Data from Fig 3b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.

Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.

Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.

Data from Fig 4 and Fig 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for beta = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 4 and FigAux 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for beta = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 4 and Fig 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 4 and FigAux 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.

Data from FigAux 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.

Data from Fig 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.

Data from FigAux 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.

Data from Fig 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.

Data from FigAux 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{ee}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{\mu\mu}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{e\mu}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{ee}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{\mu\mu}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{e\mu}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $ee jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Expected and observed 95% CL lower limits on first- and second-generation leptoquark masses for different values of $\beta$.

Event yields in the dimuon channel control regions with total uncertainties. The observed number of events is given in the first row. The background event numbers as obtained from the fit are shown together with the total uncertainties. The second row shows the total background expectation, the further rows show the breakdown into different background components.

Event yields in the dielectron channel control regions with total uncertainties. The observed number of events is given in the first row. The background event numbers as obtained from the fit are shown together with the total uncertainties. The second row shows the total background expectation, the further rows show the breakdown into different background components.

Distribution of $m_{LQ}^{min}$ in the training region for the BDT for the $ee jj$ and $\mu\mu jj$ channels. Data are shown together with predicted total background expectation.

Distribution of $m_{LQ}^{T}$ in the training region for the BDT for the $e\nu jj$ and $\mu\nu jj$ channels. Data are shown together with predicted total background expectation.

Data (bold boxes) and background estimates (colour fill) for m_beta vs. m_betagamma for the gluino R-hadron search (1000 GeV). The blue thin-line boxes illustrate the expected signal (on top of background) for the given R-hadron mass hypothesis. The black dashed vertical/horizontal lines at 500 GeV show the mass selection (signal region in the top-right). Two events pass this selection.

Expected (dashed black line) and observed (solid red line) 95% CL upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. The observed 8 TeV Run-1 limit and theory prediction [arXiv:1411.6795] are shown in dash-dotted and dotted lines, respectively.

Expected (dashed black line) and observed (solid red line) 95% CL upper limits on the cross section as a function of mass for the production of bottom-squark R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. The observed 8 TeV Run-1 limit and theory prediction [arXiv:1411.6795] are shown in dash-dotted and dotted lines, respectively.

Expected (dashed black line) and observed (solid red line) 95% CL upper limits on the cross section as a function of mass for the production of top-squark R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. The observed 8 TeV Run-1 limit and theory prediction [arXiv:1411.6795] are shown in dash-dotted and dotted lines, respectively.

Distribution of the truth-level beta for gluino R-hadrons in exemplary signal MC samples and muons in a Zmumu MC sample. All distributions have been normalised to one. The last bin contains the overflow of the histograms. The distributions illustrate the good discriminating power of the variables.

Distribution of the truth-level betagamma for gluino R-hadrons in exemplary signal MC samples and muons in a Zmumu MC sample. All distributions have been normalised to one. The last bin contains the overflow of the histograms. The distributions illustrate the good discriminating power of the variables.

Expected (dashed black line) and observed (solid red line) 95% confidence level upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. For meta-stable gluinos with a lifetime of 50 ns. (mass exclusion: about 1660 GeV expected, 1520 GeV observed).

Expected (dashed black line) and observed (solid red line) 95% confidence level upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. For meta-stable gluinos with a lifetime of 30 ns. (mass exclusion: about 1660 GeV expected, 1520 GeV observed).

Expected (dashed black line) and observed (solid red line) 95% confidence level upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. For meta-stable gluinos with a lifetime of 10 ns. (mass exclusion: about 1660 GeV expected, 1520 GeV observed).

Normalized differential cross section, $(1/\sigma_\text{fid})d\sigma_\text{fid}/d\log(m_{bb}/p_\text{T})$, as a function of $\log(m_{bb}/p_\text{T})$ for $m_{bb}$ the invariant mass of the two b-jets.

Absolute cross-sections differential in E(llb). Uncertainties are signed to show correlations.

Absolute cross-sections differential in mT(llvvb). Uncertainties are signed to show correlations.

Absolute cross-sections differential in m(llb). Uncertainties are signed to show correlations.

Normalised cross-sections differential in E(b). Uncertainties are signed to show correlations.

Normalised cross-sections differential in m(l1b). Uncertainties are signed to show correlations.

Normalised cross-sections differential in m(l2b). Uncertainties are signed to show correlations.

Normalised cross-sections differential in E(llb). Uncertainties are signed to show correlations.

Normalised cross-sections differential in mT(llvvb). Uncertainties are signed to show correlations.

Normalised cross-sections differential in m(llb). Uncertainties are signed to show correlations.

IRing2 for HT2>=500 GeV, NJets>=5

IRing2 for HT2>=1000 GeV, NJets>=2

IRing2 for HT2>=1000 GeV, NJets>=3

IRing2 for HT2>=1000 GeV, NJets>=4

IRing2 for HT2>=1000 GeV, NJets>=5

IRing2 for HT2>=1500 GeV, NJets>=2

IRing2 for HT2>=1500 GeV, NJets>=3

IRing2 for HT2>=1500 GeV, NJets>=4

IRing2 for HT2>=1500 GeV, NJets>=5

IRing128 for HT2>=500 GeV, NJets>=2

IRing128 for HT2>=500 GeV, NJets>=3

IRing128 for HT2>=500 GeV, NJets>=4

IRing128 for HT2>=500 GeV, NJets>=5

IRing128 for HT2>=1000 GeV, NJets>=2

IRing128 for HT2>=1000 GeV, NJets>=3

IRing128 for HT2>=1000 GeV, NJets>=4

IRing128 for HT2>=1000 GeV, NJets>=5

IRing128 for HT2>=1500 GeV, NJets>=2

IRing128 for HT2>=1500 GeV, NJets>=3

IRing128 for HT2>=1500 GeV, NJets>=4

IRing128 for HT2>=1500 GeV, NJets>=5

ICyl16 for HT2>=500 GeV, NJets>=2

ICyl16 for HT2>=500 GeV, NJets>=3

ICyl16 for HT2>=500 GeV, NJets>=4

ICyl16 for HT2>=500 GeV, NJets>=5

ICyl16 for HT2>=1000 GeV, NJets>=2

ICyl16 for HT2>=1000 GeV, NJets>=3

ICyl16 for HT2>=1000 GeV, NJets>=4

ICyl16 for HT2>=1000 GeV, NJets>=5

ICyl16 for HT2>=1500 GeV, NJets>=2

ICyl16 for HT2>=1500 GeV, NJets>=3

ICyl16 for HT2>=1500 GeV, NJets>=4

ICyl16 for HT2>=1500 GeV, NJets>=5

IRing2 covariance for HT2>=500 GeV, NJets>=2 (Table 1)

IRing2 covariance for HT2>=500 GeV, NJets>=3 (Table 2)

IRing2 covariance for HT2>=500 GeV, NJets>=4 (Table 3)

IRing2 covariance for HT2>=500 GeV, NJets>=5 (Table 4)

IRing2 covariance for HT2>=1000 GeV, NJets>=2 (Table 5)

IRing2 covariance for HT2>=1000 GeV, NJets>=3 (Table 6)

IRing2 covariance for HT2>=1000 GeV, NJets>=4 (Table 7)

IRing2 covariance for HT2>=1000 GeV, NJets>=5 (Table 8)

IRing2 covariance for HT2>=1500 GeV, NJets>=2 (Table 9)

IRing2 covariance for HT2>=1500 GeV, NJets>=3 (Table 10)

IRing2 covariance for HT2>=1500 GeV, NJets>=4 (Table 11)

IRing2 covariance for HT2>=1500 GeV, NJets>=5 (Table 12)

IRing128 covariance for HT2>=500 GeV, NJets>=2 (Table 13)

IRing128 covariance for HT2>=500 GeV, NJets>=3 (Table 14)

IRing128 covariance for HT2>=500 GeV, NJets>=4 (Table 15)

IRing128 covariance for HT2>=500 GeV, NJets>=5 (Table 16)

IRing128 covariance for HT2>=1000 GeV, NJets>=2 (Table 17)

IRing128 covariance for HT2>=1000 GeV, NJets>=3 (Table 18)

IRing128 covariance for HT2>=1000 GeV, NJets>=4 (Table 19)

IRing128 covariance for HT2>=1000 GeV, NJets>=5 (Table 20)

IRing128 covariance for HT2>=1500 GeV, NJets>=2 (Table 21)

IRing128 covariance for HT2>=1500 GeV, NJets>=3 (Table 22)

IRing128 covariance for HT2>=1500 GeV, NJets>=4 (Table 23)

IRing128 covariance for HT2>=1500 GeV, NJets>=5 (Table 24)

ICyl16 covariance for HT2>=500 GeV, NJets>=2 (Table 25)

ICyl16 covariance for HT2>=500 GeV, NJets>=3 (Table 26)

ICyl16 covariance for HT2>=500 GeV, NJets>=4 (Table 27)

ICyl16 covariance for HT2>=500 GeV, NJets>=5 (Table 28)

ICyl16 covariance for HT2>=1000 GeV, NJets>=2 (Table 29)

ICyl16 covariance for HT2>=1000 GeV, NJets>=3 (Table 30)

ICyl16 covariance for HT2>=1000 GeV, NJets>=4 (Table 31)

ICyl16 covariance for HT2>=1000 GeV, NJets>=5 (Table 32)

ICyl16 covariance for HT2>=1500 GeV, NJets>=2 (Table 33)

ICyl16 covariance for HT2>=1500 GeV, NJets>=3 (Table 34)

ICyl16 covariance for HT2>=1500 GeV, NJets>=4 (Table 35)

ICyl16 covariance for HT2>=1500 GeV, NJets>=5 (Table 36)

IRing2 covariance, complete

1-IRing128 covariance, complete

1-ICyl16 covariance, complete

Table of systematic uncertainties for the relative differential cross-section at particle level for the top quark transverse momentum in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the absolute value of the top quark rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the absolute value of the top quark rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the absolute value of the top quark rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the absolute value of the top quark rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the tt̄ system transverse momentum in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the tt̄ system transverse momentum in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the tt̄ system transverse momentum in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the tt̄ system transverse momentum in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the absolute value of the tt̄ system rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the absolute value of the tt̄ system rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the absolute value of the tt̄ system rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the absolute value of the tt̄ system rapidity in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the mass of the tt̄ system in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the mass of the tt̄ system in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the mass of the tt̄ system in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the mass of the tt̄ system in the resolved regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the top quark transverse momentum in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the top quark transverse momentum in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the top quark transverse momentum in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the top quark transverse momentum in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the absolute value of the top quark rapidity in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the absolute differential cross-section at particle level for the absolute value of the top quark rapidity in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the absolute value of the top quark rapidity in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Table of systematic uncertainties for the relative differential cross-section at particle level for the absolute value of the top quark rapidity in the boosted regime. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text.

Missing transverse momentum distribution after SR3b selection, beside the $E_\mathrm{T}^\mathrm{miss}$ requirement. The results in the signal region correspond to the last inclusive bin. The systematic uncertainties include theory uncertainties for the backgrounds with prompt SS/3L and the full systematic uncertainties for data-driven backgrounds. For illustration the distribution for a benchmark SUSY scenario ($pp\to \tilde g\tilde g$, $\tilde g\to t\bar t\tilde\chi_1^0$, $m_{\tilde g}=1.2$ TeV, $m_{\tilde\chi_1^0}=0.7$ TeV) is also shown.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar q(\tilde\ell\ell/\tilde\nu\nu)$ decay: signal acceptance (in %) in the signal region SR0b3j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar q(\tilde\ell\ell/\tilde\nu\nu)$ decay: reconstruction efficiency (in %) in the signal region SR0b3j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar qWZ\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR0b5j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar qWZ\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR0b5j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde b_1\tilde b_1^*$ production and $\tilde b_1\to tW\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR1b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde b_1\tilde b_1^*$ production and $\tilde b_1\to tW\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR1b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to t\bar t\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR3b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to t\bar t\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR3b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

$p_{T}^{t,2}$ absolute differential cross-section at particle level

$|{y}^{t,2}|$ absolute differential cross-section at particle level

$m^{t\bar{t}}$ absolute differential cross-section at particle level

$p_{T}^{t\bar{t}}$ absolute differential cross-section at particle level

$|y^{t\bar{t}}|$ absolute differential cross-section at particle level

$\chi^{t\bar{t}}$ absolute differential cross-section at particle level

$|y_{B}^{t\bar{t}}|$ absolute differential cross-section at particle level

$|p_{out}^{t\bar{t}}|$ absolute differential cross-section at particle level

$\Delta \phi(t_{1}, t_{2})$ absolute differential cross-section at particle level

$H_{T}^{t\bar{t}}$ absolute differential cross-section at particle level

$|\cos\theta^{*}|$ absolute differential cross-section at particle level

$p_{T}^{t,1}$ normalized differential cross-section at particle level

$|{y}^{t,1}|$ normalized differential cross-section at particle level

$p_{T}^{t,2}$ normalized differential cross-section at particle level

$|{y}^{t,2}|$ normalized differential cross-section at particle level

$m^{t\bar{t}}$ normalized differential cross-section at particle level

$p_{T}^{t\bar{t}}$ normalized differential cross-section at particle level

$|y^{t\bar{t}}|$ normalized differential cross-section at particle level

$\chi^{t\bar{t}}$ normalized differential cross-section at particle level

$|y_{B}^{t\bar{t}}|$ normalized differential cross-section at particle level

$|p_{out}^{t\bar{t}}|$ normalized differential cross-section at particle level

$\Delta \phi(t_{1}, t_{2})$ normalized differential cross-section at particle level

$H_{T}^{t\bar{t}}$ normalized differential cross-section at particle level

$|\cos\theta^{*}|$ normalized differential cross-section at particle level

$p_{T}^{t,1}$ covariance matrix for the absolute differential cross-section at particle level

$p_{T}^{t,1}$ correlation matrix for the absolute differential cross-section at particle level

$p_{T}^{t,1}$ covariance matrix for the normalized differential cross-section at particle level

$p_{T}^{t,1}$ correlation matrix for the normalized differential cross-section at particle level

$|{y}^{t,1}|$ covariance matrix for the absolute differential cross-section at particle level

$|{y}^{t,1}|$ correlation matrix for the absolute differential cross-section at particle level

$|{y}^{t,1}|$ covariance matrix for the normalized differential cross-section at particle level

$|{y}^{t,1}|$ correlation matrix for the normalized differential cross-section at particle level

$p_{T}^{t,2}$ covariance matrix for the absolute differential cross-section at particle level

$p_{T}^{t,2}$ correlation matrix for the absolute differential cross-section at particle level

$p_{T}^{t,2}$ covariance matrix for the normalized differential cross-section at particle level

$p_{T}^{t,2}$ correlation matrix for the normalized differential cross-section at particle level

$|{y}^{t,2}|$ covariance matrix for the absolute differential cross-section at particle level

$|{y}^{t,2}|$ correlation matrix for the absolute differential cross-section at particle level

$|{y}^{t,2}|$ covariance matrix for the normalized differential cross-section at particle level

$|{y}^{t,2}|$ correlation matrix for the normalized differential cross-section at particle level

$m^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at particle level

$m^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at particle level

$m^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at particle level

$m^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at particle level

$p_{T}^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at particle level

$p_{T}^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at particle level

$p_{T}^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at particle level

$p_{T}^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at particle level

$|y^{t\bar{t}}|$ covariance matrix for the absolute differential cross-section at particle level

$|y^{t\bar{t}}|$ correlation matrix for the absolute differential cross-section at particle level

$|y^{t\bar{t}}|$ covariance matrix for the normalized differential cross-section at particle level

$|y^{t\bar{t}}|$ correlation matrix for the normalized differential cross-section at particle level

$\chi^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at particle level

$\chi^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at particle level

$\chi^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at particle level

$\chi^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at particle level

$|y_{B}^{t\bar{t}}|$ covariance matrix for the absolute differential cross-section at particle level

$|y_{B}^{t\bar{t}}|$ correlation matrix for the absolute differential cross-section at particle level

$|y_{B}^{t\bar{t}}|$ covariance matrix for the normalized differential cross-section at particle level

$|y_{B}^{t\bar{t}}|$ correlation matrix for the normalized differential cross-section at particle level

$|p_{out}^{t\bar{t}}|$ covariance matrix for the absolute differential cross-section at particle level

$|p_{out}^{t\bar{t}}|$ correlation matrix for the absolute differential cross-section at particle level

$|p_{out}^{t\bar{t}}|$ covariance matrix for the normalized differential cross-section at particle level

$|p_{out}^{t\bar{t}}|$ correlation matrix for the normalized differential cross-section at particle level

$\Delta \phi(t_{1}, t_{2})$ covariance matrix for the absolute differential cross-section at particle level

$\Delta \phi(t_{1}, t_{2})$ correlation matrix for the absolute differential cross-section at particle level

$\Delta \phi(t_{1}, t_{2})$ covariance matrix for the normalized differential cross-section at particle level

$\Delta \phi(t_{1}, t_{2})$ correlation matrix for the normalized differential cross-section at particle level

$H_{T}^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at particle level

$H_{T}^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at particle level

$H_{T}^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at particle level

$H_{T}^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at particle level

$|\cos\theta^{*}|$ covariance matrix for the absolute differential cross-section at particle level

$|\cos\theta^{*}|$ correlation matrix for the absolute differential cross-section at particle level

$|\cos\theta^{*}|$ covariance matrix for the normalized differential cross-section at particle level

$|\cos\theta^{*}|$ correlation matrix for the normalized differential cross-section at particle level

Statistical correlation matrix for the absolute differential cross-section of all 13 variables at particle level. The observables are arranged as follows: leading top pT - ${p_{{T}}}^{t,1}$ [bins 1-8], leading top rapidity - $|y^{t,1}|$ [bins 9-14], subleading top pT - ${p_{{T}}}^{t,2}$ [bins 15-21], subleading top rapidity - $|y^{t,2}|$ [bins 22-27], ttbar mass - $m^{t\bar{t}}$ [bins 28-37], ttbar pT - ${p_{{T}}}^{t\bar{t}}$ [bins 38-43], ttbar rapidity - $|y^{t\bar{t}}|$ [bins 44-49], chi ttbar - ${\chi}^{t\bar{t}}$ [bins 50-56], delta phi ttbar - ${\Delta\phi}(t_1,t_2)$ [bins 57-60], ttbar out of plane momentum - $|p_{out}^{t\bar{t}}|$ [bins 61-67], yboost ttbar - $|y_{B}^{t\bar{t}}|$ [68-74], cos theta star - $|{\cos{\theta}^{\star}}|$ [bins 75-80], HT ttbar - $H_{T}^{t\bar{t}}$ [bins 81-87].

Statistical correlation matrix for the normalized differential cross-section of all 13 variables at particle level. The observables are arranged as follows: leading top pT - ${p_{{T}}}^{t,1}$ [bins 1-8], leading top rapidity - $|y^{t,1}|$ [bins 9-14], subleading top pT - ${p_{{T}}}^{t,2}$ [bins 15-21], subleading top rapidity - $|y^{t,2}|$ [bins 22-27], ttbar mass - $m^{t\bar{t}}$ [bins 28-37], ttbar pT - ${p_{{T}}}^{t\bar{t}}$ [bins 38-43], ttbar rapidity - $|y^{t\bar{t}}|$ [bins 44-49], chi ttbar - ${\chi}^{t\bar{t}}$ [bins 50-56], delta phi ttbar - ${\Delta\phi}(t_1,t_2)$ [bins 57-60], ttbar out of plane momentum - $|p_{out}^{t\bar{t}}|$ [bins 61-67], yboost ttbar - $|y_{B}^{t\bar{t}}|$ [68-74], cos theta star - $|{\cos{\theta}^{\star}}|$ [bins 75-80], HT ttbar - $H_{T}^{t\bar{t}}$ [bins 81-87].

${p_{{T}}}^{t}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|y^{t}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t,1}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|y^{t,1}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t,2}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{y}^{t,2}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$m^{t\bar{t}}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t\bar{t}}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{y}^{t\bar{t}}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${\chi}^{t\bar{t}}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{y_{B}}^{t\bar{t}}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{p_{out}}^{t\bar{t}}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${\Delta\phi}(t_1,t_2)$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${H_{T}}^{t\bar{t}}$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{\cos{\theta}^{\star}}|$ absolute differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|y^{t}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t,1}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|y^{t,1}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t,2}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{y}^{t,2}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$m^{t\bar{t}}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t\bar{t}}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{y}^{t\bar{t}}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${\chi}^{t\bar{t}}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{y_{B}}^{t\bar{t}}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{p_{out}}^{t\bar{t}}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${\Delta\phi}(t_1,t_2)$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$H_{T}^{t\bar{t}}$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

$|{\cos{\theta}^{\star}}|$ normalized differential cross-section at parton level. The parton level phase-space is limited to the region $p_T^{t,1} > 500~$GeV, $p_T^{t,2} > 350~$GeV.

${p_{{T}}}^{t}$ covariance matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t}$ correlation matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t}$ covariance matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t}$ correlation matrix for the normalized differential cross-section at parton level

$|y^{t}|$ covariance matrix for the absolute differential cross-section in parton level

$|y^{t}|$ correlation matrix for the absolute differential cross-section at parton level

$|y^{t}|$ covariance matrix for the normalized differential cross-section in parton level

$|y^{t}|$ correlation matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t,1}$ covariance matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t,1}$ correlation matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t,1}$ covariance matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t,1}$ correlation matrix for the normalized differential cross-section at parton level

$|y^{t,1}|$ covariance matrix for the absolute differential cross-section at parton level

$|y^{t,1}|$ correlation matrix for the absolute differential cross-section at parton level

$|y^{t,1}|$ covariance matrix for the normalized differential cross-section at parton level

$|y^{t,1}|$ correlation matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t,2}$ covariance matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t,2}$ correlation matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t,2}$ covariance matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t,2}$ correlation matrix for the normalized differential cross-section at parton level

$|{y}^{t,2}|$ covariance matrix for the absolute differential cross-section at parton level

$|{y}^{t,2}|$ correlation matrix for the absolute differential cross-section at parton level

$|{y}^{t,2}|$ covariance matrix for the normalized differential cross-section at parton level

$|{y}^{t,2}|$ correlation matrix for the normalized differential cross-section at parton level

$m^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at parton level

$m^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at parton level

$m^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at parton level

$m^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at parton level

${p_{{T}}}^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at parton level

${p_{{T}}}^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at parton level

$|{y}^{t\bar{t}}|$ covariance matrix for the absolute differential cross-section at parton level

$|{y}^{t\bar{t}}|$ correlation matrix for the absolute differential cross-section at parton level

$|{y}^{t\bar{t}}|$ covariance matrix for the normalized differential cross-section at parton level

$|{y}^{t\bar{t}}|$ correlation matrix for the normalized differential cross-section at parton level

${\chi}^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at parton level

${\chi}^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at parton level

${\chi}^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at parton level

${\chi}^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at parton level

$|{y_{B}}^{t\bar{t}}|$ covariance matrix for the absolute differential cross-section at parton level

$|{y_{B}}^{t\bar{t}}|$ correlation matrix for the absolute differential cross-section at parton level

$|{y_{B}}^{t\bar{t}}|$ covariance matrix for the normalized differential cross-section at parton level

$|{y_{B}}^{t\bar{t}}|$ correlation matrix for the normalized differential cross-section at parton level

$|{p_{out}}^{t\bar{t}}|$ covariance matrix for the absolute differential cross-section at parton level

$|{p_{out}}^{t\bar{t}}|$ correlation matrix for the absolute differential cross-section at parton level

$|{p_{out}}^{t\bar{t}}|$ covariance matrix for the normalized differential cross-section at parton level

$|{p_{out}}^{t\bar{t}}|$ correlation matrix for the normalized differential cross-section at parton level

${\Delta\phi}(t_1,t_2)$ covariance matrix for the absolute differential cross-section at parton level

${\Delta\phi}(t_1,t_2)$ correlation matrix for the absolute differential cross-section at parton level

${\Delta\phi}(t_1,t_2)$ covariance matrix for the normalized differential cross-section at parton level

${\Delta\phi}(t_1,t_2)$ correlation matrix for the normalized differential cross-section at parton level

$H_{T}^{t\bar{t}}$ covariance matrix for the absolute differential cross-section at parton level

$H_{T}^{t\bar{t}}$ correlation matrix for the absolute differential cross-section at parton level

$H_{T}^{t\bar{t}}$ covariance matrix for the normalized differential cross-section at parton level

$H_{T}^{t\bar{t}}$ correlation matrix for the normalized differential cross-section at parton level

$|{\cos{\theta}^{\star}}|$ covariance matrix for the absolute differential cross-section at parton level

$|{\cos{\theta}^{\star}}|$ correlation matrix for the absolute differential cross-section at parton level

$|{\cos{\theta}^{\star}}|$ covariance matrix for the normalized differential cross-section at parton level

$|{\cos{\theta}^{\star}}|$ correlation matrix for the normalized differential cross-section at parton level

Statistical correlation matrix for the absolute differential cross-section of all 15 variables at parton level. The observables are arranged as follows: random top pT - ${p_{{T}}}^{t}$ [bins 1-8], random top rapidity - $|y^{t}|$ [bins 9-16], leading top pT - ${p_{{T}}}^{t,1}$ [bins 17-24], leading top rapidity - $|y^{t,1}|$ [bins 25-32], subleading top pT - ${p_{{T}}}^{t,2}$ [bins 33-39], subleading top rapidity - $|y^{t,2}|$ [bins 40-46], ttbar mass - $m^{t\bar{t}}$ [bins 48-57], ttbar pT - ${p_{{T}}}^{t\bar{t}}$ [bins 58-63], ttbar rapidity - $|y^{t\bar{t}}|$ [bins 66-71], chi ttbar ${\chi}^{t\bar{t}}$ - [bins 74-80], delta phi ttbar - ${\Delta\phi}(t_1,t_2)$ [bins 81-84], ttbar out of plane momentum - $|p_{out}^{t\bar{t}}|$ [bins 85-91], yboost ttbar - $|y_{B}^{t\bar{t}}|$ [92-98], cos theta star - $|{\cos{\theta}^{\star}}|$ [bins 99-104], HT ttbar $H_{T}^{t\bar{t}}$ [bins 105-114].

Statistical correlation matrix for the normalized differential cross-section of all 15 variables at parton level. The observables are arranged as follows: random top pT - ${p_{{T}}}^{t}$ [bins 1-8], random top rapidity - $|y^{t}|$ [bins 9-16], leading top pT - ${p_{{T}}}^{t,1}$ [bins 17-24], leading top rapidity - $|y^{t,1}|$ [bins 25-32], subleading top pT - ${p_{{T}}}^{t,2}$ [bins 33-39], subleading top rapidity - $|y^{t,2}|$ [bins 40-46], ttbar mass - $m^{t\bar{t}}$ [bins 48-57], ttbar pT - ${p_{{T}}}^{t\bar{t}}$ [bins 58-63], ttbar rapidity - $|y^{t\bar{t}}|$ [bins 66-71], chi ttbar ${\chi}^{t\bar{t}}$ - [bins 74-80], delta phi ttbar - ${\Delta\phi}(t_1,t_2)$ [bins 81-84], ttbar out of plane momentum - $|p_{out}^{t\bar{t}}|$ [bins 85-91],yboost ttbar - $|y_{B}^{t\bar{t}}|$ [92-98], cos theta star - $|{\cos{\theta}^{\star}}|$ [bins 99-104], HT ttbar $H_{T}^{t\bar{t}}$ [bins 105-114].

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eleven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eleven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eleven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with twelve-or-more jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with twelve-or-more jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with twelve-or-more jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

Cut flow for a model of gluino pair production where the gluino decays to two (u, d, s, c) quarks and the neutralino, which then decays to two (u, d, s, c) quarks and a lepton via a lambda' RPV coupling, where each RPV decay can produce any of the four first and second generation leptons (e, mu, nu_e, nu_mu) with equal probability (m_gluino = 1800 GeV, m_neutralino = 900 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where the gluino decays to two (u, d, s, c) quarks and the neutralino, which then decays to two (u, d, s, c) quarks and a lepton via a lambda' RPV coupling, where each RPV decay can produce any of the four first and second generation leptons (e, mu, nu_e, nu_mu) with equal probability (m_gluino = 1800 GeV, m_neutralino = 900 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where the gluino decays to two (u, d, s, c) quarks and the neutralino, which then decays to two (u, d, s, c) quarks and a lepton via a lambda' RPV coupling, where each RPV decay can produce any of the four first and second generation leptons (e, mu, nu_e, nu_mu) with equal probability (m_gluino = 1800 GeV, m_neutralino = 900 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays to a top quark and a top squark, with the top squark decaying to an s- and a b- quark via a non-zero lambda''_323 RPV coupling (m_gluino = 1600 GeV, m_stop = 1000 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays to a top quark and a top squark, with the top squark decaying to an s- and a b- quark via a non-zero lambda''_323 RPV coupling (m_gluino = 1600 GeV, m_stop = 1000 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays to a top quark and a top squark, with the top squark decaying to an s- and a b- quark via a non-zero lambda''_323 RPV coupling (m_gluino = 1600 GeV, m_stop = 1000 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays via an off-shell top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino -> uds) via the RPV coupling lambda''_112 (m_gluino = 2000 GeV, m_neutralino = 941 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays via an off-shell top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino -> uds) via the RPV coupling lambda''_112 (m_gluino = 2000 GeV, m_neutralino = 941 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays via an off-shell top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino -> uds) via the RPV coupling lambda''_112 (m_gluino = 2000 GeV, m_neutralino = 941 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of right-handed top squark pair production with the top squark decaying to the lightest supersymmetric particle (LSP) which is considered to be purely higgsino. The higgsino-like LSP decays through the non-zero RPV coupling lambda''_323 (m_stop = 975 GeV, m_neutralino = 600 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of right-handed top squark pair production with the top squark decaying to the lightest supersymmetric particle (LSP) which is considered to be purely higgsino. The higgsino-like LSP decays through the non-zero RPV coupling lambda''_323 (m_stop = 975 GeV, m_neutralino = 600 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of right-handed top squark pair production with the top squark decaying to the lightest supersymmetric particle (LSP) which is considered to be purely higgsino. The higgsino-like LSP decays through the non-zero RPV coupling lambda''_323 (m_stop = 975 GeV, m_neutralino = 600 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.