Measured and expected fiducial cross section for Z$\gamma$ production. Corresponding to Table 1 in the paper.

Data vs. the ABCD method background prediction for 2017 in $\mathrm{SR_{b}^{mm}}$. Events are divided by the bin width, hence fractional data counts. Error bars show statistical uncertainties of data. The blue band shows the propagated uncertainty of all individual fit variations in a given bin, which we consider to be uncorrelated. The lower panels show the ratio of the observed data to the background estimation.

Data vs. the ABCD method background prediction for 2018 in $\mathrm{SR_{b}^{mm}}$. Events are divided by the bin width, hence fractional data counts. Error bars show statistical uncertainties of data. The blue band shows the propagated uncertainty of all individual fit variations in a given bin, which we consider to be uncorrelated. The lower panels show the ratio of the observed data to the background estimation.

Data vs. the ABCD method background prediction for 2016 in $\mathrm{SR_{b+\textrm{j}/b}^{mm}}$. Events are divided by the bin width, hence fractional data counts. Error bars show statistical uncertainties of data. The blue band shows the propagated uncertainty of all individual fit variations in a given bin, which we consider to be uncorrelated. The lower panels show the ratio of the observed data to the background estimation.

Data vs. the ABCD method background prediction for 2017 in $\mathrm{SR_{b+\textrm{j}/b}^{mm}}$. Events are divided by the bin width, hence fractional data counts. Error bars show statistical uncertainties of data. The blue band shows the propagated uncertainty of all individual fit variations in a given bin, which we consider to be uncorrelated. The lower panels show the ratio of the observed data to the background estimation.

Data vs. the ABCD method background prediction for 2018 in $\mathrm{SR_{b+\textrm{j}/b}^{mm}}$. Events are divided by the bin width, hence fractional data counts. Error bars show statistical uncertainties of data. The blue band shows the propagated uncertainty of all individual fit variations in a given bin, which we consider to be uncorrelated. The lower panels show the ratio of the observed data to the background estimation.

The asymptotic 95% CL limits on the product of cross section ($\sigma$), branching fraction into a pair of muons ($\mathcal{B}$) to dimuons, acceptance (A), and efficiency ($\epsilon$) in $\mathrm{SR_{b}^{mm}}$. A combination of both limits depends on the relative acceptance contributions in each region and is omitted here.

The asymptotic 95% CL limits on the product of cross section ($\sigma$), branching fraction into a pair of muons ($\mathcal{B}$) to dimuons, acceptance (A), and efficiency ($\epsilon$) in $\mathrm{SR_{b+\textrm{j}/b}^{mm}}$. A combination of both limits depends on the relative acceptance contributions in each region and is omitted here.

Cutflow for signal samples in 2016. Samples are divided by generated mass, signal region, and the b to s quark flavor changing coupling of the $Z^\prime$, dbs. For some mass values, multiple values of this coupling strength have been generated. It is described in this minimal lagrangian: $\mathcal{L} \supset Z^{\prime}_{\eta} \Big[ g_{\mu}\, \bar{\mu}\, \gamma^{\eta}\, \mu + \big( g_b\, \delta_{bs}\, \bar{s}\, \gamma^{\eta}\, P_{L}\, b + \text{h.c.} \big) \Big]$. The cutflow shows the selection into signal regions, and then the effects of specialized HTLT and RelMET selections, which grow with greater mass.

Cutflow for signal samples in 2017. Samples are divided by generated mass, signal region, and the b to s quark flavor changing coupling of the $Z^\prime$, dbs. For some mass values, multiple values of this coupling strength have been generated. It is described in this minimal lagrangian: $\mathcal{L} \supset Z^{\prime}_{\eta} \Big[ g_{\mu}\, \bar{\mu}\, \gamma^{\eta}\, \mu + \big( g_b\, \delta_{bs}\, \bar{s}\, \gamma^{\eta}\, P_{L}\, b + \text{h.c.} \big) \Big]$. The cutflow shows the selection into signal regions, and then the effects of specialized HTLT and RelMET selections, which grow with greater mass.

Cutflow for signal samples in 2018. Samples are divided by generated mass, signal region, and the b to s quark flavor changing coupling of the $Z^\prime$, dbs. For some mass values, multiple values of this coupling strength have been generated. It is described in this minimal lagrangian: $\mathcal{L} \supset Z^{\prime}_{\eta} \Big[ g_{\mu}\, \bar{\mu}\, \gamma^{\eta}\, \mu + \big( g_b\, \delta_{bs}\, \bar{s}\, \gamma^{\eta}\, P_{L}\, b + \text{h.c.} \big) \Big]$. The cutflow shows the selection into signal regions, and then the effects of specialized HTLT and RelMET selections, which grow with greater mass.

Distributions of DNN score for the data and post-fit backgrounds (stacked histograms), in the SRs of the ZV channel for the b tag (left) and the b veto (right) channels, for the resolved (merged) category in the first (second) row. The post-fit VBS EW ZV signal is shown overlaid as a red solid line. The overflow is included in the last bin. The lower panels show the ratios of the data to the pre-fit background prediction and post-fit background yield as red open squares and blue points, respectively. The gray band in the lower panels indicates the systematic component of the post-fit background uncertainty. The vertical bars on the data points represent statistical uncertainties. The last bin includes overflow.

Distributions of M(ZV) for the data and post-fit backgrounds (stacked histograms), in the SRs of the merged b tag (left) and the merged b veto (right) channels. The template for one signal hypothesis is shown overlaid as a red solid line. The overflow is included in the last bin. The lower panels show the ratios of the data to the pre-fit background prediction and post-fit background yield as red open squares and blue points, respectively. The gray band in the lower panels indicates the systematic component of the post-fit background uncertainty. The vertical bars on the data points represent statistical uncertainties. The last bin includes overflow.

Distributions of M(ZV) for the data and post-fit backgrounds (stacked histograms), in the SRs of the merged b tag (left) and the merged b veto (right) channels. The template for one signal hypothesis is shown overlaid as a red solid line. The overflow is included in the last bin. The lower panels show the ratios of the data to the pre-fit background prediction and post-fit background yield as red open squares and blue points, respectively. The gray band in the lower panels indicates the systematic component of the post-fit background uncertainty. The vertical bars on the data points represent statistical uncertainties. The last bin includes overflow.

Distributions of M(WV) for the data and post-fit backgrounds (stacked histograms), in the SRs of the electron (left) and muon (right) channels in the merged regime. The template for one signal hypothesis is shown overlaid as a red solid line. The overflow is included in the last bin. The lower panels show the ratios of the data to the pre-fit background prediction and post-fit background yield as red open squares and blue points, respectively. The gray band in the lower panels indicates the systematic component of the post-fit background uncertainty. The vertical bars on the data points represent statistical uncertainties. The last bin includes overflow.

Distributions of M(WV) for the data and post-fit backgrounds (stacked histograms), in the SRs of the electron (left) and muon (right) channels in the merged regime. The template for one signal hypothesis is shown overlaid as a red solid line. The overflow is included in the last bin. The lower panels show the ratios of the data to the pre-fit background prediction and post-fit background yield as red open squares and blue points, respectively. The gray band in the lower panels indicates the systematic component of the post-fit background uncertainty. The vertical bars on the data points represent statistical uncertainties. The last bin includes overflow.

Observed and expected 95% CL intervals on the parameters of quartic operators in the WV and ZV channels, and their combination.

Measured and expected signal strength μ for EW ZV production.

Sequential impact of each requirement on the number of events passing the selection for the high-$E_T$ analysis.

Sequential impact of each requirement on the number of events passing the selection for the low-$E_T$ analysis.

Application of the modified ABCD method to the final high-$E_T$ and low-$E_T$ selections.

The extrapolated signal efficiencies as a function of proper decay length of the $s$ for several simulated samples in the low-$E_T$ selections.

The extrapolated signal efficiencies as a function of proper decay length of the $s$ for several simulated samples in the high-$E_T$ selections.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 5 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 15 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 40 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 55 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 100 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 400 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 5 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 15 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 40 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 100 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 400 ~\mathrm{GeV}$.

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.