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.

Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.

Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.

The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.

Cutflow for an example higgsino signal with mass 225 GeV and lifetime 10 ns, in the H decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.

Cutflow for an example higgsino signal with mass 225 GeV and lifetime 10 ns, in the Z decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.

Cutflow for an example higgsino signal with mass 225 GeV and lifetime 2 ns, in the H decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.

Cutflow for an example higgsino signal with mass 225 GeV and lifetime 2 ns, in the Z decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.

Acceptance across the H decay mode signal grid, calculated using truth information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns).

Acceptance across the Z decay mode signal grid, calculated using truth information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns).

Efficiency across the H decay mode signal grid, calculated using reco information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns). Here, the numerator is the signal yield passing the reco selection and the denominator is the signal yield passing the truth selection.

Efficiency across the Z decay mode signal grid, calculated using reco information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns). Here, the numerator is the signal yield passing the reco selection and the denominator is the signal yield passing the truth selection.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-2 $q\bar{q}\to X^0 \to Z\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-2 $gg\to X^0 \to Z\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

The $m_{J\gamma}$ distributions of data events selected for the spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ search in the WMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-0 $gg \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-2 $gg \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-2 $q\bar{q} \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The jet mass distribution of large-$R$ jets originating from the hadronic decay of $W$ and $Z$ bosons produced from the decay of BSM bosons with mass $m_X = 2000$ GeV. The decays simulated are for the production models $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ with a spin-1 resonance $X^{\pm}$ and $gg\to X^0 \to Z\gamma$ with a spin-0 resonance $X^{0}$.

The photon $E_{T}$ selection criteria as a function of resonance mass for D2 categories. Selections for four different signal channels are shown together.

The photon $E_{T}$ selection criteria as a function of resonance mass for ZMASS or WMASS categories. Selections for four different signal channels are shown together.

Background-only fit to the observed numbers of events (black points) as a function of the diphoton invariant mass $m_{\gamma\gamma}$.

Expected and observed 95% CL upper limits on the signal fiducial cross section $\sigma_{\textrm{fid}}$, assuming 100% branching ratio for ALP decay into two photons, as a function of the hypothetical ALP mass $m_{\textrm{X}}$. The 1$\sigma$ and 2$\sigma$ confidence intervals are shown by the coloured bands.

Expected and observed 95% CL upper limits on the the ALP coupling constant, assuming 100% branching ratio for ALP decay into two photons, as a function of the hypothetical ALP mass $m_{\textrm{X}}$. The 1$\sigma$ and 2$\sigma$ confidence intervals are shown by the coloured bands. Contours of the ALP natural width $\Gamma$ are illustrated by the smooth blue solid lines.

Fiducial region acceptance of signal events for coupling constant f$^{-1}=0.05$ TeV$^{-1}$. EL, SD, and DD stand for the exclusive, single-dissociative, and double-dissociative processes. The acceptances are parameterised as $p_0 + p^{i}_{1} e^{p_{2}^{i} m_{\textrm{X}}} + p_{3}^{i} e^{p_{4}^{i} m_{\textrm{X}}} $ , where $i$ specifies the process type, exclusive, single-dissociative, or double-dissociative.

Correction factors $A_{\textrm{X}}/A_{0}$ and $C_{\textrm{X}}$ for the exclusive signal events with ALP coupling constant f$^{-1}$=0.05 TeV$^{-1}$, where acceptance $A_{\textrm{X}}$ and efficiency $C_{\textrm{X}}$ are defined in arXiv:2102.13405. $A_{\textrm{X}}$/$A_{0}$ and $C_{\textrm{X}}$ are parameterised as $p_0 + p^{i}_{1} e^{p_{2}^{i} m_{\textrm{X}}} + p_{3}^{i} e^{p_{4}^{i} m_{\textrm{X}}}$, where $i$ specifies the process type.

Simulated ALP signal cutflow for $m_{\rm{X}}$=300 GeV and $m_{\rm{X}}$=1200 GeV. The yields are normalized to a luminosity of 14.6 fb$^{-1}$. Selections are applied for each side after the selection on acoplanarity. The union of the sets of selected events is taken finally.

Figure 4a

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Acceptance $\times$ efficiency for bSR1e.

Acceptance $\times$ efficiency for bSR2e.

Acceptance $\times$ efficiency for bSR2mu.

Acceptance $\times$ efficiency for rSROS2e.

Acceptance $\times$ efficiency for rSROS2mu.

Acceptance $\times$ efficiency for rSRSS2e.

Acceptance $\times$ efficiency for rSRSS2mu.

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

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

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

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

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

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

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

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

$\gamma p \rightarrow J/\psi p$ differential cross sections 10.36–11.44 GeV beam energy range, average $t$ and beam energy in bins of $t$. The first cross section uncertainties are statistical, and the second are systematic. The overall average beam energy is 10.82 GeV. There is an additional fully correlated systematic uncertainty of 19.5% on the total cross section, not included here.

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 $\hat{H}$.

The measured and expected cross-sections of $t\bar{t}t\bar{t}$ production.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the lower part of the contour.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the upper part of the contour.

Observed 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the lower part of the contour.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the upper part of the contour.

Observed 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

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

Data bootstrap post unfolding for the inclusive cross-section. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $|\eta^{l}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $|\Delta\phi^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/rad].

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

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

Data bootstrap post unfolding for the differential absolute cross-section for $|y^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

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

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

Data bootstrap post unfolding for the double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The highest invariant mass bin of the two distributions contains the overflows.

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

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

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest invariant mass bin of the two distributions contains the overflows.

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

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

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

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

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

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $|\eta^{l}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $|\Delta\phi^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/rad].

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

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

Data bootstrap post unfolding for the differential normalised cross-section for $|y^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry.

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

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

Data bootstrap post unfolding for the double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The highest invariant mass bin of the two distributions contains the overflows.

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

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

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest invariant mass bin of the two distributions contains the overflows.

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

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

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

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

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

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.