Fig. 3 bottom figure - Distributions of MC simulation compared with data for reconstructed shower energy in the 1$e$0$p$0$\pi$ signal channel, along with the LEE Signal Model 2. The signal and background event categories are summed to form the unconstrained prediction (excluding LEE). Signal events correspond to $\nu_e$ CC events. Background events include $\nu$ with $\pi^0$ events, $\nu$ other events, and cosmic ray events. In Fig. 3, the LEE component is plotted on top of the constrained prediction (excluding LEE) for illustrative purposes. In all statistical tests (results summarized in Table I), the prediction under an LEE hypothesis corresponds to a constrained prediction including LEE. The statistical uncertainties of data use a combined Neyman-Pearson (CNP) version (Eq.(19) in https://doi.org/10.1016/j.nima.2020.163677).

Fig. 4 top figure - Distributions of MC simulation compared with data for reconstructed shower cos($\theta$) in the 1$e$N$p$0$\pi$ signal channel, along with the LEE Signal Model 2. The signal and background event categories are summed to form the unconstrained prediction (excluding LEE). Signal events correspond to $\nu_e$ CC events. Background events include $\nu$ with $\pi^0$ events, $\nu$ other events, and cosmic ray events. In Fig. 4, the LEE component is plotted on top of the constrained prediction (excluding LEE) for illustrative purposes. In all statistical tests (results summarized in Table I), the prediction under an LEE hypothesis corresponds to a constrained prediction including LEE. The statistical uncertainties of data use a combined Neyman-Pearson (CNP) version (Eq.(19) in https://doi.org/10.1016/j.nima.2020.163677).

Fig. 4 bottom figure - Distributions of MC simulation compared with data for reconstructed shower cos($\theta$) in the 1$e$0$p$0$\pi$ signal channel, along with the LEE Signal Model 2. The signal and background event categories are summed to form the unconstrained prediction (excluding LEE). Signal events correspond to $\nu_e$ CC events. Background events include $\nu$ with $\pi^0$ events, $\nu$ other events, and cosmic ray events. In Fig. 4, the LEE component is plotted on top of the constrained prediction (excluding LEE) for illustrative purposes. In all statistical tests (results summarized in Table I), the prediction under an LEE hypothesis corresponds to a constrained prediction including LEE. The statistical uncertainties of data use a combined Neyman-Pearson (CNP) version (Eq.(19) in https://doi.org/10.1016/j.nima.2020.163677).

Fig. 1 top figure - Reconstructed neutrino energy distributions of the 1$\mu$N$p$0$\pi$ control sample (sideband). The signal and background event categories are summed to form the unconstrained prediction. Signal events correspond to $\nu_\mu$ CC events. Background events include $\nu_e$ CC events, NC without $\pi^0$ events, NC with $\pi^0$ events, and cosmic ray events. The statistical uncertainties of data use a combined Neyman-Pearson (CNP) version (Eq.(19) in https://doi.org/10.1016/j.nima.2020.163677).

Fig. 1 middle figure - Reconstructed neutrino energy distributions of the 1$\mu$0$p$0$\pi$ control sample (sideband). The signal and background event categories are summed to form the unconstrained prediction. Signal events correspond to $\nu_\mu$ CC events. Background events include $\nu_e$ CC events, NC without $\pi^0$ events, NC with $\pi^0$ events, and cosmic ray events. The statistical uncertainties of data use a combined Neyman-Pearson (CNP) version (Eq.(19) in https://doi.org/10.1016/j.nima.2020.163677).

Fig. 1 bottom figure - Reconstructed neutrino energy distributions of the NC$\pi^{0}$ control sample (sideband). The signal and background event categories are summed to form the unconstrained prediction. Signal events correspond to NC with $\pi^0$ events. Background events include $\nu_\mu$ CC events, $\nu_e$ CC events, NC without $\pi^0$ events, and cosmic ray events. Only bins up to 1 GeV in this sideband are released and are used in the constraint procedure due to low statistics above this energy. The statistical uncertainties of data use a combined Neyman-Pearson (CNP) version (Eq.(19) in https://doi.org/10.1016/j.nima.2020.163677).

The covariance matrix includes both signal channels and sideband channels, with the LEE component either excluded or included in the signal channels. All channels are binned in reconstructed neutrino energy. Fig. 2 in Supplemental Material shows the corresponding correlation matrix. The constraint procedure uses the correlations with sideband channels to update the prediction and its uncertainty in the signal region. The matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. Data statistical uncertainties are not included.

The covariance matrix includes both signal channels and sideband channels, with the LEE component either excluded or included in the signal channels. Signal channels are binned shower energy and sideband channels are binned in reconstructed neutrino energy. The constraint procedure uses the correlations with sideband channels to update the prediction and its uncertainty in the signal region. The matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. Data statistical uncertainties are not included.

The covariance matrix includes both signal channels and sideband channels, with the LEE component either excluded or included in the signal channels. Signal channels are binned shower cos($\theta$) and sideband channels are binned in reconstructed neutrino energy. The constraint procedure uses the correlations with sideband channels to update the prediction and its uncertainty in the signal region. The matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. Data statistical uncertainties are not included.

The covariance matrices of the sideband channels, binned in reconstructed neutrino energy. The systematic covariance matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. The total covariance matrix additionally includes data statistical uncertainties (CNP version). The $\chi^2$ values shown in Fig. 1 are calculated using this total covariance matrix, together with the data and MC predictions from the released sideband distribtions.

The unconstrained covariance matrices of the signal channels, binned in reconstructed neutrino energy, with the LEE component either excluded or included. The systematic covariance matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. The total covariance matrix additionally includes data statistical uncertainties (CNP version). The unconstrained systematic uncertainty in neutrino energy, shown in Supplemental Material Fig. 19, is derived from this systematic covariance matrix (excluding LEE).

The constrained covariance matrices of the signal channels, binned in reconstructed neutrino energy, with the LEE component either excluded or included. The constrained systematic uncertainty in neutrino energy, shown in Supplemental Material Fig. 19, is derived from the systematic covariance matrix (excluding LEE). The $\chi^2$ values shown in Fig. 2 and Table I are calculated using these total covariance matrices, together with the data and constrained predictions from the released signal distributions under the $H_0$ hypothesis (LEE excluded) and $H_1$ hypothesis (LEE included).

The unconstrained covariance matrices of the signal channels, binned in reconstructed shower energy, with the LEE component either excluded or included. The systematic covariance matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. The total covariance matrix additionally includes data statistical uncertainties (CNP version). The unconstrained systematic uncertainty in shower energy, shown in Supplemental Material Fig. 20, is derived from this systematic covariance matrix (excluding LEE).

The constrained covariance matrices of the signal channels, binned in reconstructed shower energy, with the LEE component either excluded or included. The constrained systematic uncertainty in shower energy, shown in Supplemental Material Fig. 20, is derived from the systematic covariance matrix (excluding LEE). The $\chi^2$ values shown in Fig. 3 and Table I are calculated using these total covariance matrices, together with the data and constrained predictions from the released signal distributions under the $H_0$ hypothesis (LEE excluded) and $H_1$ hypothesis (LEE included).

The unconstrained covariance matrices of the signal channels, binned in reconstructed shower cos($\theta$), with the LEE component either excluded or included. The systematic covariance matrix accounts for uncertainties and correlations between bins due to flux, cross-section, hadron reinteraction, detector systematics, MC statistics, and cosmic ray statistics. The total covariance matrix additionally includes data statistical uncertainties (CNP version). The unconstrained systematic uncertainty in shower cos($\theta$), shown in Supplemental Material Fig. 21, is derived from this systematic covariance matrix (excluding LEE).

The constrained covariance matrices of the signal channels, binned in reconstructed shower cos($\theta$), with the LEE component either excluded or included. The constrained systematic uncertainty in shower cos($\theta$), shown in Supplemental Material Fig. 21, is derived from the systematic covariance matrix (excluding LEE). The $\chi^2$ values shown in Fig. 4 and Table I are calculated using these total covariance matrices, together with the data and constrained predictions from the released signal distributions under the $H_0$ hypothesis (LEE excluded) and $H_1$ hypothesis (LEE included).

Target asymmetry for $p\pi^0$ as a function of the polar angle for bins of the incident photon energy in the range of $E_\gamma$ = 650-2600 MeV.

Target asymmetry for $p\pi^0$ as a function of the $p\pi0$ invariant mass for bins of the incident photon energy in the range of $E_\gamma$ = 650-2600 MeV.

Target asymmetry for $p\pi^0$ as a function of the $\phi^*$ angle for bins of the incident photon energy in the range of $E_\gamma$ = 650-2600 MeV.

Double polarization observables for $\pi^0\pi^0$ as a function of the polar angle for bins of the incident photon energy in the range of $E_\gamma$ = 650-950 MeV.

Double polarization observables for $\pi^0\pi^0$ as a function of the $\pi^0\pi0$ invariant mass for bins of the incident photon energy in the range of $E_\gamma$ = 650-950 MeV.

Double polarization observables for $\pi^0\pi^0$ as a function of the polar angle for bins of the incident photon energy in the range of $E_\gamma$ = 650-950 MeV.

Double polarization observables for $p\pi^0$ as a function of the $p\pi0$ invariant mass for bins of the incident photon energy in the range of $E_\gamma$ = 650-950 MeV.

Double polarization observables for $\pi^0\pi^0$ as a function of the $\phi^*$ angle for bins of the incident photon energy in the range of $E_\gamma$ = 650-950 MeV.

Double polarization observables for $p\pi^0$ as a function of the $\phi^*$ angle for bins of the incident photon energy in the range of $E_\gamma$ = 650-950 MeV.

Target asymmetries for $\pi^0\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 650-800 MeV.

Target asymmetries for $\pi^0\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 800-950 MeV.

Target asymmetries for $\pi^0\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 950-1100 MeV.

Target asymmetries for $\pi^0\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 1100-1250 MeV.

Target asymmetries for $p\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 650-800 MeV.

Target asymmetries for $p\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 800-950 MeV.

Target asymmetries for $p\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 950-1100 MeV.

Target asymmetries for $p\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 1100-1250 MeV.

Double polarization observables for $\pi^0\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 650-800 MeV.

Double polarization observables for $\pi^0\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 800-950 MeV.

Double polarization observables for $p\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 650-800 MeV.

Double polarization observables for $p\pi^0$ as a function of $\cos\vartheta$, invariant mass and $\phi^*$ for bins of the incident photon energy in the range of $E_\gamma$ = 800-950 MeV.

The $m_{\mathrm{eff}}$ distribution for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement in SR2. Distributions for data, the estimated SM backgrounds, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal region.

Expected 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/Z$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on wino $W/h$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on $\tilde{\ell}/\tilde{\nu}$ NLSP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{12k} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on gluino NSLP pair production with RPV $\tilde{\chi}_1^0$ decays via $\lambda_{i33} \neq 0$ where $i,k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where the signal regions are not mutually exclusive, the observed $\mathrm{CL}_s$ value is taken from the signal region with the better expected $\mathrm{CL}_s$ value.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/Z$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}/\tilde{\chi}_2^0$ masses in the context of the wino $W/h$ RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the slepton/sneutrino masses in the context of the slepton/sneutrino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0A. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0A, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{12k} \neq 0$ couplings. The signal region used to obtain the limits is SR0B. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the gluino masses in the context of the gluino RPV scenario with $\lambda_{i33} \neq 0$ couplings. The signal region used to obtain the limits is the combination of SR0B, SR1 and SR2. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}\tilde{\chi}_1^{0}$ masses in the context of the higgsino GGM scenario in SR0C. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

Observed exclusion limits on the $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}\tilde{\chi}_1^{0}$ masses in the context of the higgsino GGM scenario in SR0D. All limits are computed at 95% CL. The grey numbers represent the 95% CL upper limits on the production cross-section (in fb) obtained using the signal efficiency and acceptance specific to this model.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/Z$ model for $\lambda_{12k} \neq 0$ RPV couplings. For the $\lambda_{12k} \neq 0$ case, the results from SR0B are adopted everywhere in the final exclusion limit contours, as is found to be the most powerful signal region among SR0A and SR0B in the majority of the signal grid points of this model.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/Z$ model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2. The results from the combination SR0B + SR1 + SR2 are finally adopted everywhere in the final exclusion limit contour since they provide the best expected limit.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/h$ model for $\lambda_{12k} \neq 0$ RPV couplings. For the $\lambda_{12k} \neq 0$ case, the results from SR0B are adopted everywhere in the final exclusion limit contours, as is found to be the most powerful signal region among SR0A and SR0B in the majority of the signal grid points of this model.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the wino $W/h$ model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2. The results from the combination SR0B + SR1 + SR2 are finally adopted everywhere in the final exclusion limit contour since they provide the best expected limit.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the slepton/sneutrino model for $\lambda_{12k} \neq 0$ RPV couplings.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the slepton/sneutrino model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the gluino model for $\lambda_{12k} \neq 0$ RPV couplings.

The best expected exclusion power between signal regions at each signal point as is adopted in the limit calculation of the gluino model for $\lambda_{i33} \neq 0$ RPV couplings. A combination of SR0A + SR1 + SR2 or SR0B + SR1 + SR2 is tested to detect the best expected limit; shown on the plot is the region (SR0A or SR0B) which is chosen to provide the best expected limit in combination with SR1 and SR2.

The best expected exclusion power between the overlapping SR0C and SR0D at each signal point as is adopted in the limit combination of the GGM higgsino model.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0A. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{12k} \neq 0$ model fulfilling the selection criteria of SR0B. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the GGM Higgsino models with BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=100% and BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=50% fulfilling the selection criteria of SR0C. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the GGM Higgsino models with BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=100% and BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=50% fulfilling the selection criteria of SR0D. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR1. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{+}\tilde{\chi}_1^{-}$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/Z$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the Wino $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^{0}$ $W/h$ $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the slepton/sneutrino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Acceptance ($A$) at particle level and selection efficiency ($\epsilon$) at reconstruction level for the gluino $\lambda_{i33} \neq 0$ model fulfilling the selection criteria of SR2. The implementation of "simple" cut selection at truth level uses the SimpleAnalysis framework and the code can be found in the Resources of the HepData entry for this paper.

Cutflow event yields in regions SR0A and SR0B for RPV models with the $\lambda_{12k} \neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The last entries show the normalized number of events surviving the selection requirements of SR0A and SR0B.

Cutflow event yields in regions SR0C and SR0D for GGM Higgsino models with BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=100% or BR($\tilde{\chi}_1^0 \rightarrow Z \tilde{G}$)=50%, and $\tilde{\chi}_1^{\pm}\tilde{\chi}_2^0\tilde{\chi}_1^0$ mass of 400 GeV. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The "Generator Filter" step is applied during the MC generation of the simulated events; the BR=100% sample has a generator filter of $\geq 4e/\mu$ leptons with $p_{\mathrm{T}}>4$ GeV and $|\eta|<2.8$, and the BR=50% sample has a generator filter of $\geq 4 e/\mu/\tau_{\mathrm{had-vis}}$ leptons with $p_{\mathrm{T}}(e,\mu)>4$ GeV, $p_{\mathrm{T}}(\tau_{\mathrm{had-vis}}^{\mathrm{visible}})>15$ GeV and $|\eta|<2.8$. The $ZZ$ selection cutflow step refers to the mass window cut for the leading and subleading $Z$ boson candidate between $81.2-101.2$ GeV and $61.2-101.2$ GeV, respectively. The last entries show the efficiency of events surviving the selection requirements defined in SR0C and SR0D.

Cutflow event yields in region SR1 for RPV models with the $\lambda_{i33} \neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The last entries show the normalized number of events surviving the selection requirements of SR1.

Cutflow event yields in region SR2 for RPV models with the $\lambda_{i33} \neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 36.1$ fb$^{-1}$. The "Initial" number indicates the weighted number of events before any selection cut is applied. The last entries show the normalized number of events surviving the selection requirements of SR2.

Cross sections for the slepton/snuetrino model for different NLSP masses.

Distributions of E_T^miss in the signal region of the had-had channel before the respective selection requirements, indicated by the vertical line and arrow, are applied. Here, &tau;₁ (&tau;₂) refers to the leading (subleading) &tau;_had. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty in the SM background. The error bars on the black data points represent the statistical uncertainty in the data yields. The dashed line shows the expected additional yields from a benchmark signal model. The rightmost bin includes the overflow.

<b>Exclusion contour (obs)</b> Expected (solid blue line) and observed (solid red line) exclusion-limit contours at 95% confidence level in the plane of top-squark and tau-slepton mass for the simplified model, obtained from the statistical combination of the lep-had and had-had channels, using full experimental and theory systematic uncertainties except the theoretical uncertainty in the signal cross section. The yellow band shows one-standard-deviation variations around the expected limit contour. The dotted red lines indicate how the observed limit moves when varying the signal cross section up or down by the corresponding uncertainty in the theoretical value. For comparison, the plot also shows the observed exclusion contour from the ATLAS Run-1 analysis as the area shaded in gray and the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments as a green band.

<b>Exclusion contour (exp)</b> Expected (solid blue line) and observed (solid red line) exclusion-limit contours at 95% confidence level in the plane of top-squark and tau-slepton mass for the simplified model, obtained from the statistical combination of the lep-had and had-had channels, using full experimental and theory systematic uncertainties except the theoretical uncertainty in the signal cross section. The yellow band shows one-standard-deviation variations around the expected limit contour. The dotted red lines indicate how the observed limit moves when varying the signal cross section up or down by the corresponding uncertainty in the theoretical value. For comparison, the plot also shows the observed exclusion contour from the ATLAS Run-1 analysis as the area shaded in gray and the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments as a green band.

Expected numbers of events from the SM background processes from the background fit and observed event yield in data for the signal regions in the lep-had and had-had channel, given for an integrated luminosity of 36.1 fb^-1. The expected yield for the signal model with m(t&#771;₁)=1100 GeV and m(&tau;&#771;₁)=590 GeV is shown for comparison. The uncertainties include both the statistical and systematic uncertainties and are truncated at zero. The total background from events with a fake tau lepton in the lep-had channel (fake &tau;_had + e /&mu;) is obtained from the fake-factor method.

Normalization factors obtained from the background-only fit. The normalization factor for tt&#772; events with fake tau leptons is only relevant for the had-had channel.

Left to right: observed 95% CL upper limits on the visible cross section (&lang; A &epsilon; &sigma; &rang;_obs⁹⁵) and on the number of signal events (S_obs⁹⁵ ). The third column (S_exp⁹⁵) shows the expected 95% CL upper limit on the number of signal events, given the expected number (and &plusmn; 1&sigma; excursions on the expectation) of background events. The last two columns indicate the CL_b value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)) and the corresponding significance (Z).

Signal acceptance A in percent for the signal region of the lep-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Signal acceptance A in percent for the signal region of the had-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Reconstruction efficiency &epsilon; in percent for the signal region of the lep-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Reconstruction efficiency &epsilon; in percent for the signal region of the had-had channel. The signal acceptance A is determined from a generator-level implementation of the analysis. It includes the branching ratios for the decays of the tau leptons. The selection efficiency &epsilon; is calculated using reconstructed objects, i.e. including all detector effects, and defined such that the event yields in the signal regions are given by the product A &middot; &epsilon; &middot; N_signal, where N_signal = &sigma; &middot; 36.1 fb^-1 and &sigma; is the theoretical prediction for the production cross section of top-squark pairs with the mass given on the horizontal axis.

Upper exclusion limits at 95% confidence level on the observed cross section &sigma;_obs⁹⁵ for the simplified signal model as function of the top-squark mass m(t&#771;₁) and tau-slepton mass m(&tau;&#771;₁) in GeV.

Number of events passing each selection step of SR LH (lep-had channel) before (raw number) and after applying event-based weights (weighted) for signal point m(t&#771;₁) = 1100 GeV, m(&tau;&#771;₁) = 590 GeV. The preselection step here does not include the requirements on the tau p_T and the number of bjets.

Number of events passing each selection step of SR HH (had-had channel) before (raw number) and after applying event-based weights (weighted) for signal point m(t&#771;₁) = 1100 GeV, m(&tau;&#771;₁) = 590 GeV. The preselection step here does not include the requirements on the tau p_T and the number of bjets. Here, &tau;₁ (&tau;₂) refers to the leading (subleading) &tau;_had.

Distributions of ETmiss for data and the expected SM backgrounds in the 2l+jets channel for SR2-int/high, without the final ETmiss requirement applied. Two signal points are added for comparison.

Distributions of ETmiss for data and the expected SM backgrounds in the 2l+jets channel for SR2-low, without the final ETmiss requirement applied. Two signal points are added for comparison.

Distributions of ETmiss for data and the estimated SM backgrounds in the 3l channel for SR3-slep-a. Two signal points are added for comparison.

Distributions of ETmiss for data and the estimated SM backgrounds in the 3l channel for SR3-slep-b. Two signal points are added for comparison.

Distributions of the third leading lepton pT in SR3-slep-c,d,e. Two signal points are added for comparison.

Distributions of ETmiss for data and the estimated SM backgrounds in the 3l channel for SR3-WZ-0Ja,b,c. Two signal points are added for comparison.

Distributions of ETmiss for data and the estimated SM backgrounds in the 3l channel for SR3-WZ-1Ja. Two signal points are added for comparison.

Distributions of ETmiss for data and the estimated SM backgrounds in the 3l channel for SR3-WZ-1Jb. Two signal points are added for comparison.

Distributions of ETmiss for data and the estimated SM backgrounds in the 3l channel for SR3-WZ-1Jc. Two signal points are added for comparison.

Expected 95% CL exclusion limit for chargino-pair production.

Observed 95% CL exclusion limit for chargino-pair production.

Expected 95% CL exclusion limit for direct slepton production.

Observed 95% CL exclusion limit for direct slepton production.

Expected 95% CL exclusion limit for chargino-neutralino production with slepton-mediated decays.

Observed 95% CL exclusion limit for chargino-neutralino production with slepton-mediated decays.

Expected 95% CL exclusion limit for chargino-neutralino production with W/Z-mediated decays.

Observed 95% CL exclusion limit for chargino-neutralino production with W/Z-mediated decays.

The mT2 distributions for data and the estimated SM backgrounds in the exclusive SF signal regions of the 2l+0jets channel in the regions 111 < mll < 150 GeV (corresponding to SR2-SF-a,b,c,d). Two signal points are added for comparison.

The mT2 distributions for data and the estimated SM backgrounds in the exclusive SF signal regions of the 2l+0jets channel in the regions 150 < mll < 200 GeV (corresponding to SR2-SF-e,f,g,h). Two signal points are added for comparison.

The mT2 distributions for data and the estimated SM backgrounds in the exclusive SF signal regions of the 2l+0jets channel in the regions 200 < mll < 300 GeV (corresponding to SR2-SF-i,j,k,l). Two signal points are added for comparison.

The mT2 distributions for data and the estimated SM backgrounds in the exclusive SF signal regions of the 2l+0jets channel in the regions mll > 300 GeV (corresponding to SR2-SF-m). Two signal points are added for comparison.

Signal acceptance for C1C1 production in SR2-SFloose for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$ .

Signal efficiency for C1C1 production in SR2-SFloose for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal acceptance for C1C1 production in SR2-SFtight for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal efficiency for C1C1 production in SR2-SFtight for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal acceptance for C1C1 production in SR2-DF100 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal efficiency for C1C1 production in SR2-DF100 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal acceptance for C1C1 production in SR2-DF150 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal efficiency for C1C1 production in SR2-DF150 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal acceptance for C1C1 production in SR2-DF200 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal efficiency for C1C1 production in SR2-DF200 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal acceptance for C1C1 production in SR2-DF300 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal efficiency for C1C1 production in SR2-DF300 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

Signal acceptance for direct Slepton production in SR2-SF-Loose for the process P P -> $\tilde{\ell}^\pm \tilde{\ell}^\mp$ -> $\ell^\pm \ell^\mp \widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for direct Slepton production in SR2-SF-Loose for the process P P -> $\tilde{\ell}^\pm \tilde{\ell}^\mp$ -> $\ell^\pm \ell^\mp \widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for direct Slepton production in SR2-SF-Tight for the process P P -> $\tilde{\ell}^\pm \tilde{\ell}^\mp$ -> $\ell^\pm \ell^\mp \widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for direct Slepton production in SR2-SF-Tight for the process P P -> $\tilde{\ell}^\pm \tilde{\ell}^\mp$ -> $\ell^\pm \ell^\mp \widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for C1N2 production in SR2-low for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->2j) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for C1N2 production in SR2-low for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->2j) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for C1N2 production in SR2-int for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->2j) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for C1N2 production in SR2-int for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->2j) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for C1N2 production in SR2-high for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->2j) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for C1N2 production in SR2-high for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->2j) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for Slep production in SR3-slepa for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal efficiency for Slep production in SR3-slepa for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal acceptance for Slep production in SR3-slepb for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal efficiency for Slep production in SR3-slepb for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal acceptance for Slep production in SR3-slepc for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal efficiency for Slep production in SR3-slepc for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal acceptance for Slep production in SR3-slepd for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal efficiency for Slep production in SR3-slepd for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal acceptance for Slep production in SR3-slepe for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal efficiency for Slep production in SR3-slepe for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Signal acceptance for WZ production in SR3-WZ-0Ja for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for WZ production in SR3-WZ-0Ja for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for WZ production in SR3-WZ-0Jb for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for WZ production in SR3-WZ-0Jb for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for WZ production in SR3-WZ-0Jc for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for WZ production in SR3-WZ-0Jc for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for WZ production in SR3-WZ-1Ja for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for WZ production in SR3-WZ-1Ja for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for WZ production in SR3-WZ-1Jb for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for WZ production in SR3-WZ-1Jb for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal acceptance for WZ production in SR3-WZ-1Jc for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal efficiency for WZ production in SR3-WZ-1Jc for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

Signal regions contributing to the observed exclusion limit for chargino-neutralino production with W/Z-mediated decays.

Expected 95% CL exclusion limit for chargino-neutralino production with W/Z-mediated decays in the 2l+jets channel.

Observed 95% CL exclusion limit for chargino-neutralino production with W/Z-mediated decays in the 2l+jets channel.

Expected 95% CL exclusion limit for chargino-neutralino production with W/Z-mediated decays in the 3l channel.

Observed 95% CL exclusion limit for chargino-neutralino production with W/Z-mediated decays in the 2l+jets channel.

Expected 95% CL exclusion limit for left-handed slepton production.

Observed 95% CL exclusion limit for left-handed slepton production.

Expected 95% CL exclusion limit for right-handed slepton production.

Observed 95% CL exclusion limit for right-handed slepton production.

95% upper limit on production cross-section for chargino-pair production.

95% upper limit on production cross-section for direct slepton production.

95% upper limit on production cross-section for chargino-neutralino production with slepton-mediated decays.

95% upper limit on production cross-section for chargino-neutralino production with W/Z-mediated decays

<b>Cutflow 1</b> Event counts for a signal point in SR2-SF-loose for the process P P -> $\tilde{\ell}^\pm \tilde{\ell}^\mp$ -> $\ell^\pm \ell^\mp \widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

<b>Cutflow 2</b> Event counts for a signal point in SR2-SF-loose and SR2-DF-100 for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

<b>Cutflow 3</b> Event counts for two signal points in SR2-int for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

<b>Cutflow 4</b> Event counts for two signal points in SR2-low for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_1^\mp$ -> 2x $\ell \nu \widetilde{\chi}_1^0$.

<b>Cutflow 5</b> Event counts for two signal points in SR3-WZ-0Ja/b/c and SR3-WZ-1Ja/b/c for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $W$(->l $\nu$) $Z$(->2l) $\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$.

<b>Cutflow 6</b> Event counts for two signal points in SR3-slepa-e for the process P P -> $\widetilde{\chi}_1^\pm \widetilde{\chi}_2^0$ -> $\tilde{\ell}_L \tilde{\ell}_L l (\tilde{\nu}\nu), l \tilde{\nu} \tilde{\ell}_L (\tilde{\nu}\nu)$ -> $l \nu \widetilde{\chi}_1^0 l l (\nu \nu) \widetilde{\chi}_1^0$.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 4-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and $p_{T}^{t,had}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 6-jet inclusive configuration and $p_{T}^{t,had}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 6-jet inclusive configuration and $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 6-jet inclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 6-jet inclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration and $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 5-jet exclusive configuration and $p_{T}^{t,had}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 5-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t,had}$ in the 5-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration and $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Statistical correlation matrix between |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration and |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, obtained through the Bootstrap Method.

Covariance matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of $p_{T}^{t,had}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of $p_{T}^{t,had}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of $p_{T}^{t,had}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of $p_{T}^{t,had}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of $p_{T}^{t,had}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of $p_{T}^{t,had}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Covariance matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of $p_{T}^{t,had}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of $p_{T}^{t,had}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of $p_{T}^{t,had}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of $p_{T}^{t,had}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of $p_{T}^{t,had}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of $p_{T}^{t,had}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the absolute cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Correlation matrix of the relative cross-section as function of |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration, accounting for the statistical and systematic uncertainties.

Systematic uncertanties for the absolute differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 4-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for $p_{T}^{t,had}$ in the 4-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for $p_{T}^{t,had}$ in the 4-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for $p_{T}^{t\bar{t}}$ in the 4-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 6-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for $p_{T}^{t,had}$ in the 6-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for $p_{T}^{t,had}$ in the 6-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for $p_{T}^{t\bar{t}}$ in the 6-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 5-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for $p_{T}^{t,had}$ in the 5-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for $p_{T}^{t,had}$ in the 5-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for $p_{T}^{t\bar{t}}$ in the 5-jet exclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the absolute differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Systematic uncertanties for the relative differential cross-section at particle-level for |$p_{out}^{t\bar{t}}$| in the 4-jet inclusive configuration. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Measured differential cross section with associated uncertainties as a function of COS(THETA*). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of DELTAYRAP(2GAMMA). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of MULT(JET,PT>30 GEV). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of MULT(JET,PT>50 GEV). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(JET1). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(JET2). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of HT. Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of YRAP(JET1). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of YRAP(JET2). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of M(2JET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of DELTAYRAP(2JET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of ABSDPHI(2JET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of DPHI(2JET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(2GAMMA2JET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of DPHI(2GAMMA,2JET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of TAUJET. Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of SUM(TAUJET). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(2GAMMA) [NJET=0,PT>30 GEV]. Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(2GAMMA) [NJET=1,PT>30 GEV]. Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(2GAMMA) [NJET=2,PT>30 GEV]. Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of PT(2GAMMA) [NJET>=3,PT>30 GEV]. Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

The measured cross sections or cross section limits of the diphoton, VBF-enhanced, Nlepton $\geq$ 1, high $E_{T}^{miss}$, and ttH-enhanced fiducial regions are shown.

Measured differential cross section with associated uncertainties as a function of diphoton transverse momentum in bins of ABS(COS(THETA*)). Each systematic uncertainty sources is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.Each systematic uncertainty sources is fully uncorrelated with the other sources.

Non-perturbative correction factors in percent accounting for the impact of hadronisation and the underlying event activity for all measured variables and fiducial regions. Regions of phase space where no reliable estimate could be obtained are listed as 100 without uncertainties. Uncertainties are evaluated by deriving these factors using different generators and tunes as described in the text. No factor are given for the Nlepton $\geq$ 1 and High-$E_{T}^{miss}$ fiducial regions as the gluon fusion contamination in both is negligible.

Isolation efficiencies in percent for gluon fusion $H\rightarrow\gamma\gamma$ for each fiducial region/variable bin measured in this analysis. The isolation efficiency is defined as the probability for both photons to fulfil the isolation criteria (as described in Section 9.1) for events that pass the diphoton kinematic criteria. Regions of phase space where no reliable estimate could be obtained are listed as 100 without uncertainties. Uncertainties are assigned in the same way as for the non-perturbative correction factors: by varying the fragmentation and underlying event modelling. These factors can be multiplied by the kinematic acceptance factors (see Table 29) to extrapolate an inclusive gluon fusion Higgs prediction to the fiducial volume used in this analysis. No factors for the Nlepton $\geq$ 1 and High $E_{T}^{miss}$ fiducial regions are provided as the gluon fusion contamination is negligible.

Combined non-perturbative (Table 27) and particle-level isolation correction factors (Table 28) in percent accounting for the impact of hadronisation and the underlying event activity for all measured variables and fiducial regions. Regions of phase space where no reliable estimate could be obtained are listed as 100 without uncertainties. The uncertainties on the combined values properly take into account the correlations between both multiplicative factors.

Diphoton kinematic acceptances in percent for gluon-gluon fusion for the diphoton fiducial region and all differential variable bins studied in this paper, defined as the probability to fulfill the diphoton kinematic criteria: $p_{T}$/$m_{\gamma\gamma}$ < 0.35 (0.25) for the leading (subleading) photon and $|\eta_{\gamma\gamma}|$ < 2.37. The factors are evaluated using the Powheg NNLOPSevent generator. Uncertainties are taken from PDF variations. QCD scale variations have a negligible impact on these factors. The range of each bin is given in Table 26.

observed statistical correlations between pTyy, Njets, mjj, |DeltaPhijj| and pTj1

ggH default MC + XH predictions

XH ( = VBF + VH + ttH + bbH ) MC predictions

Best-fit values and uncertainties of the production-mode cross sections times branching ratio.

Best-fit values and uncertainties of the simplified template cross sections times branching ratio.

Observed correlations between the measured simplified template cross sections, including both the statistical and systematic uncertainties.

Best-fit values and uncertainties of the simplified template cross sections times branching ratio.

Observed correlations between the measured simplified template cross sections, including both the statistical and systematic uncertainties.

Observed correlations between the measured simplified template cross sections, including both the statistical and systematic uncertainties.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for the ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,100) GeV and ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,800) GeV models. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma\gamma}_{W-L}$ and $\mathrm{SR}^{\gamma\gamma}_{W-H}$. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of combined statistical and systematic uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for the ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,100) GeV and ($m_{\tilde{W}}$, $m_{\tilde{\chi}^{0}_{1}}$) = (1000,800) GeV models. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma\gamma}_{W-L}$ and $\mathrm{SR}^{\gamma\gamma}_{W-H}$. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of combined statistical and systematic uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Comparisons between expected and observed content of the validation and signal regions for the photon+jets analysis. The uncertainties in the expected numbers of events are the combined statistical and systematic uncertainties. The lower panel shows the pull (difference between observed and expected event counts normalized by the uncertainty) for each region.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma j}_{H}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for points in the $m_{\tilde{g}}-m_{\tilde{\chi}^{0}_{1}}$ parameter space of the GGM model relevant to the photon+jets analysis (mass values in GeV). The value of the gluino mass arises from the choice $M_{3}$ = 1900 GeV. The $\tilde{\chi}^{0}_{1}$ mass values of 1868, 1920, 442 and 652 GeV arise from the choices $\mu$ = 1810, 1868, 400 and 600 GeV, respectively, combined with the constraint that the branching fraction of $\tilde{\chi}^{0}_{1}$ $\to \gamma \tilde{G}$ be 50%. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma j}_{H}$ and $\mathrm{SR}^{\gamma j}_{L}$ for $\mathrm{SR}^{\gamma j}_{L200}$, the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement is 200 GeV rather than 300 GeV. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of statistical uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Distribution of the missing transverse momentum $E_{\mathrm{T}}^{\mathrm{miss}}$ for the sample satisfying all requirements of the $\mathrm{SR}^{\gamma j}_{L}$ or $\mathrm{SR}^{\gamma j}_{L200}$ selection except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement itself. Overlaid are the expected SM backgrounds, separated into the various contributing sources. Also shown are the signal expectations for points in the $m_{\tilde{g}}-m_{\tilde{\chi}^{0}_{1}}$ parameter space of the GGM model relevant to the photon+jets analysis (mass values in GeV). The value of the gluino mass arises from the choice $M_{3}$ = 1900 GeV. The $\tilde{\chi}^{0}_{1}$ mass values of 1868, 1920, 442 and 652 GeV arise from the choices $\mu$ = 1810, 1868, 400 and 600 GeV, respectively, combined with the constraint that the branching fraction of $\tilde{\chi}^{0}_{1} \to \gamma \tilde{G}$ be 50%. The vertical dashed lines and right-pointing arrows show the region of the $E_{\mathrm{T}}^{\mathrm{miss}}$ observable selected for inclusion in $\mathrm{SR}^{\gamma j}_{H}$ and $\mathrm{SR}^{\gamma j}_{L}$ for $\mathrm{SR}^{\gamma j}_{L200}$, the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement is 200 GeV rather than 300 GeV. The lower panels show the ratio of observed data to the combined SM expectation. For these plots, the band represents the range of statistical uncertainty in the SM expectation. Events outside the range of the displayed region are included in the highest-value bin.

Expected exclusion limits in the gluino-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 1600$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 1600$ GeV.

Observed exclusion limits in the gluino--bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 1600$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 1600$ GeV.

Expected exclusion limit in the squark-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 900$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 900$ GeV.

Observed exclusion limit in the squark--bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 900$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}} < 900$ GeV.

Expected exclusion limit in the wino-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 400$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}}$ < 400$ GeV.

Observed exclusion limit in the wino-bino mass plane, using the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ analysis for $m_{\tilde{\chi}^{0}_{1}} > 400$ GeV and the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ analysis for $m_{\tilde{\chi}^{0}_{1}}$ < 400$ GeV.

Expected exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis.

Observed exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis.

Distribution of the transverse momentum $p_{\mathrm{T}} (\ell\gamma\gamma)$ of events in the $\ell\gamma\gamma$ control region (except without a cut on $p_{\mathrm{T}} (\ell\gamma\gamma)$). Also shown is the expected contribution from various SM sources, including $W(\to\ell\nu) + \gamma\gamma$ production itself. The displayed uncertainties are a combination of those from all SM sources except $W(\to\ell\nu) + \gamma\gamma$ production, and include statistical and systematic uncertainties.

Distribution of $E_{\mathrm{T}}^{\mathrm{miss}}$ for diphoton events in a validation region defined by a requirement of $H_{\mathrm{T}} > 1750$ GeV. Also shown is the expected contribution from various SM sources, as well as their combined statistical and systematic uncertainties.

Distribution of $H_{\mathrm{T}}$ for diphoton events in a validation region defined by requirement of $E_{\mathrm{T}}^{\mathrm{miss}} > 100$ GeV. Also shown is the expected contribution from various SM sources, as well as their combined statistical and systematic uncertainties.

Distribution of $m_{\mathrm{eff}}$ for events satisfying all requirements $\mathrm{SR}^{\gamma j}_{H}$ save the $m_{\mathrm{eff}}$ requirement itself. Also shown is the expected contribution from various SM sources, and their combined statistical uncertainties.

Distribution of $m_{\mathrm{eff}}$ for events satisfying all requirements $\mathrm{SR}^{\gamma j}_{L}$ save the $m_{\mathrm{eff}}$ requirement itself. Also shown is the expected contribution from various SM sources, and their combined statistical uncertainties.

Derived exclusion limits for the gluino-bino GGM model explored by the diphoton analysis. For each point in the gluino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the squark-bino GGM model explored by the diphoton analysis. For each point in the squark-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the wino-bino GGM model explored by the diphoton analysis. For each point in the wino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{W-L}$ or $\mathrm{SR}^{\gamma\gamma}_{W-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis. For each point in the higgsino-bino parameter space, the SR ($\mathrm{SR}^{\gamma j}_{L}$ or $\mathrm{SR}^{\gamma j}_{H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The model dependent upper limits on cross-section (fb) are shown by grey numbers for each signal point.

Derived exclusion limits for the gluino-bino GGM model explored by the diphoton analysis. For each point in the gluino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where SL and SH mean $\mathrm{SR}^{\gamma\gamma}_{S-L}$ and $\mathrm{SR}^{\gamma\gamma}_{S-H}$, respectively.

Derived exclusion limits for the squark-bino GGM model explored by the diphoton analysis. For each point in the squark-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{S-L}$ or $\mathrm{SR}^{\gamma\gamma}_{S-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where SL and SH mean $\mathrm{SR}^{\gamma\gamma}_{S-L}$ and $\mathrm{SR}^{\gamma\gamma}_{S-H}$, respectively.

Derived exclusion limits for the wino--bino GGM model explored by the diphoton analysis. For each point in the wino-bino parameter space, the SR ($\mathrm{SR}^{\gamma\gamma}_{W-L}$ or $\mathrm{SR}^{\gamma\gamma}_{W-H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where WL and WH mean $\mathrm{SR}^{\gamma\gamma}_{W-L}$ and $\mathrm{SR}^{\gamma\gamma}_{W-H}$, respectively.

Derived exclusion limits for the $\mu > 0$ higgsino-bino GGM model explored by the photon+jets analysis. For each point in the higgsino-bino parameter space, the SR ($\mathrm{SR}^{\gamma j}_{L}$ or $\mathrm{SR}^{\gamma j}_{H}$) that provides the best expected sensitivity is used to estimate the exclusion likelihood. The labels indicate the best-expected signal region for each point, where L and H mean $\mathrm{SR}^{\gamma j}_{L}$ and $\mathrm{SR}^{\gamma j}_{H}$, respectively.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-L}$ for the signal models of the gluino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-H}$ for the signal models of the gluino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-L}$ for the signal models of the squark-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{S-H}$ for the signal models of the squark-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{W-L}$ for the signal models of the wino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma\gamma}_{W-H}$ for the signal models of the wino-bino GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma j}_{L}$ for the signal models of the photon+jets GGM grid.

Acceptance and efficiency for $\mathrm{SR}^{\gamma j}_{H}$ for the signal models of the photon+jets GGM grid.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ selection for one relevant signal point in the gluino-bino model, where the gluinos have mass of 1900 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 300 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ selection for one relevant signal point in the gluino-bino model, where the gluinos have mass of 1900 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 1700 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-L}$ selection for one relevant signal point in the squark-bino model, where the squarks have mass of 1700 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 200 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{S-H}$ selection for one relevant signal point in the squark-bino model, where the squarks have mass of 1700 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 1600 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{W-L}$ selection for one relevant signal point in the wino-bino model, where the winos have mass of 1000 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 200 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma\gamma}_{W-H}$ selection for one relevant signal point in the wino-bino model, where the winos have mass of 1000 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 800 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma j}_{L}$ selection, for two relevant signal points in the higgsino-bino model, where the gluinos have mass of 1974 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 442 GeV (10000 generated events), and 652 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.

Cutflow for the $\mathrm{SR}^{\gamma j}_{H}$ selection, for two relevant signal points in the higgsino-bino model, where the gluinos have mass of 1974 GeV and the $\tilde{\chi}^{0}_{1}$ has a mass of 1868 GeV (10000 generated events), and 1920 GeV (10000 generated events). The numbers are normalized to a luminosity of 36.1 fb$^{-1}$.