Dijet invariant mass distributions data compared to the fitted background estimates for the $j b b$ channel. The distributions are shown here with the $m_{jj}$ resolution binning.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Observed event yields in $\textrm{SR}$ compared with post-fit expectations from Monte Carlo simulations, as a function of the scalar sum of lepton and jet transverse momenta, $H_{\mathrm{T}}$. The last bin includes overflow events. `Signal (prod.)' and `Signal (dec.)' refer to the single-top-quark production and top-quark pair decay signal contributions, respectively.

Expected and observed 95$\%$ CL upper limits on the branching ratio corresponding to the decay of a top quark to a muon and a tau lepton through a cLFV process for scalar, vector and tensor couplings. In the vector case, all vector operators are assumed to contribute simultaneously with the same effective coupling strength. The table shows the endpoint values of $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{u})$ and $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{c})$, which are interpolated linearly for each limit.

Expected and observed 95$\%$ CL upper limits on the Wilson coefficients for scalar, vector and tensor couplings of a top quark to a muon and a tau lepton through a cLFV process. In the vector case, all vector operators are assumed to contribute simultaneously with the same effective coupling strength. The table shows the endpoint values of the $|c^{\mu\tau\mathrm{ut}}|$ and $|c^{\mu\tau\mathrm{ct}}|$ Wilson coefficients, which are interpolated assuming a linear relationship between $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{u})$ and $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{c})$.

Observed event yields in $\textrm{SR}$ compared with pre-fit expectations from Monte Carlo simulations, as a function of the scalar sum of lepton and jet transverse momenta, $H_{\mathrm{T}}$. The last bin includes overflow events. The signal yields represent a leptoquark mass of $m_{S_{1}} = 1$ TeV and a coupling strength of $\lambda^{\textrm{LQ}} = 2.0$.

Observed event yields in $\textrm{SR}$ compared with post-fit expectations from Monte Carlo simulations, as a function of the scalar sum of lepton and jet transverse momenta, $H_{\mathrm{T}}$. The last bin includes overflow events. The signal yields represent a leptoquark mass of $m_{S_{1}} = 1$ TeV and a coupling strength of $\lambda^{\textrm{LQ}} = 2.0$.

Observed and expected exclusion 95$\%$ CL upper limits on the $S_{1}$ leptoquark coupling strength $\lambda^{\textrm{LQ}}$ as a function of LQ mass, $m_{S_{1}}$.

Theoretical cross-sections for single-top-quark production and top-quark decays through cLFV interactions involving vector, scalar and tensor EFT Wilson coefficients. The column titled as $c^{(ijk3)}_{\textrm{vector}}$ represents the individual cross-section contributions from each of $c_{\mathsf{lq}}^{-(ijk3)}$, $c_{\mathsf{eq}}^{(ijk3)}$, $c_{\mathsf{lu}}^{(ijk3)}$ and $c_{\mathsf{eu}}^{(ijk3)}$. The coefficient indices represent the lepton flavour generations ($i,j = 1,2,3$ where $i \neq j$) and light quark flavour generations ($k = 1,2$). The single-top-quark production cross-sections are quoted for $u$- and $c$-quark couplings separately, while they are combined for the $t\bar{t}$ decay process ($q_k = u,c$). The scale and PDF uncertainties are given. The value of each Wilson coefficient is set to 1.0 for the calculation of the cross-section.

Requirements for each analysis region. The symbol $\ell_{3}$ denotes the lowest $p_{\mathrm{T}}$ lepton. In $\mathrm{CR}t\bar{t}\mu$ an additional requirement is placed on the leading muon $p_{\mathrm{T}}$ ($p^{\mu_1}_\mathrm{T}$) and dilepton invariant masses in order to reject signal events.

Post-fit event yields for each analysis region entering the fit, with correlations on the full systematic uncertainties taken into account as determined in the fit under a signal+background hypothesis. The 'fake electron' category in the CR$t\bar{t}\mu$ control region collects small contributions primarily from $t\bar{t}\gamma$ and $Z\gamma$ which enter the event selection due to photon conversions.

Expected and observed 95$\%$ CL upper limits on Wilson coefficients corresponding to 2Q2L EFT operators which could introduce a cLFV top decay in the $\mu\tau$ channel, and existing limits from JHEP04 (2019) 014 (previous). The previous limits shown for $c^{1(ijk3)}_{\mathsf{lequ}}$ and $c^{3(ijk3)}_{\mathsf{lequ}}$ are tightened by a factor of $\sqrt{2}$, as JHEP04 (2019) 014 does not assume that these operators are Hermitian. Results are shown separately for the $\mu\tau ut$ and $\mu\tau ct$ interactions. The lepton generations are denoted by $i,j=2,3$ for $\mu$ and $\tau$ (where $i \neq j$) and the quark generations are denoted by $k=1,2$ for $u$ and $c$, respectively.

Expected and observed 95$\%$ CL upper limits on the branching ratio ($\mathcal{B}$) corresponding to the decay of a top quark to a muon and a $\tau$ lepton through a cLFV process using specific Wilson coefficients corresponding to 2Q2L EFT operators. Results are shown separately for $\mu\tau ut$ and $\mu\tau ct$ interactions. The lepton generations are denoted by $i,j=2,3$ for $\mu$ and $\tau$ (where $i \neq j$) and the quark generations are denoted by $k=1,2$ for $u$ and $c$, respectively.

Expected and observed 95% CL upper limits on the inclusive branching ratio ($\mathcal{B}$) corresponding to the decay of a top quark to a muon and a $\tau$-lepton through a cLFV process. Limits are shown for the statistical uncertainty only and for the full set of statistical and systematic uncertainties.

Expected and observed 95$\%$ CL upper limits on the Wilson coefficients corresponding to EFT operators inducing the decay of a top quark to a muon, a tau lepton and an up quark through a cLFV process ($\mu\tau ut$ interaction).

Expected and observed 95$\%$ CL upper limits on the Wilson coefficients corresponding to EFT operators inducing the decay of a top quark to a muon, a tau lepton and a charm quark through a cLFV process ($\mu\tau ct$ interaction).

Ranking of nuisance parameters by post-fit impact on cLFV signal strength $\mu$ (the parameter of interest, POI). The pre-fit (post-fit) impact of each nuisance parameter is calculated as the difference to the fitted value of cLFV signal strength between the nominal fit and a fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. The pulls of the nuisance parameters are calculated relative to zero. These pulls and their post-fit errors, $\Delta\hat{\theta} / \Delta\theta$, are given in the second column of the table.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2\mu+\text{jets}$ region of the incluse jet phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2e+\text{jets}$ region of the incluse jet phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $1\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $1e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $2\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $2e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{miss}$ differential cross-sections in the $p_\text{T}^\text{miss}+\text{jets}$ region of the VBF phase spaces, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $1\mu+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $1e+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2\mu+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2e+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $1\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $1e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $2\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $2e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $p_\text{T}^\text{miss}+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $1\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $1e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $2\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $2e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $1\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $2\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $2e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $p_\text{T}^\text{miss}+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $1\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $1e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $2\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $2e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $1\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $1e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $2\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $2e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured Z → νν cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured Z → νν cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured Z → νν cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured Z → νν cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets,(e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured Δφ_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) γ+jets (c) 2e+jets (d) 2μ+jets (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets,(e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured Δφ_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) γ+jets (c) 2e+jets (d) 2μ+jets (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets,(e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured Δφ_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) γ+jets (c) 2e+jets (d) 2μ+jets (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets,(e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^γ differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) γ+jets, (c) 2e+jets, (d) 2μ+jets, (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured Δφ_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) γ+jets (c) 2e+jets (d) 2μ+jets (e) e+jets and (f) μ+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured γ+jets cross-section, differential in p_T^γ in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured γ+jets cross-section, differential in p_T^γ in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured γ+jets cross-section, differential in p_T^γ in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured γ+jets cross-section, differential in p_T^γ in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) Δφ_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

SM processes contributing to the fiducial SM predictions shown in Figure 7a

SM processes contributing to the fiducial SM predictions shown in Figure 7b

SM processes contributing to the fiducial SM predictions shown in Figure 8a

SM processes contributing to the fiducial SM predictions shown in Figure 8b

SM processes contributing to the fiducial SM predictions shown in Figure 8c

SM processes contributing to the fiducial SM predictions shown in Figure 8d

SM processes contributing to the fiducial SM predictions shown in Figure 9a

SM processes contributing to the fiducial SM predictions shown in Figure 9b

SM processes contributing to the fiducial SM predictions shown in Figure 9c

SM processes contributing to the fiducial SM predictions shown in Figure 9d

SM processes contributing to the fiducial SM predictions shown in Figure 3b of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 3c of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 3d of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 3e of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 5b of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 5c of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 5d of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 7b of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 7c of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 7d of Auxiliary Material

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 2c (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 2c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, $\geq 1$ jet phase space (Figure 10a) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, $\geq 1$ jet phase space (Figure 10a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 2d (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 2d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, $\geq 1$ jet phase space (Figure 10b) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, $\geq 1$ jet phase space (Figure 10b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b) and $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b) and $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)) and $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)) and $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)) and $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)) and $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)) and $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)) and $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)) and $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)) and $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 4c (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 4c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 4a (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 4a (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 4d (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 4d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 4b (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 4b (aux)).

Statistical correlations between $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a) and $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a).

Statistical correlations between $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)) and $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)).

Statistical correlations between $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)) and $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)).

Statistical correlations between $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c) and $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c).

Statistical correlations between $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)) and $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)).

Statistical correlations between $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 6c (aux)) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 6c (aux)).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 6a (aux)) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 6a (aux)).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10c) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10c).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 6b (aux)) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 6b (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 8c (aux)) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 8c (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 8a (aux)) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 8a (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10d) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10d).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 8b (aux)) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 8b (aux)).

Fraction of events where both bosons are longitudinally polarized in the region with $p_T^Z>200$ GeV using two unconstrained parameters.

Numbers of observed and expected events in the 00-enhanced signal regions, before the fit. The total uncertainties are quoted.

Summary of the relative uncertainties in the measured longitudinal-longitudinal fractions $f_{00}$. The uncertainties are reported as percentages.

Numbers of observed and expected events in the 00-enhanced signal regions, after the fit. The total uncertainties are quoted.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of the TT state.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of the TT state.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of the TT state.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of of the sum of all polarization.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of of the sum of all polarization.

$|\Delta Y(l_{W}Z)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of of the sum of all polarization.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of the TT state.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of the TT state.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of the TT state.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 20$ GeV of of the sum of all polarization.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 40$ GeV of of the sum of all polarization.

$|\Delta Y(WZ)|$ Migration matrix RAZ Region $p_{T}^{WZ}< 70$ GeV of of the sum of all polarization.

The $\mathcal{D}$ value for the unfolded $|\Delta Y(l_{W}Z)|$ distributions of the TT polarization state as a function of the $p_T^{WZ}$ cut value.

The $\mathcal{D}$ value for the unfolded $|\Delta Y(WZ)|$ distributions of the TT polarization state as a function of the $p_T^{WZ}$ cut value.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the TT state in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(l_{W}Z)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 20$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 40$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Measured normalized differential $|\Delta Y(WZ)|$ cross-section of the sum of polarization states in the RAZ Region $p_{T}^{WZ}< 70$ GeV. The total uncertainties are quoted. The last bin covers all events above the lower end of the bin.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the TT state in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(l_{W}Z)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<20$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<40$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of the relative uncertainties in the measured depth of the sum of polarization states in the RAZ Region $p_{T}^{WZ}<70$ GeV using $|\Delta Y(WZ)|$. The uncertainties are reported as percentages.

Summary of observed and expected background yields in the SR after the likelihood fit under the background-only hypothesis.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.