Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-0 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.

Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-2 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-0.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-2.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-0 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.

Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-2 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.

Comparison of the background model (stacked histograms) with data (dots) in the $2b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $3b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

Comparison of the background model (stacked histograms) with data (dots) in the $4b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.

The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-0 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-2 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The theoretical prediction for the bulk RS model with $k/\bar{M}_{\text{Pl}} = 1$ (solid red line) is shown; the decrease below 350 GeV is due to a sharp reduction in the $G^{*}_{\text{KK}} \rightarrow HH$ branching ratio. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the High-pT jet SR for observed data events

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the High-pT jet SR for expected signal events in the strong gluino pair pair production model with m(gluino)=1.8 TeV, m(chi0)=0.2 TeV, tau(chi0)=0.1 ns

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the Trackless jet SR for observed data events

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the Trackless jet SR for expected signal events in the electroweak pair production model

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Exclusion limits at 95% CL on the production cross section in the electroweak pair production model.

Exclusion limits at 95% CL on the production cross section in the strong gluino pair production models and m(gluino)=2.4 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Exclusion limits at 95% CL on the production cross section in the strong gluino pair production models and m($ ilde{\chi}^0_1$)=1.25 TeV

Acceptance cutflow for the High-pT SR for representative points in the strong gluino pair production model. See additional resources for more information.

Acceptance cutflow for the Trackless SR for representative points in the electroweak pair production model. See additional resources for more information.

Acceptance cutflow for the Trackless SR for representative points in the electroweak pair production model with heavy-flavor quarks final state. See additional resources for more information.

Acceptance cutflow for the High-pT SR for representative points in the electroweak pair production model with heavy-flavor quarks final state. See additional resources for more information.

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R < 1150 mm

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R [1150, 3870] mm

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R > 3870 mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R < 1150 mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R [1150, 3870] mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R > 3870 mm

Reinterpretation Material: Vertex-level Efficiency for R < 22 mm

Reinterpretation Material: Vertex-level Efficiency for R [22, 25] mm

Reinterpretation Material: Vertex-level Efficiency for R [25, 29] mm

Reinterpretation Material: Vertex-level Efficiency for R [29, 38] mm

Reinterpretation Material: Vertex-level Efficiency for R [38, 46] mm

Reinterpretation Material: Vertex-level Efficiency for R [46, 73] mm

Reinterpretation Material: Vertex-level Efficiency for R [73, 84] mm

Reinterpretation Material: Vertex-level Efficiency for R [84, 111] mm

Reinterpretation Material: Vertex-level Efficiency for R [111, 120] mm

Reinterpretation Material: Vertex-level Efficiency for R [120, 145] mm

Reinterpretation Material: Vertex-level Efficiency for R [145, 180] mm

Reinterpretation Material: Vertex-level Efficiency for R [180, 300] mm

Cutflow (acceptance x efficiency) for the High-pT SR for representative points in the strong gluino pair production model. See additional resources for more information.

Cutflow (acceptance x efficiency) for the Trackless SR for representative points in the electroweak pair production model. See additional resources for more information.

Cutflow (acceptance x efficiency) for the Trackless SR for representative points in the electroweak pair production model with heavy-flavor quarks. See additional resources for more information.

Cutflow (acceptance x efficiency) for the High-pT SR for representative points in the electroweak pair production model with heavy-flavor quarks. See additional resources for more information.

Bayesian upper exclusion limits on the ratio $g_{W'}/g_{W}$ for an SSM-like W$\prime$ boson are shown. The unity coupling ratio (blue dotted curve) corresponds to the SSM common benchmark. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian lower exclusion limits on the NUGIM G(221) mixing angle $\cot(\theta_{E})$ are shown as a function of the W$\prime$ boson mass. The theoretically excluded region is shaded in grey. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian upper exclusion limits at 95% CL on the product of the production cross section and branching fraction of a QBH in an associated $ au$ lepton and neutrino final state. Minimum threshold masses $m_{th}$ of up to 6.6 TeV are excluded at 95% CL. The observed limit (solid line) is obtained from the intersection with the LO QBH cross section (blue dotted curve). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian upper limits at 95% CL on the cross section of the process $pp\rightarrow\tau\nu$ mediated via LQ exchange in the t-channel. The inner (green) band and the outer (yellow) band indicate the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The predicted LQ cross section at LO in the three coupling benchmark scenarios is depicted in different colors for $g_{U}=1$. The uncertainty bandy correspond to the sum in quadrature of PDF and scale variations. The first benchmark scenario considers only couplings to left-handed SM fermions (i.e. $\beta_{\text{R}}^{ij}=0$) and is referred to as "best fit LH". The second benchmark, referred to as "best fit LH+RH", considers $|\beta_{\text{R}}^{\text{b}\tau}|=1$ and all other $\beta_{\text{R}}^{ij}=0$. In the third "democratic" benchmark, equal couplings only to LH fermions are assumed, i.e. $\beta_{\text{L}}^{ij}=1$ and $\beta_{\text{R}}^{ij}=0$ for all $i$ and $j$.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH+RH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the democratic scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Bayesian upper exclusion limits at 95% CL on each of the Wilson coefficients described by the EFT model based on 2016-2018 data. The three different coupling types represent a left-handed vector coupling ($\epsilon^{cb}_{L}$), tensor-like coupling ($\epsilon^{cb}_{T}$), and scalar-tensor-like coupling ($\epsilon^{cb}_{S_{L}}$). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Summary of exclusion limits (expected and observed) calculated at 95% CL for full Run-2 CMS data.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning from Figure 4.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning used in Figure 4.

Predicted signal yields for the 2017 data-taking period, corresponding to 41.3 fb$^{-1}$, after the application of each search requirement (cumulative) for various signal hypothesis. The requirements listed are presented as total efficiencies w.r.t. the previous selection step.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-2 $q\bar{q}\to X^0 \to Z\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-2 $gg\to X^0 \to Z\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

The $m_{J\gamma}$ distributions of data events selected for the spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ search in the WMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-0 $gg \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-2 $gg \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-2 $q\bar{q} \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The jet mass distribution of large-$R$ jets originating from the hadronic decay of $W$ and $Z$ bosons produced from the decay of BSM bosons with mass $m_X = 2000$ GeV. The decays simulated are for the production models $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ with a spin-1 resonance $X^{\pm}$ and $gg\to X^0 \to Z\gamma$ with a spin-0 resonance $X^{0}$.

The photon $E_{T}$ selection criteria as a function of resonance mass for D2 categories. Selections for four different signal channels are shown together.

The photon $E_{T}$ selection criteria as a function of resonance mass for ZMASS or WMASS categories. Selections for four different signal channels are shown together.

Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the FCNC top-quark candidate in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the SM top-quark candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction before the fit (Pre-Fit) for the transverse momentum of the Z boson candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the third lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

The rejection factor (inverse of the efficiency) for jets that have the other origins (background jets) are shown. The background jet rejection factor is calculated using pre-selected large-$R$ jets in the sample of simulated $Z\rightarrow\nu\nu$ + jets events, dominated by initial state radiation (ISR) jets. The efficiency correction factors are applied on the background rejection for the $W_{qq}$/$Z_{qq}$-tagging. The systematic uncertainty is represented by the hashed bands.

The boson-tagging efficiency for jets arising from $Z/h$ bosons decaying into $b\bar{b}$ (signal jets). The signal jet efficiency of $Z_{bb}$/$h_{bb}$-tagging is evaluated using a sample of pre-selected large-$R$ jets ($p_{\textrm{T}}>200~\textrm{GeV}, |\eta|<2.0, m_{J} > 40~\textrm{GeV}$) in the simulated $(\tilde{W},\tilde{B})$ simplified model signal events with $\Delta m(\tilde{\chi}_{\textrm{heavy}},~\tilde{\chi}_{\textrm{light}}) \ge 400~\textrm{GeV}$. The jets are matched with generator-level $Z/h$-bosons by $\Delta R<1.0$ which decay into $b\bar{b}$. The systematic uncertainty is represented by the hashed bands.

The rejection factor (inverse of the efficiency) for jets that have the other origins (background jets) are shown. The background jet rejection factor is calculated using pre-selected large-$R$ jets in the sample of simulated $Z\rightarrow\nu\nu$ + jets events, dominated by initial state radiation (ISR) jets. As for the $Z_{bb}$/$h_{bb}$-tagging, the rejection is shown as the function of number of $b$- or $c$-quarks contained in the large-$R$ jet within $\Delta R<1.0$. The systematic uncertainty is represented by the hashed bands.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The signal jet efficiency and background jet rejection for the $W_{qq}$-/$Z_{qq}$-tagging working point used in the analysis in comparison with those for the nominal working points (''50% efficiency W/Z tagger'' in ATL-PHYS-PUB-2020-017). The definition of the efficiency and rejection follow the caption of Figure 4. Note that the efficiency for the nominal working point is not 50% since they are tuned using jet samples with different pre-selection.

The total post-fit uncertainty in each of the SRs and VRs.

Summary of the observed data and predicted SM background in all SRs. The background prediction in SR-4Q (SR-2B2Q) is obtained by a background-only fit to CR0L-4Q (CR0L-2B2Q). The total systematic uncertainty on the background prediction is shown by the hatched area. Distributions of a few representative signals are overlaid. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\textrm{GeV}$. The bottom panel shows the statistical significance of the discrepancy between the observed number of events and the SM expectation.

Number of observed data events and the SM backgrounds in the SRs and the CR0L bins. The SM backgrounds are predicted by the background-only fits. Note that the relative uncertainty on the expected yield in the CRs between the reducible backgrounds are identical, given that a common normalization factor is assigned for all of them in the fit. Yields for negligible backgrounds are indicated by "0.0000001±0.0000001" in the table.

Number of observed data events and the post-fit SM background prediction in the VR1L(1Y) bins and the corresponding CR1L(1Y) bins. "-" indicates negligibly small contribution. Note that the relative uncertainty on the expected yield in the CRs between the reducible backgrounds are identical, given that a common normalization factor is assigned for all of them in the fit. Yields for negligible backgrounds are indicated by "0.0000001±0.0000001" in the table.

$m_{\textrm{eff}}$ distribution in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{1})$ in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $D_2(J_{1})$ in SR-4Q-VV. For $D_2$, the cut value applied for $V_{qq}$-tagging ($D_{2,\text{cut}}$) is subtracted as the off-set so that $D_2-D_{2,\text{cut}}(J)<0$ represents $J$ passing the $D_2$ selection. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{2})$ in SR-4Q-VV. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $D_2(J_{2})$ in SR-4Q-VV. For $D_2$, the cut value applied for $V_{qq}$-tagging ($D_{2,\text{cut}}$) is subtracted as the off-set so that $D_2-D_{2,\text{cut}}(J)<0$ represents $J$ passing the $D_2$ selection. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

$m_{\textrm{T2}}$ distributions in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{bb})$ in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m_{\textrm{eff}} $ in SR-2B2Q-VZ. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

$m_{\textrm{T2}}$ distributions in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of a few representative signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m(J_{bb})$ in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Distribution of $m_{\textrm{eff}} $ in SR-2B2Q-Vh. The post-fit SM background expectation using the background-only fit is shown in a histogram stack. Distributions of relevant signals are overlaid. The bottom panels show the ratio of the observed data to the background prediction. The selection criteria on the variable shown by each plot is removed, while the arrow indicates the cut value to define the region. A few representative signals are overlaid. For the $(\tilde{W},\tilde{B})$ simplified model models, the label $(x,y)$ indicates $(m(\tilde{\chi}_{1}^{\pm}), m(\tilde{\chi}_{1}^{0}))=(x,y)~\text{GeV}$.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1C1-WW. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-WZ.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for $(\tilde{W},\tilde{B})$ simplifiec model as a function of the produced wino mass ($m(\tilde{\chi}_{1}^{\pm}/\tilde{\chi}_{2}^{0})$) and the bino LSP mass ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},\tilde{B})$ simplified models; C1N2-Wh.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Observed limits for various $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z \tilde{\chi}_{1}^{0})$ hypotheses overlaid. The outer and inner bundles correspond to the limits for the $(\tilde{W},\tilde{B})$ and $(\tilde{H},\tilde{B})$ models respectively. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

Exclusion limits for the $(\tilde{H},\tilde{B})$ models shown as a function of the mass of wino/higgsino chargino ($m(\tilde{\chi}_{1}^{\pm})$) and the mass of bino LSP ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed) and observed (solid red) 95% CL exclusion limits for a representative branching ratio $\mathcal{B}(\tilde{\chi}_{2}^{0} \rightarrow Z\tilde{\chi}_{1}^{0})=50$%. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane of the physical electroweakino masses $(m(\tilde{\chi}_{2}^{\pm}),m(\tilde{\chi}_{1}^{0}))$ representing $(m(\tilde{\chi}_{\textrm{heavy}}),m(\tilde{\chi}_{\textrm{light}}))$. The expected limit for the 1$\sigma$ down variation is not shown as no mass points could be excluded.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{W},\tilde{H})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

95% CL exclusion limits for the $(\tilde{H},\tilde{W})$ models. The limits are projected on to a two-dimensional plane as a function of the wino/higgsino mass parameters: $(M_2,\mu)$. For the limits shown on the $(M_2,\mu)$ plane, the excluded regions are indicated by the area inside the contours. The expected limits for the 1$\sigma$ down variation are not shown as no mass points could be excluded. The round excluded area in the top part corresponds to the excluded parameter space in the $(\tilde{W},~\tilde{H})$ model $(M_{2} > |\mu|)$, while the two small areas in at the bottom are that in the $(\tilde{H},~\tilde{W})$ model $(M_{2} < |\mu|)$.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

Expected (dashed) and observed (solid red) 95% CL exclusion limit derived for the $(\tilde{H},\tilde{G})$ model, as a function of the lightest higgsino mass ($m(\tilde{\chi}_{1}^{0})$) and the branching ratio $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{G}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{G}))$. The excluded region is indicated by the area inside the contour.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed line) and observed (solid red line) limits calculated for $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=100$%.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=75$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=75$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=50$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=50$% as no mass point on the plane can be excluded.

95% CL exclusion limits for the $(\tilde{H},\tilde{a})$ model as the function of axino mass ($m(\tilde{a})$) and the lightest higgsino ($m(\tilde{\chi}_{1}^{0})$). Expected (dashed lines) and observed (solid lines) limits with various $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a}) (=1-\mathcal{B}(\tilde{\chi}_{1}^{0} \to h\tilde{a}))$ hypotheses. No expected limit is derived for the case with $\mathcal{B}(\tilde{\chi}_{1}^{0} \to Z\tilde{a})=25$% as no mass point on the plane can be excluded.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{W},\tilde{H})$ model ($\tan\beta=10, \mu>0$). The branching ratios of wino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and wino-like neutralino ($\tilde{\chi}_{3}^{0}$) are shown as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of the lightest higgsino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively. The branchings into the two neutral higgsinos are summed up in the presentation i.e. $\mathcal{B}(\dots \rightarrow \tilde{\chi}_{1,2}^{0}) := \mathcal{B}(\dots \rightarrow \tilde{\chi}_{1}^{0})+\mathcal{B}(\dots \rightarrow \tilde{\chi}_{2}^{0})$.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

The $\tilde{\chi}_{\textrm{heavy}}$ branching ratios in the $(\tilde{H},\tilde{W})$ model ($\tan\beta=10, \mu>0$). The branching ratios of higgsino-like chargino ($\tilde{\chi}_{2}^{\pm}$) and higgsino-like neutralinos ($\tilde{\chi}_{1}^{0}$, $\tilde{\chi}_{3}^{0}$) as the function of the mass of $\tilde{\chi}_{2}^{\pm}$ and that of wino-like neutralino ($\tilde{\chi}_{1}^{0}$), representing $m(\tilde{\chi}_{\textrm{light}})$ and $m(\tilde{\chi}_{\textrm{heavy}})$ respectively.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1C1-WW). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-WZ). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-Wh). The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{H},\tilde{G})$ models. The black numbers represents the expected cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1C1-WW). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-WZ). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{W},~\tilde{B})$-SIM model (C1N2-Wh). The black numbers represents the observed cross-section upper-limits.

Expected (dashed) and observed (solid) 95% CL exclusion limits on $(\tilde{H},\tilde{G})$ models. The black numbers represents the observed cross-section upper-limits.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1C1-WW) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{W},\tilde{B})$ simplified models (C1N2-Wh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-Zh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-4Q-VV, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-2B2Q-VZ, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Signal acceptance of the $(\tilde{H},\tilde{G})$ model by SR-2B2Q-Vh, evaluated using MC simulation. The acceptance is given by the ratio of weighted selected events by the SR to the weighted total generated events including all the $W/Z/h$ decays. The selection is based on generator-level particle information. The efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the $W_{qq}$/$Z_{qq}$-tagging are treated as 100%. Acceptance below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1C1-WW) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-WZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{W},\tilde{B})$ simplified models (C1N2-Wh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-ZZ) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-Zh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) in their most relevant SRs. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-4Q-VV. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-2B2Q-VZ. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Efficiency for $(\tilde{H},\tilde{G})$ in SR-2B2Q-Vh. The efficiency in a given SR is defined by the ratio of weighted events selected based on the generator-level particle information and that based on detector simulation and particle reconstruction. This mainly accounts for the efficiency of lepton reconstruction/identification, $b$-tagging, as well as the $D_2$ and $n_{\textrm{trk}}$ selection in the and $W_{qq}$/$Z_{qq}$-tagging. Efficiency below 0.005\% is rounded to 0.00 in the entry. The spiky efficiency values (e.g. $m(\tilde{\chi}_{1}^{0})=600~\textrm{GeV}$ in SR-4Q-VV) are because of the difference between the truth and reconstructed samples amplified by a statistical fluctuation due to the limited statistics.

Measured differential cross section with associated uncertainties as a function of $N$($b$-jets). Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate. For the meaning of the bins please refer to the figure.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{j1}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>30\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $m_{jj}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $\Delta\phi_{jj}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma}$ in bins of $|y_{\gamma\gamma}|$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $\Delta\phi_{jj}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Observed statistical correlations, evaluated with a bootstrapping technique, between $p_\mathrm{T}^{\gamma\gamma}$, $N_\mathrm{jets}$, $m_{jj}$, $\Delta\phi_{jj}$, and $p_\mathrm{T}^{j1}$

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$.

Post-fit correlation matrix for the differential cross section measured as a function of $N_\mathrm{jets}$.

Post-fit correlation matrix for the differential cross section measured as a function of $N$($b$-jets).

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{j1}$.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>30\ \mathrm{GeV}}=0)$.

Post-fit correlation matrix for the differential cross section measured as a function of $m_{jj}$.

Post-fit correlation matrix for the differential cross section measured as a function of $\Delta\phi_{jj}$.

Post-fit correlation matrix for the differential cross section measured as a function of $\Delta\phi_{jj}$ in the VBF fiducial region.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$ in bins of $|y_{\gamma\gamma}|$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma1}/m_{\gamma\gamma}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma1}/m_{\gamma\gamma}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma2}/m_{\gamma\gamma}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma2}/m_{\gamma\gamma}$.

Measured differential cross section with associated uncertainties as a function of $|y_{\gamma\gamma}|$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $|y_{\gamma\gamma}|$.

Measured differential cross section with associated uncertainties as a function of $m_{\gamma\gamma j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $m_{\gamma\gamma j}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma j}$.

Measured differential cross section with associated uncertainties as a function of $H_{T}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $H_{T}$.

Measured differential cross section with associated uncertainties as a function of $\tau_{C,j1}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $\tau_{C,j1}$.

Measured differential cross section with associated uncertainties as a function of $\sum\tau_{C,j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $\sum\tau_{C,j}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>40\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>40\ \mathrm{GeV}}=0)$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>50\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>50\ \mathrm{GeV}}=0)$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>60\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>60\ \mathrm{GeV}}=0)$.

Measured differential cross section with associated uncertainties as a function of $\pi - |\Delta\phi_{\gamma\gamma,jj}|$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $\pi - |\Delta\phi_{\gamma\gamma,jj}|$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma jj}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma jj}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma}$ in bins of $p_{T}^{\gamma\gamma j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$ in bins of $p_{T}^{\gamma\gamma j}$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma}$ in bins of $\tau_{C,j1}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$ in bins of $\tau_{C,j1}$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $(p_{T}^{\gamma1}-p_{T}^{\gamma2})/m_{\gamma\gamma}$ in bins of $(p_{T}^{\gamma1}+p_{T}^{\gamma2})/m_{\gamma\gamma}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $(p_{T}^{\gamma1}-p_{T}^{\gamma2})/m_{\gamma\gamma}$ in bins of $(p_{T}^{\gamma1}+p_{T}^{\gamma2})/m_{\gamma\gamma}$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $|\eta^*|$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $|\eta^*|$ in the VBF fiducial region.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma jj}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma jj}$ in the VBF fiducial region.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{j1}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{j1}$ in the VBF fiducial region.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{j1}$ in bins of $\Delta\phi_{jj}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{j1}$ in bins of $\Delta\phi_{jj}$ in the VBF fiducial region. The bins are the same as in the corresponding plot of the differential cross section.