Figure 1b, lower panel, subevent

Figure 2a, full-event

Figure 2b, subevent

Figure 3b

Figure 3c

Figure 3e

Figure 3f

Figure 4a

Figure 4b

Figure 5a, full-event

Figure 5b, subevent

Figure 5c, full-event

Figure 5d, subevent

Figure 6a

Figure 6b

Figure 7a, full-event

Figure 7b, subevent

Figure 7c, full-event

Figure 7d, subevent

Figure 8a, full-event

Figure 8b, subevent

Figure 9a, full-event

Figure 9b, subevent

Figure 10a, full-event

Figure 10b, subevent

Figure 11a, full-event

Figure 11b, full-event

Figure 11c, full-event

Figure 11d, subevent

Figure 11e, subevent

Figure 11f, subevent

Figure 12a, full-event

Figure 12b, subevent

Figure 13

Figure 14a

Figure 14b

Transverse dilepton-$p_{T}^{miss}$ mass pre-fit distribution for selected events in SR2 of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Dilepton mass pre-fit distribution for selected events in SR3 of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Dilepton $p_T$ pre-fit distribution for selected events in SR3 of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Dijet $p_T$ pre-fit distribution for selected events in the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Lepton-dijet $p_T$ pre-fit distribution for selected events in the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Maximum $p_T$ of the visible particles divided by the missing $p_T$, for selected events in the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between jets, for selected events in the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the lepton and missing $p_T$, for selected events in the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the lepton and the dijet system, for selected events in the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta R$ distance between the leptons, for selected events in the non-prompt validation region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the di-lepton system and the missing $p_T$, for selected events in the non-prompt validation region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the lepton and the di-jet system, for selected events in the non-prompt validation region of the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the lepton-dijet system and the missing $p_T$, for selected events in the non-prompt validation region of the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Leading lepton $p_T$ pre-fit distribution for selected events in the top control region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Trailing lepton $p_T$ pre-fit distribution for selected events in the top control region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Dilepton mass pre-fit distribution for selected events in the Drell-Yan control region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Missing $p_T$ pre-fit distribution for selected events in the Drell-Yan control region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Missing $p_T$ pre-fit distribution for selected events in the $WW$ control region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Dilepton $p_T$ pre-fit distribution for selected events in the $WW$ control region of the di-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Minimum $p_T$ of the visible particles divided by the missing $p_T$, for selected events in the top control region of the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between jets, for selected events in the top control region of the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the lepton and missing $p_T$, for selected events in the $W$+jets control region of the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Pre-fit distribution of the $\Delta \phi$ angle between the lepton and the dijet system, for selected events in the $W$+jets control region of the semi-leptonic channel for the full dataset. The error bars on the data points represent the statistical uncertainty of the data, and the error bars on the predicted yields represent the combined systematic and statistical uncertainty in each bin. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$. The last bin includes the overflow.

Unrolled $m_{ll}-m_{T}^{l min, p_{T}^{miss}}$ post-fit distributions in the di-leptonic channel in SR1 for the full data set. The histogram bins are spaced uniformly. Each group of five bins (from left to right) corresponds to $m_{T}^{l min, p_{T}^{miss}}$ distribution in a $m_{ll}$ region, placed in ascending order. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$.

Unrolled $m_{ll}-m_{T}^{l min, p_{T}^{miss}}$ post-fit distributions in the di-leptonic channel in SR2 for the full data set. The histogram bins are spaced uniformly. Each group of five bins (from left to right) corresponds to $m_{T}^{l min, p_{T}^{miss}}$ distribution in a $m_{ll}$ region, placed in ascending order. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$.

Unrolled $m_{ll}-m_{T}^{l min, p_{T}^{miss}}$ post-fit distributions in the di-leptonic channel in SR3 for the full data set. The histogram bins are spaced uniformly. Each group of five bins (from left to right) corresponds to $m_{T}^{l min, p_{T}^{miss}}$ distribution in a $m_{ll}$ region, placed in ascending order. The signal prediction represents the mass point: $m_s = 160 GeV, m_{\chi} = 100 GeV, m_{Z'} = 500 GeV$.

Post-fit BDT distributions in the semi-leptonic channel for the full data set in the top control region. The signal prediction represents the mass point: $m_{s} = 160$ GeV, $m_{\chi} = 100$ GeV, $m_{Z'} = 500$ GeV

Post-fit BDT distributions in the semi-leptonic channel for the full data set in the $W$+jets control region. The signal prediction represents the mass point: $m_{s} = 160$ GeV, $m_{\chi} = 100$ GeV, $m_{Z'} = 500$ GeV

Post-fit BDT distributions in the semi-leptonic channel in the 2016 signal region. The signal prediction represents the mass point: $m_{s} = 160$ GeV, $m_{\chi} = 100$ GeV, $m_{Z'} = 500$ GeV

Post-fit BDT distributions in the semi-leptonic channel in the 2017+2018 signal region. The signal prediction represents the mass point: $m_{s} = 160$ GeV, $m_{\chi} = 100$ GeV, $m_{Z'} = 500$ GeV

Contour of the 95% CL observed upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

Contour of the 95% CL expected + 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

Contour of the 95% CL expected + 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

Contour of the 95% CL expected upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

Contour of the 95% CL expected - 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

Contour of the 95% CL expected - 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

Contour of the 95% CL observed upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

Contour of the 95% CL expected + 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

Contour of the 95% CL expected + 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

Contour of the 95% CL expected upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

Contour of the 95% CL expected - 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

Contour of the 95% CL expected - 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

Contour of the 95% CL observed upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

Contour of the 95% CL expected + 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

Contour of the 95% CL expected + 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

Contour of the 95% CL expected upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

Contour of the 95% CL expected - 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

Contour of the 95% CL expected - 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

Contour of the 95% CL observed upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

Contour of the 95% CL expected + 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

Contour of the 95% CL expected + 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

Contour of the 95% CL expected upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

Contour of the 95% CL expected - 1 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

Contour of the 95% CL expected - 2 std. upper limits representing $\sigma / \sigma_{theory} = 1$ for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

The $\sigma / \sigma_{theory}$ 95% CL upper limit values for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 100 GeV$.

The $\sigma / \sigma_{theory}$ 95% CL upper limit values for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 150 GeV$.

The $\sigma / \sigma_{theory}$ 95% CL upper limit values for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 200 GeV$.

The $\sigma / \sigma_{theory}$ 95% CL upper limit values for the dark Higgs model in the ($m_{s}$, $m_{Z'}$) plane for $m_{\chi} = 300 GeV$.

One-dimensional transverse Doppler profiles of the Ps cloud with and without interaction with the 243 nm cooling laser beam at a fixed frequency detuning of −200 GHz. The semitransparent bands represent the statistical measurement error (one standard deviation of the average).

Number of Ps atoms with v$_{1D}$ < 3.7 × 10$^4$ m/s, as a function of the cooling laser frequency detuning, normalized to the number of Ps atoms in the absence of all lasers. The dashed horizontal line represents the reference population of Ps in this velocity range with the cooling laser off. The highest observed relative increase is 58(9)% at a cooling laser frequency detuning of −350 GHz. The semitransparent bands represent the statistical uncertainties (one standard deviation of the average).

Covariance matrix of the two parameters $A$ and $m$ of the linear 2-to-5-jets-bin adjustment in the background Monte Carlo. Please see the text of the paper for an explanation of $A$ and $m$. Best fit values of $A$ and m are available as a separate table in this HEPData record.

Best fit values of the two parameters $A$ and $m$ of the linear 2-to-6-jets-bin adjustment in the background Monte Carlo. Please see the text of the paper for an explanation of $A$ and $m$. Uncertainties on $A$ and $m$ can be obtained from the covariance matrix of the fit, available as a separate table in this HEPData Record.

Covariance matrix of the two parameters $A$ and $m$ of the linear 2-to-6-jets-bin adjustment in the background Monte Carlo. Please see the text of the paper for an explanation of $A$ and $m$. Best fit values of $A$ and m are available as a separate table in this HEPData record.

This table contains the pre-fit background model (i.e. setting all nuisance parameters to 0), the post-fit background model (i.e. setting all nuisance parameters to their best fit values given the observed data), and the observed data in the 4 Jets bin. Uncertainties are provided, but per-bin uncertainties do not capture the correlations between bins with nuisance parameter variations. For a full analysis, the full covariance matrix between the search regions, also present in this HEPData record, should be used.

This table contains the pre-fit background model (i.e. setting all nuisance parameters to 0), the post-fit background model (i.e. setting all nuisance parameters to their best fit values given the observed data), and the observed data in the 5 Jets bin. Uncertainties are provided, but per-bin uncertainties do not capture the correlations between bins with nuisance parameter variations. For a full analysis, the full covariance matrix between the search regions, also present in this HEPData record, should be used.

This table contains the pre-fit background model (i.e. setting all nuisance parameters to 0), the post-fit background model (i.e. setting all nuisance parameters to their best fit values given the observed data), and the observed data in the $\geq 6$ Jets bin. Uncertainties are provided, but per-bin uncertainties do not capture the correlations between bins with nuisance parameter variations. For a full analysis, the full covariance matrix between the search regions, also present in this HEPData record, should be used.

Covariance matrix between search regions, estimated by toy MC runs, varying the nuisance parameters according to their covariance matrix before performing any fit. Note that the two columns ($S_T$ Bin (axis 1)) and ($N_{jets}$ Bin (axis 1)) together define the x-axis of the covariance, and the two columns ($S_T$ Bin (axis 2)) and ($N_{jets}$ Bin (axis 2)) together define the y-axis of the covariance.

Covariance matrix between search regions, estimated by toy MC runs, varying the nuisance parameters according to their covariance matrix in the background-only fit. Note that the two columns ($S_T$ Bin (axis 1)) and (NJets Bin (axis 1)) together define the x-axis of the covariance, and the two columns ($S_T$ Bin (axis 2)) and (NJets Bin (axis 2)) together define the y-axis of the covariance.

Scan through (gluino mass, neutralino mass) parameter-space. This table has the gluino and neutralino mass as the independent parameters, and the following as dependent parameters: 1. Expected upper limits (95% $CL_s$) on signal strength, with $\pm 1\sigma$ and $\pm 2\sigma$ uncertainty terms from limited statistics. 2. Observed upper limits (95% $CL_s$) on signal strength, with a $\pm 1\sigma$ uncertainty from the theoretical prediction of the gluino production cross section. 3. Observed upper limits (95% $CL_s$) on gluino production cross section.

Scan through (squark mass, neutralino mass) parameter-space. This table has the squark and neutralino mass as the independent parameters, and the following as dependent parameters: 1. Expected upper limits (95% $CL_s$) on signal strength, with $\pm 1\sigma$ and $\pm 2\sigma$ uncertainty terms from limited statistics. 2. Observed upper limits (95% $CL_s$) on signal strength, with a $\pm 1\sigma$ uncertainty from the theoretical prediction of the squark production cross section. 3. Observed upper limits (95% $CL_s$) on squark production cross section.

Full experimental (statistical and systematic) correlations (in \%) of the normalized partial decay rates over the $\overline{B}^0\to D^{*+} e^- \bar\nu_e$ and $\overline{B}^0\to D^{*+} \mu^- \bar\nu_\mu$ decays.

Measured partial decay rates $\Delta\Gamma$ (in units of $10^{-15}$ GeV) using the matrix inversion unfolding method

Average of normalized decay rates that are determined using the matrix inversion unfolding method

Full experimental (statistical and systematic) correlations (in \%) of the partial decay rates determined with the matrix inversion unfolding method for the $\overline{B}^0\to D^{*+} e^- \bar\nu_e$ and $\overline{B}^0\to D^{*+} \mu^- \bar\nu_\mu$ decays.

Full experimental (statistical and systematic) correlations (in \%) of the normalized partial decay rates determined using the matrix inversion unfolding method

Comparison between the data and SM background predictions for the number of jets in the long track final state.

Comparison between the data and SM background predictions for the hard missing transverse momentum in the long track final state.

Comparison between the data and SM background predictions for the hard missing transverse momentum in the long track final state.

Comparison between the data and SM background predictions for the number of muons in the long track final state.

Comparison between the data and SM background predictions for the number of muons in the long track final state.

Comparison between the data and SM background predictions for the m_{DTk; stopping power} in the long track final state.

Comparison between the data and SM background predictions for the m_{DTk; stopping power} in the long track final state.

Comparison between the data and SM background predictions for the number of electrons in the long track final state.

Comparison between the data and SM background predictions for the number of electrons in the long track final state.

Comparison between the data and SM background predictions for the number of b-tagged jets in the short track final state.

Comparison between the data and SM background predictions for the number of b-tagged jets in the short track final state.

Comparison between the data and SM background predictions for the number of jets in the short track final state.

Comparison between the data and SM background predictions for the number of jets in the short track final state.

Comparison between the data and SM background predictions for the hard missing transverse momentum in the short track final state.

Comparison between the data and SM background predictions for the hard missing transverse momentum in the short track final state.

Comparison between the data and SM background predictions for the number of muons in the short track final state.

Comparison between the data and SM background predictions for the number of muons in the short track final state.

Comparison between the data and SM background predictions for the m_{DTk; stopping power} in the short track final state.

Comparison between the data and SM background predictions for the m_{DTk; stopping power} in the short track final state.

Comparison between the data and SM background predictions for the number of electrons in the short track final state.

Comparison between the data and SM background predictions for the number of electrons in the short track final state.

Comparison between the data and post-fit SM background predictions for the 49 search regions in the global search region.

Comparison between the data and post-fit SM background predictions for the 49 search regions in the global search region.

95% CL upper limit on the cross section in pb for the T6bt 10 cm model.

Expected 95% CL upper limit on the cross section in pb for the T6bt 10 cm model.

Limit in the mass plane of the T6bt 10 cm model

Observed cross 95% CL upper limit on the cross section in pb for the T6bt 10 cm model.

95% CL upper limit on the cross section in pb for the T6bt 200 cm model.

Limit in the mass plane of the T6bt 10 cm model

Limit in the mass plane of the T6bt 200 cm model

Expected 95% CL upper limit on the cross section in pb for the T6bt 200 cm model.

95% CL upper limit on the cross section in pb for the T6tb 10 cm model.

Observed cross 95% CL upper limit on the cross section in pb for the T6bt 200 cm model.

Limit in the mass plane of the T6tb 10 cm model

Limit in the mass plane of the T6bt 200 cm model

95% CL upper limit on the cross section in pb for the T6tb 200 cm model.

Expected 95% CL upper limit on the cross section in pb for the T6tb 10 cm model.

Limit in the mass plane of the T6tb 200 cm model

Observed cross 95% CL upper limit on the cross section in pb for the T6tb 10 cm model.

95% CL upper limit on the cross section in pb for the T5btbt 10 cm model.

Limit in the mass plane of the T6tb 10 cm model

Limit in the mass plane of the T5btbt 10 cm model

Expected 95% CL upper limit on the cross section in pb for the T6tb 200 cm model.

95% CL upper limit on the cross section in pb for the T5btbt 200 cm model.

Observed cross 95% CL upper limit on the cross section in pb for the T6tb 200 cm model.

Limit in the mass plane of the T5btbt 200 cm model

Limit in the mass plane of the T6tb 200 cm model

95% CL upper limit on the cross section in pb for the pure Wino model.

Expected 95% CL upper limit on the cross section in pb for the T5btbt 10 cm model.

Limit in the mass plane of the pure Wino model

Observed cross 95% CL upper limit on the cross section in pb for the T5btbt 10 cm model.

95% CL upper limit on the cross section in pb for the pure Higgsino model.

Limit in the mass plane of the T5btbt 10 cm model

Limit in the mass plane of the pure Higgsino model

Expected 95% CL upper limit on the cross section in pb for the T5btbt 200 cm model.

Cut flow table showing object-level efficiency at each stage of the track selection. The denominator of the efficiency is the number of chargino-matched tracks that have been reconstructed and satisfy the pT requirements, for which the chargino decays within the pixel detector volume; the numerator is the number of such tracks which also satisfy all requirements up to and including the associated criterion.

Observed cross 95% CL upper limit on the cross section in pb for the T5btbt 200 cm model.

Cut flow table showing object-level efficiency at each stage of the track selection. The denominator of the efficiency is the number of chargino-matched tracks that have been reconstructed and satisfy the pT requirements, for which the chargino decays within the strip detector volume; the numerator is the number of such tracks which also satisfy all requirements up to and including the associated criterion.

Limit in the mass plane of the T5btbt 200 cm model

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Expected 95% CL upper limit on the cross section in pb for the pure Wino model.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Observed cross 95% CL upper limit on the cross section in pb for the pure Wino model.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Limit in the mass plane of the pure Wino model

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Expected 95% CL upper limit on the cross section in pb for the pure Higgsino model.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Observed cross 95% CL upper limit on the cross section in pb for the pure Higgsino model.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Limit in the mass plane of the pure Higgsino model

Cut flow table showing object-level efficiency at each stage of the track selection. The denominator of the efficiency is the number of chargino-matched tracks that have been reconstructed and satisfy the pT requirements, for which the chargino decays within the pixel detector volume; the numerator is the number of such tracks which also satisfy all requirements up to and including the associated criterion.

Cut flow table showing object-level efficiency at each stage of the track selection. The denominator of the efficiency is the number of chargino-matched tracks that have been reconstructed and satisfy the pT requirements, for which the chargino decays within the strip detector volume; the numerator is the number of such tracks which also satisfy all requirements up to and including the associated criterion.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:pp -> chi1p-chi1m / chi10-chi1pmHiggsino model: ctau calculated as eq. 12 in arXiv:1703.09675.Individual events weighted according to the resulting decay lengths.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:pp -> chi1p-chi1m / chi10-chi1pmPure Wino model: ctau calculated as eq. 61 in arXiv:1410.4549.Individual events weighted according to the resulting decay lengths.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:pp -> chi20-chi1pm / chi1p-chi1m / chi10-chi1pm; dm(chi20 - chi10) = 2dm(chi1pm - chi10)Higgsino model: ctau calculated as eq. 12 in arXiv:1703.09675.Individual events weighted according to the resulting decay lengths.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:pp -> chi20-chi1pm / chi1p-chi1m / chi10-chi1pm; dm(chi20 - chi10) = 2dm(chi1pm - chi10)Pure Higgsino model: ctau calculated as eq. 12 in arXiv:1703.09675.Individual events weighted according to the resulting decay lengths.

Percent efficiencies for sum of all SR bins, each SR bin, SR bin groups:

Efficiency as a function of transverse (x-y) decay length of the chargino for short track selection in Run 2.

Efficiency as a function of transverse (x-y) decay length of the chargino for long track selection in Run 2.

Efficiency as a function of transverse (x-y) decay length of the chargino for combined track selection in Run 2.

Cut flow for model T2tb (m(~b)=1 TeV, m(~chi10)=900 GeV, c#tau=10 cm), showing events passing each cut stage.

Cut flow for model T1btbt (m(~g)=1.5 TeV, m(~chi10)=1.1 TeV, c#tau=200 cm), showing events passing each cut stage.

Cut flow for model T2tb (m(~b)=1 TeV, m(~chi10)=900 GeV, c#tau=200 cm), showing events passing each cut stage.

Cut flow for model T2tb (m(~b)=1 TeV, m(~chi10)=900 GeV, c#tau=200 cm), showing events passing each cut stage.

Cut flow for model T1btbt (m(~g)=1.5 TeV, m(~chi10)=1.1 TeV, c#tau=200 cm), showing events passing each cut stage.

Cut flow for model T1btbt (m(~g)=1.5 TeV, m(~chi10)=1.1 TeV, c#tau=10 cm), showing events passing each cut stage.

Cut flow for model T2tb (m(~b)=1 TeV, m(~chi10)=900 GeV, c#tau=10 cm), showing events passing each cut stage.

Cut flow for model T1btbt (m(~g)=1.5 TeV, m(~chi10)=1.1 TeV, c#tau=10 cm), showing events passing each cut stage.

Observed upper limits at 90% CL on the square of the kinetic mixing coefficient $\epsilon$ in the minimal model of a dark photon

Observed upper limits at 90% CL on the mixing angle $\theta_H$ for the type IV 2HDM+S scenario with fixed $tan\beta=0.5$

The combined efficiency of the dimuon scouting trigger and the MVA muon selection, averaged between 2017 and 2018, weighted by the integrated luminosity of each year. The trigger and selection efficiencies are also included in the table.

Theory cross-section times branching fraction to muons times acceptance for the dark photon and 2HDM+S models.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{1})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}^{\mathrm{add.}}_{2})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{2})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{add.}})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\Delta\mathrm{R}_{\mathrm{b}\mathrm{b}}^{\mathrm{avg}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}_{3})|$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}_{3})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}_{3})$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}_{3})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}_{4})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}_{4})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{b}}_{\mathrm{T}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{b}}_{\mathrm{T}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $|\eta(\mathrm{b}^{\mathrm{extra}}_{1})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{1})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}^{\mathrm{extra}}_{2})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{2})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{extra}})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $H^{\mathrm{j}}_{\mathrm{T}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{j}}_{\mathrm{T}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\mathrm{m}_{\mathrm{b}\mathrm{b}}^{\mathrm{max}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{light}}_{\mathrm{T}}$ in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $H^{\mathrm{light}}_{\mathrm{T}}$ in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $N_{\mathrm{jets}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $N_{\mathrm{jets}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $N_{\mathrm{b}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{add.}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\Delta\mathrm{R}_{\mathrm{b}\mathrm{b}}^{\mathrm{avg}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}_{4})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}_{4})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{extra}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\mathrm{m}_{\mathrm{b}\mathrm{b}}^{\mathrm{max}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $N_{\mathrm{jets}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $N_{\mathrm{jets}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $N_{\mathrm{b}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{add.}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\Delta\mathrm{R}_{\mathrm{b}\mathrm{b}}^{\mathrm{avg}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}_{4})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}_{4})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{extra}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\mathrm{m}_{\mathrm{b}\mathrm{b}}^{\mathrm{max}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $N_{\mathrm{jets}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $N_{\mathrm{jets}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $N_{\mathrm{b}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Fiducial cross sections from the measurements of all observables, compared to predictions from different ttbb simulation approaches. For each of the normalized differential measurements the fiducial cross section in the respective phase space is also determined. In the paper only one representative observable is quoted for each fiducial phase space, while here the measured cross section with the uncertainties from the fit to the respective observable is summarized.