Expected CLs values in the High mA SR, mA vs ma signal grid.

Observed CLs values in the High mA SR, mA vs ma signal grid.

Expected CLs values in the Low mA SR, mA vs ma signal grid.

Observed CLs values in the High mA SR, mA vs ma signal grid.

CLs+b values in the Low mA SR, mA vs tanB signal grid.

CLs+b values in the High mA SR, mA vs ma signal grid.

CLs+b values in the Low mA SR, mA vs ma signal grid.

Cut flow of the 2HDM+a signal points, gluon–gluon fusion production, Low mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

Cut flow of the 2HDM+a signal points, gluon–gluon fusion production, High mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

Cut flow of the 2HDM+a signal points, bb annihilation production, Low mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

Cut flow of the 2HDM+a signal points, bb annihilation production, High mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

A comparison of the observed and expected yields in the four bins of the Low mA SR.

A comparison of the observed and expected yields in the two bins of the High mA SR.

Expected exclusion contours at 95% CL as a function of mA and ma.

Observed exclusion contours at 95% CL as a function of mA and ma.

Expected +- 1sigma exclusion contours at 95% CL as a function of mA and ma.

Expected exclusion contours at 95% CL as a function of mA and ma.

Observed exclusion contours at 95% CL as a function of mA and ma.

Expected +- 1sigma exclusion contours at 95% CL as a function of mA and ma.

Acceptance, High mA SR, mA vs tanB grid, 400-750 GeV, bb prod

Acceptance, High mA SR, mA vs tanB grid, >750 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, 100-250 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, 250-400 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, 400-550 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, >550 GeV, bb prod

Acceptance, High mA SR, mA vs ma grid, 400-750 GeV, bb prod

Acceptance, High mA SR, mA vs ma grid, >750 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, 100-250 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, 250-400 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, 400-550 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, >550 GeV, bb prod

Acceptance, High mA SR, mA vs tanB grid, 400-750 GeV, ggF prod

Acceptance, High mA SR, mA vs tanB grid, >750 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, 100-250 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, 250-400 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, 400-550 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, >550 GeV, ggF prod

Acceptance, High mA SR, mA vs ma grid, 400-750 GeV, ggF prod

Acceptance, High mA SR, mA vs ma grid, >750 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, 100-250 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, 250-400 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, 400-550 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, >550 GeV, ggF prod

Efficiency, High mA SR, mA vs tanB grid, 400-750 GeV, bb prod

Efficiency, High mA SR, mA vs tanB grid, >750 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, 100-250 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, 250-400 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, 400-550 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, >550 GeV, bb prod

Efficiency, High mA SR, mA vs ma grid, 400-750 GeV, bb prod

Efficiency, High mA SR, mA vs ma grid, >750 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, 100-250 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, 250-400 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, 400-550 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, >550 GeV, bb prod

Efficiency, High mA SR, mA vs tanB grid, 400-750 GeV, ggF prod

Efficiency, High mA SR, mA vs tanB grid, >750 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, 100-250 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, 250-400 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, 400-550 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, >550 GeV, ggF prod

Efficiency, High mA SR, mA vs ma grid, 400-750 GeV, ggF prod

Efficiency, High mA SR, mA vs ma grid, >750 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, 100-250 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, 250-400 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, 400-550 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, >550 GeV, ggF prod

Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the FCNC top-quark candidate in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the SM top-quark candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction before the fit (Pre-Fit) for the transverse momentum of the Z boson candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the third lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.16 < $\alpha$ < 0.18.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.18 < $\alpha$ < 0.20.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.20 < $\alpha$ < 0.22.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.22 < $\alpha$ < 0.24.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.24 < $\alpha$ < 0.26.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.26 < $\alpha$ < 0.28.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.28 < $\alpha$ < 0.30.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.30 < $\alpha$ < 0.32.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.32 < $\alpha$ < 0.34.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.10 < $\alpha$ < 0.12.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.12 < $\alpha$ < 0.14.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.14 < $\alpha$ < 0.16.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.16 < $\alpha$ < 0.18.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.18 < $\alpha$ < 0.20.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.20 < $\alpha$ < 0.22.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.22 < $\alpha$ < 0.24.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.24 < $\alpha$ < 0.26.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.26 < $\alpha$ < 0.28.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.28 < $\alpha$ < 0.30.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.30 < $\alpha$ < 0.32.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The product of the analysis acceptance and selection efficiency for all analysis selection criteria is shown as a function of $m_Y$ and $m_{X}/m_{Y}$.

The cross-section of the signal samples is shown as a function of $m_Y$ and $m_{X}/m_{Y}$.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson, as a function of the dark-photon mean proper decay length c&tau;. The limits are shown for all search channels combined, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio is larger than unity.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for a &gamma;_d mass of 100&nbsp;MeV, as a function of the dark-photon mean proper decay length c&tau;. The solid black curve shows the observed exclusion limit from their statistical combination. The hatched band denotes the region in which the branching ratio is larger than unity.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for a &gamma;_d mass of 10&nbsp;GeV, as a function of the dark-photon mean proper decay length c&tau;. The solid black curve shows the observed exclusion limit from their statistical combination. The hatched band denotes the region in which the branching ratio is larger than unity.

Acceptance times efficiency extrapolated as a function of c&tau;_&gamma;d for the &mu;DPJ channel (SR_&mu;), obtained for the different FRVZ MC signal mass points.

Acceptance times efficiency extrapolated as a function of c&tau;_&gamma;d for the low E_T^miss caloDPJ channel (SR_c^L), obtained for the different FRVZ MC signal mass points.

Acceptance times efficiency extrapolated as a function of c&tau;_&gamma;d for the high E_T^miss caloDPJ channel (SR_c^H), obtained for the different FRVZ MC signal mass points.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 17&nbsp;MeV.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 50&nbsp;MeV.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 400&nbsp;MeV.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 900&nbsp;MeV.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 2&nbsp;GeV.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 6&nbsp;GeV.

Observed (solid line) and expected (dashed line) 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for FRVZ processes with a 125&nbsp;GeV Higgs boson as a function of dark photon mean proper decay length for the ggF, WH and VBF analyses. The full combination is shown as solid or dashed black lines for a dark photon mass of 15&nbsp;GeV.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson, as a function of the dark-photon mean proper decay length c&tau;. The limits are shown for the SR_&mu; search channel, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio is larger than unity.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson, as a function of the dark-photon mean proper decay length c&tau;. The limits are shown for the SR_c^L search channel, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio is larger than unity.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson, as a function of the dark-photon mean proper decay length c&tau;. The limits are shown for the SR_c^H search channel, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio is larger than unity.

Observed 95% CL upper limits on B(H&rarr;&nbsp;2&gamma;_d+X) for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson, as a function of the dark-photon mean proper decay length c&tau;. The limits are shown for all search channels combined, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio is larger than unity.

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 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.

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR1 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR2 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR3 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR4 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR5 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR6 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Observed and expected distributions of the invariant mass of the four-lepton system in the $A\to ZH\to 4\ell+X$ search for SR7 under a background-only fit to data. The total background includes the $q\overline{q}\to ZZ$, $gg\to ZZ$, $q\overline{q}\to ZZ$ (EW), $VVV$, $t\overline{t}V$, $t\overline{t}$, $Z$+jets and $WZ$ processes. The distribution of the $(m_{A}, m_{H}) = (510, 380)$ GeV signal is normalised to the observed upper limit on the cross-section (29.9 fb).

Local p0-values in the $(m_{H}, m_{R})$ plane for the $R\to SH\to 4\ell+E^{\textrm{miss}}_{\textrm{T}}$ search with $m_{S} = 160$ GeV.

Local p0-values in the $(m_{H}, m_{A})$ plane for the $A\to ZH\to 4\ell+X$ search.

The observed upper limits at 95% confidence level on $\sigma(gg\to R)\times \mathcal{B}(R\to SH)\times (H\to ZZ)$ across the $(m_{H}, m_{R})$ plane with $m_{S} = 160$ GeV for the $R\to SH\to 4\ell+E^{\textrm{miss}}_{\textrm{T}}$ search.

The expected upper limits at 95% confidence level on $\sigma(gg\to R)\times \mathcal{B}(R\to SH)\times (H\to ZZ)$ across the $(m_{H}, m_{R})$ plane with $m_{S} = 160$ GeV for the $R\to SH\to 4\ell+E^{\textrm{miss}}_{\textrm{T}}$ search.

The observed upper limits at 95% confidence level on $\sigma(gg\to A)\times \mathcal{B}(A\to ZH)\times (H\to ZZ)$ across the $(m_{H}, m_{A})$ plane for the $A\to ZH\to 4\ell+X$ search.

The expected upper limits at 95% confidence level on $\sigma(gg\to A)\times \mathcal{B}(A\to ZH)\times (H\to ZZ)$ across the $(m_{H}, m_{A})$ plane for the $A\to ZH\to 4\ell+X$ search.

Cut-flow of the raw events at each selection stage for the $R\rightarrow SH\rightarrow 4\ell + E^{\textrm{miss}}_{\textrm{T}}$ signal with a mass point of $(m_R, m_H ) = (390, 220)$ GeV and $m_S = 160$ GeV. The events are shown for the individual and combined four-lepton channels. The SFOS denotes same flavour and opposite sign lepton pairs selection and the final selection is shown for each signal region. The final selection criteria for SR1 to SR3 are defined in Table 2 of the paper.

Cut-flow of the raw events at each selection stage for the $A\rightarrow Z(\rightarrow jj/\ell^+\ell^-/\textrm{invisible})H(\rightarrow 4\ell)$ signal with a mass point of $(m_A, m_H ) = (330, 220)$ GeV. The events are shown for the individual and combined four-lepton channels. The SFOS denotes same flavour and opposite sign lepton pairs selection and the final selection is shown for each signal region. The final selection criteria for SR1 to SR7 are defined in Table 2 of the paper.

Cut-flow of the raw events at each selection stage for the $A\rightarrow Z(\rightarrow 2\ell)H(\rightarrow 2\ell+jj/\textrm{invisible})$ signal with a mass point of $(m_A, m_H ) = (330, 220)$ GeV. The events are shown for the individual and combined four-lepton channels. The SFOS denotes same flavour and opposite sign lepton pairs selection and the final selection is shown for each signal region. The final selection criteria for SR1 to SR7 are defined in Table 2 of the paper.

Expected 95% CL exclusion contours in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for boosted.

Observed 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the electron channel for boosted.

Expected 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the electron channel for boosted.

Observed 95% CL exclusion contours for the assumpition of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for boosted.

Expected 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for boosted.

Observed 95% CL exclusion contours for the assumption of Majorana type in the $(m(W_{R}), m(N_{R}))$ plane in the electron channel for resolved.

Expected 95% CL exclusion contours for the assumption of Majorana type in the $(m(W_{R}), m(N_{R}))$ plane in the electron channel for resolved.

Observed 95% CL exclusion contours for the assumption of Majorana type in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for resolved.

Expected 95% CL exclusion contours for the assumption of Majorana type in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for resolved.

Observed 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the electron channel for resolved.

Expected 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the electron channel for resolved.

Observed 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for resolved.

Expected 95% CL exclusion contours for the assumption of Dirac type in the $(m(W_{R}), m(N_{R}))$ plane in the muon channel for resolved.

The $m_{eJ}$ distribution in the boosted one electron channel.

The $m_{eeJ}$ distribution in the boosted two electron channel.

The $m_{\mu\mu J}$ distribution in the boosted two muon channel.

The $m_{jj}$ distribution in the resolved electron channel.

The $m_{jj}$ distribution in the resolved muon channel.

The $m_{eejj}$ distribution in the resolved electron channel.

The $m_{\mu\mu jj}$ distribution in the resolved muon channel.

The $h_{T}$ distribution in the resolved electron channel.

The $h_{T}$ distribution in the resolved muon channel.

Acceptance $\times$ efficiency for bSR1e.

Acceptance $\times$ efficiency for bSR2e.

Acceptance $\times$ efficiency for bSR2mu.

Acceptance $\times$ efficiency for rSROS2e.

Acceptance $\times$ efficiency for rSROS2mu.

Acceptance $\times$ efficiency for rSRSS2e.

Acceptance $\times$ efficiency for rSRSS2mu.

Expected and observed 95% CL upper limits for $\sigma(pp\to W_{R})\times B(W_{R}\to eeqq)$ as a function of $m(W_{R})$ for the special case of $m(W_{R}) = 2\times m(N_{R})$. The theoretical cross section corresponds to either the Dirac or Majorana neutrino interpretation by ignoring the charge of the leptons.

Expected and observed 95% CL upper limits for $\sigma(pp\to W_{R})\times B(W_{R} \to \mu\mu qq)$ as a function of $m(W_{R})$ for the special case of $m(W_{R}) = 2\times m(N_{R})$. The theoretical cross section corresponds to either the Dirac or Majorana neutrino interpretation by ignoring the charge of the leptons.

Expected and observed 95% CL upper limits for $\sigma(pp\to W_{R})\times B(W_{R} \to eeqq)$ as a function of $m(W_{R})$ for the special case of $m(N_{R})=200$ GeV. The theoretical cross section corresponds to either the Dirac or Majorana neutrino interpretation by ignoring the charge of the leptons.

Expected and observed 95% CL upper limits for $\sigma(pp\to W_{R})\times B(W_{R} \to \mu \mu qq)$ as a function of $m(W_{R})$ for the special case of $m(N_{R})=200$ GeV. The theoretical cross section corresponds to either the Dirac or Majorana neutrino interpretation by ignoring the charge of the leptons.

Expected and observed 95% CL upper limits for $\sigma(pp \to W_{R}) \times B(W_{R} \to eeqq)$ as a function of $m(W_{R})$ for the Majorana neutrino interpretations for the special case of $m(W_{R}) = 2\times m(N_{R})$.

Expected and observed 95% CL upper limits for $\sigma(pp \to W_{R}) \times B(W_{R} \to \mu\mu qq)$ as a function of $m(W_{R})$ for the Majorana neutrino interpretations for the special case of $m(W_{R}) = 2 \times m(N_{R})$.

Expected and observed 95% CL upper limits for $\sigma(pp \to W_{R}) \times B(W_{R} \to eeqq)$ as a function of $m(W_{R})$ for the Dirac neutrino interpretations for the special case of $m(W_{R}) = 2 \times m(N_{R})$.

Expected and observed 95% CL upper limits for $\sigma(pp \to W_{R}) \times B(W_{R} \to \mu \mu qq)$ as a function of $m(W_{R})$ for the Dirac neutrino interpretations for the special case of $m(W_{R}) = 2 \times m(N_{R})$.

Observed 95% CL upper limits on the cross section for all masses and charges of photon-fusion pair-produced spin-1/2 monopoles as a function of mass for magnetic charges $|g|=1g_D$ and $|g|=2g_D$.

Observed 95% CL upper limits on the cross section for all masses and charges of Drell-Yan spin-0 HECOs production as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Observed 95% CL upper limits on the cross section for all masses and charges of Drell-Yan spin-1/2 HECOs production as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Observed 95% CL upper limits on the cross section for all masses and charges of photon-fusion pair-produced spin-0 HECOs as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Observed 95% CL upper limits on the cross section for all masses and charges of photon-fusion pair-produced spin-1/2 HECOs as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 4000 GeV.