Observed 95% CL exclusion limits on the simplified models of chargino-pair production decaying via W bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino-pair production decaying via W bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino-pair production decaying via W bosons.

Expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

Observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

Expected 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

Observed 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

Observed upper limit on the signal cross section in fb for the production of $\tilde{\chi}_1^{+}\tilde{\chi}_{1}^{-}$.

The analyses used in combination for each scenario to set limits in models of the production of $\tilde{\chi}_1^{+}\tilde{\chi}_{1}^{-}$.

Observed upper limit on the signal cross section in fb for chargino--neutralino production decaying via W and Z bosons.

The analyses used in combination for each scenario to set limits in models of chargino--neutralino production decaying via W and Z bosons.

Expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Observed upper limit on the signal cross section in fb for chargino--neutralino production decaying via W and h bosons.

The analyses used in combination for each scenario to set limits in models of chargino--neutralino production decaying via W and h bosons.

Observed upper limit on the signal cross section in fb for higgsino GGM scenarios.

The analyses used in combination for each scenario to set limits in higgsino GGM scenarios.

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the High-pT jet SR for observed data events

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the High-pT jet SR for expected signal events in the strong gluino pair pair production model with m(gluino)=1.8 TeV, m(chi0)=0.2 TeV, tau(chi0)=0.1 ns

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the Trackless jet SR for observed data events

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the Trackless jet SR for expected signal events in the electroweak pair production model

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Exclusion limits at 95% CL on the production cross section in the electroweak pair production model.

Exclusion limits at 95% CL on the production cross section in the strong gluino pair production models and m(gluino)=2.4 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Exclusion limits at 95% CL on the production cross section in the strong gluino pair production models and m($ ilde{\chi}^0_1$)=1.25 TeV

Acceptance cutflow for the High-pT SR for representative points in the strong gluino pair production model. See additional resources for more information.

Acceptance cutflow for the Trackless SR for representative points in the electroweak pair production model. See additional resources for more information.

Acceptance cutflow for the Trackless SR for representative points in the electroweak pair production model with heavy-flavor quarks final state. See additional resources for more information.

Acceptance cutflow for the High-pT SR for representative points in the electroweak pair production model with heavy-flavor quarks final state. See additional resources for more information.

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R < 1150 mm

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R [1150, 3870] mm

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R > 3870 mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R < 1150 mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R [1150, 3870] mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R > 3870 mm

Reinterpretation Material: Vertex-level Efficiency for R < 22 mm

Reinterpretation Material: Vertex-level Efficiency for R [22, 25] mm

Reinterpretation Material: Vertex-level Efficiency for R [25, 29] mm

Reinterpretation Material: Vertex-level Efficiency for R [29, 38] mm

Reinterpretation Material: Vertex-level Efficiency for R [38, 46] mm

Reinterpretation Material: Vertex-level Efficiency for R [46, 73] mm

Reinterpretation Material: Vertex-level Efficiency for R [73, 84] mm

Reinterpretation Material: Vertex-level Efficiency for R [84, 111] mm

Reinterpretation Material: Vertex-level Efficiency for R [111, 120] mm

Reinterpretation Material: Vertex-level Efficiency for R [120, 145] mm

Reinterpretation Material: Vertex-level Efficiency for R [145, 180] mm

Reinterpretation Material: Vertex-level Efficiency for R [180, 300] mm

Cutflow (acceptance x efficiency) for the High-pT SR for representative points in the strong gluino pair production model. See additional resources for more information.

Cutflow (acceptance x efficiency) for the Trackless SR for representative points in the electroweak pair production model. See additional resources for more information.

Cutflow (acceptance x efficiency) for the Trackless SR for representative points in the electroweak pair production model with heavy-flavor quarks. See additional resources for more information.

Cutflow (acceptance x efficiency) for the High-pT SR for representative points in the electroweak pair production model with heavy-flavor quarks. See additional resources for more information.

Definition of the tetralepton signal regions.

Definition of the fiducial volumes at particle- and parton-level. Leptons refer exclusively to electrons and muons - they are dressed with additional radiation at particle-level, but not at parton-level.

Definition of the dilepton $t\bar{t}$ validation regions.

Pre-fit distribution of the number of $b$-jets in 2L-$e\mu$-6j2b, this distribution is not used in the fit.

Pre-fit distribution of the DNN output 2L-$e\mu$-6j1b, this distribution is not used in the fit.

Pre-fit distribution of the DNN output 2L-$e\mu$-5j2b, this distribution is not used in the fit.

Pre-fit distribution of the DNN output 2L-$e\mu$-6j2b, this distribution is not used in the fit.

Definition of the tetralepton control region.

Definition of the trilepton fakes control regions.

Pre-fit distribution of jet multiplicity in CR-$t\bar{t}$-e region.

Pre-fit distribution of loose lepton transverse momentum in CR-$t\bar{t}$-$\mu$ region.

Pre-fit distribution of the transverse mass of the trailing lepton and the missing transverse momentum in CR-Z-e region.

Post-fit distribution of jet multiplicity in CR-$t\bar{t}$-e region

Post-fit distribution of loose lepton transverse momentum in CR-$t\bar{t}$-$\mu$ region

Post-fit distribution of the transverse mass of the trailing lepton and the missing transverse momentum in CR-Z-e region

Post-fit distribution of NN output in SR-2L-5j2b region.

Post-fit distribution of NN output in SR-2L-6j1b region.

Post-fit distribution of NN output in SR-2L-6j2b region.

Post-fit distribution of DNN-$t\bar{t}Z$ output in 3L-SR-ttZ region.

Post-fit distribution of DNN-$t\bar{t}Z$ outputt in 3L-SR-tZq region.

Post fit events yields in 3L-SR-WZ region.

Post-fit distribution of NN output in 4L-SR-SF region.

Post-fit distribution of NN output in 4L-SR-DF region.

Post-fit distribution of b-tagger output for leading b-jet in 4L-CR-ZZ region.

Measured values of the background normalizations obtained from the combined fit. The uncertainties include statistical and systematic sources.

Measured $\sigma_{t\bar{t}\text{Z}}$ cross sections obtained from the fits in the different lepton channels. The uncertainties include statistical and systematic sources.

Grouped impact of systematic uncertainties in the combined inclusive fit to data.

Unfolded absolute cross section as a function of $p^{Z}_{T}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 8 top-left).

Unfolded absolute cross section as a function of $p^{Z}_{T}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 8 top-right).

Unfolded normalized cross section as a function of $p^{Z}_{T}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 8 bottom-left).

Unfolded normalized cross section as a function of $p^{Z}_{T}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 8 bottom-right).

Unfolded absolute cross section as a function of $|y^{Z}$| in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 17 top-left and Figure 11 top-left).

Unfolded absolute cross section as a function of $|y^{Z}$| in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 17 top-right).

Unfolded normalized cross section as a function of $|y^{Z}$| in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 17 bottom-left).

Unfolded normalized cross section as a function of $|y^{Z}$| in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 17 bottom-right).

Unfolded absolute cross section as a function of cos $\theta_{Z}^{*}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 18 top-left and Figure 11 top-right).

Unfolded absolute cross section as a function of cos $\theta_{Z}^{*}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 18 top-right).

Unfolded normalized cross section as a function of cos $\theta_{Z}^{*}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 18 bottom-left).

Unfolded normalized cross section as a function of cos $\theta_{Z}^{*}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 18 bottom-right).

Unfolded absolute cross section as a function of $p_{T}^{\mathrm{top}}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 19 top-left and Figure 11 bottom-left).

Unfolded absolute cross section as a function of $p_{T}^{\mathrm{top}}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 19, top-right).

Unfolded normalized cross section as a function of $p_{T}^{\mathrm{top}}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 19, bottom-left).

Unfolded normalized cross section as a function of $p_{T}^{\mathrm{top}}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 19, bottom-right).

Unfolded absolute cross section as a function of $p_{T}^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 20 top-left and Figure 11 bottom-right).

Unfolded absolute cross section as a function of $p_{T}^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 20, top-right).

Unfolded normalized cross section as a function of $p_{T}^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 20, bottom-left)

Unfolded normalized cross section as a function of $p_{T}^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 20, bottom-right)

Unfolded absolute cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 21 top-left and Figure 12 top-left).

Unfolded absolute cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 21, top-right).

Unfolded normalized cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 21, bottom-left).

Unfolded normalized cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 21, top-right).

Unfolded absolute cross section as a function of $m^{t\bar{t}Z}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 22 top-left and Figure 12 bottom-left).

Unfolded absolute cross section as a function of $m^{t\bar{t}Z}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 22, top-right).

Unfolded normalized cross section as a function of $m^{t\bar{t}Z}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 22, bottom-left).

Unfolded normalized cross section as a function of $m^{t\bar{t}Z}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 22, bottom-right).

Unfolded absolute cross section as a function of $m^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 23 top-left and Figure 12 bottom-right).

Unfolded absolute cross section as a function of $m^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 23, top-right).

Unfolded normalized cross section as a function of $m^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 23, bottom-left).

Unfolded normalized cross section as a function of $m^{t\bar{t}}$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 23, bottom-right).

Unfolded absolute cross section as a function of $|y^{t\bar{t}Z}|$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 24 top-left and Figure 12 top-right).

Unfolded absolute cross section as a function of $|y^{t\bar{t}Z}|$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 24, top-right).

Unfolded normalized cross section as a function of $|y^{t\bar{t}Z}|$ in the combination of $3\ell$ and $4\ell$ channels at particle-level (Figure 24, bottom-left).

Unfolded normalized cross section as a function of $|y^{t\bar{t}Z}|$ in the combination of $3\ell$ and $4\ell$ channels at parton-level (Figure 24, bottom-right).

Unfolded absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at particle-level (Figure 25 top-left and Figure 9 top-left).

Unfolded absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at parton-level (Figure 25 top-right).

Unfolded normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at particle-level (Figure 25 bottom-left).

Unfolded normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at parton-level (Figure 25 bottom-right).

Unfolded absolute cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ in the trilepton channel at particle-level (Figure 26 top-left and Figure 10 bottom-left).

Unfolded absolute cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ in the trilepton channel at parton-level (Figure 26 top-right).

Unfolded normalized cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ in the trilepton channel at particle-level (Figure 26 bottom-left).

Unfolded normalized cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ in the trilepton channel at parton-level (Figure 26 bottom-right).

Unfolded absolute cross section as a function of $|\Delta y(Z, t_{lep})|$ in the trilepton channel at particle-level (Figure 27 top-left and Figure 10 bottom-right).

Unfolded absolute cross section as a function of $|\Delta y(Z, t_{lep})|$ in the trilepton channel at parton-level (Figure 27 top-right).

Unfolded normalized cross section as a function of $|\Delta y(Z, t_{lep})|$ in the trilepton channel at particle-level (Figure 27 bottom-left).

Unfolded normalized cross section as a function of $|\Delta y(Z, t_{lep})|$ in the trilepton channel at parton-level (Figure 27 bottom-right).

Unfolded absolute cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ in the trilepton channel at particle-level (Figure 28 top-left and Figure 10 top-left).

Unfolded absolute cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ in the trilepton channel at parton-level (Figure 28 top-right).

Unfolded normalized cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ in the trilepton channel at particle-level (Figure 28 bottom-left).

Unfolded normalized cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ in the trilepton channel at parton-level (Figure 28 bottom-right).

Unfolded absolute cross section as a function of $N_{\text{jets}}$ in the trilepton channel at particle-level (Figure 29 left and Figure 9 bottom-left).

Unfolded normalized cross section as a function of $N_{\text{jets}}$ in the trilepton channel at particle-level (Figure 29 right).

Unfolded absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ in the tetralepton channel at particle-level (Figure 30 top-left and Figure 9 top-right).

Unfolded absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ in the tetralepton channel at parton-level (Figure 30 top-right).

Unfolded normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the tetralepton channel at particle-level (Figure 30 bottom-left).

Unfolded normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the tetralepton channel at parton-level (Figure 30 bottom-right).

Unfolded absolute cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the tetralepton channel at particle-level (Figure 31 top-left and Figure 10 top-right).

Unfolded absolute cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the tetralepton channel at parton-level (Figure 31 top-right).

Unfolded normalized cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the tetralepton channel at particle-level (Figure 31 bottom-left).

Unfolded normalized cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the tetralepton channel at parton-level (Figure 31 bottom-right).

Unfolded absolute cross section as a function of $N_{\text{jets}}$ in the tetralepton channel at particle-level (Figure 32 left and Figure 9 bottom-right).

Unfolded normalized cross section as a function of $N_{\text{jets}}$ in the tetralepton channel at particle-level (Figure 32 right).

Bootstrap replicas (0-499) for data in all regions used in inclusive cross section measurement. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data in all regions used in inclusive cross section measurement. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $|\Delta\Phi(t\bar{t}, Z)|/\pi$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $|\Delta\Phi(t\bar{t}, Z)|/\pi$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $|\Delta\Phi(Z, t_{lep})|/\pi$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $|\Delta\Phi(Z, t_{lep})|/\pi$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $m^{t\bar{t}}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $m^{t\bar{t}}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $N_{\text{jets}}$ in $3\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $N_{\text{jets}}$ in $3\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $|y^{t\bar{t}Z}|$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $|y^{t\bar{t}Z}|$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $H_{\text{T}}^{\text{l}}$ in $3\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $H_{\text{T}}^{\text{l}}$ in $3\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $y^{Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $y^{Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $p_{T}^{\mathrm{top}}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $p_{T}^{\mathrm{top}}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable cos $\theta^{*}_{Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable cos $\theta^{*}_{Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $p_{\text{T}}^{\ell, non-Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $p_{\text{T}}^{\ell, non-Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $H_{\text{T}}^{\text{l}}$ in $4\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $H_{\text{T}}^{\text{l}}$ in $4\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $m^{t\bar{t}Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $m^{t\bar{t}Z}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $N_{\text{jets}}$ in $4\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $N_{\text{jets}}$ in $4\ell$ channel. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $|\Delta y(Z, t_{lep})|$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $|\Delta y(Z, t_{lep})|$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $p^{Z}_{T}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $p^{Z}_{T}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (0-499) for data, variable $p_{T}^{t\bar{t}}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Bootstrap replicas (500-999) for data, variable $p_{T}^{t\bar{t}}$. The used bootstrap method is described in ATL-PHYS-PUB-2021-011 (https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2021-011/).

Parton-level acceptance and selection efficiency histograms for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable.

Parton-level acceptance and selection efficiency histograms for $|\Delta y(Z, t_{lep})|$ variable.

Parton-level acceptance and selection efficiency histograms for $H_{\text{T}}^{\text{ l}}$ variable.

Parton-level acceptance and selection efficiency histograms for $p_{\text{T}}^{\ell, non-Z}$ variable.

Parton-level acceptance and selection efficiency histograms for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable.

Parton-level acceptance and selection efficiency histograms for $H_{\text{T}}^{\text{ l}}$ variable.

Parton-level acceptance and selection efficiency histograms for cos $\theta_{Z}^{*}$ variable.

Parton-level acceptance and selection efficiency histograms for $p^{Z}_{T}$ variable.

Parton-level acceptance and selection efficiency histograms for $|y^{Z}$| variable.

Parton-level acceptance and selection efficiency histograms for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable.

Parton-level acceptance and selection efficiency histograms for $m^{t\bar{t}}$ variable.

Parton-level acceptance and selection efficiency histograms for $m^{t\bar{t}Z}$ variable.

Parton-level acceptance and selection efficiency histograms for $p_{T}^{\mathrm{top}}$ variable.

Parton-level acceptance and selection efficiency histograms for $p_{T}^{t\bar{t}}$ variable.

Parton-level acceptance and selection efficiency histograms for $|y^{t\bar{t}Z}|$ variable.

Particle-level acceptance and selection efficiency histograms for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable.

Particle-level acceptance and selection efficiency histograms for $|\Delta y(Z, t_{lep})|$ variable.

Particle-level acceptance and selection efficiency histograms for $H_{\text{T}}^{\text{ l}}$ variable.

Particle-level acceptance and selection efficiency histograms for $N_{\text{jets}}$ variable.

Particle-level acceptance and selection efficiency histograms for $p_{\text{T}}^{\ell, non-Z}$ variable.

Particle-level acceptance and selection efficiency histograms for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable.

Particle-level acceptance and selection efficiency histograms for $H_{\text{T}}^{\text{ l}}$ variable.

Particle-level acceptance and selection efficiency histograms for $N_{\text{jets}}$ variable.

Particle-level acceptance and selection efficiency histograms for cos $\theta_{Z}^{*}$ variable.

Particle-level acceptance and selection efficiency histograms for $p^{Z}_{T}$ variable.

Particle-level acceptance and selection efficiency histograms for $|y^{Z}$| variable.

Particle-level acceptance and selection efficiency histograms for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable.

Particle-level acceptance and selection efficiency histograms for $m^{t\bar{t}}$ variable.

Particle-level acceptance and selection efficiency histograms for $m^{t\bar{t}Z}$ variable.

Particle-level acceptance and selection efficiency histograms for $p_{T}^{\mathrm{top}}$ variable.

Particle-level acceptance and selection efficiency histograms for $p_{T}^{t\bar{t}}$ variable.

Particle-level acceptance and selection efficiency histograms for $|y^{t\bar{t}Z}|$ variable.

Migration matrix for cos $\theta_{Z}^{*}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for cos $\theta_{Z}^{*}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for cos $\theta_{Z}^{*}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for cos $\theta_{Z}^{*}$ variable at particle-level in region SR-4L-DF.

Migration matrix for cos $\theta_{Z}^{*}$ variable at particle-level in region SR-4L-SF.

Migration matrix for cos $\theta_{Z}^{*}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for cos $\theta_{Z}^{*}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for cos $\theta_{Z}^{*}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for cos $\theta_{Z}^{*}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for cos $\theta_{Z}^{*}$ variable at parton-level in region SR-4L-DF.

Migration matrix for cos $\theta_{Z}^{*}$ variable at parton-level in region SR-4L-SF.

Migration matrix for cos $\theta_{Z}^{*}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at particle-level in region SR-4L-DF.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at particle-level in region SR-4L-SF.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at parton-level in region SR-4L-DF.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at parton-level in region SR-4L-SF.

Migration matrix for $|\Delta\Phi(t\bar{t}, Z)|/\pi$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable at particle-level in region SR-4L-DF.

Migration matrix for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable at particle-level in region SR-4L-SF.

Migration matrix for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable at parton-level in region SR-4L-DF.

Migration matrix for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable at parton-level in region SR-4L-SF.

Migration matrix for $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $|\Delta\Phi(Z, t_{lep})|/\pi$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $|\Delta y(Z, t_{lep})|$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $|\Delta y(Z, t_{lep})|$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $|\Delta y(Z, t_{lep})|$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $|\Delta y(Z, t_{lep})|$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $|\Delta y(Z, t_{lep})|$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $|\Delta y(Z, t_{lep})|$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at parton-level in region SR-4L-DF.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at parton-level in region SR-4L-SF.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $H_{\text{T}}^{\text{ l}}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $m^{t\bar{t}Z}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $m^{t\bar{t}Z}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $m^{t\bar{t}Z}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $m^{t\bar{t}Z}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $m^{t\bar{t}Z}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $m^{t\bar{t}Z}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $m^{t\bar{t}Z}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $m^{t\bar{t}Z}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $m^{t\bar{t}Z}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $m^{t\bar{t}Z}$ variable at parton-level in region SR-4L-DF.

Migration matrix for $m^{t\bar{t}Z}$ variable at parton-level in region SR-4L-SF.

Migration matrix for $m^{t\bar{t}Z}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $m^{t\bar{t}}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $m^{t\bar{t}}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $m^{t\bar{t}}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $m^{t\bar{t}}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $m^{t\bar{t}}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $m^{t\bar{t}}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $m^{t\bar{t}}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $m^{t\bar{t}}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $m^{t\bar{t}}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $m^{t\bar{t}}$ variable at parton-level in region SR-4L-DF.

Migration matrix for $m^{t\bar{t}}$ variable at parton-level in region SR-4L-SF.

Migration matrix for $m^{t\bar{t}}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $N_{\text{jets}}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $N_{\text{jets}}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $N_{\text{jets}}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $N_{\text{jets}}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $N_{\text{jets}}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $N_{\text{jets}}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $p^{Z}_{T}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $p^{Z}_{T}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $p^{Z}_{T}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $p^{Z}_{T}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $p^{Z}_{T}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $p^{Z}_{T}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $p^{Z}_{T}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $p^{Z}_{T}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $p^{Z}_{T}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $p^{Z}_{T}$ variable at parton-level in region SR-4L-DF.

Migration matrix for $p^{Z}_{T}$ variable at parton-level in region SR-4L-SF.

Migration matrix for $p^{Z}_{T}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at parton-level in region SR-4L-DF.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at parton-level in region SR-4L-SF.

Migration matrix for $p_{T}^{\mathrm{top}}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at particle-level in region SR-4L-DF.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at particle-level in region SR-4L-SF.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at parton-level in region SR-4L-DF.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at parton-level in region SR-4L-SF.

Migration matrix for $p_{T}^{t\bar{t}}$ variable at parton-level in region CR-4L-ZZ.

Migration matrix for $p_{\text{T}}^{\ell, non-Z}$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $p_{\text{T}}^{\ell, non-Z}$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $p_{\text{T}}^{\ell, non-Z}$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $p_{\text{T}}^{\ell, non-Z}$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $p_{\text{T}}^{\ell, non-Z}$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $p_{\text{T}}^{\ell, non-Z}$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $|y^{Z}$| variable at particle-level in region SR-3L-ttZ.

Migration matrix for $|y^{Z}$| variable at particle-level in region SR-3L-tZq.

Migration matrix for $|y^{Z}$| variable at particle-level in region SR-3L-WZ.

Migration matrix for $|y^{Z}$| variable at particle-level in region SR-4L-DF.

Migration matrix for $|y^{Z}$| variable at particle-level in region SR-4L-SF.

Migration matrix for $|y^{Z}$| variable at particle-level in region CR-4L-ZZ.

Migration matrix for $|y^{Z}$| variable at parton-level in region SR-3L-ttZ.

Migration matrix for $|y^{Z}$| variable at parton-level in region SR-3L-tZq.

Migration matrix for $|y^{Z}$| variable at parton-level in region SR-3L-WZ.

Migration matrix for $|y^{Z}$| variable at parton-level in region SR-4L-DF.

Migration matrix for $|y^{Z}$| variable at parton-level in region SR-4L-SF.

Migration matrix for $|y^{Z}$| variable at parton-level in region CR-4L-ZZ.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at particle-level in region SR-3L-ttZ.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at particle-level in region SR-3L-tZq.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at particle-level in region SR-3L-WZ.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at particle-level in region SR-4L-DF.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at particle-level in region SR-4L-SF.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at particle-level in region CR-4L-ZZ.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at parton-level in region SR-3L-ttZ.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at parton-level in region SR-3L-tZq.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at parton-level in region SR-3L-WZ.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at parton-level in region SR-4L-DF.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at parton-level in region SR-4L-SF.

Migration matrix for $|y^{t\bar{t}Z}|$ variable at parton-level in region CR-4L-ZZ.

Covariance matrix for absolute cross section as a function of $p_{T}^{\mathrm{top}}$ at particle-level.

Covariance matrix for normalized cross section as a function of $p_{T}^{\mathrm{top}}$ at particle-level.

Covariance matrix for absolute cross section as a function of $p_{T}^{\mathrm{top}}$ at parton-level.

Covariance matrix for normalized cross section as a function of $p_{T}^{\mathrm{top}}$ at parton-level.

Covariance matrix for absolute cross section as a function of $p_{T}^{t\bar{t}}$ at particle-level.

Covariance matrix for normalized cross section as a function of $p_{T}^{t\bar{t}}$ at particle-level.

Covariance matrix for absolute cross section as a function of $p_{T}^{t\bar{t}}$ at parton-level.

Covariance matrix for normalized cross section as a function of $p_{T}^{t\bar{t}}$ at parton-level.

Covariance matrix for absolute cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ at particle-level.

Covariance matrix for normalized cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ at particle-level.

Covariance matrix for absolute cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ at parton-level.

Covariance matrix for normalized cross section as a function of $|\Delta\Phi(t\bar{t}, Z)|/\pi$ at parton-level.

Covariance matrix for absolute cross section as a function of $m^{t\bar{t}Z}$ at particle-level.

Covariance matrix for normalized cross section as a function of $m^{t\bar{t}Z}$ at particle-level.

Covariance matrix for absolute cross section as a function of $m^{t\bar{t}Z}$ at parton-level.

Covariance matrix for normalized cross section as a function of $m^{t\bar{t}Z}$ at parton-level.

Covariance matrix for absolute cross section as a function of $m^{t\bar{t}}$ at particle-level.

Covariance matrix for normalized cross section as a function of $m^{t\bar{t}}$ at particle-level.

Covariance matrix for absolute cross section as a function of $m^{t\bar{t}}$ at parton-level.

Covariance matrix for normalized cross section as a function of $m^{t\bar{t}}$ at parton-level.

Covariance matrix for absolute cross section as a function of $|y^{t\bar{t}Z}|$ at particle-level.

Covariance matrix for normalized cross section as a function of $|y^{t\bar{t}Z}|$ at particle-level.

Covariance matrix for absolute cross section as a function of $|y^{t\bar{t}Z}|$ at parton-level.

Covariance matrix for normalized cross section as a function of $|y^{t\bar{t}Z}|$ at parton-level.

Covariance matrix for absolute cross section as a function of cos $\theta_{Z}^{*}$ at particle-level.

Covariance matrix for normalized cross section as a function of cos $\theta_{Z}^{*}$ at particle-level.

Covariance matrix for absolute cross section as a function of cos $\theta_{Z}^{*}$ at parton-level.

Covariance matrix for normalized cross section as a function of cos $\theta_{Z}^{*}$ at parton-level.

Covariance matrix for absolute cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ at particle-level.

Covariance matrix for normalized cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ at particle-level.

Covariance matrix for absolute cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ at parton-level.

Covariance matrix for normalized cross section as a function of $|\Delta\Phi(l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ at parton-level.

Covariance matrix for absolute cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ at particle-level.

Covariance matrix for normalized cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ at particle-level.

Covariance matrix for absolute cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ at parton-level.

Covariance matrix for normalized cross section as a function of $|\Delta\Phi(Z, t_{lep})|/\pi$ at parton-level.

Covariance matrix for absolute cross section as a function of $|\Delta y(Z, t_{lep})|$ at particle-level.

Covariance matrix for normalized cross section as a function of $|\Delta y(Z, t_{lep})|$ at particle-level.

Covariance matrix for absolute cross section as a function of $|\Delta y(Z, t_{lep})|$ at parton-level.

Covariance matrix for normalized cross section as a function of $|\Delta y(Z, t_{lep})|$ at parton-level.

Covariance matrix for absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ at in the tetralepton channel particle-level.

Covariance matrix for normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ at in the tetralepton channel particle-level.

Covariance matrix for absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ at in the tetralepton channel parton-level.

Covariance matrix for normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the tetralepton channel at parton-level.

Covariance matrix for absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at particle-level.

Covariance matrix for normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at particle-level.

Covariance matrix for absolute cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at parton-level.

Covariance matrix for normalized cross section as a function of $H_{\text{T}}^{\text{l}}$ in the trilepton channel at parton-level.

Covariance matrix for absolute cross section as a function of $N_{\text{jets}}$ in the tetralepton channel at particle-level.

Covariance matrix for normalized cross section as a function of $N_{\text{jets}}$ in the tetralepton channel at particle-level.

Covariance matrix for absolute cross section as a function of $N_{\text{jets}}$ in the trilepton channel at particle-level.

Covariance matrix for normalized cross section as a function of $N_{\text{jets}}$ in the trilepton channel at particle-level.

Covariance matrix for absolute cross section as a function of $p^{Z}_{T}$ at particle-level.

Covariance matrix for normalized cross section as a function of $p^{Z}_{T}$ at particle-level.

Covariance matrix for absolute cross section as a function of $p^{Z}_{T}$ at parton-level.

Covariance matrix for normalized cross section as a function of $p^{Z}_{T}$ at parton-level.

Covariance matrix for absolute cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ at particle-level.

Covariance matrix for normalized cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ at particle-level.

Covariance matrix for absolute cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ at parton-level.

Covariance matrix for normalized cross section as a function of $p_{\text{T}}^{\ell, non-Z}$ at parton-level.

Covariance matrix for absolute cross section as a function of $|y^{Z}$| at particle-level.

Covariance matrix for normalized cross section as a function of $|y^{Z}$| at particle-level.

Covariance matrix for absolute cross section as a function of $|y^{Z}$| at parton-level.

Covariance matrix for normalized cross section as a function of $|y^{Z}$| at parton-level.

Ranking of nuisance parameters and background normalizations on signal strength for inclusive cross section measurement in combination of all channels

Observed and expected 68% and 95% credible intervals for the top-boson operators, in the marginalised linear fit.

Observed and expected 68% and 95% credible intervals for the top-boson operators, in the marginalised quadratic fit.

Observed and expected 68% and 95% credible intervals for the top-boson operators, in the independent quadratic fits (allowing only one Wilson Coefficient to be non-zero).

Observed and expected 68% and 95% credible intervals for the four-quark operators, in the marginalised linear fit.

Observed and expected 68% and 95% credible intervals for the four-quark operators, in the marginalised quadratic fit.

Observed and expected 68% and 95% credible intervals for the four-quark operators, in the independent quadratic fits (allowing only one Wilson Coefficient to be non-zero).

Observed and expected 68% and 95% credible intervals for Fisher-rotated directions of EFT sensitivity, in the marginalised linear fit.

Correlation matrix of the input particle-level observables used in the EFT fit.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The impact of the eight most important systematic uncertainties on \(R_t\), in %.

The post-fit yields in the two SRs. All uncertainties applied in the analysis are included.

Limits on EFT parameters and Vtb extracted from the analysis. The upper and lower limits correspond to 95% CL.

Results of the main analysis.

Pre-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR plus.

Post-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR plus.

Post-fit distribution of the \(\Delta\phi(\vec{p}_{T}^{miss},\mu)\) variable in the muon-plus CR.

Pre-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR minus.

Post-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR minus.

Post-fit distribution of the \(\Delta\phi(\vec{p}_{T}^{miss},\mu)\) variable in the muon-minus CR.

Pre-fit distribution of the invariant mass of the untagged jet and the b-tagged jet, \(m(jb)\), in SR plus.

Pre-fit distribution of the absolute value of the pseudorapidity of the untagged jet, \(|\eta(j)|\), in SR plus.

Pre-fit distribution of the absolute value of the difference in \(p_{\text{T}}\) between the reconstructed W boson and the jet pair, \(|\Delta p_{\text{T}}(W, jb)|\), in SR plus.

Pre-fit distribution of the difference in azimuth angle between the reconstructed W boson and the jet pair, \(|\Delta\phi(W, jb)|\), in SR plus.

Pre-fit distribution of the invariant mass of the reconstructed top quark, \(m(t)\), in SR plus.

Pre-fit distribution of the absolute value of the difference in pseudorapidity between the charged lepton and the untagged jet, \(|\Delta\eta(\ell ,j)|\), SR plus.

Pre-fit distribution of the angular distance of the charged lepton and the untagged jet, \(\Delta R(\ell ,j)\), in SR plus.

Pre-fit distribution of the absolute value of the difference in pseudorapidity between the b-tagged jet and the charged lepton, \(|\Delta\eta(b,\ell )|\), in SR plus.

Distribution (events/100 GeV) of the reconstructed $m_{tb}$ for data and backgrounds in the 0-lepton channel's the signal region 3 after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Distribution (events/100 GeV) of the reconstructed $m_{tb}$ for data and backgrounds in the 0-lepton channel's validation region before the fit to data. The systematics uncertainty is shown for the pre-fit background sum, including the background statistical uncertainty. The individual background components are obtained before the fit, too. There is also the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 2j1b signal region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 3j1b signal region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 2j2b signal region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 3j2b signal region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 2j1b control region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 3j1b control region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 2j1b validation region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Reconstructed $m_{tb}$ distributions (events/50 GeV) for data and backgrounds in the 1-lepton channel's 3j1b validation region after the background-only fit to data. The systematics uncertainty is shown for the post-fit background sum, including the background statistical uncertainty. The individual background components are obtained after the fit, too. There are also the pre-fit background sum and the expected signal distribution. The distribution of the $W^{\prime}$ boson signal for a mass of 3 TeV is normalised to the predicted cross-section. The last bin in each distribution includes overflow.

Observed and expected 95% CL limits on the cross-section times branching ratio for the production of a $W^{\prime}$ boson with decay to tb and left-handed couplings as a function of the mass of the $W^{\prime}$ boson and a coupling value of $g^{\prime}/g$=0.5. The observed and expected limits are derived using a linear interpolation between simulated signal mass hypotheses. The uncertainty on the theory prediction includes components from factorisation and renormalisation scales, PDFs, the strong coupling constant, and the top-quark mass.

Observed and expected 95% CL limits on the cross-section times branching ratio for the production of a $W^{\prime}$ boson with decay to tb and left-handed couplings as a function of the mass of the $W^{\prime}$ boson and a coupling value of $g^{\prime}/g$=1.0. The observed and expected limits are derived using a linear interpolation between simulated signal mass hypotheses. The uncertainty on the theory prediction includes components from factorisation and renormalisation scales, PDFs, the strong coupling constant, and the top-quark mass.

Observed and expected 95% CL limits on the cross-section times branching ratio for the production of a $W^{\prime}$ boson with decay to tb and left-handed couplings as a function of the mass of the $W^{\prime}$ boson and a coupling value of $g^{\prime}/g$=2.0. The observed and expected limits are derived using a linear interpolation between simulated signal mass hypotheses. The uncertainty on the theory prediction includes components from factorisation and renormalisation scales, PDFs, the strong coupling constant, and the top-quark mass.

Observed and expected 95% CL limits on the cross-section times branching ratio for the production of a $W^{\prime}$ boson with decay to tb and right-handed couplings as a function of the mass of the $W^{\prime}$ boson and a coupling value of $g^{\prime}/g$=0.5. The observed and expected limits are derived using a linear interpolation between simulated signal mass hypotheses. The uncertainty on the theory prediction includes components from factorisation and renormalisation scales, PDFs, the strong coupling constant, and the top-quark mass.

Observed and expected 95% CL limits on the cross-section times branching ratio for the production of a $W^{\prime}$ boson with decay to tb and right-handed couplings as a function of the mass of the $W^{\prime}$ boson and a coupling value of $g^{\prime}/g$=1.0. The observed and expected limits are derived using a linear interpolation between simulated signal mass hypotheses. The uncertainty on the theory prediction includes components from factorisation and renormalisation scales, PDFs, the strong coupling constant, and the top-quark mass.

Observed and expected 95% CL limits on the cross-section times branching ratio for the production of a $W^{\prime}$ boson with decay to tb and right-handed couplings as a function of the mass of the $W^{\prime}$ boson and a coupling value of $g^{\prime}/g$=2.0. The observed and expected limits are derived using a linear interpolation between simulated signal mass hypotheses. The uncertainty on the theory prediction includes components from factorisation and renormalisation scales, PDFs, the strong coupling constant, and the top-quark mass.

95% CL limits on the coupling value ($g^{\prime}/g$) and the $W^{\prime}$-boson mass for left-handed $W^{\prime}$-boson couplings. The area above the line is excluded.

95% CL limits on the coupling value ($g^{\prime}/g$) and the $W^{\prime}$-boson mass for right-handed $W^{\prime}$-boson couplings. The area above the line is excluded.

The product of fiducial acceptance and selection efficiency for the three signal regions of the 0-lepton channel as a function of the mass of the $W^{\prime}$ boson with left-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=1$.

The product of fiducial acceptance and selection efficiency for the three signal regions of the 0-lepton channel as a function of the mass of the $W^{\prime}$ boson with left-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=0.5$.

The product of fiducial acceptance and selection efficiency for the three signal regions of the 0-lepton channel as a function of the mass of the $W^{\prime}$ boson with left-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=2$.

The product of fiducial acceptance and selection efficiency for the four signal regions of the 1-lepton channel as a function of the mass of the $W^{\prime}$ boson with left-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=1$.

The product of fiducial acceptance and selection efficiency for the four signal regions of the 1-lepton channel as a function of the mass of the $W^{\prime}$ boson with left-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=0.5$.

The product of fiducial acceptance and selection efficiency for the four signal regions of the 1-lepton channel as a function of the mass of the $W^{\prime}$ boson with left-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=2$.

The product of fiducial acceptance and selection efficiency for the three signal regions of the 0-lepton channel as a function of the mass of the $W^{\prime}$ boson with right-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=1$.

The product of fiducial acceptance and selection efficiency for the three signal regions of the 0-lepton channel as a function of the mass of the $W^{\prime}$ boson with right-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=0.5$.

The product of fiducial acceptance and selection efficiency for the three signal regions of the 0-lepton channel as a function of the mass of the $W^{\prime}$ boson with right-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=2$.

The product of fiducial acceptance and selection efficiency for the four signal regions of the 1-lepton channel as a function of the mass of the $W^{\prime}$ boson with right-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=1$.

The product of fiducial acceptance and selection efficiency for the four signal regions of the 1-lepton channel as a function of the mass of the $W^{\prime}$ boson with right-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=0.5$.

The product of fiducial acceptance and selection efficiency for the four signal regions of the 1-lepton channel as a function of the mass of the $W^{\prime}$ boson with right-handed chirality. The $W^{\prime}$ boson coupling strength is set to $g^{\prime}/g=2$.

Predicted and observed yields as a function of $m_{4\ell}$ in the VBS-Suppressed region. Overflow events are included in the last bin of the distribution.

Relative systematic uncertainties in the unfolded differential cross section for 4ljj production in the VBS-Enhanced region as a function of m_{jj}

Relative systematic uncertainties in the unfolded differential cross section for 4ljj production in the VBS-Enhanced region as a function of m_{4\ell}

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $m_{jj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $m_{jj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $m_{4\ell}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $m_{4\ell}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $ p_{T,4l}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $ p_{T,4l}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $\Delta\phi_{jj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $\Delta\phi_{jj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $|\Delta y_{jj}|$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $|\Delta y_{jj}|$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $cos(\theta^{*}_{12})$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $cos(\theta^{*}_{12})$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $cos(\theta^{*}_{34})$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $cos(\theta^{*}_{34})$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $p_{T,jj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $p_{T,jj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $p_{T,4ljj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $p_{T,4ljj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Enhanced region as a function of $S_{T,4ljj}$. Overflow events are included in the last bin of the distribution.

Differential cross-sections for inclusive 4ljj production in the VBS-Suppressed region as a function of $S_{T,4ljj}$. Overflow events are included in the last bin of the distribution.

Statistical correlation between different bins of the unfolded cross-section for all the measured observables in the VBS-Enhanced region.

Statistical correlation between different bins of the unfolded cross-section for all the measured observables in the VBS-Suppressed region.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-0J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.

Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-1J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.

Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

The upper panel shows the observed number of events in each of the binned SRs defined in Table 3, together with the expected SM backgrounds obtained after applying the efficiency correction method to compute the number of expected FSB events. `Others' include the non-dominant background sources, e.g. $t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. The distributions of two signal points with mass splittings $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 30$ GeV and $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 50$ GeV are overlaid. The lower panel shows the significance as defined in Ref. [115].

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The observed mass distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

List of expected and observed events, $p_{0}$-value and the corresponding $Z$ local significance, as well as the 95% CLs upper limit of the expected and observed signal events ($S^{95}_ ext{exp} and $S^{95}_ ext{obs}$ ) in each mass window for SR-Inclusive bins of the short lifetime regime.

List of expected and observed events, $p_{0}$-value and the corresponding $Z$ local significance, as well as the 95% CLs upper limit of the expected and observed signal events ($S^{95}_ ext{exp} and $S^{95}_ ext{obs}$ ) in each mass window for SR-Inclusive bins of the long lifetime regime.

The observed $p_{\rm T$ distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $|\eta|$ distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $|\eta|$ distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

Expected and observed distributions in SR-Inclusive_Low of missing transverse momentum. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of missing transverse momentum. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of relative phi-angle between pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of relative phi-angle between pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the transverse mass of pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the transverse mass of pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the relative phi-angle between the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track, and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the relative phi-angle between the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track, and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the relative phi-angle between pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the relative phi-angle between pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the transverse mass of pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the transverse mass of pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the effective mass, defined as the scalar sum pT of the signal candidate track, jets satisfying pT > 30 GeV, excluding ones within deltaR < 0.4 with respect to the signal candidate track, and pTmiss. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the effective mass, defined as the scalar sum pT of the signal candidate track, jets satisfying pT > 30 GeV, excluding ones within deltaR < 0.4 with respect to the signal candidate track, and pTmiss. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

Muon reconstruction efficiency as a function of β and |η| for (a) stable charginos and (b) stable charged R-hadrons. For weakly interacting LLPs with calorimeter materials the efficiency for the chargino is recommended to refer to. The muon reconstruction efficiency for R-hadrons is significantly lower due to having QCD interactions with materials.

Muon reconstruction efficiency as a function of β and |η| for (a) stable charginos and (b) stable charged R-hadrons. For weakly interacting LLPs with calorimeter materials the efficiency for the chargino is recommended to refer to. The muon reconstruction efficiency for R-hadrons is significantly lower due to having QCD interactions with materials.

Trigger and event selection efficiencies. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.

Trigger and event selection efficiencies. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.

Signal track selection efficiency as a function of CLLP $\beta\gamma$ for SR-Inclusive_Low and SR-Inclusive_High bins. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.

Signal selection efficiency by the mass window for SR-Inclusive_Low and SR-Inclusive_High bins.

Acceptance for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.

Acceptance for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |eta| < 1.8 and r_decay > 500 mm.

Acceptance for the chargino pair-production model for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.

Acceptance for the stau pair-production model for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.

Event-level efficiency for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Event-level efficiency for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Event-level efficiency for the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Event-level efficiency for the stau pair-production model for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Efficiency of SR-Inclusive_Highfor the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Highfor the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Highfor the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Highfor the stau pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the stau pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Passing events in event selection steps for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes.

Passing events in event selection steps for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes.

Passing events in event selection steps for the chargino pair-production model for various masses and lifetimes.

Passing events in event selection steps for the stau pair-production model for various masses and lifetimes.

Expected +- 1 sigma 95% CL exclusion limit for the Zprime-2HDM model.

Expected +- 2 sigma 95% CL exclusion limit for the Zprime-2HDM model.

Observed 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected +- 1 sigma 95% CL exclusion limit for the 2HDM+a model ggF production.

Expected +- 2 sigma 95% CL exclusion limit for the 2HDM+a model ggF production.

Observed 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected +- 1 sigma 95% CL exclusion limit for the 2HDM+a model bbA production.

Expected +- 2 sigma 95% CL exclusion limit for the 2HDM+a model bbA production.

Observed 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected +- 1 sigma 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected +- 2 sigma 95% CL exclusion limit for the Zprime-2HDM model with the benchmark used in arXiv:1707.01302.

Expected and observed upper limits at 95% CL on cross-section for Zprime-2HDM model.

Expected and observed upper limits at 95% CL on cross-section for ggF producton in the 2HDM+a model.

Expected and observed upper limits at 95% CL on cross-section for bbA producton in the 2HDM+a model.

Model-independent upper limits on the visible cross-section $σ_{vis, $h(\bar{b})+DM} ≡ σ_{h+DM} \times B(h \to b\bar{b}) \times \mathcal{A} \times \epsilon$ in the different signal regions.

Theory cross-section for Zprime-2HDM model.

Theory cross-section for bbA production in the 2HDM+a model.

Theory cross-section for ggF production in the 2HDM+a model.

Distribution of Higgs boson candidate mass in 2b region with MET=150-200 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=200-350 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=350-500 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET=500-750 GeV.

Distribution of Higgs boson candidate mass in 2b region with MET > 750 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=150-200 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=200-350 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET=350-500 GeV.

Distribution of Higgs boson candidate mass in 3b region with MET > 500 GeV.

Yields in 1-lepton control region.

Yields in 2-lepton control region.

MET distribution in 2b region of the 0-lepton channel.

MET distribution in 3b region of the 0-lepton channel.

Expected signal yields after certain selection cuts in 2b region with MET=150-200 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=200-350 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=350-500 GeV.

Expected signal yields after certain selection cuts in 2b region with MET=500-750 GeV.

Expected signal yields after certain selection cuts in 2b region with MET > 750 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=150-200 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=200-350 GeV.

Expected signal yields after certain selection cuts in 3b region with MET=350-500 GeV.

Expected signal yields after certain selection cuts in 3b region with MET > 500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 2b region with MET > 750 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for bbA production in the 2HDM+a model - 3b region with MET>500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 2b region with MET > 750 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the 2HDM+a model - 3b region with MET > 500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET=500-750 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 2b region with MET > 750 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=150-200 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=200-350 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET=350-500 GeV.

Acceptance times efficiency for ggF production in the Zprime-2HDM model - 3b region with MET > 500 GeV.