The total uncertainty covariance matrix for measured differential cross sections in $m_{4\ell}$ bins in fiducial volume. The matrix elements are in unit of $[$fb/TeV$]^2$.

The total uncertainty covariance matrix for measured differential cross sections in $p_\mathrm{T}^{4\ell}$ bins in fiducial volume. The matrix elements are in unit of $[$fb/TeV$]^2$.

Charged particle multiplicity distribution.

Average charged particle transverse momentum as a function of the number of charged particles in the event.

Transverse momentum(energy) distribution of the fourth leading muon(electron) for events with at least 4 leptons each having transverse PT(ET) > 10 GeV.

The jet multiplicity distribution for events with at least 4 leptons each having transverse PT(ET) > 10 GeV.

The distribution of missing ET for events with at least 4 leptons each having transverse PT(ET) > 10 GeV.

The invariant mass distribution of the Same-Flavour, Opposite Sign lepton pair closest to the Z0 mass for events with at least 4 leptons each having transverse PT(ET) > 10 GeV.

The distribution of effective mass for events with at least 4 leptons each having transverse PT(ET) > 10 GeV. The effeictive mass is defined as the scalar sum of the missing ET, the PT of the leptons and the PT of jets with PT> 40 GeV.

Observed CLs contour with plus 1-sigma signal cross-section uncertainty in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Expected CLs contour in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Expected CLs contour with minus 1-sigma experimental uncertainty in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Expected CLs contour with plus 1-sigma experimental uncertainty in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Observed CLs contour in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Observed CLs contour with minus 1-sigma signal cross-section uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Observed CLs contour with plus 1-sigma signal cross-section uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Expected CLs contour in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Expected CLs contour with minus 1-sigma experimental uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Expected CLs contour with plus 1-sigma experimental uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

The measured bin-averaged cross-section $d\sigma/d\Delta\phi^{\gamma \rm j}$ for isolated-photon plus jet production. Other details as in the caption to Table 1.

The measured bin-averaged cross-section $d\sigma/d{\rm m}^{\gamma \rm j}$ with the requirements on $|\cos{\Theta^{\gamma \rm J}}|$ and $|\eta^\gamma + {\rm y^{jet}}|$ for isolated-photon plus jet production. Other details as in the caption to Table 1.

The measured bin-averaged cross-section $d\sigma/d|\cos{\Theta^{\gamma \rm j}}|$ with the requirements on ${\rm m}^{\gamma \rm J}$ and $|\eta^\gamma + {\rm y^{jet}}|$ for isolated-photon plus jet production. Other details as in the caption to Table 1.

The measured bin-averaged cross-section $d\sigma/d|\cos{\Theta^{\gamma \rm j}}|$ without the requirements on ${\rm m}^{\gamma \rm J}$ and $|\eta^\gamma + {\rm y^{jet}}|$ for isolated-photon plus jet production. Other details as in the caption to Table 1.

$R_{pPb}$ as a function of $p_{T}$ integrated over rapidity range $-1.8<y*<1.3$ for the 0--90% centrality interval for the three geometric models: (a) Glauber, (b) Glauber-Gribov with $\omega_{\sigma}=0.11$ and (c) Glauber-Gribov with $\omega_{\sigma}=0.2$.

$R_{pPb}$ values as a function of $p_{T}$ in the 0--90% centrality interval averaged over $|y*|<0.5$.The $\langle T_{Pb} \rangle$ value for the ATLAS centrality correction is calculated with the Glauber model. The total systematic uncertainties, which include the uncertainty in $\langle T_{Pb} \rangle$, are indicated by lines of the same colour. Strict quantitative agreement is not expected as each measurement uses different rapidity intervals for the centrality determination and apply different event selection criteria to reject diffractive collisions.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields integrated over $-1.8<y*<1.3$ for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields integrated over $-1.8<y*<1.3$ for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields integrated over $-1.8<y*<1.3$ for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{CP}$ as a function of $p_{T}$ extracted from the invariant yields integrated over $|\eta|<2.3$ for seven centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{CP}$ as a function of $p_{T}$ extracted from the invariant yields integrated over $|\eta|<2.3$ for seven centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{CP}$ as a function of $p_{T}$ extracted from the invariant yields integrated over $|\eta|<2.3$ for seven centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $p_{T}$ extracted from the invariant yields for six rapidity ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

$R_{pPb}$ as a function of $y*$ extracted from the invariant yields for eight $p_{T}$ ranges, for eight centrality intervals, and for different geometrical models used to calculate $\langle T_{Pb} \rangle$.

Measured $\gamma+c$ fiducial differential cross section as a function of $E_\text{T}^\gamma$ for $|\eta^\gamma|<1.37$.

Measured $\gamma+c$ fiducial differential cross section as a function of $E_\text{T}^\gamma$ for $1.56<|\eta^\gamma|<2.37$.

Measured ratio of the $\gamma+b$ fiducial differential cross section as a function of $E_\text{T}^\gamma$ for $|\eta^\gamma|<1.37$ to that for $1.56<|\eta^\gamma|<2.37$.

Measured ratio of the $\gamma+c$ fiducial differential cross section as a function of $E_\text{T}^\gamma$ for $|\eta^\gamma|<1.37$ to that for $1.56<|\eta^\gamma|<2.37$.

Statistical correlation between the $\gamma+b$ and the $\gamma+c$ cross sections in a given $E_\text{T}^\gamma$ bin and $|\eta^\gamma|$ region. The two cross sections are correlated as the heavy flavour fractions are extracted simultaneously from a template fit, performed in each $E_\text{T}^\gamma$ bin and separately for the two $|\eta^\gamma|$ regions.

Signed shifts of the individual systematic uncertainties on the $\gamma+b$ cross section for $|\eta^\gamma|<1.37$. The numbers after the name of the uncertainty source refer to the individual component in that uncertainty. Each bin of the MC statistical uncertainty is independent of any other bin. The first four components of the photon energy scale uncertainty are specific to this $|\eta^\gamma|$ region and are independent of the components in the other region. The region is indicated as part of their name to indicate the independence between the $|\eta^\gamma|$ regions. The uncertainties on the prompt photon modelling, non-perturbative QCD models and particle-level migration effects are only varied once and not up and down by their nature, but are symmetrised for the final results. Only uncertainties which have at least a 1% variation in at least one bin of the $\gamma+b$ and $\gamma+c$ cross section measurements, including the ratios, are listed. The others are summed in quadrature and listed as a single entry.

Signed shifts of the individual systematic uncertainties on the $\gamma+b$ cross section for $1.56<|\eta^\gamma|<2.37$. The numbers after the name of the uncertainty source refer to the individual component in that uncertainty. Each bin of the MC statistical uncertainty is independent of any other bin. The first four components of the photon energy scale uncertainty are specific to this $|\eta^\gamma|$ region and are independent of the components in the other region. The region is indicated as part of their name to indicate the independence between the $|\eta^\gamma|$ regions. The uncertainties on the prompt photon modelling, non-perturbative QCD models and particle-level migration effects are only varied once and not up and down by their nature, but are symmetrised for the final results. Only uncertainties which have at least a 1% variation in at least one bin of the $\gamma+b$ and $\gamma+c$ cross section measurements, including the ratios, are listed. The others are summed in quadrature and listed as a single entry.

Signed shifts of the individual systematic uncertainties on the $\gamma+c$ cross section for $|\eta^\gamma|<1.37$. The numbers after the name of the uncertainty source refer to the individual component in that uncertainty. Each bin of the MC statistical uncertainty is independent of any other bin. The first four components of the photon energy scale uncertainty are specific to this $|\eta^\gamma|$ region and are independent of the components in the other region. The region is indicated as part of their name to indicate the independence between the $|\eta^\gamma|$ regions. The uncertainties on the prompt photon modelling, non-perturbative QCD models and particle-level migration effects are only varied once and not up and down by their nature, but are symmetrised for the final results. Only uncertainties which have at least a 1% variation in at least one bin of the $\gamma+b$ and $\gamma+c$ cross section measurements, including the ratios, are listed. The others are summed in quadrature and listed as a single entry.

Signed shifts of the individual systematic uncertainties on the $\gamma+c$ cross section for $1.56<|\eta^\gamma|<2.37$. The numbers after the name of the uncertainty source refer to the individual component in that uncertainty. Each bin of the MC statistical uncertainty is independent of any other bin. The first four components of the photon energy scale uncertainty are specific to this $|\eta^\gamma|$ region and are independent of the components in the other region. The region is indicated as part of their name to indicate the independence between the $|\eta^\gamma|$ regions. The uncertainties on the prompt photon modelling, non-perturbative QCD models and particle-level migration effects are only varied once and not up and down by their nature, but are symmetrised for the final results. Only uncertainties which have at least a 1% variation in at least one bin of the $\gamma+b$ and $\gamma+c$ cross section measurements, including the ratios, are listed. The others are summed in quadrature and listed as a single entry.

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.