Differential production yield per binary collision for $W^{-}$ bosons as a function of $|\eta_\ell|$.

The lepton charge asymmetry $A_{\ell}$ from $W^\pm$ bosons as a function of absolute pseudorapidity.

Selection acceptance times efficiency as a function of WPRIME mass at truth level for left- and right-handed WPRIME MC. The total efficiency curves correspond to the sum of the efficiencies of the one b-tag and two b-tag categories.

Cutflow (efficiency with respect to total number of events in %) for several WPRIME masses in the left-handed and in the right-handed model for hadronic top-quark decays.

Overview of the signal acceptance times efficiency (A x Eff) for the hadronic top-quark decay for both categories, the predicted cross section times branching ratio to TOP BOTTOM, as well as the observed 95% CL limit on the cross section times branching ratio for several WPRIME masses in the left-handed and in the right-handed model. The expected signal event yields in the two categories for 20.3 fb-1 of 8 TeV data are also shown.

Spectrum of ETmiss for the muon channel after the event selection. The spectrum is shown with the requirement MT > 252 GeV.

Spectrum of MT for the electron channel after the event selection.

Spectrum of MT for the muon channel after the event selection.

Observed and expected limits on SIGMA*B for WPRIME at 95% CL in the electron channel.

Observed and expected limits on SIGMA*B for W* at 95% CL in the electron channel.

Observed and expected limits on SIGMA*B for WPRIME at 95% CL in the muon channel.

Observed and expected limits on SIGMA*B for W* at 95% CL in the muon channel.

Observed and expected limits on SIGMA*B for WPRIME at 95% CL for the combination of the electron and muon channels.

Observed and expected limits on SIGMA*B for W* at 95% CL for the combination of the electron and muon channels.

Observed limits on M* as a function of the DM particle mass M(chi) at 90% CL for the combination of the electron and muon channel for various operators in the EFT.

Observed limits on the DM-nucleon scattering cross-section as a function of M(chi) at 90% CL for various operators in the EFT.

Observed limits on M* as a function of the DM particle mass M(chi) at 90% CL in the electron for various operators in the EFT.

Observed limits on M* as a function of the DM particle mass M(chi) at 90% CL in the muon for various operators in the EFT.

Observed limits on M* as a function of the DM particle mass M(chi) at 95% CL in the electron for various operators in the EFT.

Observed limits on M* as a function of the DM particle mass M(chi) at 95% CL in the muon for various operators in the EFT.

Observed limits on M* as a function of the DM particle mass M(chi) at 95% CL for the combination of the electron and muon channel for various operators in the EFT.

Reconstructed $\cos\theta^*$ distributions for data and the SM background estimate in the dimuon channel.

Reconstructed $A_{\rm FB}$ distributions for data and the SM background estimate as a function of dielectron mass.

Reconstructed $A_{\rm FB}$ distributions for data and the SM background estimate as a function of dimuon mass.

Summary of 95% C.L. lower exclusion limits on $\Lambda$ for the combined dilepton contact interaction search, using a uniform positive prior in 1/$\Lambda^2$.

Summary of 95% C.L. lower exclusion limits on $M_{\rm S}$ for the combined dilepton ADD large extra dimensions search, using a uniform positive prior in 1/$M_{\rm S}^8$.

Reconstructed $\cos\theta^*$ distributions for data and the SM background estimate in the electron channel.

Reconstructed $\cos\theta^*$ distributions for data and the SM background estimate in the muon channel.

Total cross sections (cross section), branching ratios (BR(gg)) and acceptances (A) for decays in gg, for the S8 model.

Total cross sections (cross section),next-to-next-to-leading order k-factors, branching ratios (BR(qqbar prime)) and acceptances for decays in q anti-q prime only (A) for the W prime model.

Total cross sections (cross section), branching ratios (BR(qqbar prime)) and acceptances for decays in q anti-q prime only (A) for the W* leptophobic model.

Total cross sections (cross section), branching ratios (BR(qqbar prime)) and acceptances for all decays (A) for the W* leptophilic model.

Total cross sections (cross section), branching ratios only involving q and g (BR(q/g)) and acceptances for all decays into jets for the QBH model from the QBH generator.

Total cross sections (cross section), branching ratios only involving q and g (BR(q/g)) and acceptances for all decays into jets for the QBH model from the BlackMax generator.

Observed limit on cross section times acceptance for each width and mass of Gaussian signal shape tested.

Observed limit on cross section times acceptance for each intrinsic width and mass of Breit-Wigner signal shape tested. The limits in this table correspond to the qq PDF.

Observed limit on cross section times acceptance for each intrinsic width and mass of Breit-Wigner signal shape tested. The limits in this table correspond to the qg PDF.

Observed limit on cross section times acceptance for each intrinsic width and mass of Breit-Wigner signal shape tested. The limits in this table correspond to the gg PDF.

Observed limit on cross section times acceptance for each intrinsic width and mass of Breit-Wigner signal shape tested. The limits in this table correspond to the q anti-q PDF.

Expected and observed $am_{T2}$ distribution for bCd_high1 SR, before applying the $am_{T2}>200$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Expected and observed leading b-jet $p_T$ distribution for bCd_high2 SR, before applying the leading b-jet $p_T>170$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Expected and observed $E_T^{miss}$ distribution for tNbC_mix SR, before applying the $E_T^{miss}>270$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Expected and observed lepton $p_T$ distribution for bCa_low SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Expected and observed lepton $p_T$ distribution for bCa_med SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Expected and observed $am_T2$ distribution for bCb_med1 SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Expected and observed $am_T2$ distribution for bCb_high SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.

Best expected signal region for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$). This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$). This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV. This mapping is used for the final combined exclusion limits.

Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV. This mapping is used for the final combined exclusion limits.

Upper limits on the model cross-section for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Observed exclusion contour for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Expected exclusion contour for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Upper limit on signal events for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$).

Observed exclusion contour for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$).

Expected exclusion contour for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$).

Upper limit on signal events for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$).

Observed exclusion contour for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$).

Expected exclusion contour for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$).

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV.

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV.

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV.

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV.

Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV.

Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV.

Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV.

Acceptance of tN_diag SR ($E_T^{miss}>150$ GeV, $m_T>140$ GeV) for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCb_med2 SR ($am_{T2}>250$ GeV, $m_T>60$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCd_bulk SR ($am_{T2}>175$ GeV, $m_T>120$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCa_med for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCa_low for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCb_med1 for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of bCb_high for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of 3-body SR ($80<am_{T2}<90$ GeV, $m_T>120$ GeV) for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$). The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance of tNbC_mix SR for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Efficiency of tN_diag SR ($E_T^{miss}>150$ GeV, $m_T>140$ GeV) for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCb_med2 SR ($am_{T2}>250$ GeV, $m_T>60$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCd_bulk SR ($am_{T2}>175$ GeV, $m_T>120$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCa_med for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCa_low for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCb_med1 for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of bCb_high for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of 3-body SR ($80<am_{T2}<90$ GeV, $m_T>120$ GeV) for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$). The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency of tNbC_mix SR for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Number of generated events for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Number of generated events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Number of generated events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV; $E_T^{miss}$(gen)$>60$ GeV.

Number of generated events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV; $E_T^{miss}$(gen)$>250$ GeV.

Number of generated events for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).

Number of generated events for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Cross-section for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Cross-section for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Cross-section for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Cross-section for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).

Cross-section for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Combined experimental systematic uncertainty of expected tN_diag SR yields for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$, using the 2 highest $E_T^{miss}$ and 2 highest $m_T$ bins.

Combined experimental systematic uncertainty of expected tN_med SR yields for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Combined experimental systematic uncertainty of expected tN_boost SR yields for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Combined experimental systematic uncertainty of expected bCb_med2 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$, using the 2 highest $am_{T2}$ and 2 highest $m_T$ bins.

Combined experimental systematic uncertainty of expected bCc_diag SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Combined experimental systematic uncertainty of expected bCd_bulk SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$, using the 2 highest $am_{T2}$ and 2 highest $m_T$ bins.

Combined experimental systematic uncertainty of expected bCd_high1 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Combined experimental systematic uncertainty of expected bCd_high2 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Combined experimental systematic uncertainty of expected bCa_med SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Combined experimental systematic uncertainty of expected bCa_low SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Combined experimental systematic uncertainty of expected bCb_med1 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Combined experimental systematic uncertainty of expected bCb_high SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Combined experimental systematic uncertainty of expected 3-body SR yields for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$), using the 2 lowest $am_{T2}$ and 2 highest $m_T$ bins.

Combined experimental systematic uncertainty of expected tNbC_mix SR yields for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed CLs in tN_diag SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Observed CLs in tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Observed CLs in tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Observed CLs in bCb_med2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed CLs in bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed CLs in bCd_bulk SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed CLs in bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed CLs in bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Observed CLs in bCa_med SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Observed CLs in bCa_low SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Observed CLs in bCb_med1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Observed CLs in bCb_high SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Observed CLs in 3-body SR for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).

Observed CLs in tNbC_mix SR for the mixed scenario (50% $\tilde t_1\to t\chi^0_1$, 50% $\tilde t_1\to b\chi^0_1$).

Expected CLs in tN_diag SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Expected CLs in tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Expected CLs in tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.

Expected CLs in bCb_med2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Expected CLs in bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Expected CLs in bCd_bulk SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Expected CLs in bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Expected CLs in bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.

Expected CLs in bCa_med SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Expected CLs in bCa_low SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Expected CLs in bCb_med1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Expected CLs in bCb_high SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.

Expected CLs in 3-body SR for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).

Expected CLs in tNbC_mix SR for the mixed scenario (50% $\tilde t_1\to t\chi^0_1$, 50% $\tilde t_1\to b\chi^\pm_1$).

The measured leading jet $p_T$ distribution in the $W \rightarrow \mu \nu$ control region, for the M1 selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in the $W \rightarrow e \nu$ control region, for the M1 selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured leading jet $p_T$ distribution in the $W \rightarrow e \nu$ control region, for the M1 selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in the $Z \rightarrow \mu \mu$ control region, for the M1 selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured leading jet $p_T$ distribution in the $Z \rightarrow \mu \mu$ control region, for the M1 selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in the $W\rightarrow \mu \nu$ control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured leading jet $p_T$ distribution in the $W\rightarrow \mu \nu$ control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in the $W\rightarrow e \nu$ control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured leading jet $p_T$ distribution in the $W\rightarrow e \nu$ control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in the $Z\rightarrow ll$ control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured leading jet $p_T$ distribution in the $Z\rightarrow ll$ control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in the t-(anti)-t control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured leading jet $p_T$ distribution in the t-(anti)-t control region, for the c-tagged selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

Measured leading jet $p_T$ distribution for the V3 selections compared to the SM predictions.

Measured $E_T^{miss}$ distribution for the V4 selections compared to the SM predictions.

Measured leading jet $p_T$ distribution for the V5 selections compared to the SM predictions.

Measured $E_T^{miss}$ distribution for the V5 selections compared to the SM predictions.

Measured $E_T^{miss}$ distribution for the M1 selection compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured leading jet $p_T$ distribution for the M1 selection compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured $E_T^{miss}$ distribution for the M2 selection compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured leading jet $p_T$ distribution for the M2 selection compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured $E_T^{miss}$ distribution for the M3 selection compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured leading jet $p_T$ distribution for the M3 selection compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured $E_T^{miss}$ distribution for the C1 selection before the cut in the variable shown is applied. The data are compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured leading jet $p_T$ distribution for the C1 selection before the cut in the variable shown is applied. The data are compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured leading jet $p_T$ for the C2 selection. The data are compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

Measured jet multiplicity for the C2 selection. The data are compared to the SM predictions. For illustration purposes, the distribution of two different SUSY scenarios are included.

The measured $W$ transverse mass distribution in the $W \rightarrow \mu \nu$ control region for the M1 monojet-like selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $W$ transverse mass distribution in the $W \rightarrow e \nu$ control region for the M1 monojet-like selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured dimuon invariant mass distribution in the $Z \rightarrow \mu \mu$ control region for the M1 monojet-like selection, compared to the background predictions. The latter include the global normalization factors extracted from the fit.

The measured $E_T^{miss}$ distribution in a multijet control sample for the monojet-like analysis. This region is defined with similar $E_T^{miss}$ and leading jet $p_T$ cuts as M1, but releasing the jet-veto and inverting the $\Delta \phi (jet, p_T^{miss})$ cut, now requiring it to be less than 0.4. The data are compared to predictions.

Measured jet multiplicity in the $W \rightarrow e \nu$ control region for the c-tagged analysis, compared to background predictions. The latter include the global normalization factors extracted from the fit.

Measured jet multiplicity in the $W \rightarrow \mu \nu$ control region for the c-tagged analysis, compared to background predictions. The latter include the global normalization factors extracted from the fit.

Measured jet multiplicity in the $Z \rightarrow ll$ control region for the c-tagged analysis, compared to background predictions. The latter include the global normalization factors extracted from the fit.

Measured jet multiplicity in the t-(anti)-t control region for the c-tagged analysis, compared to background predictions. The latter include the global normalization factors extracted from the fit.

Measured dilepton invariant mass in the $Z \rightarrow ll$ control region for the c-tagged analysis. The predictions include the global normalization factors extracted from the fit.

Measured jet multiplicity distribution for the M1 selection compared to the SM predictions. For illustration purposes, the impact of two different SUSY scenarios are included.

Measured leading jet $\eta$ distribution for the M1 selection compared to the SM predictions. For illustration purposes, the impact of two different SUSY scenarios are included.

Measured $\Delta \phi (jet, p_T^{miss})$ distribution for the M1 selection compared to the SM predictions. For illustration purposes, the impact of two different SUSY scenarios are included.

Measured $E_T^{miss}$ distribution after preselection cuts compared to background predictions.

Measured jet multiplicity distribution in the C1 c-tagged selection compared to background predictions.

Multiplicity of jets with loose heavy-flavor tags in the C1 selection.

Multiplicity of jets with medium heavy-flavor tags in the C1 selection.

Expected and observed CLs, number of events, acceptance and efficiency for M1 SR for $\tilde{t}_1 \rightarrow c + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M2 SR for $\tilde{t}_1 \rightarrow c + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M3 SR for $\tilde{t}_1 \rightarrow c + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for C1 SR for $\tilde{t}_1 \rightarrow c + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for C2 SR for $\tilde{t}_1 \rightarrow c + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M1 SR for $\tilde{b}_1 \rightarrow b + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M2 SR for $\tilde{b}_1 \rightarrow b + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M3 SR for $\tilde{b}_1 \rightarrow b + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M1 SR for $\tilde{t}_1 \rightarrow b + ff' + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M2 SR for $\tilde{t}_1 \rightarrow b + ff' + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Expected and observed CLs, number of events, acceptance and efficiency for M3 SR for $\tilde{t}_1 \rightarrow b + ff' + \tilde{\chi}^0_1$. The expected event yield in 20.3 fb$^{-1}$, $N_{\text{events}}$, is used to calculate $\sigma\times A\times\varepsilon$ and $A\times\varepsilon$. The acceptance $A$ is defined as the percentage of events passing the selection at truth level, and the efficiency $\varepsilon$ is the ratio of events passing the selection at reconstructed level over the number of events passing the selection at truth level. The modified efficiency $\varepsilon\prime$ is the ratio of events passing the selection at truth and reconstructed level over the number of events passing the selection at truth level.

Detailed list of the contribution of each source of uncertainty to the total uncertainty on the measured values of $\sigma(tq)$, $\sigma(\bar{t}q)$, $R_t$, and $\sigma(tq+\bar{t}q)$. The evaluation of the systematic uncertainties has a statistical uncertainty of $0.3\,\%$. Uncertainties contributing less than $1.0\,\%$ are marked with "$<1$" in the paper. To provide numerical values for this table in HEPdata, these uncertainties are approximated with $\pm 0.5\,\%$. This approximation is applied to all measurements for the following uncertainties$:$ JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by-jets, JES pileup, $b$-JES, jet vertex fraction, mistag efficiency and $W+\;$jets shape variation. For the measurement of $\sigma(tq)$ the approximation is applied in addition to the following uncertainties$:$ JES flavor response, $c$-tagging efficiency, $t\bar{t}$ generator + parton shower and $t\bar{t}$ ISR/FSR. For the measurement of $\sigma(\bar{t}q)$ the approximation is applied in addition to these uncertainties$:$ JES flavor response, $b/\bar{b}$ acceptance, and $t\bar{t}$ ISR/FSR. For the measurement of $R_t$ the approximation is applied in addition to these uncertainties$:$ JES detector, $b$-tagging efficiency, $c$-tagging efficiency, $b/\bar{b}$ acceptance and $tq$ scale variations. For the measurement of $\sigma(tq+\bar{t}q)$ the approximation is applied in addition to these uncertainties$:$ JES flavor response, $c$-tagging efficiency, $b/\bar{b}$ acceptance, $t\bar{t}$ generator + parton shower and $t\bar{t}$ ISR/FSR.

Differential t-channel top-quark production cross sections and normalized differential t-channel top-quark production cross sections as functions of ABS(YRAP(T)).

The cross sections for top-quark and top-antiquark production in the t-channel, together with the cross-section ratio.

Differential t-channel top-quark production cross sections and normalized differential t-channel top-quark production cross sections as functions of ABS(YRAP(TBAR)).

Parametrization factors for the $m_{t}$ dependence [see Eq. (4) in the paper] of $\sigma(tq)$, $\sigma(\bar tq)$, and $\sigma(t q+\bar t q)$.

The cross sections for top-quark and top-antiquark production in the t-channel, together with the cross-section ratio.

The value of $|V_{tb}|^{2}$ is extracted by dividing the measured value of $\sigma(t q+\bar t q)$ by the prediction of the approximate NNLO calculation. The experimental and theoretical uncertainties are added in quadrature.

Event yields for the 2-jet-$\ell^{+}$ and 2-jet-$\ell^{-}$ HPR channels. The expectation for the signal and background yields correspond to the result of the binned maximum-likelihood fit described in Sec. $\mathrm{VI~D}$ in the paper. The uncertainty of the expectations is the normalization uncertainty of each process after the fit, as described in Sec. $\mathrm{VII~F}$ in the paper.

Migration matrix relating the parton level $p_{\mathrm{T}}(t)$ shown on the $y$ axis and the reconstruction level $p_{\mathrm{T}}(\ell \nu b)$ shown on the $x$ axis for the top-quark $p_{\mathrm{T}}$ distribution.

Migration matrix relating the parton level $p_{\mathrm{T}}(\bar t)$ shown on the $y$ axis and the reconstruction level $p_{\mathrm{T}}(\ell \nu b)$ shown on the $x$ axis for the top-antiquark $p_{\mathrm{T}}$ distribution.

Migration matrix relating the parton level $|y(t)|$ shown on the $y$ axis and the reconstruction level $|y(\ell \nu b)|$ shown on the $x$ axis for the top-quark $|y|$ distribution.

Migration matrix relating the parton level $|y(\bar t)|$ shown on the $y$ axis and the reconstruction level $|y(\ell \nu b)|$ shown on the $x$ axis for the top-antiquark $|y|$ distribution.

Differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(t)$ with the uncertainties for each bin given in percent.

Differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(\bar t)$ with the uncertainties for each bin given in percent.

Differential t-channel top-quark production cross section as a function of $|y(t)|$ with the uncertainties for each bin given in percent.

Differential t-channel top-quark production cross section as a function of $|y(\bar t)|$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(t)$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(\bar t)$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $|y(t)|$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $|y(\bar t)|$ with the uncertainties for each bin given in percent.

Comparison between the measured differential cross sections and the predictions from the NLO calculation using the MSTW2008 PDF set. For each variable and prediction a $\chi^{2}$ value is calculated with HERAfitter using the covariance matrix of each measured spectrum. The theory uncertainties of the predictions are treated as uncorrelated. The number of degrees of freedom (NDF) is equal to the number of bins in the measured spectrum.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, jet-vertex fraction, $b/\bar{b}$ acceptance, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(\bar t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, mistag efficiency, $b/\bar{b}$ acceptance, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, jet-vertex fraction, $b/\bar{b}$ acceptance, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(\bar t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The JES $\eta$ intercalibration uncertainty has a sign switch from the first to the second bin. For the $tq$ generator $+$ parton shower uncertainty a sign switch is denoted with $\mp$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, $c-$tagging efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(\bar t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. Sign switches within one uncertainty are denoted with $\mp$ and $\pm$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. Sign switches within one uncertainty are denoted with $\mp$ and $\pm$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, b-JES, jet-vertex fraction, $b/\bar{b}$ acceptance, $b-$tagging efficiency, $c-$tagging efficiency, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(\bar t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. Sign switches within one uncertainty are denoted with $\mp$ and $\pm$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content $:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, b-JES, jet energy resolution, jet-vertex fraction, $b/\bar{b}$ acceptance, $b-$tagging efficiency, $c-$ tagging efficiency, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Statistical correlation matrices for the differential cross section as a function of $p_{\mathrm{T}}(t)$.

Statistical correlation matrices for the differential cross section as a function of $p_{\mathrm{T}}(\bar t)$.

Statistical correlation matrices for the differential cross section as a function of $|y(t)|$.

Statistical correlation matrices for the differential cross section as a function of $|y(\bar t)|$.

Statistical correlation matrices for the normalized differential cross section as a function of $p_{\mathrm{T}}(t)$.

Statistical correlation matrices for the normalized differential cross section as a function of $p_{\mathrm{T}}(\bar t)$.

Statistical correlation matrices for the normalized differential cross section as a function of $|y(t)|$.

Statistical correlation matrices for the normalized differential cross section as a function of $|y(\bar t)|$.

Etmiss distribution for SRC1 after all selection requirements except those on Etmiss.

Etmiss distribution for SRC2 after all selection requirements except those on Etmiss.

Etmiss distribution for SRC3 after all selection requirements except those on Etmiss.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=50%.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=50%.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=100%.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=100%.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=75%.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=75%.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=50%.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=50%.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=25%.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=25%.

Observed exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=0%.

Expected exclusion limit at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=0%.

Nominal observed excluded cross sections at 95% CL in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario, once corrected by the recorded luminosity and the efficiency times acceptance of the model itself.

Signal region (SR) combination providing the lowest expected CLs in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario.

Signal region (SR) combination providing the lowest expected CLs in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=75%.

Signal region (SR) combination providing the lowest expected CLs in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=50%.

Signal region (SR) combination providing the lowest expected CLs in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=25%.

Signal region (SR) combination providing the lowest expected CLs in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where BR(stop --> top+neutralino)=0%.

Signal acceptance for the different signal regions (SR) in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario with both stops decaying to top+neutralino. The acceptance is defined in Appendix A of arXiv:1403.4853.

Signal efficiency for the different signal regions (SR) in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario with both stops decaying to top+neutralino.

Signal acceptance for the different signal regions (SR) in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario with both stops decaying to b+chargino. The acceptance is defined in Appendix A of arXiv:1403.4853.

Signal efficiency for the different signal regions (SR) in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario with both stops decaying to b+chargino.

Number of generated Monte Carlo events in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino.

Number of generated Monte Carlo events in the ( M(STOP), M(NEUTRALINO) ) mass plane in the stop pair production scenario where both stops decay to b+chargino.

Stop signal production cross sections in the ( M(STOP), M(NEUTRALINO) ) mass plane.

Total experimental systematic uncertainty in percent on the signal yield for SRA1 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRA2 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRA3 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRA4 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRB in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRC1 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRC2 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Total experimental systematic uncertainty in percent on the signal yield for SRC3 in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario where both stops decay to top+neutralino. The uncertainty does not include Monte Carlo statistical uncertainties, nor theoretical uncertainties on the signal cross section.

Observed and expected CLs in the ( M(STOP), M(NEUTRALINO) ) mass plane for the stop pair production scenario. The value for the best expected signal region combination is shown.

The effective mass distribution in VR2Z.

The ETmiss distribution in SR0noZa.

The effective mass distribution in SR0noZa.

The ETmiss distribution in SR1noZa.

The effective mass distribution in SR1noZa.

The ETmiss distribution in SR2noZa.

The effective mass distribution in SR2noZa.

The ETmiss distribution in SR0noZb.

The effective mass distribution in SR0noZb.

The ETmiss distribution in SR1noZb.

The effective mass distribution in SR1noZb.

The ETmiss distribution in SR2noZb.

The effective mass distribution in SR2noZb.

The ETmiss distribution in SR0Z.

The effective mass distribution in SR0Z.

The ETmiss distribution in SR1Z.

The effective mass distribution in SR1Z.

The ETmiss distribution in SR2Z.

The effective mass distribution in SR2Z.

Observed 95% CL exclusion contour for the RPV chargino NLSP model with lambda_121 != 0.

Expected 95% CL exclusion contour for the RPV chargino NLSP model with lambda_121 != 0.

Observed 95% CL exclusion contour for the RPV chargino NLSP model with lambda_122 != 0.

Expected 95% CL exclusion contour for the RPV chargino NLSP model with lambda_122 != 0.

Observed 95% CL exclusion contour for the RPV chargino NLSP model with lambda_133 != 0.

Expected 95% CL exclusion contour for the RPV chargino NLSP model with lambda_133 != 0.

Observed 95% CL exclusion contour for the RPV chargino NLSP model with lambda_233 != 0.

Expected 95% CL exclusion contour for the RPV chargino NLSP model with lambda_233 != 0.

Observed 95% CL exclusion contour for the RPV gluino NLSP model with lambda_121 != 0.

Expected 95% CL exclusion contour for the RPV gluino NLSP model with lambda_121 != 0.

Observed 95% CL exclusion contour for the RPV gluino NLSP model with lambda_122 != 0.

Expected 95% CL exclusion contour for the RPV gluino NLSP model with lambda_122 != 0.

Observed 95% CL exclusion contour for the RPV gluino NLSP model with lambda_133 != 0.

Expected 95% CL exclusion contour for the RPV gluino NLSP model with lambda_133 != 0.

Observed 95% CL exclusion contour for the RPV gluino NLSP model with lambda_233 != 0.

Expected 95% CL exclusion contour for the RPV gluino NLSP model with lambda_233 != 0.

Observed 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_121 != 0.

Expected 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_121 != 0.

Observed 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_122 != 0.

Expected 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_122 != 0.

Observed 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_133 != 0.

Expected 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_133 != 0.

Observed 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_233 != 0.

Expected 95% CL exclusion contour for the RPV Lslepton NLSP model with lambda_233 != 0.

Observed 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_121 != 0.

Expected 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_121 != 0.

Observed 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_122 != 0.

Expected 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_122 != 0.

Observed 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_133 != 0.

Expected 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_133 != 0.

Observed 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_233 != 0.

Expected 95% CL exclusion contour for the RPV Rslepton NLSP model with lambda_233 != 0.

Observed 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_121 != 0.

Expected 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_121 != 0.

Observed 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_122 != 0.

Expected 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_122 != 0.

Observed 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_133 != 0.

Expected 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_133 != 0.

Observed 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_233 != 0.

Expected 95% CL exclusion contour for the RPV sneutrino NLSP model with lambda_233 != 0.

Observed 95% CL exclusion contour for the R-slepton RPC model.

Expected 95% CL exclusion contour for the R-slepton RPC model.

Observed and expected 95% CL cross-section upper limits for the Stau RPC model, together with the theoretically predicted cross-section.

Observed and expected 95% CL cross-section upper limits for the Z RPC model, together with the theoretically predicted cross-section.

Observed 95% CL exclusion contour for the GGM tan beta = 1.5 model.

Expected 95% CL exclusion contour for the GGM tan beta = 1.5 model.

Observed 95% CL exclusion contour for the GGM tan beta = 30 model.

Expected 95% CL exclusion contour for the GGM tan beta = 30 model.

Observed 95% CL cross-section upper limit for the RPV chargino NLSP models with lambda_121 != 0 and lambda_122 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV chargino NLSP models with lambda_133 != 0 and lambda_233 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV gluino NLSP models with lambda_121 != 0 and lambda_122 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV gluino NLSP models with lambda_133 != 0 and lambda_233 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV Lslepton NLSP models with lambda_121 != 0 and lambda_122 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV Lslepton NLSP models with lambda_133 != 0 and lambda_233 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV Rslepton NLSP models with lambda_121 != 0 and lambda_122 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV Rslepton NLSP models with lambda_133 != 0 and lambda_233 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV sneutrino NLSP models with lambda_121 != 0 and lambda_122 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the RPV sneutrino NLSP models with lambda_133 != 0 and lambda_233 != 0, and the selection of Z-veto signal regions used to set limits in these models. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bba' means that the regions SR0noZb, SR1noZb and SR2noZa were used, in addition to the three Z-rich regions (SR0-2Z).

Observed 95% CL cross-section upper limit for the R-slepton RPC model, and the selection of Z-veto signal regions used to set limits in this model. The combination of regions used is ordered by the minimum number of hadronic taus required. For example, ``bbb' means that the regions SR0noZb, SR1noZb and SR2noZb were used, in addition to the three Z-rich regions (SR0-2Z). For the RPC stau and Z models, the ``aaa' combination of regions was used throughout.

Performance of the SR0noZa selection in the R-slepton RPC model: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Performance of the SR0noZb selection in the RPV chargino NLSP model with lambda_121 != 0: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Performance of the SR1noZa selection in the RPV sneutrino NLSP model with lambda_233 != 0: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Performance of the SR1noZb selection in the RPV gluino NLSP model with lambda_133 != 0: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Performance of the SR2noZa selection in the RPV sneutrino NLSP model with lambda_233 != 0: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Performance of the SR2noZb selection in the RPV gluino NLSP model with lambda_133 != 0: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Performance of the SR0Z selection in the GGM tan beta = 30 model: number of generated signal events; total signal cross-section; acceptance; efficiency; total experimental systematic uncertainty, not including Monte Carlo statistics; observed CL using this region alone; expected CL using this region alone.

Cut flows for a representative selection of SUSY signal points in the Z-veto signal regions. In each case, m2 and m1 refer to the axes of the plots in Sec. XI, where m2 is the larger of the two masses. The number of events expected for a luminosity of 20.3 fb-1 is quoted at each step of the selection. The preselection requires four baseline leptons, at least two of which are light leptons; the signal lepton selection is made at the ``Lepton Multiplicity' stage. ``Event Cleaning' refers to the selection criteria applied to remove non-collision backgrounds and detector noise.

Cut flows for a representative selection of SUSY signal points in the Z-rich signal regions. In each case, m2 and m1 refer to the axes of the plots in Sec. XI, where m2 is the larger of the two masses (or the value of mu in the case of GGM models). The number of events expected for a luminosity of 20.3 fb-1 is quoted at each step of the selection. The preselection requires four baseline leptons, at least two of which are light leptons; the signal lepton selection is made at the ``Lepton Multiplicity' stage. ``Event Cleaning' refers to the selection criteria applied to remove non-collision backgrounds and detector noise.

Cut flows by lepton channel for a representative selection of SUSY signal points in the SR0noZa signal region. In each case, m2 and m1 refer to the axes of the plots in Sec. XI, where m2 is the larger of the two masses. The number of events expected for a luminosity of 20.3 fb-1 is quoted at each step of the selection. The preselection requires four baseline leptons, at least two of which are light leptons; the signal lepton selection is made at the ``Lepton Multiplicity' stage. ``Event Cleaning' refers to the selection criteria applied to remove non-collision backgrounds and detector noise. The RPC R-slepton model is used, with (m2,m1) = (450,300) GeV.

Cut flows by lepton channel for a representative selection of SUSY signal points in the SR1noZb signal region. In each case, m2 and m1 refer to the axes of the plots in Sec. XI, where m2 is the larger of the two masses. The number of events expected for a luminosity of 20.3 fb-1 is quoted at each step of the selection. The preselection requires four baseline leptons, at least two of which are light leptons; the signal lepton selection is made at the ``Lepton Multiplicity' stage. ``Event Cleaning' refers to the selection criteria applied to remove non-collision backgrounds and detector noise. The RPV gluino NLSP model is used, with lambda_133 != 0 and (m2,m1) = (800,400) GeV.

Cut flows by lepton channel for a representative selection of SUSY signal points in the SR0Z signal region. In each case, m2 and m1 refer to the axes of the plots in Sec. XI, where m2 is the value of mu. The number of events expected for a luminosity of 20.3 fb-1 is quoted at each step of the selection. The preselection requires four baseline leptons, at least two of which are light leptons; the signal lepton selection is made at the ``Lepton Multiplicity' stage. ``Event Cleaning' refers to the selection criteria applied to remove non-collision backgrounds and detector noise. The GGM tan beta = 30 model is used, with (m2,m1) = (200,1000) GeV.