Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Post-fit SR yields of the high-mass channel. The upper panel shows the observed number of events, as well the post-fit background predictions in each region. The bottom panel shows the ratio of the observed data and the total background prediction. The shaded areas correspond to the total statistical and systematic uncertainties obtained after the fit and described in Section 6.

Pre-fit data and background (reweighted $2b$) predictions for each $4b$ SR $E_\text{T}^\text{miss}$ and $m_\text{eff}$ bin of the low-mass channel for the 2016 data-taking period. The bottom panel shows the significance of any differences between the observed $4b$ data and the background prediction. The $1\sigma$ and $2\sigma$ bands are shown in green and yellow, respectively. All systematics are included except the background normalization, which is 2.3%.

Pre-fit data and background (reweighted $2b$) predictions for each $4b$ SR $E_\text{T}^\text{miss}$ and $m_\text{eff}$ bin of the low-mass channel for the 2017 data-taking period. The bottom panel shows the significance of any differences between the observed $4b$ data and the background prediction. The $1\sigma$ and $2\sigma$ bands are shown in green and yellow, respectively. All systematics are included except the background normalization, which is 3.7%.

Pre-fit data and background (reweighted $2b$) predictions for each $4b$ SR $E_\text{T}^\text{miss}$ and $m_\text{eff}$ bin of the low-mass channel for the 2018 data-taking period. The bottom panel shows the significance of any differences between the observed $4b$ data and the background prediction. The $1\sigma$ and $2\sigma$ bands are shown in green and yellow, respectively. All systematics are included except the background normalization, which is 1.8%.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. Results from a previous ATLAS search using 24.3-36.1 fb$^{-1}$ [13] are shown by the solid (observed) and dashed (expected) blue lines. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the 95% CL observed (solid) and expected (dashed) upper limits on $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})$, assuming the theory cross section for higgsino pair production. The higgsinos are assumed to decay as $\tilde{H}\rightarrow h + \tilde{G}$ or $\tilde{H}\rightarrow Z + \tilde{G}$. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the 95% CL observed (solid) and expected (dashed) upper limits on $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})$, assuming the theory cross section for higgsino pair production. The higgsinos are assumed to decay as $\tilde{H}\rightarrow h + \tilde{G}$ or $\tilde{H}\rightarrow Z + \tilde{G}$. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the 95% CL observed (solid) and expected (dashed) upper limits on $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})$, assuming the theory cross section for higgsino pair production. The higgsinos are assumed to decay as $\tilde{H}\rightarrow h + \tilde{G}$ or $\tilde{H}\rightarrow Z + \tilde{G}$. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the 95% CL observed (solid) and expected (dashed) upper limits on $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})$, assuming the theory cross section for higgsino pair production. The higgsinos are assumed to decay as $\tilde{H}\rightarrow h + \tilde{G}$ or $\tilde{H}\rightarrow Z + \tilde{G}$. The phase space above the lines is excluded.

Exclusion limits of the low-mass and high-mass channels. The low-mass channel is used for $m_{\tilde{H}}<250$ GeV while the high-mass channel is used for $m_{\tilde{H}}\ge250$ GeV. The plot shows the 95% CL observed (solid) and expected (dashed) upper limits on $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})$, assuming the theory cross section for higgsino pair production. The higgsinos are assumed to decay as $\tilde{H}\rightarrow h + \tilde{G}$ or $\tilde{H}\rightarrow Z + \tilde{G}$. The phase space above the lines is excluded.

Exclusion limits of the low-mass channel. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the low-mass channel. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the high-mass channel. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Exclusion limits of the high-mass channel. The plot shows the observed (solid) and expected (dashed) 95% CL upper limits on the cross section of higgsino pair production, assuming a higgsino decay branching ratio of $\mathcal{B}(\tilde{H}\rightarrow h + \tilde{G})=100\%$. The theory cross section and its uncertainty are shown by the solid red line and red shading. The bottom panel shows the ratio of the limits to the theory cross section. The phase space above the lines is excluded.

Results of the background-only fit in the low-mass channel discovery region SR_LM_150. Both pre-fit and post-fit values are shown.

Results of the background-only fit in the low-mass channel discovery region SR_LM_300. Both pre-fit and post-fit values are shown.

The experimental efficiency of the low-mass channel for the exclusion and discovery signal regions as a function of higgsino mass. The experimental efficiency is defined as the number of events passing the detector-level event selections divided by the number of events passing the event selections for a perfect detector. The denominator is obtained by implementing particle-level event selections that emulate the detector-level selections. This treats the lack of availability of $b$-jet triggers as an inefficiency.

The particle-level acceptance for the low-mass exclusion and discovery signal regions, shown as a function of higgsino mass. The acceptance is defined as the fraction of signal events passing the particle-level event selection that emulates the detector-level selection. The acceptance calculation considers only those signal events where both higgsinos decay to Higgs bosons.

The experimental efficiency of the high-mass channel discovery regions as a function of higgsino mass. For each higgsino mass, the efficiency is shown for the SR-1 region corresponding to the mass. For masses above 1100 GeV, SR-1-1100 is used. The experimental efficiency is defined as the number of events passing the detector-level event selections divided by the number of events passing the event selections for a perfect detector. The denominator is obtained by implementing particle-level event selections that emulate the detector-level selections. The efficiency calculation considers only those signal events where both higgsinos decay to Higgs bosons.

The particle-level acceptance for the high-mass signal regions, shown as a function of higgsino mass. For each higgsino mass, the acceptance is shown for the SR-1 region corresponding to the mass. For masses above 1100 GeV, SR-1-1100 is used. The acceptance is defined as the fraction of signal events passing the particle-level event selection that emulates the detector-level selection. The acceptance calculation considers only those signal events where both higgsinos decay to Higgs bosons.

Cutflow for the low-mass channel for a representative 130 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 150 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 200 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 250 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 300 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 400 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 500 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 600 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 700 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 800 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 900 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 1000 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the low-mass channel for a representative 1100 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. The $b$-jet cut requires 4 or more $b$-jets with $p_\text{T}>40$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$, with the availability of $b$-jet triggers lowering the luminosity to 126 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 200 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 250 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 300 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 400 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 500 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 600 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 700 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 800 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 900 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 1000 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 1100 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 1200 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 1300 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 1400 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Cutflow for the high-mass channel for a representative 1500 GeV signal. The preselection requires 4 or more jets with $p_\text{T}>25$ GeV and 2 or more $b$-jets with $p_\text{T}>25$ GeV. As the samples are generated with $\mathcal{B}(\tilde{H}\rightarrow h\tilde{G})$=50%, $\mathcal{B}(\tilde{H}\rightarrow Z\tilde{G})$=50% to allow for both decays to be studied, the $hh$ events selection is used to select the events where each of the higgsinos decays to a Higgs boson. Expected yields are normalized to a luminosity of 139 fb$^{-1}$. All selections are cumulative, with the exception of the SR cuts, which are each applied separately.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, pT,jet

R32 for delta y

R32 for delta y max

R32 for $m_{jj}$

R32 for $m_{jj, max}$

R43 for $H_{T2}$, 60 GeV < $p_{T,3}$

R43 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R43 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R43 for $H_{T2}$, pT,jet

R43 for $Delta y_{jj}$

R43 for $Delta y_{jj, max}$

R43 for $m_{jj}$

R43 for $m_{jj, max}$

R54 for $H_{T2}$, 60 GeV < $p_{T,3}$

R54 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R54 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R54 for $Delta y_{jj}$

R54 for $Delta y_{jj, max}$

R54 for $m_{jj}$

R54 for $m_{jj, max}$

R42 for $H_{T2}$, 60 GeV < $p_{T,3}$

R42 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R42 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R42, pT,jet

R42 for $Delta y_{jj}$

R42 for $Delta y_{jj, max}$

R42 for $m_{jj}$

R42 for $m_{jj}$

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 2

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=3

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=4

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 5

pTnincl

pTnincl

pTnincl

mjj max, $N_{jet}$ >= 2

mjj max, $N_{jet}$ >= 3

mjj max, $N_{jet}$ >= 4

mjj max, $N_{jet}$ >= 5

$m_{jj}$ $N_{jet}$ >= 2

$m_{jj}$ $N_{jet}$ >= 3

$m_{jj}$ $N_{jet}$ >= 4

$m_{jj}$ $N_{jet}$ >= 5

$Delta y_{jj}$ $N_{jet}$ >= 2

$Delta y_{jj}$ $N_{jet}$ >= 3

$Delta y_{jj}$ $N_{jet}$ >= 4

$Delta y_{jj}$ $N_{jet}$ >= 5

$Delta y_{jj, max}$, $N_{jet}$ >= 2

$Delta y_{jj, max}$, $N_{jet}$ >= 3

$Delta y_{jj, max}$, $N_{jet}$ >= 4

$Delta y_{jj, max}$, $N_{jet}$ >= 5

R32 for $H_{T2}$, 60 GeV < $p_{T,3}$

R32 for $H_{T2}$, 0.05 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

Observed and expected upper limits at 95% CL on the production cross-section times branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 50 GeV

Observed and expected upper limits at 95% CL on the production cross-section times branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 60 GeV

Observed and expected upper limits at 95% CL on the production cross-section times branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 70 GeV

Observed 90% CL upper limits on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 20 GeV

Observed 90% CL upper limits on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 30 GeV

Observed 90% CL upper limits on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 40 GeV

Observed 90% CL upper limits on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 50 GeV

Observed 90% CL upper limits on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 60 GeV

Observed 90% CL upper limits on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 70 GeV

Observed 90% CL upper limits on $\epsilon^{2}$, assuming of $\alpha_{D}=0.1$, as a function of $m_{A'}$ at dark Higgs boson mass of 20 GeV

Observed 90% CL upper limits on $\epsilon^{2}$, assuming of $\alpha_{D}=0.1$, as a function of $m_{A'}$ at dark Higgs boson mass of 30 GeV

Observed 90% CL upper limits on $\epsilon^{2}$, assuming of $\alpha_{D}=0.1$, as a function of $m_{A'}$ at dark Higgs boson mass of 40 GeV

Observed 90% CL upper limits on $\epsilon^{2}$, assuming of $\alpha_{D}=0.1$, as a function of $m_{A'}$ at dark Higgs boson mass of 50 GeV

Observed 90% CL upper limits on $\epsilon^{2}$, assuming of $\alpha_{D}=0.1$, as a function of $m_{A'}$ at dark Higgs boson mass of 60 GeV

Observed 90% CL upper limits on $\epsilon^{2}$, assuming of $\alpha_{D}=0.1$, as a function of $m_{A'}$ at dark Higgs boson mass of 70 GeV

Observed and expected upper limits at 95% CL on the branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 20 GeV

Observed and expected upper limits at 95% CL on the branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 30 GeV

Observed and expected upper limits at 95% CL on the branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 40 GeV

Observed and expected upper limits at 95% CL on the branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 50 GeV

Observed and expected upper limits at 95% CL on the branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 60 GeV

Observed and expected upper limits at 95% CL on the branching fraction as a function of $m_{A'}$ at dark Higgs boson mass of 70 GeV

Observed and expected upper limits at 95% CL on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 20 GeV

Observed and expected upper limits at 95% CL on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 30 GeV

Observed and expected upper limits at 95% CL on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 40 GeV

Observed and expected upper limits at 95% CL on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 50 GeV

Observed and expected upper limits at 95% CL on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 60 GeV

Observed and expected upper limits at 95% CL on $\alpha_{D}\epsilon^{2}$ as a function of $m_{A'}$ at dark Higgs boson mass of 70 GeV

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.56<|\eta^{\gamma}|<1.81$ and photon isolation cone radius $R=0.4$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.81<|\eta^{\gamma}|<2.01$ and photon isolation cone radius $R=0.4$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $2.01<|\eta^{\gamma}|<2.37$ and photon isolation cone radius $R=0.4$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $|\eta^{\gamma}|<0.6$ and photon isolation cone radius $R=0.2$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $0.6<|\eta^{\gamma}|<0.8$ and photon isolation cone radius $R=0.2$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $0.8<|\eta^{\gamma}|<1.37$ and photon isolation cone radius $R=0.2$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.56<|\eta^{\gamma}|<1.81$ and photon isolation cone radius $R=0.2$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.81<|\eta^{\gamma}|<2.01$ and photon isolation cone radius $R=0.2$.

Measured cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $2.01<|\eta^{\gamma}|<2.37$ and photon isolation cone radius $R=0.2$.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $|\eta^{\gamma}|<0.6$ and isolation cone radius $0.4$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $0.6<|\eta^{\gamma}|<0.8$ and isolation cone radius $0.4$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $0.8<|\eta^{\gamma}|<1.37$ and isolation cone radius $0.4$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.56<|\eta^{\gamma}|<1.81$ and isolation cone radius $0.4$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.81<|\eta^{\gamma}|<2.01$ and isolation cone radius $0.4$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $2.01<|\eta^{\gamma}|<2.37$ and isolation cone radius $0.4$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $|\eta^{\gamma}|<0.6$ and isolation cone radius $0.2$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $0.6<|\eta^{\gamma}|<0.8$ and isolation cone radius $0.2$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $0.8<|\eta^{\gamma}|<1.37$ and isolation cone radius $0.2$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.56<|\eta^{\gamma}|<1.81$ and isolation cone radius $0.2$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $1.81<|\eta^{\gamma}|<2.01$ and isolation cone radius $0.2$ at NNLO QCD.

Predicted cross sections for inclusive isolated-photon production as a function of $E_{\rm T}^{\gamma}$ for $2.01<|\eta^{\gamma}|<2.37$ and isolation cone radius $0.2$ at NNLO QCD.

Measured ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $|\eta^{\gamma}|<0.6$.

Measured ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $0.6<|\eta^{\gamma}|<0.8$.

Measured ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $0.8<|\eta^{\gamma}|<1.37$.

Measured ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $1.56<|\eta^{\gamma}|<1.81$.

Measured ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $1.81<|\eta^{\gamma}|<2.01$.

Measured ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $2.01<|\eta^{\gamma}|<2.37$.

Predicted ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $|\eta^{\gamma}|<0.6$ at NNLO QCD.

Predicted ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $0.6<|\eta^{\gamma}|<0.8$ at NNLO QCD.

Predicted ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $0.8<|\eta^{\gamma}|<1.37$ at NNLO QCD.

Predicted ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $1.56<|\eta^{\gamma}|<1.81$ at NNLO QCD.

Predicted ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $1.81<|\eta^{\gamma}|<2.01$ at NNLO QCD.

Predicted ratio of the differential cross sections for inclusive isolated-photon production for $R=0.2$ and $R=0.4$ as a function of $E_{\rm T}^{\gamma}$ for $2.01<|\eta^{\gamma}|<2.37$ at NNLO QCD.

Measured fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $|\eta^{\gamma}|<0.6$.

Measured fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $0.6<|\eta^{\gamma}|<0.8$.

Measured fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $0.8<|\eta^{\gamma}|<1.37$.

Measured fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $1.56<|\eta^{\gamma}|<1.81$.

Measured fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $1.81<|\eta^{\gamma}|<2.01$.

Measured fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $2.01<|\eta^{\gamma}|<2.37$.

Predicted fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $|\eta^{\gamma}|<0.6$ at NNLO QCD.

Predicted fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $0.6<|\eta^{\gamma}|<0.8$ at NNLO QCD.

Predicted fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $0.8<|\eta^{\gamma}|<1.37$ at NNLO QCD.

Predicted fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $1.56<|\eta^{\gamma}|<1.81$ at NNLO QCD.

Predicted fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $1.81<|\eta^{\gamma}|<2.01$ at NNLO QCD.

Predicted fiducial integrated cross section for inclusive isolated-photon production as a function of $R$ for $2.01<|\eta^{\gamma}|<2.37$ at NNLO QCD.

Di-muon mass distribution for the $\mu\mu\mu$ signal region for data and expected background. The expected signal distribution for $m_a = 35$ GeV is shown assuming $\sigma(t\bar{t}a)\times \text{Br}(a\rightarrow\mu\mu) = 4$ fb. Rare background processes include $ZZ+$jets, $WWZ$, $WZZ$, $ZZZ$, and $t\bar{t}t\bar{t}$.

Fit of the Double Crystal Ball width as a function of the $a$-boson mass in the $e\mu\mu$ channel.

Expected and observed 95% CL upper limit on signal cross section as a function of $m_a$ for $t\bar{t}a$.

Expected and observed 95% CL upper limit on signal cross section as a function of $m_a$ for $H^{+}\rightarrow aW$, considering a top pair cross section of $\sigma(pp\rightarrow t\bar{t})= 833$ pb for the charged Higgs boson mass, $m_{H^{+}} = 120$ GeV.

Expected and observed 95% CL upper limit on signal cross section as a function of $m_a$ for $H^{+}\rightarrow aW$, considering a top pair cross section of $\sigma(pp\rightarrow t\bar{t})= 833$ pb for the charged Higgs boson mass, $m_{H^{+}} = 140$ GeV.

Expected and observed 95% CL upper limit on signal cross section as a function of $m_a$ for $H^{+}\rightarrow aW$, considering a top pair cross section of $\sigma(pp\rightarrow t\bar{t})= 833$ pb for the charged Higgs boson mass, $m_{H^{+}} = 160$ GeV.

Comparison of the expected background with data in a region dominated by non-prompt background. The region is defined exactly like the $e\mu\mu$ signal region but requiring the two selected muons to have the same electrical charge.

Scan of the observed p-value as a function of $m_a$ for the background-only hypothesis.

Scan of the observed p-value as a function of $m_a$ for the background-only hypothesis for $e\mu\mu$ channel.

Scan of the observed p-value as a function of $m_a$ for the background-only hypothesis for $\mu\mu\mu$ channel.

Acceptance times efficiency for the $t\bar{t}a$, $a\rightarrow \mu\mu$ signal as a function of the $m_a$ hypothesis.

Acceptance times efficiency for the $t \rightarrow bH^+$, $H^+ \rightarrow W^+ a$, $a\rightarrow \mu\mu$ signal as a function of the $m_a$ and $m_{H^+}$ hypotheses, for the $e\mu\mu$ signal region. The upper left region of the plot corresponds to signals for which on-shell decays of $H^+ \rightarrow Wa$ are not possible. The CP conjugate process is also included.

Acceptance times efficiency for the $t \rightarrow bH^+ $, $H^+ \rightarrow W^+ a$, $a\rightarrow \mu\mu$ signal as a function of the $m_a$ and $m_{H^+}$ hypotheses, for the $\mu\mu\mu$ signal region. The upper left region of the plot corresponds to signals for which on-shell decays of $H^+ \rightarrow Wa$ are not possible. The CP conjugate process is also included.

Expected lower limits on $\tan\beta$ for the $t\rightarrow H^{+}b, H^{+}\rightarrow aW, a\rightarrow\mu\mu$ signal in the context of the 2HDM as a function of $m_{H^{+}}$ and $m_a$.

Observed lower limits on $\tan\beta$ for the $t\rightarrow H^{+}b, H^{+}\rightarrow aW, a\rightarrow\mu\mu$ signal in the context of the 2HDM as a function of $m_{H^{+}}$ and $m_a$.

Cutflow for two signal mass points for $t\bar{t}a$, $m_{a} = 20$ GeV and $m_{a} = 60$ GeV, and one signal mass point for $H^+ \rightarrow Wa$ for the electron channel $e\mu\mu$. For the signal yields, a cross section times branching ratio of 1 fb is assumed. The yields are presented before the profile likelihood fit.

Cutflow for the dominant backgrounds estimated from simulation ($t\bar{t}Z$, $WZ$, $t\bar{t}H$) and data, for the electron channel $e\mu\mu$. The yields are presented before the profile likelihood fit.

Cutflow for the signal mass point $t\bar{t}a$, $m_{a} = 20$ GeV, as well as the dominant backgrounds estimated from simulation ($t\bar{t}Z$, $WZ$, $t\bar{t}H$) and data, for the corresponding signal mass hypothesis, for the muon channel $\mu\mu\mu$. For the signal yields, a cross section times branching ratio of 1 fb is assumed. The yields are presented before the profile likelihood fit.

Cutflow for the signal mass point $t\bar{t}a$, $m_{a} = 60$ GeV, as well as the dominant backgrounds estimated from simulation ($t\bar{t}Z$, $WZ$, $t\bar{t}H$) and data, for the corresponding signal mass hypothesis, for the muon channel $\mu\mu\mu$. For the signal yields, a cross section times branching ratio of 1 fb is assumed. The yields are presented before the profile likelihood fit.

Cutflow for $H^+ \rightarrow W a$ with $m_{H^{+}} = 120$ GeV, $m_{a} = 30$ GeV for the muon channel $\mu\mu\mu$. A cross section times branching ratio of 1 fb is assumed. The yields are presented before the profile likelihood fit.

Total yields in the $e\mu\mu$ and $\mu\mu\mu$ signal regions for the expected background and data after the profile likelihood fit for a $m_a = 30$ GeV signal mass hypothesis.

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Post-fit distributions for the number of jets ($N_{j}$) in CR 1b(-). The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distribution for the difference between the number of positive events and the number of negative events ($N_{+}-N_{-}$) as a function of the number of jets ($N_{j}$) in the sum of four $t\bar{t}W$ CRs and the SR.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR HF e. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR HF $\mu$. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR Mat. Conv.. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR Low $m_{\gamma*}$. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and the predictions after a fit to data for the GNN distribution in the SR. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the number of jets. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the number of b-jets. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the sum of the four highest PCBT scores of jets in the event. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the sum of transverse momenta over all jets and leptons in the event. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Two-dimensional negative log-likelihood contour for the $t\bar{t}t$ cross section ($\sigma_{t\bar{t}t}$) versus the $t\bar{t}t\bar{t}$ cross section ($\sigma_{t\bar{t}t\bar{t}}$) when the normalisation of both processes are treated as free parameters in the fit.

The negative log-likelihood values as a function of the Higgs oblique parameter $\hat{H}$.

Cutflow for two representative signal samples used in this analysis. The excited tau mass $m_{\tau^\ast} = 2.75$ TeV and the contact interaction scale $\Lambda=10$ TeV. The LQ mass $m_\mathrm{LQ} = 1.3$ TeV. The event yields include all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Acceptance x efficiency of the $\tau^\ast$ signal SR selection

Acceptance x efficiency of the LQ signal SR selection

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Forward $\eta$

Inclusive $\lt N_{Lund} \gt$

Inclusive $\lt N_{Lund}^{Primary} \gt$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$