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.

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Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

95% CL upper limits on the cross section [fb].

Number of signal events.

Acceptance [%].

Efficiency [%].

Experimental uncertainty.

Expected CLs.

Observed CLs.

Best signal region.

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

95% CL upper limits on the cross section [fb].

Observed 95% CL exclusion contour.

Expected 95% CL exclusion contour.

95% CL upper limits on the cross section [fb].

Number of signal events.

Acceptance [%].

Efficiency [%].

Uncertainty [%].

Expected CLs.

Observed CLs.

Best signal region.

Best signal region.

Distribution of HT after the MTtau requirement for the 1-tau ''Tight'' SR. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in the CRs corresponding to MTtau below 130 GeV. Also shown is the expected signal from typical mSUGRA, GMSB and bRPV samples. The last bin in the expected background distribution is an overflow bin.

Distribution of MTtau1 + MTtau2 in the 2tau channel after all analysis requirements but the final SR requirements on MTtau1 + MTtau2 and HT2j. To reduce the contributions from events with Z bosons decaying into tau leptons, the requirement MTtau1 + MTtau2 > 150 GeV is applied to all distributions. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in the CRs corresponding to HT2j below 550 GeV.

Distribution of HT2j in the 2tau channel after all analysis requirements but the final SR requirements on MTtau1 + MTtau2 and HT2j. To reduce the contributions from events with Z bosons decaying into tau leptons, the requirement MTtau1 + MTtau2 > 150 GeV is applied to all distributions. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in the CRs corresponding to HT2j below 550 GeV.

Distribution of the jet multiplicity in the 2tau channel after all analysis requirements but the final SR requirements on MTtau1 + MTtau2 and HT2j. To reduce the contributions from events with Z bosons decaying into tau leptons, the requirement MTtau1 + MTtau2 > 150 GeV is applied to all distributions. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in the CRs corresponding to HT2j below 550 GeV.

Distribution of the final kinematic variables in the tau+e channel after all analysis requirements but the final SR selections on Meff for the bRPV model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+e channel after all analysis requirements but the final SR selections on MEFF for the GMSB model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+e channel after all analysis requirements but the final SR selections on MET for the mSUGRA model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+e channel after all analysis requirements but the final SR selections on MET for the nGM model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+mu channel after all analysis requirements but the final SR selections on MEFF for the bRPV model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+mu channel after all analysis requirements but the final SR selections on MEFF for the GMSB model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+mu channel after all analysis requirements but the final SR selections on MET for the mSUGRA model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Distribution of the final kinematic variables in the tau+mu channel after all analysis requirements but the final SR selections on MET for the nGM model. The SM prediction includes the data-driven corrections discussed in the paper. MC events are normalized to data in specific CRs. The last bin in the expected background distribution is an overflow bin. There are no data events in the overflow bin after all analysis requirements are applied.

Observed 95% CL lower limits on the minimal GMSB model parameters Lambda and tan(beta) using a combination of all channels. The result is obtained using 20.3 fb-1 of sqrt(s) = 8 TeV ATLAS data. Additional model parameters are M(mess) = 250 TeV, N5 = 3, mu>0 and Cgrav =1.

Expected 95% CL lower limits on the minimal GMSB model parameters Lambda and tan(beta) using a combination of all channels. The result is obtained using 20.3 fb-1 of sqrt(s) = 8 TeV ATLAS data. Additional model parameters are M(mess) = 250 TeV, N5 = 3, mu>0 and Cgrav =1.

Observed 95% CL lower limits on the mSUGRA/CMSSM model parameters m0 and m1/2 for the combination of the 1tau, tau+e and tau+mu channels. Additional model parameters are A0 = -2m0, tan(beta) = 30 and sign(mu) = +1.

Expected 95% CL lower limits on the mSUGRA/CMSSM model parameters m0 and m1/2 for the combination of the 1tau, tau+e and tau+mu channels. Additional model parameters are A0 = -2m0, tan(beta) = 30 and sign(mu) = +1.

Observed 95% CL lower limits on the simplified nGM model parameters m(stau) and m(gluino) for the combination of the 2tau, tau+e and tau+mu channels. Additional squark and slepton mass parameters are set to 2.5 TeV, M1 = M2 = 2.5 TeV, and all trilinear coupling terms are set to zero. Also, the parameter mu is fixed to mu = 400 GeV.

Expected 95% CL lower limits on the simplified nGM model parameters m(stau) and m(gluino) for the combination of the 2tau, tau+e and tau+mu channels. Additional squark and slepton mass parameters are set to 2.5 TeV, M1 = M2 = 2.5 TeV, and all trilinear coupling terms are set to zero. Also, the parameter mu is fixed to mu = 400 GeV.

Observed 95% CL lower limits on the bRPV model parameters m0 and m1/2 for the combination of all channels. Additional model parameters are A0 = -2m0 , tan(beta) = 30 and sign(mu) = +1.

Expected 95% CL lower limits on the bRPV model parameters m0 and m1/2 for the combination of all channels. Additional model parameters are A0 = -2m0 , tan(beta) = 30 and sign(mu) = +1.

Cross section predictions for the nGM grid. For each signal point 25000 MC events have been generated.

Observed upper cross section limits for the nGM grid. For each signal point 25000 MC events have been generated. The limit is derived for the combination of the 2tau and the tau+lepton channels.

Systematic uncertainty for the GMSB grid in the 1-tau analysis.

Acceptance for the GMSB grid in the 1-tau analysis.

Efficiency for the GMSB grid in the 1-tau analysis.

The product of acceptance and efficiency for the GMSB grid in the 1-tau analysis.

Expected CLs values for the GMSB grid in the 1tau analysis.

Observed CLs values for the GMSB grid in the 1tau analysis.

Systematic uncertainty for the mSUGRA grid in the 1-tau analysis.

Acceptance for the mSUGRA grid in the 1-tau analysis.

Efficiency for the mSUGRA grid in the 1-tau analysis.

Product of acceptance and efficiency for the mSUGRA grid in the 1-tau analysis.

Expected CLs values for the mSUGRA grid in the 1tau analysis.

Observed CLs values for the mSUGRA grid in the 1tau analysis.

Systematic uncertainty for the bRPV grid in the 1-tau analysis.

Acceptance for the bRPV grid in the 1-tau analysis.

Efficiency for the bRPV grid in the 1-tau analysis.

Product of acceptance and efficiency for the bRPV grid in the 1-tau analysis.

Expected CLs values for the bRPV grid in the 1tau analysis.

Observed CLs values for the bRPV grid in the 1tau analysis.

Systematic uncertainty for the GMSB grid in the 2tau analysis.

Acceptance for the GMSB grid in the 2tau analysis.

Efficiency for the GMSB grid in the 2tau analysis.

Product of acceptance and efficiency for the GMSB grid in the 2tau analysis.

Expected CLs values for the GMSB grid in the 2tau analysis.

Observed CLs values for the GMSB grid in the 2tau analysis.

Systematic uncertainty for the nGM grid in the 2tau analysis.

Acceptance for the nGM grid in the 2tau analysis.

Efficiency for the nGM grid in the 2tau analysis.

Product of acceptance and efficiency for the nGM grid in the 2tau analysis.

Expected CLs values for the nGM grid in the 2tau analysis.

Observed CLs values for the nGM grid in the 2tau analysis.

Systematic uncertainty for the bRPV grid in the 2tau analysis.

Acceptance for the bRPV grid in the 2tau analysis.

Efficiency for the bRPV grid in the 2tau analysis.

Product of acceptance and efficiency for the bRPV grid in the 2tau analysis.

Expected CLs values for the bRPV grid in the 2tau analysis.

Observed CLs values for the bRPV grid in the 2tau analysis.

Systematic Uncertainty for the GMSB grid in the tau+e analysis.

Acceptance for the GMSB grid in the tau+e analysis.

Efficiency for the GMSB grid in the tau+e analysis.

Product of acceptance and efficiency for the GMSB grid in the tau+e analysis.

Expected CLs values for the GMSB grid in the tau+e analysis.

Observed CLs values for the GMSB grid in the tau+e analysis.

Systematic Uncertainty for the nGM grid in the tau+e analysis.

Acceptance for the nGM grid in the tau+e analysis.

Efficiency for the nGM grid in the tau+e analysis.

Product of acceptance and efficiency for the nGM grid in the tau+e analysis.

Expected CLs values for the nGM grid in the tau+e analysis.

Observed CLs values for the nGM grid in the tau+e analysis.

Systematic Uncertainty for the bRPV grid in the tau+e analysis.

Acceptance for the bRPV grid in the tau+e analysis.

Efficiency for the bRPV grid in the tau+e analysis.

Product of acceptance and efficiency for the bRPV grid in the tau+e analysis.

Expected CLs values for the bRPV grid in the tau+e analysis.

Observed CLs values for the bRPV grid in the tau+e analysis.

Systematic Uncertainty for the mSUGRA grid in the tau+e analysis.

Acceptance for the mSUGRA grid in the tau+e analysis.

Efficiency for the mSUGRA grid in the tau+e analysis.

Product of acceptance and efficiency for the mSUGRA grid in the tau+e analysis.

Expected CLs values for the mSUGRA grid in the tau+e analysis.

Observed CLs values for the mSUGRA grid in the tau+e analysis.

Systematic Uncertainty for the GMSB grid in the tau+mu analysis.

Acceptance for the GMSB grid in the tau+mu analysis.

Efficiency for the GMSB grid in the tau+mu analysis.

Product of acceptance and efficiency for the GMSB grid in the tau+mu analysis.

Expected CLs values for the GMSB grid in the tau+mu analysis.

Observed CLs values for the GMSB grid in the tau+mu analysis.

Systematic Uncertainty for the nGM grid in the tau+mu analysis.

Acceptance for the nGM grid in the tau+mu analysis.

Efficiency for the nGM grid in the tau+mu analysis.

Product of acceptance and efficiency for the nGM grid in the tau+mu analysis.

Expected CLs values for the nGM grid in the tau+mu analysis.

Observed CLs values for the nGM grid in the tau+mu analysis.

Systematic Uncertainty for the bRPV grid in the tau+mu analysis.

Acceptance for the bRPV grid in the tau+mu analysis.

Efficiency for the bRPV grid in the tau+mu analysis.

Product of acceptance and efficiency for the bRPV grid in the tau+mu analysis.

Expected CLs values for the bRPV grid in the tau+mu analysis.

Observed CLs values for the bRPV grid in the tau+mu analysis.

Systematic Uncertainty for the mSUGRA grid in the tau+mu analysis.

Acceptance for the mSUGRA grid in the tau+mu analysis.

Efficiency for the mSUGRA grid in the tau+mu analysis.

Product of acceptance and efficiency for the mSUGRA grid in the tau+mu analysis.

Expected CLs values for the mSUGRA grid in the tau+mu analysis.

Observed CLs values for the mSUGRA grid in the tau+mu analysis.

Example cutflow for three benchmark signal points in the 1-tau channel. For the GMSB point 50000 events have been generated, 20000 for nGM and 20000 for mSUGRA respectively. These event numbers are then normalised to 21 fb^{-1} luminosity.

Example cutflow for three benchmark signal points in the 2tau analysis. Specific SRs are indicated at the respective cut. Event numbers are normalised to 21 fb^{-1} luminosity. For the GMSB point 50000 events have been generated, 20000 for nGM and 25000 for bRPV respectively.

Example cutflow for four benchmark signal points in the tau+e analysis. Event numbers are normalised to 21 fb^{-1} luminosity. For the GMSB point 50000 events have been generated, 20000 for nGM, 20000 for mSUGRA and 25000 for bRPV respectively.

Example cutflow for four benchmark signal points in the tau+mu analysis. Event numbers are normalised to 21 fb^{-1} luminosity. For the GMSB point 50000 events have been generated, 20000 for nGM, 20000 for mSUGRA and 25000 for bRPV respectively.

Normalized differential cross-sections for the absolute rapidity (|y|) of the ttbar system. The cross-section in each bin is given as the integral of the normalized differential cross-section over the bin width, divided by the bin width. The calculation of the cross-sections in the last bins includes events falling outside of the bin edges, and the normalization is done within the quoted bin width. The full covariance matrice is provided in Table 8 below.

Bin-wise full covariance matrices for the normalized differential cross-sections of the hadronically decaying top-quark PT. The elements of the covariance matrices are in units of TeV$^{-2}$.

Bin-wise full covariance matrices for the normalized differential cross-sections of the mass of the ttbar system. The elements of the covariance matrices are in units of TeV$^{-2}$.

Bin-wise full covariance matrices for the normalized differential cross-sections of the PT of the ttbar system. The elements of the covariance matrices are in units of TeV$^{-2}$.

Bin-wise full covariance matrices for the normalized differential cross-sections of the absolute rapidity (|y|) of the ttbar system.

Effective mass distribution in the multi-jet CR DS-lowMass.

Distribution of Met in the W+jets control region with 20.3/fb.

Distribution of Meff in the W+jets control region with 20.3/fb.

Distribution of Mt2 in the W+jets control region with 20.3/fb.

Distribution of Mt(tau1)+Mt(tau2) in the W+jets control region with 20.3/fb.

Distribution of Meff in the W+jets control region with 20.3/fb.

Distribution of Meff in the Z+jets control region with 20.3/fb.

The stransverse mass distribution in the WW-C1N2 validation region.

Mt(tau1)+Mt(tau2) distribution in Top-VR.

The stransverse mass distribution in SR-C1N2.

Mt(tau1)+Mt(tau2) distribution in SR-C1C1.

Effective mass distribution in SRDS-highMass.

Effective mass distribution in SRDS-lowMass.

Expected 95% CL exclusion limits for simplified models with a combination of chargino-neutralino and chargino-chargino production.

Observed 95% CL exclusion limits for simplified models with a combination of chargino-neutralino and chargino-chargino production.

Expected 95% CL exclusion limits for simplified models with chargino-chargino production.

Observed 95% CL exclusion limits for simplified models with chargino-chargino production.

Upper limits on the cross section for production of only right-handed stau pairs.

Upper limits on the cross section for production of only left-handed stau pairs.

Upper limits on the signal strength for the associated production of right-handed and left-handed stau pairs.

Expected 95% CL exclusion limits for the pMSSM models with fixed stau mass, M1=50 GeV.

Observed 95% CL exclusion limits for the pMSSM models with fixed stau mass, M1=50 GeV.

Expected 95% CL exclusion limits for the pMSSM models with variable stau mass, M1=75 GeV.

Observed 95% CL exclusion limits for the pMSSM models with variable stau mass, M1=75 GeV.

Numbers give 95% CL excluded model cross sections for the simplified models with chargino-chargino production.

Numbers give 95% CL excluded model cross sections for the simplified models with chargino-neutralino production.

SR with best expected p0-value for each point for the simplified models with chargino-neutralino and chargino-chargino production. SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

SR with best expected p0-value for each point for the simplified models with chargino-chargino production. SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

SR with best expected p0-value for each point for direct stau pair production. SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

SR with best expected p0-value for each point for direct stau pair production (left-handed). SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

SR with best expected p0-value for each point for direct stau pair production (right-handed). SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

SR with best expected p0-value for each point for the pMSSM models with fixed stau mass, M1=50 GeV. SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

SR with best expected p0-value for each point for the pMSSM models with variable stau mass, M1=75 GeV. SR-C1N2 (SR-C1C1) is indicated with 1 (2) and SR-DS-highMass (SR-DS-lowMass) is indicated with 3 (4).

Cutflows for the three SUSY reference points. The event yields have been normalised to 20.3/fb.

Cutflows for data and the SM backgrounds estimated from MC.

Detailed information for the three SUSY benchmark grid points (from left to right): the reference point(1: (chargino1(neutralino2),neutralino1)=(250,100)GeV, 2: (chargino1,neutralino1)=(250,50) GeV, 3: (stauR (stauL), neutralino1)=(127(129),0) GeV), production process(1:chargino1-neutralino2, 2:chargino1-chargino1, 3:stauL-stauL, 4:stauR-stauR), cross section, number of generated MC events, considered signal region(1:C1N2, 2:C1C1, 3:DS-highMass,4:DS-lowMass), event yield after normalization to 20.3 fb-1, acceptance (Acc.), efficiency (Eff.) on the signal selection, absolute systematic uncertainty on signal yields, 95% CL observed and expected limits on the number of events.

Number of generated events for the simplified models with chargino-neutralino production.

Number of generated events for the simplified models with chargino-chargino production.

Number of generated events for direct left-handed stau pair production.

Number of generated events for direct right-handed stau pair production.

Production cross section for the simplified models with chargino-neutralino production.

Production cross section for the simplified models with chargino-chargino production.

Production cross section for direct left-handed stau pair production.

Production cross section for direct right-handed stau pair production.

Acceptance for the simplified models with chargino-neutralino production.

Acceptance for the simplified models with chargino-chargino production.

Acceptance for direct left-handed stau pair production.

Acceptance for direct right-handed stau pair production.

Efficiency for the simplified models with chargino-neutralino production.

Efficiency for the simplified models with chargino-chargino production.

Efficiency for direct stau (the left-handed) pair production.

Efficiency for direct stau (the right-handed) pair production.

Experimental systematics (in percentage) for the simplified models with chargino-neutralino production.

Experimental systematics (in percentage) for the simplified models with chargino-chargino production.

Experimental systematics (in percentage) for direct stau (the left-handed) pair production.

Experimental systematics (in percentage) for direct stau (the right-handed) pair production.

Observed CLs for the simplified models with chargino-neutralino production.

Observed CLs for the simplified models with chargino-chargino production.

Observed CLs for direct stau (the left-handed) pair production.

Observed CLs for direct stau (the right-handed) pair production.

Expected CLs for the simplified models with chargino-neutralino production.

Expected CLs for the simplified models with chargino-chargino production.

Expected CLs for direct stau (the left-handed) pair production.

Expected CLs for direct stau (the right-handed) pair production.

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 $|y(\bar t)|$.

The differential cross section measurement as a function of the transverse momentum of the second leading jet.

The differential cross section measurement as a function of the transverse momentum of the third leading jet.

The differential cross section measurement as a function of the transverse momentum of the fourth leading jet.

The differential cross section measurement as a function of the scalar sum of the transverse momenta of jets (HT) for jet multiplicities of one or more.

The differential cross section measurement as a function of the scalar sum of the transverse momenta of jets (HT) for jet multiplicities of two or more.

The differential cross section measurement as a function of the scalar sum of the transverse momenta of jets (HT) for jet multiplicities of three or more.

The differential cross section measurement as a function of the scalar sum of the transverse momenta of jets (HT) for jet multiplicities of four or more.

The differential cross section measurement as a function of the absolute value of the pseudorapidity of the first leading jet.

The differential cross section measurement as a function of the absolute value of the pseudorapidity of the first second jet.

The differential cross section measurement as a function of the absolute value of the pseudorapidity of the third leading jet.

The differential cross section measurement as a function of the absolute value of the pseudorapidity of the fourth leading jet.

The differential cross section measurement as a function of the absolute value of the azimuthal separation between the first leading jet and the muon.

The differential cross section measurement as a function of the absolute value of the azimuthal separation between the second leading jet and the muon.

The differential cross section measurement as a function of the absolute value of the azimuthal separation between the third leading jet and the muon.

The differential cross section measurement as a function of the absolute value of the azimuthal separation between the fourth leading jet and the muon.