Data and SM predictions in SRs for the $1lb\bar{b}$ analysis for $E_{\mathrm{T}}^{\mathrm{miss}}$ in SR1Lbb-Medium. All SRs selections but the one on the quantity shown are applied. All uncertainties are included in the uncertainty band. Example SUSY models are superimposed for illustrative purposes.

Distributions of $m_{\gamma\gamma}$ before the final requirement on $m_{\gamma\gamma}$ in SR1Lyy-a. The expected contributions from both the peaking and non-peaking backgrounds are shown as stacked colored histograms. Two example SUSY models are superimposed for illustrative purposes.

Distributions of $m_{\gamma\gamma}$ before the final requirement on $m_{\gamma\gamma}$ in SR1Lyy-b. The expected contributions from both the peaking and non-peaking backgrounds are shown as stacked colored histograms. Two example SUSY models are superimposed for illustrative purposes.

Observed and predicted distributions for $m_{jj}$ in SRSS-j1. All SRs selections but the one on the quantity shown are applied. All uncertainties are included in the uncertainty band. An example SUSY model is superimposed for illustrative purposes.

Observed and predicted distributions for $m_{T2}$ in SRSS-j23. All SRs selections but the one on the quantity shown are applied. All uncertainties are included in the uncertainty band. An example SUSY model is superimposed for illustrative purposes.

The observed exclusion for the $0lb\bar{b}$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The expected exclusion for the $0lb\bar{b}$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The observed exclusion for the $1lb\bar{b}$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The expected exclusion for the $1lb\bar{b}$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The expected exclusion for the $1l\gamma\gamma$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The observed exclusion for the $l^{\pm}l^{\pm}$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The expected exclusion for the $l^{\pm}l^{\pm}$ channel. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $3l$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $3l$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1lb\bar{b}$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1lb\bar{b}$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1l\gamma\gamma$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1l\gamma\gamma$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $l^{\pm}l^{\pm}$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $l^{\pm}l^{\pm}$ analysis for signal models with $m(\tilde{\chi}_{2}^{0}) - m(\tilde{\chi}_{1}^{0}) = 130$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $0lb\bar{b}$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $0lb\bar{b}$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1lb\bar{b}$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1lb\bar{b}$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1l\gamma\gamma$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $1l\gamma\gamma$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $l^{\pm}l^{\pm}$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $l^{\pm}l^{\pm}$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The observed cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $3l$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

The expected cross-section exclusion as a function of the $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ masses for the $3l$ analysis for signal models assuming $m(\tilde{\chi}_{1}^{0}) = 0$ GeV.

Acceptance for $0lb\bar{b}$ SRHad-Low signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $0lb\bar{b}$ SRHad-Low signal region.

Acceptance for $0lb\bar{b}$ SRHad-High signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $0lb\bar{b}$ SRHad-High signal region.

Upper cross section limits for the $0lb\bar{b}$ analysis.

Acceptance for $1lb\bar{b}$ SR1Lbb-Low signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiency for $1lb\bar{b}$ SR1Lbb-Low signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $1lb\bar{b}$ SR1Lbb-Medium signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiency for $1lb\bar{b}$ SR1Lbb-Medium signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $1lb\bar{b}$ SR1Lbb-High signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiency for $1lb\bar{b}$ SR1Lbb-High signal region. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Upper cross section limits for the $1lb\bar{b}$ analysis.

Acceptance for $1l\gamma\gamma$ signal region SR1Lyy-a. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $1l\gamma\gamma$ signal region SR1Lyy-a.

Acceptance for $1l\gamma\gamma$ signal region SR1Lyy-b. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $1l\gamma\gamma$ signal region SR1Lyy-b.

Upper cross section limits for the $1l\gamma\gamma$ analysis.

Acceptance for $l^{\pm}l^{\pm}$ signal region SRSS-j1. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $l^{\pm}l^{\pm}$ signal region SRSS-j1. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $l^{\pm}l^{\pm}$ signal region SRSS-j23. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $l^{\pm}l^{\pm}$ signal region SRSS-j23. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $3L$ SFOS signal region SR3L-SFOS-1J. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $3L$ SFOS signal region SR3L-SFOS-1J. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $3L$ SFOS signal region SR3L-SFOS-0Ja. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $3L$ SFOS signal region SR3L-SFOS-0Ja. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $3L$ SFOS signal region SR3L-SFOS-0Jb. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $3L$ SFOS signal region SR3L-SFOS-0Jb. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $3L$ DFOS signal region SR3L-DFOS-0J. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $3L$ DFOS signal region SR3L-DFOS-0J. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $3L$ DFOS signal region SR3L-DFOS-1Ja. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $3L$ DFOS signal region SR3L-DFOS-1Ja. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Acceptance for $3L$ DFOS signal region SR3L-DFOS-1Jb. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Efficiencies for $3L$ DFOS signal region SR3L-DFOS-1Jb. Note that the acceptance is relative to the total chargino-neutralino production cross section.

Upper cross section limits for the $l^{\pm}l^{\pm}$ analysis.

Upper cross section limits for the $3l$ analysis.

Event selection cutflow for SM background (pre-fit) and representative signal samples for the $0lb\bar{b}$ SR. The masses of next-lightest-neutralinos and LSPs are reported. Only statistical uncertainties are shown. The MC statistics for the signal samples is 50K and 44K events, respectively. Samples are produced with generator filters which selects $h \rightarrow b\bar{b}$ and $W \rightarrow qq'$ decays. ``All events" for SUSY scenarios represent the total number of signal events for the models considered taking into account the BR of $W \rightarrow qq'$ (0.674) and $h \rightarrow b\bar{b}$ (0.582).

Event selection cutflow for SM background (pre-fit) and representative signal samples for the $1lb\bar{b}$ channel. The masses of next-lightest-neutralinos and LSPs are reported. Only statistical uncertainties are shown. The MC statistics for the signal samples is 28K, 29K and 29K events, respectively. Samples are produced with generator filters which selects $h \rightarrow b\bar{b}$ and $W \rightarrow l \nu$ decays. ``All events" for SUSY scenarios represent the total number of signal events for the models considered taking into account the BR of $W \rightarrow l \nu$ (0.324) and $h \rightarrow b\bar{b}$ (0.582).

Event selection cutflow is presented for SM background (pre-fit) and three signal scenarios for the $1l\gamma\gamma$ channel SRs. The masses of next-lightest-neutralinos and LSPs are reported. The uncertainties only include statistical uncertainties. The MC statistics for the signal samples is 10K events in all cases. Samples are produced with generator filters which selects $h \rightarrow \gamma\gamma$ and $W \rightarrow l \nu$ decays. The data-driven-estimated non-peaking background is not evaluated at each stage of the selection, hence only the peaking background cutflow is presented.

Event selection cutflow is presented for one signal scenario for SRSS-j23. The model with next-to-lightest neutralino mass of 175 GeV and massless $\tilde{\chi}^{0}_{1})$ with 100% BR is considered. The MC statistics for the signal samples is 75K events. Samples are produced with a generator filter which selects events with at least two leptons with $p_{\mathrm{T}} > 7$ GeV. ``2SS leptons" indicates the selection of two same-sign electrons or muons as described in the text (Section 6.4).

Event selection cutflow is presented for one signal scenario for SRSS-j1. The model with next-to-lightest neutralino mass of 175 GeV and massless $\tilde{\chi}^{0}_{1})$ with 100% BR is considered. The MC statistics for the signal samples is 75K events. Samples are produced with a generator filter which selects events with at least two leptons with $p_{\mathrm{T}} > 7$ GeV. ``2SS leptons" indicates the selection of two same-sign electrons or muons as described in the text (Section 6.4).

Event selection cut-flow is presented for one signal scenario for each signal regions. The model with next-to-lightest neutralino mass of 150 GeV and massless LSP with 100 \% BR is considered. The MC statistics for the signal samples is 123K events. Samples are produced with a generator filter which selects events with at least two leptons with pT $>$ 7 GeV. ``3L+Trigger" indicates three-lepton and trigger requirements. ``Preselection" includes the veto on bjets, ETmiss above 20 GeV and invariant mass of the three leptons above 20 GeV.

The systematic uncertainty on the unfolded distribution as a function of minimax-mbl, broken down by components.

The covariance matrix for the unfolded, normalized data in bins of minimax-mbl shown in Figure 2. Redundant entries are suppressed.

Leading lepton transverse momentum distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Sub-leading lepton transverse momentum distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Leading lepton pseudorapidity distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Sub-leading lepton pseudorapidity distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Leading b-jet transverse momentum distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Sub-leading b-jet transverse momentum distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Leading b-jet pseudorapidity distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

Sub-leading b-jet pseudorapidity distribution for events passing the signal selection in bins of minimax-mbl. The data here are not unfolded.

The detector-level minimax-m(bl) distribution for events entering the Z+jets control region.

The detector-level minimax-m(bl) distribution for events entering the same-sign lepton control region.

Relative differential cross section as a function of the b-jet multiplicity in emu channel

Relative differential cross section as a function of H_T in emu channel

Relative differential cross section as a function of H_T in emu channel

Relative differential cross section as a function of H_Thad in emu channel

Relative differential cross section as a function of H_Thad in emu channel

Relative differential cross section as a function of H_T in leptons+jets channel

Relative differential cross section as a function of H_T in leptons+jets channel

Relative differential cross section as a function of H_Thad in leptons+jets channel

Relative differential cross section as a function of H_Thad in leptons+jets channel

Relative differential cross section as a function of pT of leading b-jet in emu channel

Relative differential cross section as a function of pT of leading b-jet in emu channel

Relative differential cross section as a function of pT of sub-leading b-jet in emu channel

Relative differential cross section as a function of pT of sub-leading b-jet in emu channel

Relative differential cross section as a function of pT of third-leading b-jet in emu channel

Relative differential cross section as a function of pT of third-leading b-jet in emu channel

Relative differential cross section as a function of pT of leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of sub-leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of sub-leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of third-leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of third-leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of fourth-leading b-jet in lepton+jets channel

Relative differential cross section as a function of pT of fourth-leading b-jet in lepton+jets channel

Relative differential cross section as a function of invarant mass of two highest pT b-jets in emu channel

Relative differential cross section as a function of invarant mass of two highest pT b-jets in emu channel

Relative differential cross section as a function of pT of two highest pT b-jets in emu channel

Relative differential cross section as a function of pT of two highest pT b-jets in emu channel

Relative differential cross section as a function of deltaR of two highest pT b-jets in emu channel

Relative differential cross section as a function of deltaR of two highest pT b-jets in emu channel

Relative differential cross section as a function of invariant mass of two highest pT b-jets in lepton+jets channel

Relative differential cross section as a function of invariant mass of two highest pT b-jets in lepton+jets channel

Relative differential cross section as a function of pT of two highest pT b-jets in lepton+jets channel

Relative differential cross section as a function of pT of two highest pT b-jets in lepton+jets channel

Relative differential cross section as a function of deltaR of two highest pT b-jets in lepton+jets channel

Relative differential cross section as a function of deltaR of two highest pT b-jets in lepton+jets channel

Relative differential cross section as a function of invariant mass of two closest b-jets in deltaR in emu channel

Relative differential cross section as a function of invariant mass of two closest b-jets in deltaR in emu channel

Relative differential cross section as a function of pT of two closest b-jets in deltaR in emu channel

Relative differential cross section as a function of pT of two closest b-jets in deltaR in emu channel

Relative differential cross section as a function of deltaR of two closest b-jets in deltaR in emu channel

Relative differential cross section as a function of deltaR of two closest b-jets in deltaR in emu channel

Relative differential cross section as a function of invariant mass of two closest b-jets in deltaR in lepton+jets channel

Relative differential cross section as a function of invariant mass of two closest b-jets in deltaR in lepton+jets channel

Relative differential cross section as a function of pT of two closest b-jets in deltaR in lepton+jets channel

Relative differential cross section as a function of pT of two closest b-jets in deltaR in lepton+jets channel

Relative differential cross section as a function of deltaR of two closest b-jets in deltaR in lepton+jets channel

Relative differential cross section as a function of deltaR of two closest b-jets in deltaR in lepton+jets channel

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 316.22 < pTjet < 398.10 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 398.10 < pTjet < 501.18 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 398.10 < pTjet < 501.18 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 398.10 < pTjet < 501.18 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 398.10 < pTjet < 501.18 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 126.00 < pTjet < 158.49 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 158.49 < pTjet < 199.53 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 199.53 < pTjet < 251.19 and 0.0 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 0.3.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.3 < eta < 0.8.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.8 < eta < 1.2.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 1.2 < eta < 2.1.

The D(z) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

Excess transverse momenta in jet in PbPb compared to pp collisions in different centrality selections for abs(jet rapidity) < 2.1.

The D(pT) distributions in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

Excess particles in jet in PbPb compared to pp collisions in different centrality selections for abs(jet rapidity) < 2.1.

The ratio of the D(z) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

Ratio of R(D(z)) distributions in a given abs(jet rapidity) region to R(D(z)) for abs(jet rapidity) < 0.3 for 126 < pTJet < 158.5 GeV.

The ratio of the D(pT) in different centrality intervals in PbPb and in pp for 251.19 < pTjet < 316.22 and 0.0 < eta < 2.1.

Ratio of R(D(z)) distributions in a given abs(jet rapidity) region to R(D(z)) for abs(jet rapidity) < 0.3 for 158.5 < pTJet < 199.5 GeV.

Excess transverse momenta in jet in PbPb compared to pp collisions in different centrality selections for abs(jet rapidity) < 2.1.

Ratio of R(D(z)) distributions in a given abs(jet rapidity) region to R(D(z)) for abs(jet rapidity) < 0.3 for 199.5 < pTJet < 251.8 GeV.

Excess particles in jet in PbPb compared to pp collisions in different centrality selections for abs(jet rapidity) < 2.1.

Ratio of R(D(z)) distributions in a given abs(jet rapidity) region to R(D(z)) for abs(jet rapidity) < 0.3 for 126 < pTJet < 158.5 GeV.

Ratio of R(D(z)) distributions in a given abs(jet rapidity) region to R(D(z)) for abs(jet rapidity) < 0.3 for 158.5 < pTJet < 199.5 GeV.

Ratio of R(D(z)) distributions in a given abs(jet rapidity) region to R(D(z)) for abs(jet rapidity) < 0.3 for 199.5 < pTJet < 251.8 GeV.

Lower mass requirement for signal regions.

Lower mass requirement for signal regions.

Lower mass requirement for signal regions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Upper cross-section limit in stau search.

Upper cross-section limit in stau search.

Upper cross-section limit in chargino search.

Upper cross-section limit in chargino search.

Lower mass limit as function of gluino lifetime.

Lower mass limit as function of gluino lifetime.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Candidate reconstruction efficiency for loose selection (LooseEff).

Candidate reconstruction efficiency for loose selection (LooseEff).

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

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

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

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

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

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

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

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

The measured normalized differential cross section as a function of the photon $|\eta|$ in the single lepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The measured normalized differential cross section as a function of the $\Delta R$ between the photon and the lepton in the single lepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The measured normalized differential cross section as a function of the photon pT in the dilepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The measured normalized differential cross section as a function of the photon $|\eta|$ in the dilepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The measured normalized differential cross section as a function of minimum $\Delta R) between the photon and the leptons in the dilepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The measured normalized differential cross section as a function of $|\Delta\eta|$ between the two leptons in the dilepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The measured normalized differential cross section as a function of $\Delta\phi$ between the two leptons in the dilepton channel. The uncertainty is decomposed into five components which are the signal modelling uncertainty, the experimental uncertainty, the ttbar modelling uncertainty, the other background estimation uncertainty, and the data statistical uncertainty.

The total correlation matrix of the measured normalized differential cross section as a function of the photon pT in the single lepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the photon $|\eta|$ in the single lepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the $\Delta R$ between the photon and the lepton in the single lepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the photon pT in the dilepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the photon $|\eta|$ in the dilepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the minimum $\Delta R$ between the photon and the leptons in the dilepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the $|\Delta\eta|$ between the two leptons in the dilepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The total correlation matrix of the measured normalized differential cross section as a function of the $\Delta\phi$ between the two leptons in the dilepton channel. The individual systematic uncertainties are symmetrized before deriving the correlation matrix.

The statistical correlation matrix of all the measured normalized differential cross sections in the single lepton channel.

The statistical correlation matrix of all the measured normalized differential cross sections in the dilepton channel.

List of relative uncertainties in the measured cross sections of the $t\bar{t}Z$ and $t\bar{t}W$ processes from the fit, grouped in categories. All uncertainties are symmetrized. The sum in quadrature may not be equal to the total due to correlations between uncertainties introduced by the fit.

The expected and observed 68% and 95% confidence intervals, which include the value 0, for $\mathcal{C}_{i}/\Lambda^{2}$ for the EFT coefficients $\mathcal{C}_{\phi Q}^{(3)}$, $\mathcal{C}_{\phi t}$, $\mathcal{C}_{tB}$ and $\mathcal{C}_{tW}$. The intervals for $\mathcal{C}_{\phi Q}^{(3)}$ are derived setting $\mathcal{C}_{\phi Q}^{(1)}$ to zero; the measurement is sensitive to the difference $\mathcal{C}_{\phi Q}^{(3)}-\mathcal{C}_{\phi Q}^{(1)}$. All results are given in units of 1/TeV$^{2}$. Limits from fits for the EFT coefficients with only the linear term are also shown.

The signal selection acceptance times efficiency (A$\times \epsilon$), defined as the ratio of the number of MC signal events in the category to the number of generated signal events, is presented as a function of the HVT resonance mass in the VBF category. The A$\times \epsilon$ is shown for the combination of all decay channels. For the HVT VBF samples, generator cuts are: m$_{jj} >$ 150 GeV. The decay of $W$ and $Z$ are to all flavors of leptons. The uncertainty shown represents the total statistical and systematic uncertainties.

Signal selection acceptance times efficiency (A$\times \epsilon$), defined as the ratio of the number of MC signal events in the category to the number of generated signal events, as a function of the HVT resonance mass for the quark-antiquark production. The $A\times\epsilon$ is shown for each decay channel and the inclusive one. The decay of $W$ and $Z$ are to all flavors of leptons. The uncertainty shown represents the total statistical and systematic uncertainties.

Signal selection acceptance times efficiency (A$\times \epsilon$), defined as the ratio of the number of MC signal events in the category to the number of generated signal events, as a function of the HVT resonance mass for the quark-antiquark production. The $A\times\epsilon$ is shown for each decay channel and the inclusive one. The decay of $W$ and $Z$ are to all flavors of leptons. The uncertainty shown represents the total statistical and systematic uncertainties.

Observed and expected distributions of the $WZ$ invariant mass in the q$\bar{\mathrm q}$ category after applying all selection cuts. Background contributions are obtained from background-only likelihood fit to the data.

Observed and expected distributions of the $WZ$ invariant mass in the q$\bar{\mathrm q}$ category after applying all selection cuts. Background contributions are obtained from background-only likelihood fit to the data.

Observed and expected distributions of the $WZ$ invariant mass in the $VBF$ category after applying all selection cuts. Background contributions are obtained from background-only likelihood fit to the data.

Observed and expected distributions of the $WZ$ invariant mass in the $VBF$ category after applying all selection cuts. Background contributions are obtained from background-only likelihood fit to the data.

Observed and expected 95% C.L. upper limits on $\sigma\times$BR($W'\to W^\pm Z$) for the q$\bar{\mathrm q}$ production of a $W'$ boson in the HVT model as a function of its mass. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty in the expected limits are given. Limits are calculated in the asymptotic approximation below 900 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on $\sigma\times$BR($W'\to W^\pm Z$) for the q$\bar{\mathrm q}$ production of a $W'$ boson in the HVT model as a function of its mass. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty in the expected limits are given. Limits are calculated in the asymptotic approximation below 900 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on $\sigma\times$BR($W'\to W^\pm Z$) for the $VBF$ production of a $W'$ boson in the HVT model as a function of its mass. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty are given in the expected limits. Limits are calculated in the asymptotic approximation below 700 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on $\sigma\times$BR($W'\to W^\pm Z$) for the $VBF$ production of a $W'$ boson in the HVT model as a function of its mass. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty are given in the expected limits. Limits are calculated in the asymptotic approximation below 700 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on $\sigma\times$BR($H_5^\pm\to W^\pm Z$) of the Georgi-Machacek Model as a function of $m_{H_5^\pm}$. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty in the expected limits are given. Limits are calculated in the asymptotic approximation below 700 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on $\sigma\times$BR($H_5^\pm\to W^\pm Z$) of the Georgi-Machacek Model as a function of $m_{H_5^\pm}$. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty in the expected limits are given. Limits are calculated in the asymptotic approximation below 700 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on the parameter $\sin(\theta_H)$ of the Georgi-Machacek Model as a function of $m_{H_5^\pm}$. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty are given in the expected limits. Limits are calculated in the asymptotic approximation below 700 GeV and are obtained from pseudo-experiments above that.

Observed and expected 95% C.L. upper limits on the parameter $\sin(\theta_H)$ of the Georgi-Machacek Model as a function of $m_{H_5^\pm}$. The $\pm1\sigma$ and $\pm2\sigma$ uncertainty are given in the expected limits. Limits are calculated in the asymptotic approximation below 700 GeV and are obtained from pseudo-experiments above that.

Observed top-quark mass distribution and expected background in Region A after the fit (Post-Fit) under the background-only hypothesis for the resolved analysis.

Observed top-quark mass distribution and expected background in Region B after the fit (Post-Fit) under the background-only hypothesis for the resolved analysis.

Observed top-quark mass distribution and expected background in Region C after the fit (Post-Fit) under the background-only hypothesis for the resolved analysis.

Observed top-quark mass distribution and expected background in Region D after the fit (Post-Fit) under the background-only hypothesis for the resolved analysis.

Observed top-quark mass distribution and expected background in Region SR1 Medium 1b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR1 Medium 2b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR1 Tight 1b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR1 Tight 2b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR2 Medium 1b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR2 Medium 2b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR2 Tight 1b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Observed top-quark mass distribution and expected background in Region SR2 Tight 2b after the fit (Post-Fit) under the background-only hypothesis for the boosted analysis.

Expected and observed upper limits on the cross-section times branching fraction of topcolor-assisted-technicolor Z$^{\prime}_{TC2}$ decaying into top-quark pair as a function of the Z$^{\prime}_{TC2}$ mass.

Expected and observed upper limits on the cross-section times branching fraction of A1 axial-vector mediator decaying into top-quark pair as a function of the mediator mass.

Expected and observed upper limits on the cross-section times branching fraction of V1 vector mediator decaying into top-quark pair as a function of the mediator mass.

Expected and observed upper limits on the cross-section times branching fraction of Kaluza-Klein graviton decaying into top-quark pair as a function of the graviton mass.

Expected and observed upper limits on the cross-section times branching fraction of Kaluza-Klein gluon decaying into top-quark pair as a function of the gluon mass.

Expected and observed upper limits on cross-section times branching fraction of Kaluza-Klein gluon decaying into top-quark pair as a function of the width of Kaluza-Klein gluon for masses of 0.5 TeV.

Expected and observed upper limits on cross-section times branching fraction of Kaluza-Klein gluon decaying into top-quark pair as a function of the width of Kaluza-Klein gluon for masses of 1 TeV.

Expected and observed upper limits on cross-section times branching fraction of Kaluza-Klein gluon decaying into top-quark pair as a function of the width of Kaluza-Klein gluon for masses of 1.5 TeV.

Expected upper limits on cross-section times branching fraction of Kaluza-Klein gluon decaying into top-quark pair as a function of the width of Kaluza-Klein gluon for masses of 2 TeV.

Expected upper limits on cross-section times branching fraction of Kaluza-Klein gluon decaying into top-quark pair as a function of the width of Kaluza-Klein gluon for masses of 5 TeV.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for all Stealth SUSY benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=100$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=125$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for all Stealth SUSY benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=100$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=125$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for all Stealth SUSY benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=100$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=125$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for all Stealth SUSY benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=5$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=5$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=5$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=8$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=15$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=15$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=15$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=25$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=40$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=40$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=40$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=250$ GeV for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=500$ GeV for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=500$ GeV for 1MSVx strategy.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=500$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=800$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=800$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=800$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1200$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1200$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1200$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1500$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1500$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1500$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=2000$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=2000$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=2000$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=100$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=100$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=200$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=200$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=200$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=400$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=400$ GeV and $m_{s}=100$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=600$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=600$ GeV and $m_{s}=150$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=1000$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=1000$ GeV and $m_{s}=150$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=1000$ GeV and $m_{s}=400$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.