1$\tau$ MediumMass SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ multibin SR eff.

2$\tau$ multibin SR eff.

2$\tau$ GMSB SR eff.

2$\tau$ GMSB SR eff.

1$\tau$ Compressed SR eff.

1$\tau$ Compressed SR eff.

1$\tau$ MediumMass SR eff.

1$\tau$ MediumMass SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ multibin SR eff.

2$\tau$ multibin SR eff.

2$\tau$ GMSB SR eff.

2$\tau$ GMSB SR eff.

1$\tau$ Compressed SR acceptance.

1$\tau$ Compressed SR acceptance.

1$\tau$ MediumMass SR acceptance.

1$\tau$ MediumMass SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ GMSB SR acceptance.

2$\tau$ GMSB SR acceptance.

1$\tau$ Compressed SR acceptance.

1$\tau$ Compressed SR acceptance.

1$\tau$ MediumMass SR acceptance.

1$\tau$ MediumMass SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ GMSB SR acceptance.

2$\tau$ GMSB SR acceptance.

Cutflow table of the $1\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $1\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $1\tau$ medium-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $1\tau$ medium-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ high-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ high-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ multibin SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ multibin SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ GMSB SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ GMSB SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Best performing fit setups entering the final combination as a function of the LSP mass and the gluino mass. 'S' marks the simultaneous fit of the four simplified model single-bin SRs, 'M' denotes the simultaneous fit of the two $1\tau$ SRs and the $2\tau$ multibin SR.

Best performing fit setups entering the final combination as a function of the LSP mass and the gluino mass. 'S' marks the simultaneous fit of the four simplified model single-bin SRs, 'M' denotes the simultaneous fit of the two $1\tau$ SRs and the $2\tau$ multibin SR.

Observed exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Observed exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Expected exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Expected exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Observed exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Observed exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Expected exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Expected exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Observed upper limits on the production cross section at 95% CL in pb as a function of tanBeta and SUSY breaking mass scale Lambda.

Observed upper limits on the production cross section at 95% CL in pb as a function of tanBeta and SUSY breaking mass scale Lambda.

Observed upper limits on the production cross section at 95% CL in pb as a function of the LSP mass and the gluino mass.

Observed upper limits on the production cross section at 95% CL in pb as a function of the LSP mass and the gluino mass.

Yields of the expected background from the SM in the bins of the multibin SR of the $2\tau$ channel with all bins being simultaneously used to constrain the background prediction. Expectation is given with the scalings computed in the combined fit applied. Uncertainties are statistial plus systematrics. Only the subsamples contributing the respective region are considered.

Yields of the expected background from the SM in the bins of the multibin SR of the $2\tau$ channel with all bins being simultaneously used to constrain the background prediction. Expectation is given with the scalings computed in the combined fit applied. Uncertainties are statistial plus systematrics. Only the subsamples contributing the respective region are considered.

$m_{\mathrm{T}}^{\tau}$ in the compressed $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the compressed $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the compressed $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the compressed $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the medium-mass $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the medium-mass $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the medium-mass $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the medium-mass $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the medium-mass $H_{\mathrm{T}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the medium-mass $H_{\mathrm{T}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the top VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the top VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the $W$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the $W$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the $Z$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the $Z$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the compressed SR of the $1\tau$ channel before application of the $m_{\mathrm{T}}^{\tau}$ > 80 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$m_{\mathrm{T}}^{\tau}$ in the compressed SR of the $1\tau$ channel before application of the $m_{\mathrm{T}}^{\tau}$ > 80 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the medium-mass SR of the $1\tau$ channel before application of the $H_{\mathrm{T}}$ > 1000 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the medium-mass SR of the $1\tau$ channel before application of the $H_{\mathrm{T}}$ > 1000 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$m_{\mathrm{T}}^{\mathrm{sum}}$ in the compressed SR of the $2\tau$ channel before application of the $m_{\mathrm{T}}^{\mathrm{sum}}$ > 1600 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$m_{\mathrm{T}}^{\mathrm{sum}}$ in the compressed SR of the $2\tau$ channel before application of the $m_{\mathrm{T}}^{\mathrm{sum}}$ > 1600 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the high-mass SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1100 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the high-mass SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1100 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

mT(tau_1) + mT(tau_2) in the multibin SR of the 2T channel. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

mT(tau_1) + mT(tau_2) in the multibin SR of the 2T channel. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the GMSB SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1900 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the GMSB SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1900 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

Lower mass requirement for signal regions.

Lower mass requirement for signal regions.

Lower mass requirement for signal regions.

Expected and observed events in the 16 discovery regions along with the according control regions.

Expected and observed events in the 16 discovery regions along with the according control regions.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic R-hadron search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic R-hadron search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector R-hadron search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector R-hadron search.

p0-values and model-independent upper limits on cross-section x acceptance x efficiency for the 16 discovery regions.

p0-values and model-independent upper limits on cross-section x acceptance x efficiency for the 16 discovery regions.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic search for metastable gluino R-hadrons.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic search for metastable gluino R-hadrons.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector direct-stau search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector direct-stau search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector chargino search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector chargino search.

Upper cross-section limit in gluino R-hadron search.

Upper cross-section limit in gluino R-hadron search.

Upper cross-section limit in sbottom R-hadron search.

Upper cross-section limit in sbottom R-hadron search.

Upper cross-section limit in stop R-hadron search.

Upper cross-section limit in stop R-hadron search.

Upper cross-section limit in stau search.

Upper cross-section limit in stau search.

Upper cross-section limit in chargino search.

Upper cross-section limit in chargino search.

Lower mass limit as function of gluino lifetime.

Lower mass limit as function of gluino lifetime.

Acceptance x efficiency, acceptance and efficiency for the full range of simulated masses in the MS-agnostic R-hadron search.

Acceptance x efficiency, acceptance and efficiency for the full range of simulated masses in the MS-agnostic R-hadron search.

Upper cross-section limit in meta-stable gluino R-hadron search.

Upper cross-section limit in meta-stable gluino R-hadron search.

Flavor composition of 800 GeV stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

ETmiss trigger efficiency as function of true ETmiss (EtmissTurnOn).

ETmiss trigger efficiency as function of true ETmiss (EtmissTurnOn).

Single-muon trigger efficiency as function of $|\eta|$ and $\beta$ (SingleMuTurnOn).

Single-muon trigger efficiency as function of $|\eta|$ and $\beta$ (SingleMuTurnOn).

Candidate reconstruction efficiency for ID+Calo selection (IDCaloEff).

Candidate reconstruction efficiency for ID+Calo selection (IDCaloEff).

Candidate reconstruction efficiency for loose selection (LooseEff).

Candidate reconstruction efficiency for loose selection (LooseEff).

Efficiency for a loose candidate to be promoted to a tight candidate (TightPromotionEff).

Efficiency for a loose candidate to be promoted to a tight candidate (TightPromotionEff).

Resolution and average of reconstructed dE/dx mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed dE/dx mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for FullDet candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for FullDet candidates.

Upper limits at the 95% CL on the cross section for SSM $W^\prime$ production and decay to the muon+neutrino channel as a function of the $W^\prime$ pole mass.

Combined (electron and muon channels) upper limits at the 95% CL on the cross section for SSM $W^\prime$ production and decay to a single lepton generation as a function of the $W^\prime$ pole mass.

Observed upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the electron+neutrino channel as a function of the $W^\prime$ pole mass.

Observed upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the muon+neutrino channel as a function of the $W^\prime$ pole mass.

Combined (electron and muon channels) observed upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to a single lepton generation as a function of the $W^\prime$ pole mass.

Observed upper limits at the 95% CL on the visible cross section in the electron+neutrino channel as a function of the transverse mass threshold.

Observed upper limits at the 95% CL on the visible cross section in the muon+neutrino channel as a function of the transverse mass threshold.

Product of acceptance and efficiency for the electron and muon selections as a function of the $W^\prime$ pole mass.

Expected upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the electron+neutrino channel as a function of the $W^\prime$ pole mass.

Expected upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the muon+neutrino channel as a function of the $W^\prime$ pole mass.

Combined (electron and muon channels) expected upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to a single lepton generation as a function of the $W^\prime$ pole mass.

Distribution of the resonance mass in the c-tag Signal Region of the $ ce$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the b-tag Signal Region of the $ ce$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the untagged Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the c-tag Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the b-tag Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 0-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 1-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 2-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 0-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 1-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 2-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $qe$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $q\mu$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $ce$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $c\mu$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $be$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $b\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $qe$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $q\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $ce$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $c\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $be$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $b\mu$ channel.

The signal selection efficiency x acceptance summed over all signal regions, for all masses and LQ decay channels considered.

The observed and expected limits for all masses and LQ decay channels considered.

Cutflow Table in the electron channel, considering signal samples with LQ mass of 1 TeV.

Cutflow Table in the muon channel, considering signal samples with LQ mass of 1 TeV.

The observed exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

The expected exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

The observed exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown for the region $m_{\tilde{t}} - m_{\tilde{\chi}^0_{1,2}, \tilde{\chi}^\pm_{1}} \geq m_\text{top}$ where $B(\tilde{t} \rightarrow b \chi^{+}_{1}) = B(\tilde{t} \rightarrow t \chi^{0}_{1,2}) = 0.5$.

The expected exclusion contour at 95% CL as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$. Masses that are within the contours are excluded. Limits are shown for the region $m_{\tilde{t}} - m_{\tilde{\chi}^0_{1,2}, \tilde{\chi}^\pm_{1}} \geq m_\text{top}$ where $B(\tilde{t} \rightarrow b \chi^{+}_{1}) = B(\tilde{t} \rightarrow t \chi^{0}_{1,2}) = 0.5$.

Observed model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1})$ signal grid. Limits are shown for $B(\tilde{t} \rightarrow b \chi^{+}_{1})$ equal to unity.

Observed model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1} / \tilde{\chi}^{0}_{1,2})$ signal grid. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

Expected model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1})$ signal grid. Limits are shown for $B(\tilde{t} \rightarrow b \chi^{+}_{1})$ equal to unity.

Expected model-dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{\pm}_{1} / \tilde{\chi}^{0}_{1,2})$ signal grid. Limits are shown in the case of a higgsino LSP. The results are constrained by the kinematic limits of the top-squark decay into a chargino and a bottom quark (upper diagonal line) and into a neutralino and a top quark (lower diagonal line), respectively.

Expected background and observed number of events in different jet and $b$-tag multiplicity bins.

Cut flow for a model of top-squark pair production with the top squark decaying to a $b$-quark and a chargino. The chargino decays through the non-zero RPV coupling $\lambda^{''}_{323}$ via a virtual top squark to $bbs$ quark triplets ($m_{\tilde{t}}$ = 800 GeV, $m_{\tilde{\chi}^{\pm}_{1}}$ = 750 GeV). The multijet trigger consists of four jets satisfying $p_{\text{T}}\geq(100)120$ GeV for the 2015-2016 (2017-2018) data period. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed. The numbers in $N_{\text{weighted}}$ are normalized by the integrated luminosity of 139 fb$^{-1}$.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal efficiency for $\tilde{t} \rightarrow b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the efficiency given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Signal acceptance for $\tilde{t} \rightarrow t\tilde{\chi}^{0}_{1,2}(\tilde{\chi}^{0}_{1,2} \rightarrow tbs) / b\tilde{\chi}^{+}_{1}(\tilde{\chi}^{+}_{1} \rightarrow \bar{b}\bar{b}\bar{s}) $ and c.c. model. Please mind that the acceptance given in the table is reported in %.

Distributions in SR-low of the data and post-fit background prediction for H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for m_eff^3ℓ/H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for m_eff^3ℓ/H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for p_T^soft/(p_T^soft + m_eff^3ℓ). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for p_T^soft/(p_T^soft + m_eff^3ℓ). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for m_T. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for m_T. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for R(E_T^miss,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for R(E_T^miss,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^soft. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^soft. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^jets. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^jets. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.

Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.

Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.

Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.

Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.

Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.

Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.

Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

The observed upper limits on the model cross-section in units of pb for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production. Three points at $M({\tilde{\chi}}^{0}_{1})=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.

The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.

Observed and predicted mTtot distribution in the b-veto category of the 1l1tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table.The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 1l1tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 1l1tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 1l1tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 1l1tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-veto category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-veto category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-veto category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-veto category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and predicted mTtot distribution in the b-tag category of the 2tau_h channel. Please note that the bin content is divided by the bin width in the paper figure, but not in the HepData table. The last bin includes overflows. The combined prediction for A and H bosons with masses of 400, 1000 and 1500 GeV and $\tan\beta$ = 6, 12 and 25 respectively in the mh125 scenario are also provided. The combined prediction for A and H bosons with masses of 1000 and 1500 GeV is scaled by 100 in the paper figure, but not in the HepData table.

Observed and expected 95% CL upper limits on the gluon-gluon fusion Higgs boson production cross section times ditau branching fraction as a function of the Higgs boson mass.

Observed and expected 95% CL upper limits on the gluon-gluon fusion Higgs boson production cross section times ditau branching fraction as a function of the Higgs boson mass.

Observed and expected 95% CL upper limits on the gluon-gluon fusion Higgs boson production cross section times ditau branching fraction as a function of the Higgs boson mass.

Observed and expected 95% CL upper limits on the gluon-gluon fusion Higgs boson production cross section times ditau branching fraction as a function of the Higgs boson mass.

Observed and expected 95% CL upper limits on the b-associated Higgs boson production cross section times ditau branching fraction as a function of the boson mass.

Observed and expected 95% CL upper limits on the b-associated Higgs boson production cross section times ditau branching fraction as a function of the boson mass.

Observed and expected 95% CL upper limits on the b-associated Higgs boson production cross section times ditau branching fraction as a function of the boson mass.

Observed and expected 95% CL upper limits on the b-associated Higgs boson production cross section times ditau branching fraction as a function of the boson mass.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered for the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. No theoretical uncertainty is considered when computing these limits.

Acceptance times efficiency for a scalar boson produced by gluon-gluon fusion as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by gluon-gluon fusion as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by gluon-gluon fusion as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by gluon-gluon fusion as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by b-associated production as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by b-associated production as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by b-associated production as a function of the scalar boson mass.

Acceptance times efficiency for a scalar boson produced by b-associated production as a function of the scalar boson mass.

Observed 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Observed 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Observed 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Observed 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Expected 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Expected 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Expected 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Expected 95% CL upper limits on the scalar boson production cross section times ditau branching fraction as a function of the scalar boson mass and the fraction of the b-associated production. The limits are calculated from a statistical combination of the 1l1tau_h and 2tau_h channels.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

Observed two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 200 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 250 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 300 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 350 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 400 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 600 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 700 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 800 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1000 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1200 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 1500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2000 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

Expected two dimensional likelihood scan of the gluon-gluon fusion cross section times branching fraction, $\sigma(gg\phi)\times B(\phi\to\tau\tau)$, vs the b-associated production times branching fraction, $\sigma(bb\phi)\times B(\phi\to\tau\tau)$ for the scalar boson mass ($m_\phi$) indicated in the table. For each mass, 10000 points are scanned. At each point $\Delta(\mathrm{NLL})$ is calculated, defined as the negative-log-likelihood (NLL) of the conditional fit with $\sigma(gg\phi)$ and $\sigma(bb\phi)$ fixed to their values at the point and with the minimum NLL value at any point subtracted. The best-fit point and the preferred 68% and 95% boundaries are found at $2\Delta(\mathrm{NLL})$ values of 0.0, 2.30 and 5.90, respectively. The value of $2\Delta(\mathrm{NLL})$ for 2500 GeV signal mass point is shown in the HEPData table.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the hMSSM scenario. The lowest value of $\tan\beta$ considered by the hMSSM scenario is 0.8 and the highest value of mass is 2 TeV. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\chi})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\chi})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\chi})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\chi})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\chi})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\chi})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\chi})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\chi})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\chi})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\chi})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\chi})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\chi})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The observed 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\tau})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\tau})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\tau})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\tau})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\tau})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\tau})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\tau})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\tau})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\tau})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\tau})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(\widetilde{\tau})$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(\widetilde{\tau})$ scenario is 0.5. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The range of $\tan\beta$ shown in the paper figure and the HEPData is from 1 to 60. The theoretical uncertainty of signal cross section is considered.

The observed 95% CL upper limits with one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(alignment)$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 1.0. The highest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 20.0. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(alignment)$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 1.0. The highest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 20.0. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(alignment)$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 1.0. The highest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 20.0. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus one sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(alignment)$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 1.0. The highest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 20.0. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with plus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(alignment)$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 1.0. The highest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 20.0. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The theoretical uncertainty of signal cross section is considered.

The expected 95% CL upper limits with minus two sigma on $\tan\beta$ as a function of $m_{A}$ in the $M_{h}^{125}(alignment)$ scenario. The lowest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 1.0. The highest value of $\tan\beta$ considered by the $M_{h}^{125}(alignment)$ scenario is 20.0. The points in the region which is called "Not applicable" in the paper figure are kept in the HEPData table. Linear connection is applied in the range of signal mass points from 400 to 1000 GeV in the paper figure. The theoretical uncertainty of signal cross section is considered.

Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Four-body selection. (a) distributions of $E_{T}^{miss}$ in $SR^{4-body}_{Small\,\Delta m}$ and (b) distribution of $R_{2\ell 4j}$ in $SR^{4-body}_{Large\,\Delta m}$ for events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Four-body selection. (a) distributions of $E_{T}^{miss}$ in $SR^{4-body}_{Small\,\Delta m}$ and (b) distribution of $R_{2\ell 4j}$ in $SR^{4-body}_{Large\,\Delta m}$ for events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the Observed limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Two-body selection. Background fit results for $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}}$, $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}Z}$, $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t}, DF}$, $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t}, SF}$ and $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t} Z}$. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Three-body selection. Background fit results for $\mathrm{CR}^{\mathrm{3-body}}_{t\bar{t}}$, $\mathrm{CR}^{\mathrm{3-body}}_{VV}$, $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}Z}$, $\mathrm{VR}^{\mathrm{3-body}}_{VV}$, $\mathrm{VR(1)}^{\mathrm{3-body}}_{t\bar{t}}$ and $\mathrm{VR(2)}^{\mathrm{3-body}}_{t\bar{t}}$. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Four-body selection. Background fit results for $\mathrm{CR}^{\mathrm{4-body}}_{t\bar{t}}$,$\mathrm{CR}^{\mathrm{4-body}}_{VV}$, $\mathrm{VR}^{\mathrm{4-body}}_{t\bar{t}}$, $VR^{4-body}_{VV}$ and $\mathrm{VR}^{\mathrm{4-body}}_{VV,lll}$. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Two-body selection. Background fit results for the different-flavour leptons binned SRs. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Two-body selection. Background fit results for the same-flavour leptons binned SRs. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Three-body selection. Observed event yields and background fit results for the three-body selection SRs. The ''Others'' category contains contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Four-body selection. Observed event yields and background fit results for SR$^{\mathrm{4-body}}_{\mathrm{Small}\,\Delta m}$ and SR$^{\mathrm{4-body}}_{\mathrm{Large}\,\Delta m}$. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Exclusion limits contours (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with 100% branching ratio in $\tilde{t}_1--\tilde{\chi}^0_1$ masses planes. The dashed lines and the shaded bands are the expected limit and its $\pm 1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The exclusion limits contours for the two-body, three-body and four-body selections are respectively shown in blue, green and red.

Exclusion limits contours (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with 100% branching ratio in $\tilde{t}_1--\tilde{\chi}^0_1$ masses planes. The dashed lines and the shaded bands are the expected limit and its $\pm 1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The exclusion limits contours for the two-body, three-body and four-body selections are respectively shown in blue, green and red.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm 1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty.The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty.The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Four-body selection Efficiency (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Four-body selection Efficiency (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta\ m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Four-body selection acceptance (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Four-body selection acceptance (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Three-body selection The numbers indicate the upper limits on the signal strenght for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Four-body selection The numbers indicate the upper limits on the signal strenght for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Three-body selection The numbers indicate the upper limits on the signal cross-section for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Four-body selection The numbers indicate the upper limits on the signal cross-section for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Two-body selection. Background fit results for the $inclusive$ SRs. The Others category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=600~ GeV$ and $m(\tilde{\chi}^0_1)=400~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the scalar signal model $t\bar{t} + \phi $ with $m(\phi)=150~ GeV$ and $m(\chi)=1~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the pseudoscalar signal model $t\bar{t} + a $ with $m(a)=150~ GeV$ and $m(\chi)=1~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=385~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=400~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=430~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=460~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=400~ GeV$ and $m(\tilde{\chi}^0_1)=380~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=460~ GeV$ and $m(\tilde{\chi}^0_1)=415~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=400~ GeV$ and $m(\tilde{\chi}^0_1)=320~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Summary of the 95% C.L. upper limits on $\sigma_{ZH} BR(H\rightarrow aa \rightarrow (b\bar{b})(b\bar{b}))$ for the dilepton channel in the resolved analysis. The observed limits are shown, together with the expected limits (dotted black lines). In the case of the expected limits, one- and two-standard-deviation uncertainty bands are also displayed. The data was published in JHEP 10 (2018) 031.

Efficiency and acceptance for simulated $ZH(\rightarrow aa\rightarrow (b\bar{b})(b\bar{b}))$ samples in two signal regions (SR) of the analysis, one with two $a\to b\bar{b}$ candidates in the High Purity Category (HPC), and the other with one $a\to b\bar{b}$ candidate in the High Purity Category (HPC) and one in the Low Purity Category (LPC).

Efficiency and acceptance for simulated $ZH(\rightarrow aa\rightarrow (b\bar{b})(b\bar{b}))$ samples in two signal regions (SR) of the analysis, one with two $a\to b\bar{b}$ candidates in the High Purity Category (HPC), and the other with one $a\to b\bar{b}$ candidate in the High Purity Category (HPC) and one in the Low Purity Category (LPC).

Event yields for a simulated $ZH(\rightarrow aa\rightarrow (b\bar{b})(b\bar{b}))$ sample with $m_a = 17.5\,\text{GeV}$. The signal sample is produced with cross section equals to the standard model $pp\to ZH$, i.e. $0.88\,\text{pb}$. Cut 0 corresponds to the initial number of events. Cut 1 requires the single lepton trigger. Cut 2 requires 2 identified leptons. Cut 3 requires the Z-boson mass window. Cut 4 requires 2 reconstructed $a\to b\bar{b}$ candidates. Cut 5a requires 2 identified $a\to b\bar{b}$ candidates in the 1HPC1LPC region. Cut 6a requires the 2 $a\to b\bar{b}$ candidates in the 1HPC1LPC region to be inside the Higgs mass window. Cut 5b requires 2 identified $a\to b\bar{b}$ candidates in the 2HPC region. Cut 6b requires the 2 $a\to b\bar{b}$ candidates in the 2HPC region to be inside the Higgs mass window.

Event yields for a simulated $ZH(\rightarrow aa\rightarrow (b\bar{b})(b\bar{b}))$ sample with $m_a = 17.5\,\text{GeV}$. The signal sample is produced with cross section equals to the standard model $pp\to ZH$, i.e. $0.88\,\text{pb}$. Cut 0 corresponds to the initial number of events. Cut 1 requires the single lepton trigger. Cut 2 requires 2 identified leptons. Cut 3 requires the Z-boson mass window. Cut 4 requires 2 reconstructed $a\to b\bar{b}$ candidates. Cut 5a requires 2 identified $a\to b\bar{b}$ candidates in the 1HPC1LPC region. Cut 6a requires the 2 $a\to b\bar{b}$ candidates in the 1HPC1LPC region to be inside the Higgs mass window. Cut 5b requires 2 identified $a\to b\bar{b}$ candidates in the 2HPC region. Cut 6b requires the 2 $a\to b\bar{b}$ candidates in the 2HPC region to be inside the Higgs mass window.

Background yield table for Z+jets, $t\bar{t}$, and rare sources. Observed data yield. Signal $ZH(\rightarrow aa\rightarrow (b\bar{b})(b\bar{b}))$ yield with $m_a = 20\,\text{GeV}$. The signal sample is produced with cross section equals to the standard model $pp\to ZH$, i.e. $0.88\,\text{pb}$, with a branching ratio set to 1 for the $H \rightarrow aa$ decay, whereas the ATLAS figure attached to this entry instead uses the upper-limit branching ratio (smaller than 1). The table includes the yields in two signal regions with leptons consistent with an on-shell Z-boson decay, one with 2 $a\to b\bar{b}$ candidates in the 2HPC region and one with 2 $a\to b\bar{b}$ candidates in the 1HPC1LPC region. The table also includes the yields in four control regions, one with leptons consistent with an on-shell Z-boson decay and 2 $a\to b\bar{b}$ candidates in the Low Purity Category (LPC), and three others where the leptons are not consistent an on-shell Z-boson decay.

Background yield table for Z+jets, $t\bar{t}$, and rare sources. Observed data yield. Signal $ZH(\rightarrow aa\rightarrow (b\bar{b})(b\bar{b}))$ yield with $m_a = 20\,\text{GeV}$. The signal sample is produced with cross section equals to the standard model $pp\to ZH$, i.e. $0.88\,\text{pb}$. The table includes the yields in two signal regions with leptons consistent with an on-shell Z-boson decay, one with 2 $a\to b\bar{b}$ candidates in the 2HPC region and one with 2 $a\to b\bar{b}$ candidates in the 1HPC1LPC region. The table also includes the yields in four control regions, one with leptons consistent with an on-shell Z-boson decay and 2 $a\to b\bar{b}$ candidates in the Low Purity Category (LPC), and three others where the leptons are not consistent an on-shell Z-boson decay.

The ratios (&mu;) of the 95&#37; C.L. upper limits on the combined s&rarr; W^&plusmn;W^&#8723; and s&rarr; ZZ cross section to simplified model expectations for the m_Z'=1 TeV scenario, for various m_s hypotheses. The observed limits (solid line) are consistent with the expectation under the SM-only hypothesis (dashed line) within uncertainties (filled band), except for a small excess for m_s=160 GeV, discussed in the text.

The ratios (&mu;) of the 95&#37; C.L. upper limits on the combined s&rarr; W^&plusmn;W^&#8723; and s&rarr; ZZ cross section to simplified model expectations for the m_Z'=1.7 TeV scenario, for various m_s hypotheses. The observed limits (solid line) are consistent with the expectation under the SM-only hypothesis (dashed line) within uncertainties (filled band), except for a small excess for m_s=160 GeV, discussed in the text.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) × B(s&rarr; VV) for m_Z'=0.5 TeV signal points. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) × B(s&rarr; VV) process for m_Z'=0.5 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) × B(s&rarr; VV) for m_Z'=1 TeV signal points. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) × B(s&rarr; VV) process for m_Z'=1 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) × B(s&rarr; VV) for m_Z'=1.7 TeV signal points. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) × B(s&rarr; VV) process for m_Z'=1.7 TeV, shown in dashed blue.

SM background post-fit yields stacked in each SR and CR category and E_T^miss bin and data overlaid with the maximum likelihood estimators set to the conditional values of the combined signal and control region fit. The hatched uncertainty band shown includes simulation statistics uncertainties, experimental systematic uncertainties, and V+jets theory modelling systematic uncertainties. Pre-fit uncertainties cover differences between the data and pre-fit background prediction.

Cumulative efficiencies for the merged category for signal samples with m_s=160 GeV (a), m_s=235 GeV (b) and m_s=310 GeV (c), each with m_Z'=1 TeV. The dark Higgs candidate selection includes stringent jet substructure requirements and typically at most one candidate is present in signal events. Here, &Delta; &phi;_{jets_1,2,3 E_T^miss} is the smallest azimuthal angle between the E_T^miss and any of the three highest-p_T (leading) small-R jets.

Cumulative efficiencies for the intermediate category for signal samples with m_s=160 GeV (a), m_s=235 GeV (b) and m_s=310 GeV (c), each with m_Z'=1 TeV. The TAR+Comb algorithm reconstructs the dark Higgs candidate from a TAR jet with m^TAR&gt;60 GeV that is supplemented by up to two additional small-R jets within &Delta;R_cone=2.5 of the TAR jet. Here, &Delta; &phi;_{jets_1,2,3 E_T^miss} is the smallest azimuthal angle between the E_T^miss and any of the three highest-p_T (leading) small-R jets. For details see text.

The product of acceptance and efficiency (A &times; &varepsilon;), defined as the number of signal events satisfying the full set of selection criteria in the merged or intermediate signal regions, divided by the total number of generated signal events, for the s(W^&plusmn;W^&#8723;) dark Higgs signal points with dark Higgs boson mass m_s and Z' boson mass m_Z'.

The product of acceptance and efficiency (A &times; &varepsilon;), defined as the number of signal events satisfying the full set of selection criteria in the merged or intermediate signal regions, divided by the total number of generated signal events, for the s(ZZ) dark Higgs signal points with dark Higgs boson mass m_s and Z' boson mass m_Z'.

Efficiency in the Single-bin SR as a function of the M(Sbottom) vs. M(N2) with the $\Delta$ M(N2,N1) $= 130$ GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Acceptance in the Multi-bin SR, $\min_{\Theta} < 0.5$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.

Efficiency in the Multi-bin SR, $\min_{\Theta} < 0.5$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Acceptance in the Multi-bin SR, $0.5 < \min_{\Theta} < 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.

Efficiency in the Multi-bin SR, $0.5 < \min_{\Theta} < 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Acceptance in the Multi-bin SR, $\min_{\Theta} > 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.

Efficiency in the Multi-bin SR, $\min_{\Theta} > 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Observed upper limits on the signal cross section as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV.

Expected upper limits on the signal cross section as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV.

Cutflows for the bechmarl signal point M(Sbottom) = 800 GeV, M(N2) = 180 GeV. Weighted event yields are reported starting with the "Preselection" line, normalized to an integrated luminosity of $139$ fb$^{−1}$.

Comparison of the expected and observed event yields in the signal regions. The top-quark and Z(mumu) background contributions are scaled with the normalization factors obtained from the background-only fit. The other contribution includes all the backgrounds not explicitly listed in the legend (V+jets except Z(mumu)+jets, di-/triboson, multijet). The hatched band indicates the total statistical and systematic uncertainties in the SM background. The contributions from three signal models to the signal regions are also displayed, where the masses M(Sbottom) and M(N2) are given in GeV in the legend. The lower panel shows the significance of the deviation of the observed yield from the expected background yield.

Dominant systematic uncertainties in the background prediction for the signal regions after the fit to the control regions. “Other” includes the uncertainties arising from muons, jet-vertex tagging, modeling of pile-up, the $E_{T}^{miss}$ computation, multijet background, and luminosity. The individual uncertainties can be correlated and do not necessarily add up quadratically to the total uncertainty.

- - - - - - - - Overview of HEPData Record - - - - - - - - <br/><br/> <b>Background Fit results:</b> <ul> <li><a href="89413?version=4&table=Background fit 1">CRs</a> <li><a href="89413?version=4&table=Background fit 2">VRs</a> <li><a href="89413?version=4&table=Background fit 5">inclusive DF-0J SRs</a> <li><a href="89413?version=4&table=Background fit 6">inclusive DF-1J SRs</a> <li><a href="89413?version=4&table=Background fit 3">inclusive SF-0J SRs</a> <li><a href="89413?version=4&table=Background fit 4">inclusive SF-1J SRs</a> </ul> <b>Kinematic distributions in VRs:</b> <ul> <li><a href="89413?version=4&table=VR kinematics 1">$m_{T2}$ in VR-top-low</a> <li><a href="89413?version=4&table=VR kinematics 2">$m_{T2}$ in VR-top-high</a> <li><a href="89413?version=4&table=VR kinematics 3">$E_T^{miss}$ in VR-WW-0J</a> <li><a href="89413?version=4&table=VR kinematics 4">$E_T^{miss}$ in VR-WW-1J</a> <li><a href="89413?version=4&table=VR kinematics 5">$E_T^{miss}$ sig in VR-VZ</a> <li><a href="89413?version=4&table=VR kinematics 6">$E_T^{miss}$ sig in VR-top-WW</a> </ul> <b>Kinematic distributions in SRs:</b> <ul> <li><a href="89413?version=4&table=SR kinematics 1">$m_{T2}$ in SR-SF-0J</a> <li><a href="89413?version=4&table=SR kinematics 2">$m_{T2}$ in SR-SF-1J</a> <li><a href="89413?version=4&table=SR kinematics 3">$m_{T2}$ in SR-DF-0J</a> <li><a href="89413?version=4&table=SR kinematics 4">$m_{T2}$ in SR-DF-1J</a> </ul> <b>Systematic uncertaities:</b> <ul> <li><a href="89413?version=4&table=Systematic uncertainties">dominant systematic uncertainties in the inclusive SRs</a> </ul> <b>Exclusion contours:</b> <ul> <li><a href="89413?version=4&table=Exclusion contour (exp) 1">expected exclusion contour direct chargino-pair production via W decay grid</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 1">observed exclusion contour direct chargino-pair production via W decay grid</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 2">expected exclusion contour direct chargino-pair production via slepton decay grid</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 2">observed exclusion contour direct chargino-pair production via slepton decay grid</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 3">expected exclusion contour direct slepton-pair production grid</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 3">observed exclusion contour direct slepton-pair production grid</a> </ul> <br/><br/><b>AUXILIARY MATERIAL</b><br/> <b>Background Fit in binned SRs:</b> <ul> <li><a href="89413?version=4&table=Background fit 7">binned DF-0J SRs</a> <li><a href="89413?version=4&table=Background fit 8">binned DF-1J SRs</a> <li><a href="89413?version=4&table=Background fit 9">binned SF-0J SRs</a> <li><a href="89413?version=4&table=Background fit 10">binned SF-1J SRs</a> </ul> <b>Exclusion contours:</b> <ul> <li><a href="89413?version=4&table=Exclusion contour (exp) 4">expected exclusion contour left-handed slepton-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 4">observed exclusion contour left-handed slepton-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 5">expected exclusion contour right-handed slepton-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 5">observed exclusion contour right-handed slepton-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 6">expected exclusion contour selectron-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 6">observed exclusion contour selectron-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 7">expected exclusion contour left-handed selectron-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 7">observed exclusion contour left-handed selectron-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 8">expected exclusion contour right-handed selectron-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 8">observed exclusion contour right-handed selectron-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 9">expected exclusion contour smuon-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 9">observed exclusion contour smuon-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 10">expected exclusion contour left-handed smuon-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 10">observed exclusion contour left-handed smuon-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (exp) 11">expected exclusion contour right-handed smuon-pair production</a> <li><a href="89413?version=4&table=Exclusion contour (obs) 11">observed exclusion contour right-handed smuon-pair production</a> </ul> <b>Cross section upper limits:</b> <ul> <li><a href="89413?version=4&table=xsec upper limits 1">upper limits on signal cross section for direct chargino-pair production via W decay</a> <li><a href="89413?version=4&table=xsec upper limits 2">upper limits on signal cross section for direct chargino-pair production via slepton decay</a> <li><a href="89413?version=4&table=xsec upper limits 3">upper limits on signal cross section for direct slepton-pair production</a> </ul> <b>Acceptances and Efficiencies for direct chargino-pair production via W decay grid </b> <ul> <li> <b>Acceptance</b> <br/> <a href="89413?version=4&table=Acceptance SR-DF-0J-[100,inf) for C1C1WW grid">SR-DF-0J-[100,inf) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[160,inf) for C1C1WW grid">SR-DF-0J-[160,inf) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[100,120) for C1C1WW grid">SR-DF-0J-[100,120) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[120,160) for C1C1WW grid">SR-DF-0J-[120,160) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[100,105) for C1C1WW grid">SR-DF-0J-[100,105) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[105,110) for C1C1WW grid">SR-DF-0J-[105,110) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[110,120) for C1C1WW grid">SR-DF-0J-[110,120) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[120,140) for C1C1WW grid">SR-DF-0J-[120,140) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[140,160) for C1C1WW grid">SR-DF-0J-[140,160) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[160,180) for C1C1WW grid">SR-DF-0J-[160,180) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[180,220) for C1C1WW grid">SR-DF-0J-[180,220) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[220,260) for C1C1WW grid">SR-DF-0J-[220,260) </a> <a href="89413?version=4&table=Acceptance SR-DF-0J-[260,inf) for C1C1WW grid">SR-DF-0J-[260,inf) </a><br/> <a href="89413?version=4&table=Acceptance SR-DF-1J-[100,inf) for C1C1WW grid">SR-DF-1J-[100,inf) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[160,inf) for C1C1WW grid">SR-DF-1J-[160,inf) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[100,120) for C1C1WW grid">SR-DF-1J-[100,120) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[120,160) for C1C1WW grid">SR-DF-1J-[120,160) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[100,105) for C1C1WW grid">SR-DF-1J-[100,105) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[105,110) for C1C1WW grid">SR-DF-1J-[105,110) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[110,120) for C1C1WW grid">SR-DF-1J-[110,120) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[120,140) for C1C1WW grid">SR-DF-1J-[120,140) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[140,160) for C1C1WW grid">SR-DF-1J-[140,160) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[160,180) for C1C1WW grid">SR-DF-1J-[160,180) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[180,220) for C1C1WW grid">SR-DF-1J-[180,220) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[220,260) for C1C1WW grid">SR-DF-1J-[220,260) </a> <a href="89413?version=4&table=Acceptance SR-DF-1J-[260,inf) for C1C1WW grid">SR-DF-1J-[260,inf) </a><br/> <a href="89413?version=4&table=Acceptance SR-SF-0J-[100,inf) for C1C1WW grid">SR-SF-0J-[100,inf) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[160,inf) for C1C1WW grid">SR-SF-0J-[160,inf) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[100,120) for C1C1WW grid">SR-SF-0J-[100,120) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[120,160) for C1C1WW grid">SR-SF-0J-[120,160) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[100,105) for C1C1WW grid">SR-SF-0J-[100,105) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[105,110) for C1C1WW grid">SR-SF-0J-[105,110) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[110,120) for C1C1WW grid">SR-SF-0J-[110,120) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[120,140) for C1C1WW grid">SR-SF-0J-[120,140) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[140,160) for C1C1WW grid">SR-SF-0J-[140,160) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[160,180) for C1C1WW grid">SR-SF-0J-[160,180) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[180,220) for C1C1WW grid">SR-SF-0J-[180,220) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[220,260) for C1C1WW grid">SR-SF-0J-[220,260) </a> <a href="89413?version=4&table=Acceptance SR-SF-0J-[260,inf) for C1C1WW grid">SR-SF-0J-[260,inf) </a><br/> <a href="89413?version=4&table=Acceptance SR-SF-1J-[100,inf) for C1C1WW grid">SR-SF-1J-[100,inf) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[160,inf) for C1C1WW grid">SR-SF-1J-[160,inf) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[100,120) for C1C1WW grid">SR-SF-1J-[100,120) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[120,160) for C1C1WW grid">SR-SF-1J-[120,160) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[100,105) for C1C1WW grid">SR-SF-1J-[100,105) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[105,110) for C1C1WW grid">SR-SF-1J-[105,110) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[110,120) for C1C1WW grid">SR-SF-1J-[110,120) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[120,140) for C1C1WW grid">SR-SF-1J-[120,140) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[140,160) for C1C1WW grid">SR-SF-1J-[140,160) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[160,180) for C1C1WW grid">SR-SF-1J-[160,180) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[180,220) for C1C1WW grid">SR-SF-1J-[180,220) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[220,260) for C1C1WW grid">SR-SF-1J-[220,260) </a> <a href="89413?version=4&table=Acceptance SR-SF-1J-[260,inf) for C1C1WW grid">SR-SF-1J-[260,inf) </a><br/> <li> <b>Efficiency</b> <br/> <a href="89413?version=4&table=Efficiency SR-DF-0J-[100,inf) for C1C1WW grid">SR-DF-0J-[100,inf) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[160,inf) for C1C1WW grid">SR-DF-0J-[160,inf) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[100,120) for C1C1WW grid">SR-DF-0J-[100,120) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[120,160) for C1C1WW grid">SR-DF-0J-[120,160) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[100,105) for C1C1WW grid">SR-DF-0J-[100,105) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[105,110) for C1C1WW grid">SR-DF-0J-[105,110) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[110,120) for C1C1WW grid">SR-DF-0J-[110,120) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[120,140) for C1C1WW grid">SR-DF-0J-[120,140) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[140,160) for C1C1WW grid">SR-DF-0J-[140,160) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[160,180) for C1C1WW grid">SR-DF-0J-[160,180) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[180,220) for C1C1WW grid">SR-DF-0J-[180,220) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[220,260) for C1C1WW grid">SR-DF-0J-[220,260) </a> <a href="89413?version=4&table=Efficiency SR-DF-0J-[260,inf) for C1C1WW grid">SR-DF-0J-[260,inf) </a><br/> <a href="89413?version=4&table=Efficiency SR-DF-1J-[100,inf) for C1C1WW grid">SR-DF-1J-[100,inf) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[160,inf) for C1C1WW grid">SR-DF-1J-[160,inf) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[100,120) for C1C1WW grid">SR-DF-1J-[100,120) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[120,160) for C1C1WW grid">SR-DF-1J-[120,160) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[100,105) for C1C1WW grid">SR-DF-1J-[100,105) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[105,110) for C1C1WW grid">SR-DF-1J-[105,110) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[110,120) for C1C1WW grid">SR-DF-1J-[110,120) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[120,140) for C1C1WW grid">SR-DF-1J-[120,140) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[140,160) for C1C1WW grid">SR-DF-1J-[140,160) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[160,180) for C1C1WW grid">SR-DF-1J-[160,180) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[180,220) for C1C1WW grid">SR-DF-1J-[180,220) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[220,260) for C1C1WW grid">SR-DF-1J-[220,260) </a> <a href="89413?version=4&table=Efficiency SR-DF-1J-[260,inf) for C1C1WW grid">SR-DF-1J-[260,inf) </a><br/> <a href="89413?version=4&table=Efficiency SR-SF-0J-[100,inf) for C1C1WW grid">SR-SF-0J-[100,inf) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[160,inf) for C1C1WW grid">SR-SF-0J-[160,inf) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[100,120) for C1C1WW grid">SR-SF-0J-[100,120) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[120,160) for C1C1WW grid">SR-SF-0J-[120,160) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[100,105) for C1C1WW grid">SR-SF-0J-[100,105) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[105,110) for C1C1WW grid">SR-SF-0J-[105,110) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[110,120) for C1C1WW grid">SR-SF-0J-[110,120) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[120,140) for C1C1WW grid">SR-SF-0J-[120,140) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[140,160) for C1C1WW grid">SR-SF-0J-[140,160) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[160,180) for C1C1WW grid">SR-SF-0J-[160,180) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[180,220) for C1C1WW grid">SR-SF-0J-[180,220) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[220,260) for C1C1WW grid">SR-SF-0J-[220,260) </a> <a href="89413?version=4&table=Efficiency SR-SF-0J-[260,inf) for C1C1WW grid">SR-SF-0J-[260,inf) </a><br/> <a href="89413?version=4&table=Efficiency SR-SF-1J-[100,inf) for C1C1WW grid">SR-SF-1J-[100,inf) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[160,inf) for C1C1WW grid">SR-SF-1J-[160,inf) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[100,120) for C1C1WW grid">SR-SF-1J-[100,120) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[120,160) for C1C1WW grid">SR-SF-1J-[120,160) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[100,105) for C1C1WW grid">SR-SF-1J-[100,105) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[105,110) for C1C1WW grid">SR-SF-1J-[105,110) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[110,120) for C1C1WW grid">SR-SF-1J-[110,120) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[120,140) for C1C1WW grid">SR-SF-1J-[120,140) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[140,160) for C1C1WW grid">SR-SF-1J-[140,160) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[160,180) for C1C1WW grid">SR-SF-1J-[160,180) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[180,220) for C1C1WW grid">SR-SF-1J-[180,220) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[220,260) for C1C1WW grid">SR-SF-1J-[220,260) </a> <a href="89413?version=4&table=Efficiency SR-SF-1J-[260,inf) for C1C1WW grid">SR-SF-1J-[260,inf) </a><br/> </ul> <b>Cutflow:</b> <ul> <li><a href="89413?version=4&table=Cutflow 1">Cutflow for direct chargino-pair production via W decay $m(\tilde{\chi}^{\pm}_1,\tilde{\chi}^{0}_1)=(300,50) GeV$</a> <li><a href="89413?version=4&table=Cutflow 2">Cutflow for direct chargino-pair production via slepton decay $m(\tilde{\chi}^{\pm}_1,\tilde{l},\tilde{\chi}^{0}_1)=(600,300,1) GeV$</a> <li><a href="89413?version=4&table=Cutflow 3">Cutflow for direct slepton-pair production $m(\tilde{l},\tilde{\chi}^{0}_1)=(400,200) GeV$</a> </ul> <b>SimpleAnalysis framework implementation</b> of the search SRs is available under "Resources" (purple button on the left)

Observed events and predicted background yields from the fit for the CRs. For backgrounds whose normalisation is extracted from the fit, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit for the CRs. For backgrounds whose normalisation is extracted from the fit, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit for the CRs. For backgrounds whose normalisation is extracted from the fit, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit for the CRs. For backgrounds whose normalisation is extracted from the fit, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields in the VRs. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields in the VRs. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields in the VRs. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields in the VRs. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Distributions of $m_{T2}$ in VR-top-low for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-low for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-low for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-low for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-high for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-high for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-high for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $m_{T2}$ in VR-top-high for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ in VR-WW-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-VZ for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-VZ for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-VZ for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-VZ for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-top-WW for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-top-WW for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-top-WW for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Distributions of $E_T^{miss}$ significance in VR-top-WW for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow.

Breakdown of the dominant systematic uncertainties on background estimates in the inclusive SRs requiring $m_{T2}$>100 GeV after performing the profile likelihood fit. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty. The percentages show the size of the uncertainty relative to the total expected background. "Top theoretical uncertainties" refers to $t\bar t$ theoretical uncertainties and the uncertainty associated to $Wt-t\bar t$ interference added quadratically.

Breakdown of the dominant systematic uncertainties on background estimates in the inclusive SRs requiring $m_{T2}$>100 GeV after performing the profile likelihood fit. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty. The percentages show the size of the uncertainty relative to the total expected background. "Top theoretical uncertainties" refers to $t\bar t$ theoretical uncertainties and the uncertainty associated to $Wt-t\bar t$ interference added quadratically.

Breakdown of the dominant systematic uncertainties on background estimates in the inclusive SRs requiring $m_{T2}$>100 GeV after performing the profile likelihood fit. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty. The percentages show the size of the uncertainty relative to the total expected background. "Top theoretical uncertainties" refers to $t\bar t$ theoretical uncertainties and the uncertainty associated to $Wt-t\bar t$ interference added quadratically.

Breakdown of the dominant systematic uncertainties on background estimates in the inclusive SRs requiring $m_{T2}$>100 GeV after performing the profile likelihood fit. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty. The percentages show the size of the uncertainty relative to the total expected background. "Top theoretical uncertainties" refers to $t\bar t$ theoretical uncertainties and the uncertainty associated to $Wt-t\bar t$ interference added quadratically.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the DF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted post-fit background yields for the SF inclusive SRs. The model independent upper limits at 95% confidence level (CL) on the observed and expected number of beyond the SM events $S^{0.95}_{obs/exp}$ and the effective beyond the SM cross-section $\sigma^{0.95}_{obs}$ are also reported. The last row reports the $p_0$-value of the SM-only hypothesis. For SRs where the data yield is smaller than expected, the $p$-value is truncated at 0.50. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$+V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Distributions of $m_{T2}$ in SRSF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRSF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-0J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Distributions of $m_{T2}$ in SRDF-1J for data and the estimated SM backgrounds. The normalisation factors extracted from the corresponding CRs are used to rescale the $t\bar t$, single top, WW, WZ and ZZ backgrounds. The fake and non-prompt leptons background (FNP) is calculated using the data-driven matrix method. The uncertainty band includes all sources of systematic and statistical errors and the last bin includes the overflow. Distributions for three benchmark signal points are overlaid for comparison.

Observed exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with $W$ boson mediated decays. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for chargino-pair production with slepton/sneutrino mediated mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for selectron-pair production, with left and right handed selectron production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed selectron-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for smuon-pair production, with left and right handed smuon production combined. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed smuon-pair production. All limits are computed at 95% CL.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned DF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=0$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed events and predicted background yields from the fit in the binned SF SRs with $n_{non-b-tagged jets}=1$. For backgrounds whose normalisation is extracted from the fit in the CRs, the yield expected from the simulation before the fit is also reported. The background denoted as "Other" in the Table includes the non-dominant background sources for this analysis, i.e. Z+jets, $t\bar t$ +V, Higgs and Drell-Yan events. A "–" symbol indicates that the background contribution is negligible.

Observed exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for left-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Observed exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Expected exclusion limits on SUSY simplified models for right-handed slepton-pair production. All limits are computed at 95% CL.

Upper limits on signal cross-section (fb) for chargino-pair production with W -boson-mediated decays.

Upper limits on signal cross-section (fb) for chargino-pair production with W -boson-mediated decays.

Upper limits on signal cross-section (fb) for chargino-pair production with W -boson-mediated decays.

Upper limits on signal cross-section (fb) for chargino-pair production with slepton/sneutrino-mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed.

Upper limits on signal cross-section (fb) for chargino-pair production with slepton/sneutrino-mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed.

Upper limits on signal cross-section (fb) for chargino-pair production with slepton/sneutrino-mediated decays. The mass relation $m(\tilde{l}_L)=\frac{1}{2}[m(\tilde{\chi}^{\pm}_1 + m(\tilde{\chi}^{0}_1)]$ is assumed.

Upper limits on signal cross-section (fb) for slepton-pair production.

Upper limits on signal cross-section (fb) for slepton-pair production.

Upper limits on signal cross-section (fb) for slepton-pair production.

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-1J-[100,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-SF-1J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).

Signal Acceptance for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).

Signal Efficiency for direct chargino-pair production with W-boson mediated decays in SR-DF-0J-[160,inf).