$p_{\mathrm{T}}$-differential $\mathrm{D}^{+}_\mathrm{s}$ production cross section at midrapidity ($|y|<0.5$) in pp collisions at $\sqrt{s}$ = 13 TeV Branching ratio of $\mathrm{D}^{+}_\mathrm{s}\rightarrow\phi\pi^+\rightarrow\mathrm{K}^+\mathrm{K}^-\pi^+$: $(2.22 \pm 0.06)\%$. Global relative uncertainty on BR: $2.7\%$ Global relative uncertainty on luminosity: $1.6\%$

$p_{\mathrm{T}}$-differential $\Lambda_\mathrm{c}^{+}$ production cross section at midrapidity ($|y|<0.5$) in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on luminosity: $1.6\%$

$p_{\mathrm{T}}$-differential $\Xi_\mathrm{c}^{+}$ production cross section at midrapidity ($|y|<0.5$) in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on luminosity: $1.6\%$

$p_{\mathrm{T}}$-differential $\mathrm{D}^{+}/\mathrm{D}^{0}$ ratio in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on BR: $1.9\%$

$p_{\mathrm{T}}$-differential $\mathrm{D}^{*+}/\mathrm{D}^{0}$ ratio in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on BR: $0.7\%$

$p_{\mathrm{T}}$-differential $\mathrm{D}^{+}_\mathrm{s}/\mathrm{D}^{0}$ ratio in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on BR: $2.8\%$

$p_{\mathrm{T}}$-differential $\mathrm{D}^{+}_\mathrm{s}/\mathrm{D}^{+}$ ratio in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on BR: $3.2\%$

$p_{\mathrm{T}}$-differential $\mathrm{D}^{+}_\mathrm{s}/(\mathrm{D}^{0}+\mathrm{D}^{+})$ ratio in pp collisions at $\sqrt{s}$ = 13 TeV Global relative uncertainty on BR: $3.3\%$

$\Lambda_\mathrm{c}^{+}/\mathrm{D}^{0}$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\Xi_\mathrm{c}^{+}/\mathrm{D}^{0}$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\sigma(\mathrm{D}^{0})$ $\sqrt{s}$ = 13 TeV / $\sigma(\mathrm{D}^{0})$ $\sqrt{s}$ = 5.02 TeV

$\sigma(\mathrm{D}^{+})$ $\sqrt{s}$ = 13 TeV $/ \sigma(\mathrm{D}^{+})$ $\sqrt{s}$ = 5.02 TeV

$\sigma(\mathrm{D}^{*+})$ $\sqrt{s}$ = 13 TeV / $\sigma(\mathrm{D}^{*+})$ $\sqrt{s}$ = 5.02 TeV

$\sigma(\mathrm{D}^{+}_\mathrm{s})$ $\sqrt{s}$ = 13 TeV $/ \sigma(\mathrm{D}^{+}_\mathrm{s})$ $\sqrt{s}$ = 5.02 TeV

$\sigma(\Lambda^{+}_\mathrm{c})$ $\sqrt{s}$ = 13 TeV $/ \sigma(\Lambda^{+}_\mathrm{c})$ $\sqrt{s}$ = 5.02 TeV

$\sigma(\Xi^{+}_\mathrm{c})$ $\sqrt{s}$ = 13 TeV $/ \sigma(\Xi^{0}_\mathrm{c})$ $\sqrt{s}$ = 5.02 TeV

$\sigma(\Xi^{0}_\mathrm{c})$ $\sqrt{s}$ = 13 TeV / $\sigma(\Xi^{0}_\mathrm{c})$ $\sqrt{s}$ = 5.02 TeV

$\mathrm{D}^{0}$ ratio |$y$| < 0.5 / $2 < y < 2.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{0}$ ratio |$y$| < 0.5 / $3 < y < 3.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{0}$ ratio |$y$| < 0.5 / $4 < y < 4.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{+}$ ratio |$y$| < 0.5 / $2 < y < 2.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{+}$ ratio |$y$| < 0.5 / $3 < y < 3.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{+}$ ratio |$y$| < 0.5 / $4 < y < 4.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{*+}$ ratio |$y$| < 0.5 / $2 < y < 2.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{*+}$ ratio |$y$| < 0.5 / $3 < y < 3.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{*+}$ ratio |$y$| < 0.5 / $4 < y < 4.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{+}_\mathrm{s}$ ratio |$y$| < 0.5 / $2 < y < 2.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{+}_\mathrm{s}$ ratio |$y$| < 0.5 / $3 < y < 3.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

$\mathrm{D}^{+}_\mathrm{s}$ ratio |$y$| < 0.5 / $4 < y < 4.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

Double ratio $\mathrm{D}^{0}$ 13 TeV / 5.02 TeV |$y$| < 0.5 / $2 < y < 2.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

Double ratio $\mathrm{D}^{0}$ 13 TeV / 5.02 TeV |$y$| < 0.5 / $3 < y < 3.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

Double ratio $\mathrm{D}^{0}$ 13 TeV / 5.02 TeV |$y$| < 0.5 / $4 < y < 4.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

Charm $f_\mathrm{s}/(f_\mathrm{u}+f_\mathrm{d})$ in pp collisions at $\sqrt{s}$ = 13 TeV

$f(\mathrm{c \rightarrow h_c})$ in pp collisions at $\sqrt{s}$ = 13 TeV

$f(\mathrm{c \rightarrow h_c})$ in pp collisions at $\sqrt{s}$ = 5.02 TeV

$\sigma(\mathrm{c\bar{c}})$ at $|y|<0.5$ in pp collisions vs.$\sqrt{s}$

The EW-Zy jj differential cross-section in the Signal Region as a function of the leading lepton pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The EW-Zy jj differential cross-section in the Signal Region as a function of the leading photon pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The EW-Zy jj differential cross-section in the Signal Region as a function of the leading jet pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The EW-Zy jj differential cross-section in the Signal Region as a function of the Zy system pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The EW-Zy jj differential cross-section in the Signal Region as a function of the dijet invariant mass. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The EW-Zy jj differential cross-section in the Signal Region as a function of the dijet rapidity difference. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The EW-Zy jj differential cross-section in the Signal Region as a function of the Zy and dijet azimuthal difference. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the leading lepton pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the leading photon pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the leading jet pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the Z boson pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the Zy system pT. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the dijet invariant mass. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the dijet rapidity difference. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the Zy and dijet azimuthal difference. The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

The total Zy jj differential cross-section in the Extended SR as a function of the centrality of the system ζ(Zy). The lower panels show the ratios of the MC predictions to the data. The band around unfolded data represents the total uncertainty (including statistical uncertainty) and takes into account the correlations as obtained from the fit. The hatched area represents the uncertainty in the prediction. Events beyond the upper limit of the histogram are included in the last bin.

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))

Post-fit distributon of BDT discriminants for $\rho_{tc}=1.0$ with $m_A$ = 350 GeV interfered with H.($m_A - m_H$ = 50 GeV)

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tu}$ = 0.1, 0.4, 1.0.

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tc}$ = 0.1, 0.4, 1.0.

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tu}$ = 0.1, 0.4, 1.0 with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tc}$ = 0.1, 0.4, 1.0 with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model.

Expected exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model

Observed exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model.

Expected exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model

Observed exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Expected exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Observed exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Expected exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Observed exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Raw primary antitriton-to-triton ratio as a function of the momentum p_primary in MC (sigma_inel x 4.0).

Raw TPC/TOF ratio for antitriton as a function of the momentum p_primary in exp. data.

Raw TPC/TOF ratio for antitriton as a function of the momentum p_primary in MC (sigma_inel x 0.5).

Raw TPC/TOF ratio for antitriton as a function of the momentum p_primary in MC (sigma_inel x 1.0).

Raw TPC/TOF ratio for antitriton as a function of the momentum p_primary in MC (sigma_inel x 1.5).

Inelastic interaction cross section of antitriton on an averaged material element of the ALICE detector.

Inelastic interaction cross section of antitriton on an averaged material element of the ALICE detector.

The observed and expected 95% CL upper limits on the minimal coupling vector LQ pair production cross-sections assuming B = 1 as a function of m$_\text{LQ}$.

The observed and expected 95% CL upper limits on the Yang-Mills coupling vector LQ pair production cross-sections assuming B = 1 as a function of m$_\text{LQ}$.

The observed and expected 95% CL upper limits on the branching ratio into charged leptons as a function of m$_\text{LQ}$ for the scalar LQ.

The observed and expected 95% CL upper limits on the branching ratio into charged leptons as a function of m$_\text{LQ}$ for the vector LQ minimal coupling scenario.

The observed and expected 95% CL upper limits on the branching ratio into charged leptons as a function of m$_\text{LQ}$ for the vector LQ Yang-Mills coupling scenario.

Breakdown of systematic uncertainties in the measured inclusive cross-section. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Data bootstrap post unfolding for the inclusive cross-section. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

Differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $|\eta^{l}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $|\eta^{l}|$.

Data bootstrap post unfolding for the differential absolute cross-section for $|\eta^{l}|$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

Differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $m^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $m^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $|\Delta\phi^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $|\Delta\phi^{e\mu}|$.

Data bootstrap post unfolding for the differential absolute cross-section for $|\Delta\phi^{e\mu}|$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/rad].

Differential absolute cross-section for $|y^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $|y^{e\mu}|$.

Data bootstrap post unfolding for the differential absolute cross-section for $|y^{e\mu}|$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

Double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $|\eta^{l}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $|\eta^{l}|$.

Data bootstrap post unfolding for the differential normalised cross-section for $|\eta^{l}|$. 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. All the provided numbers originate from pseudo-data, including the 0th entry.

Differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $m^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $m^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $|\Delta\phi^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $|\Delta\phi^{e\mu}|$.

Data bootstrap post unfolding for the differential normalised cross-section for $|\Delta\phi^{e\mu}|$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/rad].

Differential normalised cross-section for $|y^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $|y^{e\mu}|$.

Data bootstrap post unfolding for the differential normalised cross-section for $|y^{e\mu}|$. 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. All the provided numbers originate from pseudo-data, including the 0th entry.

Double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. 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. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

The rejection factor (inverse of the efficiency) for jets that have the other origins (background jets) are shown. The background jet rejection factor is calculated using pre-selected large-$R$ jets in the sample of simulated $Z\rightarrow\nu\nu$ + jets events, dominated by initial state radiation (ISR) jets. The efficiency correction factors are applied on the background rejection for the $W_{qq}$/$Z_{qq}$-tagging. The systematic uncertainty is represented by the hashed bands.

The boson-tagging efficiency for jets arising from $Z/h$ bosons decaying into $b\bar{b}$ (signal jets). The signal jet efficiency of $Z_{bb}$/$h_{bb}$-tagging is evaluated using a sample of pre-selected large-$R$ jets ($p_{\textrm{T}}>200~\textrm{GeV}, |\eta|<2.0, m_{J} > 40~\textrm{GeV}$) in the simulated $(\tilde{W},\tilde{B})$ simplified model signal events with $\Delta m(\tilde{\chi}_{\textrm{heavy}},~\tilde{\chi}_{\textrm{light}}) \ge 400~\textrm{GeV}$. The jets are matched with generator-level $Z/h$-bosons by $\Delta R<1.0$ which decay into $b\bar{b}$. The systematic uncertainty is represented by the hashed bands.

The rejection factor (inverse of the efficiency) for jets that have the other origins (background jets) are shown. The background jet rejection factor is calculated using pre-selected large-$R$ jets in the sample of simulated $Z\rightarrow\nu\nu$ + jets events, dominated by initial state radiation (ISR) jets. As for the $Z_{bb}$/$h_{bb}$-tagging, the rejection is shown as the function of number of $b$- or $c$-quarks contained in the large-$R$ jet within $\Delta R<1.0$. The systematic uncertainty is represented by the hashed bands.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The total post-fit uncertainty in each of the SRs and VRs.

Summary of the observed data and predicted SM background in all SRs. The background prediction in SR-4Q (SR-2B2Q) is obtained by a background-only fit to CR0L-4Q (CR0L-2B2Q). The total systematic uncertainty on the background prediction is shown by the hatched area. Distributions of a few representative signals are overlaid. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\textrm{GeV}$. The bottom panel shows the statistical significance of the discrepancy between the observed number of events and the SM expectation.

Number of observed data events and the SM backgrounds in the SRs and the CR0L bins. The SM backgrounds are predicted by the background-only fits. Note that the relative uncertainty on the expected yield in the CRs between the reducible backgrounds are identical, given that a common normalization factor is assigned for all of them in the fit. Yields for negligible backgrounds are indicated by "0.0000001±0.0000001" in the table.

Number of observed data events and the post-fit SM background prediction in the VR1L(1Y) bins and the corresponding CR1L(1Y) bins. "-" indicates negligibly small contribution. Note that the relative uncertainty on the expected yield in the CRs between the reducible backgrounds are identical, given that a common normalization factor is assigned for all of them in the fit. Yields for negligible backgrounds are indicated by "0.0000001±0.0000001" in the table.

$m_{\textrm{eff}}$ distribution in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{1})$ in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $D_2(J_{1})$ in SR-4Q-VV. For $D_2$, the cut value applied for $V_{qq}$-tagging ($D_{2,\text{cut}}$) is subtracted as the off-set so that $D_2-D_{2,\text{cut}}(J)<0$ represents $J$ passing the $D_2$ selection. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{2})$ in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $D_2(J_{2})$ in SR-4Q-VV. For $D_2$, the cut value applied for $V_{qq}$-tagging ($D_{2,\text{cut}}$) is subtracted as the off-set so that $D_2-D_{2,\text{cut}}(J)<0$ represents $J$ passing the $D_2$ selection. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

$m_{\textrm{T2}}$ distributions in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{bb})$ in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m_{\textrm{eff}} $ in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

$m_{\textrm{T2}}$ distributions in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{bb})$ in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m_{\textrm{eff}} $ in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=75$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=75$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=50$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=50$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=25$% as no mass point on the plane can be excluded.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1C1-WW). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-WZ). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-Wh). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{H},\tilde{G})$ models. The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1C1-WW). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-WZ). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-Wh). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{H},\tilde{G})$ models. The black numbers represents the observed cross-section upper-limits.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1C1-WW) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-Wh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-Zh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-4Q-VV, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-2B2Q-VZ, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-2B2Q-Vh, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1C1-WW) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-Wh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-Zh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-4Q-VV. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-2B2Q-VZ. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-2B2Q-Vh. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.