For events in the low-ETmiss validation region, the M(l tau) distribution in VR1taua.

For events in the low-ETmiss validation region, the MT2max distribution in VR2taua.

For events in the high-ETmiss + b-tagged jet validation region, the ETmiss distribution in VR0taunoZb.

For events in the high-ETmiss + b-tagged jet validation region, the ETmiss distribution in VR0tauZb.

For events in the high-ETmiss + b-tagged jet validation region, the ETmiss distribution in VR1taub.

For events in the high-ETmiss + b-tagged jet validation region, the ETmiss distribution in VR2taub.

Expected distributions of SM background events and observed data distributions in the binned signal regions SR0taua.

The distribution of ETmiss in the summation of all SR0taua regions prior to the requirements on this variable.

The distribution of MT in the summation of all SR0taua regions prior to the requirements on this variable.

The distribution of M(SFOS) in the summation of all SR0taua regions prior to the requirements on this variable.

Expected distribution of SM background events and observed data distribution for the MIN(DELTA(PHI(ll))) variable in the SR0taub region, prior to the requirements on this variable.

Expected distribution of SM background events and observed data distribution for the ETmiss variable in the SR1tau region, prior to the requirements on this variable.

Expected distribution of SM background events and observed data distribution for the MT2max variable in the SR2taua region, prior to the requirements on this variable.

Expected distribution of SM background events and observed data distribution for the M(tau tau) variable in the SR2taub region, prior to the requirements on this variable.

Observed 95% CL exclusion contour for chargino and neutralino production in the sleptonL-mediated simplified model.

Expected 95% CL exclusion contour for chargino and neutralino production in the sleptonL-mediated simplified model.

Observed 95% CL exclusion contour for chargino and neutralino production in the WZ-mediated simplified model.

Expected 95% CL exclusion contour for chargino and neutralino production in the WZ-mediated simplified model.

Observed 95% CL exclusion contour for chargino and neutralino production in the stauL-mediated simplified model.

Expected 95% CL exclusion contour for chargino and neutralino production in the stauL-mediated simplified model.

Observed 95% CL exclusion contour for chargino and neutralino production in the Wh-mediated simplified model.

Expected 95% CL exclusion contour for chargino and neutralino production in the Wh-mediated simplified model.

Observed 95% CL exclusion contour in the pMSSM model with sleptons and M1 = 100 GeV.

Expected 95% CL exclusion contour in the pMSSM model with sleptons and M1 = 100 GeV.

Observed 95% CL exclusion contour in the pMSSM model with sleptons and M1 = 140 GeV.

Expected 95% CL exclusion contour in the pMSSM model with sleptons and M1 = 140 GeV.

Observed 95% CL exclusion contour in the pMSSM model with sleptons and M1 = 250 GeV.

Expected 95% CL exclusion contour in the pMSSM model with sleptons and M1 = 250 GeV.

Observed 95% CL exclusion contour in the pMSSM model with staus.

Expected 95% CL exclusion contour in the pMSSM model with staus.

Observed 95% CL exclusion contour in the pMSSM model with no sleptons.

Expected 95% CL exclusion contour in the pMSSM model with no sleptons.

Excluded model cross-sections at 95% confidence level for the slepL-mediated simplified model.

Excluded model cross-sections at 95% confidence level for WZ-mediated simplified model.

Excluded model cross-sections at 95% confidence level for stauL-mediated simplified model.

Excluded model cross-sections at 95% confidence level for Wh-mediated simplified model.

Number of generated signal events for slepL-mediated simplified model.

Number of generated signal events for WZ-mediated simplified model.

Number of generated signal events for stauL-mediated simplified model.

Number of generated signal events for Wh-mediated simplified model.

Cross-sections for slepL-mediated simplified model.

Cross-sections for WZ-mediated simplified model.

Cross-sections for stauL-mediated simplified model.

Cross-sections for Wh-mediated simplified model.

Acceptance for slepL-mediated simplified model using SR0taua bin 20.

Acceptance for WZ-mediated simplified model using SR0taua bin 16.

Acceptance for stauL-mediated simplified model using SR2taua.

Acceptance for Wh-mediated simplified model using SR2taub.

Efficiency for slepL-mediated simplified model using SR0taua bin 20.

Efficiency for WZ-mediated simplified model using SR0taua bin 16.

Efficiency for stauL-mediated simplified model using SR2taua.

Efficiency for Wh-mediated simplified model using SR2taub.

The total experimental uncertainty, not including Monte Carlo statistics, for the slepL-mediated simplified model using SR0taua bin 20.

The total experimental uncertainty, not including Monte Carlo statistics, for the WZ-mediated simplified model using SR0taua bin 16.

The total experimental uncertainty, not including Monte Carlo statistics, for the stauL-mediated simplified model using SR2taua.

The total experimental uncertainty, not including Monte Carlo statistics, for the Wh-mediated simplified model using SR2taub.

The expected CL for slepL-mediated simplified model using SR0taua bin 20, using pseudo-experiments.

The expected CL for WZ-mediated simplified model using SR0taua bin 16, using pseudo-experiments.

The expected CL for stauL-mediated simplified model using SR2taua, using pseudo-experiments.

The expected CL for Wh-mediated simplified model using SR2taub, using pseudo-experiments.

The observed CL for slepL-mediated simplified model using SR0taua bin 20, using pseudo-experiments.

The observed CL for WZ-mediated simplified model using SR0taua bin 16, using pseudo-experiments.

The observed CL for stauL-mediated simplified model using SR2taua, using pseudo-experiments.

The observed CL for Wh-mediated simplified model using SR2taub, using pseudo-experiments.

Numbers of observed and background events for SR3L low for each bin of the distribution in Meff. The table corresponds to Fig. 4(d). The statistical and systematic uncertainties are combined for the predicted numbers.

Numbers of observed and background events for SR3L high for each bin of the distribution in Meff. The table corresponds to Fig. 4(e). The statistical and systematic uncertainties are combined for the predicted numbers.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The efficiencies are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The efficiencies are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The efficiencies are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into b s and gluinos decay into t stop (see Fig. 5d in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The efficiencies are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The acceptances (in percent, %) are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The acceptances (in percent, %) are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The acceptances (in percent, %) are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The acceptances (in percent, %) are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The limits on observed cross section are calculated for all simplified models. The simplified models are for direct pair production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair-production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop)-20 GeV.

The signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

Experimental uncertainties on the signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

Experimental uncertainties on the signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

Experimental uncertainties on the signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

Statistical uncertainties on the signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

Statistical uncertainties on the signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

Statistical uncertainties on the signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W ^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The confidence levels are calculated for all simplified models. For each model, the observed and expected values are given. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The confidence levels are calculated for all simplified models. For each model, the observed and expected values are given. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The confidence levels are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluion), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The confidence levels are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the expected and observed values are given. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The confidence levels are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the expected and observed values are given. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The confidence levels are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the expected and observed values are given.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The confidence levels are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the expected and observed values are given. The model assumes tan(beta)=30, A0=2m0, and mu>0.

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.

Distribution of mT2 for events passing all the signal candidate selection requirements, except that on mT2 of the L100 selection, for DF events.

Distribution of mT2 for events passing all the signal candidate selection requirements, except that on mT2 of the L110 selection, for SF events.

Distribution of mT2 for events passing all the signal candidate selection requirements, except that on mT2 of the L110 selection, for DF events.

Observed 95% CL exclusion contour in the (STOP1, CHARGINO1) mass plane for a fixed value of m(NEUTRALINO1) = 1 GeV.

Expected 95% CL exclusion contour in the (STOP1, CHARGINO1) mass plane for a fixed value of m(NEUTRALINO1) = 1 GeV.

Observed 95% CL exclusion minus 1 sigma contour in the (STOP1, CHARGINO1) mass plane for a fixed value of m(NEUTRALINO1) = 1 GeV.

Observed 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane for a fixed value of m(STOP1) - m(CHARGINO1) = 10 GeV.

Expected 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane for a fixed value of m(STOP1) - m(CHARGINO1) = 10 GeV.

Observed 95% CL exclusion minus 1 sigma contour in the (STOP1, NEUTRALINO1) mass plane for a fixed value of m(STOP1) - m(CHARGINO1) = 10 GeV.

Observed 95% CL exclusion contour in the (CHARGINO1, NEUTRALINO1) mass plane for a fixed value of m(STOP1) = 300 GeV.

Expected 95% CL exclusion contour in the (CHARGINO1, NEUTRALINO1) mass plane for a fixed value of m(STOP1) = 300 GeV.

Observed 95% CL exclusion minus one sigma contour in the (CHARGINO1, NEUTRALINO1) mass plane for a fixed value of m(STOP1) = 300 GeV.

Observed 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane of m(CHARGINO1) = 2 m(NEUTRALINO1).

Expected 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane for m(CHARGINO1) = 2 m(NEUTRALINO1).

Observed 95% CL exclusion minus one sigma contour in the (STOP1, NEUTRALINO1) mass plane for m(CHARGINO1) = 2 m(NEUTRALINO1).

Observed 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane.

Expected 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane.

Observed 95% CL exclusion minus one sigma contour in the (STOP1, NEUTRALINO1) mass plane.

Observed 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane.

Expected 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane.

Observed 95% CL exclusion minus one sigma contour in the (STOP1, NEUTRALINO1) mass plane.

Observed 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane for a fixed value of m(CHARGINO1) = 106 GeV.

Expected 95% CL exclusion contour in the (STOP1, NEUTRALINO1) mass plane for a fixed value of m(CHARGINO1) = 106 GeV.

Observed 95% CL exclusion minus one sigma contour in the (STOP1, NEUTRALINO1) mass plane for a fixed value of m(CHARGINO1) = 106 GeV.

Expected CLs values for Fig. 14.

Observed CLs values for Fig. 14.

Observed cross-section limits for Fig. 14.

Expected CLs values for Fig. 15.

Observed CLs values for Fig. 15.

Observed cross-section limits for Fig. 15.

Expected CLs values for Fig. 16.

Observed CLs values for Fig. 16.

Observed cross-section limits for Fig. 14.

Expected CLs values for Fig. 17.

Observed CLs values for Fig. 17.

Observed cross-section limits for Fig. 17.

Expected CLs values for Fig. 19.

Observed CLs values for Fig. 19.

Observed cross-section limits for Fig. 19.

Number of generated events for Fig. 16.

Acceptance of S1 for Fig. 16.

Efficiency of S1 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S1 for Fig. 16.

Acceptance of S2 for Fig. 16.

Efficiency of S2 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S2 for Fig. 16.

Acceptance of S3 for Fig. 16.

Efficiency of S3 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S3 for Fig. 16.

Acceptance of S4 for Fig. 16.

Efficiency of S4 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S4 for Fig. 16.

Acceptance of S5 for Fig. 16.

Efficiency of S5 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S5 for Fig. 16.

Acceptance of S6 for Fig. 16.

Efficiency of S6 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S6 for Fig. 16.

Acceptance of S7 for Fig. 16.

Efficiency of S7 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in S7 for Fig. 16.

Acceptance of H160 for Fig. 16.

Efficiency of H160 for Fig. 16.

Total systematic uncertainty IN PCT on signal yields in H160 for Fig. 16.

Observed cross-section limits for Fig. 20.

Observed cross-section limits for Fig. 20.

Expected cross-section limits for Fig. 20.

Total number of generated MC events for each point of the grid.

Observed upper limit on the signal cross-section, in pb, for each point of the grid in the different flavour channel.

Observed upper limit on the signal cross-section, in pb, for each point of the grid in the same flavour channel.

The best expected signal region chosen for each point of the grid in the different flavour channel.

The best expected signal region chosen for each point of the grid in the same flavour channel.

Observed CLs for each point of the grid in the different flavour channel.

Observed CLs for each point of the grid in the same flavour channel.

Expected CLs for each point of the grid in the different flavour channel.

Expected CLs for each point of the grid in the same flavour channel.

Signal acceptance for all the analysis cuts, except the BDTG cut, for each point of the grid in the different flavour channel.

Signal acceptance for all the analysis cuts, except the BDTG cut, for each point of the grid in the same flavour channel.

Signal efficiency, including the acceptance of the BDTG cut, for each point of the grid in the different flavour channel.

Signal efficiency, including the acceptance of the BDTG cut, for each point of the grid in the same flavour channel.

Total signal experimental systematic uncertainty for each point of the grid in the different flavour channel.

Signal efficiency, including the acceptance of the BDTG cut, for each point of the grid in the same flavour channel.

Number of simulated events passing various stages of the selection in the hadronic mT2 analysis for a signal sample with m(STOP1)=300 GeV, m(CHARGINO1) = 150 GeV and m(NEUTRALINO1) = 50 GeV, and with the top squark decaying as STOP1 --> CHARGINO1+ BOTTOM -> W(*)+NEUTRALINO1+ BOTTOM with unit probability. Event weights are applied to correct simulated events to data. "Isolation" includes the effects of tight ID for electrons and the isolation selection for both electrons and muons. "Cleaning cuts" refer to cuts applied to remove non-collision backgrounds and detector noise.

Number of simulated events passing various stages of the selection in the hadronic mT2 analysis for a signal sample with m(STOP1)=250 GeV, m(CHARGINO1) = 106 GeV and m(NEUTRALINO1) = 60 GeV, and with the top squark decaying as STOP1 --> CHARGINO1+ BOTTOM -> W(*)+NEUTRALINO1+ BOTTOM with unit probability. Event weights are applied to correct simulated events to data. "Isolation" includes the effects of tight ID for electrons and the isolation selection for both electrons and muons. "Cleaning cuts" refer to cuts applied to remove non-collision backgrounds and detector noise.

Number of simulated events passing various stages of the selection in the leptonic mT2 analysis for two signal samples with the top squark decaying as STOP1 --> CHARGINO1+ BOTTOM -> W(*)+NEUTRALINO1+BOTTOM with unit probability. Event weights are applied to correct simulated events to data. "Isolation" includes the effects of tight ID for electrons and the isolation selection for both electrons and muons. "Cleaning cuts" refer to cuts applied to remove non-collision backgrounds and detector noise.

Number of simulated events passing various stages of the selection in the leptonic mT2 analysis for a signal sample with m(STOP1)=180 GeV and m(NEUTRALINO1) = 60 GeV, and with the top squark decaying as STOP1 --> W + BOTTOM + NEUTRALINO1 with unit probability. Event weights are applied to correct simulated events to data. "Isolation" includes the effects of tight ID for electrons and the isolation selection for both electrons and muons. "Cleaning cuts" refer to cuts applied to remove non-collision backgrounds and detector noise.

Number of simulated events passing various stages of the selection in the in the MVA analysis for all signal samples used to train the BDTG and with the top squark decaying as STOP1 --> TOP + NEUTRALINO1 with unit probability. Event weights are applied to correct simulated events to data. "Isolation" includes the effects of tight ID for electrons and the isolation selection for both electrons and muons. "Cleaning cuts" refer to cuts applied to remove non-collision backgrounds and detector noise. The index i in category (Ci) is the one reported in table 3 for each signal point, SF and DF.

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.

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.

$v_{2,2}^{unsub}$ and $v_{2,2}$ as a function of $\Delta\eta$ calculated from the 2-D per-trigger yields in figure 4(a) and 4(b), respectively.

$v_{3,3}^{unsub}$ and $v_{3,3}$ as a function of $\Delta\eta$ calculated from the 2-D per-trigger yields in figure 4(a) and 4(b), respectively.

$v_{4,4}^{unsub}$ and $v_{4,4}$ as a function of $\Delta\eta$ calculated from the 2-D per-trigger yields in figure 4(a) and 4(b), respectively.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

The per-trigger yield distributions $Y^{corr}(\Delta\phi)$ and $Y^{recoil}(\Delta\phi)$ for events with $N_{ch}^{rec} \geq$ 220 in the long-range region $|\Delta\eta| >$ 2.

Integrated per-trigger yield, $Y_{int}$, on the near-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the near-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the near-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the near-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the near-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the away-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the away-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the away-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the away-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

Integrated per-trigger yield, $Y_{int}$, on the away-side as a function of $p_{T}^{a}$ for 1 $< p_{T}^{b} <$ 3 GeV.

The integrated per-trigger yield, Y_{int}, on the near-side, the away-side and their difference and Y_{int} from the recoil as a function of event activity. Errors are statistical.

The integrated per-trigger yield, Y_{int}, on the near-side, the away-side and their difference and Y_{int} from the recoil as a function of event activity. Errors are statistical.

The Fourier coefficients $v_{n}$ as a function of $p_{T}^{a}$ extracted from the correlation functions, before and after the subtraction of the recoil component.

The Fourier coefficients $v_{n}$ as a function of $p_{T}^{a}$ extracted from the correlation functions, before and after the subtraction of the recoil component.

The Fourier coefficients $v_{n}$ as a function of $p_{T}^{a}$ extracted from the correlation functions, before and after the subtraction of the recoil component.

$v_{2}$, $v_{3}$, $v_{4}$ and $v_{5}$ as a function of $p_T^a$ for 1 $< p_{T}^{b} <$ 3 GeV for different $N_{ch}^{rec}$ intervals.

$v_{2}$, $v_{3}$, $v_{4}$ and $v_{5}$ as a function of $p_T^a$ for 1 $< p_{T}^{b} <$ 3 GeV for different $N_{ch}^{rec}$ intervals.

$v_{2}$, $v_{3}$, $v_{4}$ and $v_{5}$ as a function of $p_T^a$ for 1 $< p_{T}^{b} <$ 3 GeV for different $N_{ch}^{rec}$ intervals.

$v_{2}$, $v_{3}$, $v_{4}$ and $v_{5}$ as a function of $p_T^a$ for 1 $< p_{T}^{b} <$ 3 GeV for different $N_{ch}^{rec}$ intervals.

$v_{2}$, $v_{3}$, $v_{4}$ and $v_{5}$ as a function of $p_T^a$ for 1 $< p_{T}^{b} <$ 3 GeV for different $N_{ch}^{rec}$ intervals.

$v_{2}$, $v_{3}$, $v_{4}$ and $v_{5}$ as a function of $p_T^a$ for 1 $< p_{T}^{b} <$ 3 GeV for different $N_{ch}^{rec}$ intervals.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{2}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The values of factorization variable $r_{3}$ defined by Eq.(11) before and after the subtraction of the recoil component. Errors are total experimental uncertainties.

The centrality dependence of $v_{2}$ as a function of $N_{ch}^{rec}$. Values from before and after the recoil subtraction are included.

The centrality dependence of $v_{3}$ as a function of $N_{ch}^{rec}$. Values from before and after the recoil subtraction are included.

The centrality dependence of $v_{4}$ as a function of $N_{ch}^{rec}$. Values from before and after the recoil subtraction are included.

The centrality dependence of $v_{2}$ as a function of $E_{T}^{Pb}$. Values from before and after the recoil subtraction are included.

The centrality dependence of $v_{3}$ as a function of $E_{T}^{Pb}$. Values from before and after the recoil subtraction are included.

The centrality dependence of $v_{4}$ as a function of $E_{T}^{Pb}$. Values from before and after the recoil subtraction are included.

The $v_{2}$ as a function of $E_{T}^{Pb}$ obtained indirectly by mapping from the $N_{ch}^{rec}-dependence of $v_{2}$ using the correlation data shown in Fig. 2(b).

The $v_{3}$ as a function of $E_{T}^{Pb}$ obtained indirectly by mapping from the $N_{ch}^{rec}-dependence of $v_{3}$ using the correlation data shown in Fig. 2(b).

The first-order harmonic of 2PC before recoil subtraction, $v_{1,1}^{unsub}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC before recoil subtraction, $v_{1,1}^{unsub}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC before recoil subtraction, $v_{1,1}^{unsub}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC before recoil subtraction, $v_{1,1}^{unsub}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC before recoil subtraction, $v_{1,1}^{unsub}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC before recoil subtraction, $v_{1,1}^{unsub}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC after recoil subtraction, $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC after recoil subtraction, $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC after recoil subtraction, $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC after recoil subtraction, $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC after recoil subtraction, $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic of 2PC after recoil subtraction, $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic $v_1$ obtained using factorization from $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic $v_1$ obtained using factorization from $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

The first-order harmonic $v_1$ obtained using factorization from $v_{1,1}$, as a function of $p_T^a$ for different $p_T^b$ ranges for events with $N_{ch}^{rec} \geq$ 220.

$v_{2}$ for Pb+Pb collisions in 55-60% centrality interval obtained using an EP method.

$v_{2}$ for Pb+Pb collisions in 55-60% centrality interval obtained using an EP method, after the scaling.

$v_{3}$ for Pb+Pb collisions in 55-60% centrality interval obtained using an EP method.

$v_{3}$ for Pb+Pb collisions in 55-60% centrality interval obtained using an EP method, after the scaling.

$v_{4}$ for Pb+Pb collisions in 55-60% centrality interval obtained using an EP method.

$v_{4}$ for Pb+Pb collisions in 55-60% centrality interval obtained using an EP method, after the scaling.

Correlation between $E_{T}^{FCal}$ and $N_{ch}^{rec}$ for MB events (without weighting) and MB+HMT events (with weighting), errors are statistical.