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 3% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 3% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 5% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 5% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 10% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 10% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Data yields of the six event categories in the $Z\to e^+e^-$ and $\mu^+\mu^-$ decay channels. The merged-$e^+e^-$ events are included in the $e^+e^-$ channel, increasing the event yield, mainly in the leading large-$R$-jet category, by 0.6 %.

A list of mass spectra, event categories and their corresponding fit ranges, functional forms, numbers of free parameters and global $\chi^2$ $p$-values from background-only fits. The fit range values are rounded to the nearest 5 GeV. Here $f_1(x)=p_0\left(\mathrm{e}^{-p_1x}+p_2\mathrm{e}^{-(p_1+p_3)x}+p_4\mathrm{e}^{-(p_1+p_3+p_5)x}+\cdots\right)$ and $f_2(x)=p_0(1-x)^{p_1}x^{p_2+p_3\ln\!x+p_4\ln^2\!x+\cdots}$

A list of mass spectra, event categories and their corresponding signal-sensitive mass ranges. The initial BH $p$-value is obtained by using the background derived from the background-only fit in the full fit range, whereas the new BH $p$-value uses the background derived from the background-only fit in the range excluding the initial BH interval.

A list of mass spectra, event categories, relative width values of Gaussian-shaped signals and limit-sensitive mass ranges and fractions, corresponding to the mass values in the previous column, of pseudo-experiments having exclusion upper limits higher than the nominal exclusion limit at 95% CL

Cutflow of HVT model $A$ signals ($W^\prime \to ZW \to \ell\ell qq$) with $m_{W^\prime} = 1$ TeV and $m_{W^\prime} = 4$ TeV based on MC simulations.

Acceptance times efficiency ($\mathcal{A} \times \epsilon$) values in % in the dominant event category for $p^Z_{\mathrm{T}} > 100$ GeV in the $Z \to \ell^{+}\ell^{-}$ decay channel.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the hadhad category. The efficiencies are calculated with respect to HH to bbtautau decays in which both tau-leptons decay hadronically. The ‘Pre-selection’ consists of basic requirements, including that at least two tau-had-vis pass loose kinematic requirements, at least one of the tau-had-vis candidate passes identification requirement, and that events do not contain an electron or muon. The ‘Object selections’ require exactly two tau-had-vis candidates, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Loose tau ID’ requires that both tau-had-vis candidates pass identification requirements. The ‘DTT offline jet cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad SLT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad LTT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Post-fit expected number of signal and background events and observed number of data events after applying the selection criteria and requiring exactly 2 b-tagged jets and assuming a background-only hypothesis.

Observed and expected limits at 95% CL on the cross-section of HH production, for the non-resonant ggF+VBF HH search, and the resonant HH search for four values of the resonance mass mX.

Acceptance times efficiency for the full analysis selections as a function of the resonance mass mX in the hadhad, lephad SLT and lephad LTT trigger categories, and the combined lephad. The acceptance times efficiency is evaluated for HH to bbtautau decays, with respect to the targeted tau-lepton decay modes (lephad or hadhad).

Post-fit distribution of mHH in the hadhad channel.

Post-fit distribution of mHH in the lephad SLT channel.

Post-fit distribution of mHH in the lephad LTT channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of of di-b-jet mass in the hadhad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

BDT for SM HH in the hadhad channel

NN for SM HH in the lephad SLT channel

NN for SM HH in the lephad LTT channel

PNN for mX = 500 GeV resonant HH in the hadhad channel

PNN for mX = 500 GeV resonant HH in the lephad SLT channel

PNN for mX = 500 GeV resonant HH in the lephad LTT channel

PNN for mX = 1000 GeV resonant HH in the hadhad channel

PNN for mX = 1000 GeV resonant HH in the lephad SLT channel

PNN for mX = 1000 GeV resonant HH in the lephad LTT channel

Event yields as a function of log10(S/B) for data, background and non-resonant HH signal. Final discriminant bins from the hadhad, lephad SLT and lephad LTT categories are combined into bins of log10(S/B). The B is the fitted background yield assuming background-only hypothesis, and the signal S is scaled to the SM expected cross-section.

Observed and expected limits at 95% CL on the cross-section of the resonant HH production as a function of the scalar resonance mass mX.

Post-fit distribution of delta-R between the taus in the hadhad channel.

Post-fit distribution of delta-R between the b-tagged jets in the hadhad channel.

Post-fit distribution of delta-R between the taus in the lephad SLT channel.

Post-fit distribution of delta-R between the b-tagged jets in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad SLT channel.

Post-fit distribution of pT of the subleading b-tagged jet in the lephad SLT channel.

Post-fit distribution of MTW in the lephad SLT channel.

Post-fit distribution of missing transverse momentum in the lephad SLT channel.

Post-fit distribution of missing transverse momentum centrality in the lephad SLT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad LTT channel.

Post-fit distribution of delta-R between the taus in the lephad LTT channel.

Post-fit distribution of delta-phi between the lepton and the missing transverse momentum in the lephad LTT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad LTT channel.

Post-fit distribution of the total transverse momentum s in the lephad LTT channel.

PNN for mX = 300 GeV resonant HH in the hadhad channel

PNN for mX = 300 GeV resonant HH in the lephad SLT channel

PNN for mX = 300 GeV resonant HH in the lephad LTT channel

PNN for mX = 1600 GeV resonant HH in the hadhad channel

PNN for mX = 1600 GeV resonant HH in the lephad SLT channel

PNN for mX = 1600 GeV resonant HH in the lephad LTT channel

Local p-value of the background-only hypothesis as a function of the resonance mass.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the signal region, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $W$+jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Expected distributions of the MEM discriminant $P(S|X)$ in the SR, for the s-channel single-top signal, and for the $t\bar{t}$ and $W$+jets backgrounds, for MEM discriminant values larger than $2.0\times10^{-4}$. Each distribution is normalised to unity. The binning is the same as the optimised binning used in the signal extraction fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $W$+jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 3-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 4-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty bands indicate the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR before the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore not included. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$, after subtraction of all backgrounds. The fitted distribution for the simulation of the signal is shown together with the post-fit uncertainty in the backgrounds. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Pre-fit and post-fit event yields in the SR, for MEM discriminant values larger than $2.0\times10^{-4}$. The central value of the event yield for each process is calculated by summing the values of the discriminant bin contents, using the nominal expected yield for the pre-fit value, and the best-fit estimate for the post-fit value. The error includes statistical and systematic uncertainties summed in quadrature. All sources of systematic uncertainties are included, taking into account correlations and anti-correlations in the post-fit case. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore only included in the post-fit uncertainties.

Observed impact of the different sources of uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. The 'Systematic uncertainties' category combines all sources of systematic uncertainties. The statistical uncertainty is obtained by repeating the fit after having fixed all nuisance parameters, including the $t\bar{t}$ and $W$+jets normalisation factors. 'Total' gives the total uncertainty on the measurement.

Observed impact of the different sources of $t\bar{t}$ modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and 'ME/PS matching' to the matching of the ME to the parton shower.

Observed impact of the different sources of s-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of t-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of $tW$ modelling uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and '$t\bar{t}$ overlap' to the algorithm removing the overlap between $tW$ and $t\bar{t}$ production at NLO, as described in Section 7 in the paper.

Observed impact of the different sources of PDF uncertainties on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the ${E}_{T}^{miss}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the $m_{T}^{W}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Nuisance parameters ranked according to their post-fit impacts on the best-fit value of the ratio $\mu$ of the measured cross-section to the predicted cross-section. In the figure, only the 20 nuisance parameters with the largest post-fit impacts are shown. The empty (solid) blue rectangles illustrate the pre-fit (post-fit) impact on $\mu$, corresponding to the upper axis. The pre-fit (post-fit) impact of each nuisance parameter, $\Delta\mu$, is calculated as the difference in the fitted value of $\mu$ between the nominal fit and the fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. Several systematic uncertainties are split into different nuisance parameters, which are indicated by NP. JES (JER) indicates jet energy scale (resolution), and $\gamma$ indicates a nuisance parameter associated to the MC statistics in one of the 18 bins numbered from 0 to 17. The black points show the best-fit values of the nuisance parameters, with the error bars representing the post-fit uncertainties. Each nuisance parameter is shown wrt. its nominal value, $\theta_0$, and in units of its pre-fit uncertainty, except the free-floating normalisation factors of the $t\bar{t}$ and $W$+jets backgrounds, and the parameters associated to the MC statistics in each bin, for which the post-fit values and uncertainties are shown.

Correlation matrix of the nuisance parameters and of the ratio $\mu$ of the measured cross-section to the predicted cross-section. The correlations are given after the fit to data. In the figure, only the parameters which have a correlation of at least 0.2 with any other parameter are shown.

Distribution of the MEM discriminant $P(S|X)$ in the SR for MEM discriminant values larger than $2.0\times10^{-4}$, for the collision data used for the measurement, and for 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. 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. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Measured values of the signal cross-section and of the $t\bar{t}$ and $W$+jets normalisation factors, obtained by statistical-only fits to the collision data used for the measurement, and to 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The central values and their statistical uncertainties are obtained by repeating the fit after having fixed all nuisance parameters, except the $t\bar{t}$ and $W$+jets normalisation factors, which are let free-floating (unlike for the statistical uncertainty on the cross-section quoted in the paper). The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. 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. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.