The $pK\pi$ invariant mass distributions (Counts per 10 MeV/c^2 bin) for wrong-sign combinations, scaled by 1/3, in Au+Au collisions at $\sqrt{s_{\rm NN}}$ = 200\,GeV for 10--80\% centrality class.

The $\Lambda_c^{\pm}$ invariant yields measured in the 10-80% centrality class for the different $p_T$ bins, in Au+Au collisions at √sNN = 200 GeV. The $p_T$ bins are 2.5 < $p_T$ < 3.5 GeV/c, 3.5 < $p_T$ < 5.0 GeV/c and 5.0 < $p_T$ < 8.0 GeV/c.

The $\Lambda_c/D^0$ yield ratio measured in the 10-80% centrality class for the different $p_T$ bins, in Au+Au collisions at √sNN = 200 GeV. The $p_T$ bins are 2.5 < $p_T$ < 3.5 GeV/c, 3.5 < $p_T$ < 5.0 GeV/c and 5.0 < $p_T$ < 8.0 GeV/c.

The $\Lambda_c/D^0$ yield ratio measured in 3.0 < $p_T$ < 6.0 GeV/c for different centrality classes, in Au+Au collisions at √sNN = 200 GeV. The centrality bins are 50-80%, 20-50% and 0-20%.

$\eta$ distributions of fake tracks in minimum-bias collision events and chargino tracks in 3 TeV wino signal events. The normalization is arbitrary. The details of fake track reconstruction is described in Sect. 5.2. The fake-track distribution is shown only within the range $|\eta|<2$.

Probability of finding a fake track in an event as a function of the average number of $pp$ interactions per bunch crossing ($\left<\mu\right>$). The probabilities for the tracks reconstructed with $N_{\mathrm{layer}}^{\mathrm{hit}}=4$ (5) are shown by the open (filled) markers, and they are presented separately for the three tracker layouts, the default layout ($\#$1) in black circles, the alternative layouts of $\#$2 in magenta squares and $\#$3 in red upward triangles. The probabilities for the layout $\#$2 are shown only at $\left<\mu\right>=200$. The estimate from the mixed sample of non-diffractive and diffractive events for the default layout is shown by the blue diamond for comparison purpose.

Expected discovery significance for the wino model with 30 ab$^{-1}$ with the requirement of $N_{\mathrm{layer}}^{\mathrm{hit}}=5$. The grey (red) bands show the significance using the default (alternative) layout $\#$1 ($\#$3). The difference between the solid and hatched bands corresponds to the different pileup conditions of $\left<\mu\right>=200$ and 500. The band width corresponds to the significance variation due to the two models assumed for soft QCD processes.

Expected discovery significance for the higgsino model with 30 ab$^{-1}$ with the requirement of $N_{\mathrm{layer}}^{\mathrm{hit}}=5$. The grey (red) bands show the significance using the default (alternative) layout $\#$1 ($\#$3). The difference between the solid and hatched bands corresponds to the different pileup conditions of $\left<\mu\right>=200$ and 500. The band width corresponds to the significance variation due to the two models assumed for soft QCD processes.

Expected discovery significance for the wino model with 30 ab$^{-1}$ with the requirements of $N_{\mathrm{layer}}^{\mathrm{hit}}=5$ and a good time-fit quality. The background reduction rate with the time information is assumed to be the same for both pileup conditions. The grey (red) bands show the significance using the default (alternative) layout $\#$1 ($\#$3). The difference between the solid and hatched bands corresponds to the different pileup conditions of $\left<\mu\right>=200$ and 500. The band width corresponds to the significance variation due to the two models assumed for soft QCD processes.

Expected discovery significance for the higgsino model with 30 ab$^{-1}$ with the requirements of $N_{\mathrm{layer}}^{\mathrm{hit}}=5$ and a good time-fit quality. The background reduction rate with the time information is assumed to be the same for both pileup conditions. The grey (red) bands show the significance using the default (alternative) layout $\#$1 ($\#$3). The difference between the solid and hatched bands corresponds to the different pileup conditions of $\left<\mu\right>=200$ and 500. The band width corresponds to the significance variation due to the two models assumed for soft QCD processes.

Summary of reach for charginos using a disappearing-track signature. The FCC-hh 5$\sigma$-discovery reach assuming the alternative layout, the average number of $pp$ interactions per bunch crossing of 500, only non-diffractive soft-QCD processes and a use of the time information of the pixel detector is shown in blue. The reach for the wino scenario is obtained by extrapolating the sensitivity curve in Fig. 6. The 5$\sigma$-discovery reach and the 95% CL exclusion sensitivity are taken from [46]. The observed exclusion in the ATLAS search with 36 fb$^{-1}$ of Run 2 data [39, 47] are shown as well.

Radial distances (in mm) of the first five layers from the beam line for the three inner-tracker layouts considered in this study.

Signal acceptance for the 3 TeV wino and 1 TeV higgsino with $|\eta|<1$ for the three inner-tracker layouts. The acceptances are provided separately for the requirements of $N_{\mathrm{layer}}^{\mathrm{hit}}=4$ and 5. The alternative layout $\#$3 has significantly higher acceptance than the others because the relevant layers are located closer to the beam line, particularly for the higgsino case with $N_{\mathrm{layer}}^{\mathrm{hit}}=5$.

Signal and background yields as well as the discovery significance for the 3 TeV wino and 1 TeV higgsino with 30 ab$^{-1}$ with the requirements of $N_{\mathrm{layer}}^{\mathrm{hit}}=5$ under the pileup condition of $\left<\mu\right>=200$. The fake-track background rate is assumed to be same for both alternative layouts. The kinematic selection requirements on the leading jet $p_{T}$ and $E_{\text{T}}^{\text{miss}}$ are also given.

Signal and background yields as well as the discovery significance for the 3 TeV wino and 1 TeV higgsino with 30 ab$^{-1}$ with the requirements of $N_{\mathrm{layer}}^{\mathrm{hit}}=5$ under the pileup condition of $\left<\mu\right>=500$. The fake-track background rate is assumed to be same for both alternative layouts. The kinematic selection requirements on the leading jet $p_{T}$ and $E_{\text{T}}^{\text{miss}}$ are also given.

Observed and expected 95% confidence-level limits on sigma x BR as a function of mZ`.

Cutflow Table for 1 TeV, 2 TeV, 4 TeV MC signal samples and Data. Cut0: $\mathcal{L} \times \sigma \times B(Z`\to H\gamma)\times B(H \to b\bar{b})$; Cut1: Preselection; Cut2: Photon requirement; Cut3: Large-$R$ jet requirement; Cut4: Large-$R$ jet mass optimization; Cut5: Double $b$-tagged; Cut5.1: $p_T$ optimization(Double $b$-tagged); Cut6: Single $b$-tagged; Cut6.1: $p_T$ optimization(Single $b$-tagged).

The centrality dependence of e+e− invariant mass spectra within the STAR acceptance from Au+Au collisions and U+U collisions for pair pT < 0.15 GeV/c. The vertical bars on data points depict the statistical uncertainties, while the systematic uncertainties are shown as gray boxes. The hadronic cocktail yields from U+U collisions are ∼5%–12% higher than those from Au+Au collisions in given centrality bins; thus only cocktails for Au+Au collisions are shown here as solid lines, with shaded bands representing the systematic uncertainties for clarity.

The centrality dependence of e+e− invariant mass spectra within the STAR acceptance from Au+Au collisions and U+U collisions for pair pT < 0.15 GeV/c. The vertical bars on data points depict the statistical uncertainties, while the systematic uncertainties are shown as gray boxes. The hadronic cocktail yields from U+U collisions are ∼5%–12% higher than those from Au+Au collisions in given centrality bins; thus only cocktails for Au+Au collisions are shown here as solid lines, with shaded bands representing the systematic uncertainties for clarity.

The centrality dependence of e+e− invariant mass spectra within the STAR acceptance from Au+Au collisions and U+U collisions for pair pT < 0.15 GeV/c. The vertical bars on data points depict the statistical uncertainties, while the systematic uncertainties are shown as gray boxes. The hadronic cocktail yields from U+U collisions are ∼5%–12% higher than those from Au+Au collisions in given centrality bins; thus only cocktails for Au+Au collisions are shown here as solid lines, with shaded bands representing the systematic uncertainties for clarity.

The corresponding ratios of data over cocktail for Figure 1.

The corresponding ratios of data over cocktail for Figure 1.

The corresponding ratios of data over cocktail for Figure 1.

The corresponding ratios of data over cocktail for Figure 1.

The corresponding ratios of data over cocktail for Figure 1.

The corresponding ratios of data over cocktail for Figure 1.

The e+e− pair pT distributions within the STAR acceptance for different mass regions in 60%–80% Au+Au and U+U collisions compared to cocktails. The systematic uncertainties of the data are shown as gray boxes. The gray bands depict the systematic uncertainties of the cocktails.

The e+e− pair pT distributions within the STAR acceptance for different mass regions in 60%–80% Au+Au and U+U collisions compared to cocktails. The systematic uncertainties of the data are shown as gray boxes. The gray bands depict the systematic uncertainties of the cocktails.

The e+e− pair pT distributions within the STAR acceptance for different mass regions in 60%–80% Au+Au and U+U collisions compared to cocktails. The systematic uncertainties of the data are shown as gray boxes. The gray bands depict the systematic uncertainties of the cocktails.

The e+e− pair pT distributions within the STAR acceptance for different mass regions in 60%–80% Au+Au and U+U collisions compared to cocktails. The systematic uncertainties of the data are shown as gray boxes. The gray bands depict the systematic uncertainties of the cocktails.

The e+e− pair pT distributions within the STAR acceptance for different mass regions in 60%–80% Au+Au and U+U collisions compared to cocktails. The systematic uncertainties of the data are shown as gray boxes. The gray bands depict the systematic uncertainties of the cocktails.

The e+e− pair pT distributions within the STAR acceptance for different mass regions in 60%–80% Au+Au and U+U collisions compared to cocktails. The systematic uncertainties of the data are shown as gray boxes. The gray bands depict the systematic uncertainties of the cocktails.

The low-pT (pT < 0.15 GeV/c) e+e− excess mass spectra (data − cocktail) within the STAR acceptance in 60%–80% for Au + Au and U + U collisions, compared with a broadened rho meson model calculation [8]. The contributions of rho and phi from the photonuclear process are shown, as are the contributions of photon-photon process from two models [33,34]. The model calculations are for Au + Au collisions in the corresponding centrality bins.

The low-pT (pT < 0.15 GeV/c) e+e− excess mass spectra (data − cocktail) within the STAR acceptance in 60%–80% for Au + Au and U + U collisions, compared with a broadened rho meson model calculation [8]. The contributions of rho and phi from the photonuclear process are shown, as are the contributions of photon-photon process from two models [33,34]. The model calculations are for Au + Au collisions in the corresponding centrality bins.

The low-pT (pT < 0.15 GeV/c) e+e− excess mass spectra (data − cocktail) within the STAR acceptance in 40%–60% for Au + Au and U + U collisions, compared with a broadened rho meson model calculation [8]. The contributions of rho and phi from the photonuclear process are shown, as are the contributions of photon-photon process from two models [33,34]. The model calculations are for Au + Au collisions in the corresponding centrality bins.

The low-pT (pT < 0.15 GeV/c) e+e− excess mass spectra (data − cocktail) within the STAR acceptance in 40%–60% for Au + Au and U + U collisions, compared with a broadened rho meson model calculation [8]. The contributions of rho and phi from the photonuclear process are shown, as are the contributions of photon-photon process from two models [33,34]. The model calculations are for Au + Au collisions in the corresponding centrality bins.

The centrality dependence of integrated excess yields in the mass regions of 0.4–0.76, 0.76–1.2, and 1.2–2.6 GeV=c2 in Au + Au and U + U collisions. The centrality dependence of hadronic cocktail yields in the mass region of 0.76–1.2 GeV/c2 in both collisions is also shown for comparison. The systematic uncertainties are shown as gray boxes.

The centrality dependence of integrated excess yields in the mass regions of 0.4–0.76, 0.76–1.2, and 1.2–2.6 GeV=c2 in Au + Au and U + U collisions. The centrality dependence of hadronic cocktail yields in the mass region of 0.76–1.2 GeV/c2 in both collisions is also shown for comparison. The systematic uncertainties are shown as gray boxes.

The centrality dependence of integrated excess yields in the mass regions of 0.4–0.76, 0.76–1.2, and 1.2–2.6 GeV=c2 in Au + Au and U + U collisions. The centrality dependence of hadronic cocktail yields in the mass region of 0.76–1.2 GeV/c2 in both collisions is also shown for comparison. The systematic uncertainties are shown as gray boxes.

The centrality dependence of integrated excess yields in the mass regions of 0.4–0.76, 0.76–1.2, and 1.2–2.6 GeV=c2 in Au + Au and U + U collisions. The centrality dependence of hadronic cocktail yields in the mass region of 0.76–1.2 GeV/c2 in both collisions is also shown for comparison. The systematic uncertainties are shown as gray boxes.

The centrality dependence of integrated excess yields in the mass regions of 0.4–0.76, 0.76–1.2, and 1.2–2.6 GeV=c2 in Au + Au and U + U collisions. The centrality dependence of hadronic cocktail yields in the mass region of 0.76–1.2 GeV/c2 in both collisions is also shown for comparison. The systematic uncertainties are shown as gray boxes.

The centrality dependence of integrated excess yields in the mass regions of 0.4–0.76, 0.76–1.2, and 1.2–2.6 GeV/c2 in Au + Au and U + U collisions. The centrality dependence of hadronic cocktail yields in the mass region of 0.76–1.2 GeV/c2 in both collisions is also shown for comparison. The systematic uncertainties are shown as gray boxes.

The p2T distributions of excess yields within the STAR acceptance in the mass regions of 0.4–0.76 GeV/c2 in 60%–80% Au + Au and U + U collisions. The systematic uncertainties are shown as gray boxes. The solid and dotted lines are exponential fits to the data in Au + Au and U + U collisions, respectively. The dot-dashed and dot-dot- dashed lines represent the p2T distributions for the photon-photon process from two models [33,34] within the STAR acceptance in 60%–80% Au + Au collisions. The dashed lines illustrate the corresponding p2T distributions for e+e− pairs from the model [33] traversing 1 fm in a constant magnetic field of 10^14 T perpendicular to the beam line.

The p2T distributions of excess yields within the STAR acceptance in the mass regions of 0.4–0.76 GeV/c2 in 60%–80% Au + Au and U + U collisions. The systematic uncertainties are shown as gray boxes. The solid and dotted lines are exponential fits to the data in Au + Au and U + U collisions, respectively. The dot-dashed and dot-dot- dashed lines represent the p2T distributions for the photon-photon process from two models [33,34] within the STAR acceptance in 60%–80% Au + Au collisions. The dashed lines illustrate the corresponding p2T distributions for e+e− pairs from the model [33] traversing 1 fm in a constant magnetic field of 10^14 T perpendicular to the beam line.

The p2T distributions of excess yields within the STAR acceptance in the mass regions of 0.76–1.2 GeV/c2 in 60%–80% Au + Au and U + U collisions. The systematic uncertainties are shown as gray boxes. The solid and dotted lines are exponential fits to the data in Au + Au and U + U collisions, respectively. The dot-dashed and dot-dot- dashed lines represent the p2T distributions for the photon-photon process from two models [33,34] within the STAR acceptance in 60%–80% Au + Au collisions. The dashed lines illustrate the corresponding p2T distributions for e+e− pairs from the model [33] traversing 1 fm in a constant magnetic field of 10^14 T perpendicular to the beam line.

The p2T distributions of excess yields within the STAR acceptance in the mass regions of 0.76–1.2 GeV/c2 in 60%–80% Au + Au and U + U collisions. The systematic uncertainties are shown as gray boxes. The solid and dotted lines are exponential fits to the data in Au + Au and U + U collisions, respectively. The dot-dashed and dot-dot- dashed lines represent the p2T distributions for the photon-photon process from two models [33,34] within the STAR acceptance in 60%–80% Au + Au collisions. The dashed lines illustrate the corresponding p2T distributions for e+e− pairs from the model [33] traversing 1 fm in a constant magnetic field of 10^14 T perpendicular to the beam line.

The p2T distributions of excess yields within the STAR acceptance in the mass regions of 1.2–2.6 GeV/c2 in 60%–80% Au + Au and U + U collisions. The systematic uncertainties are shown as gray boxes. The solid and dotted lines are exponential fits to the data in Au + Au and U + U collisions, respectively. The dot-dashed and dot-dot- dashed lines represent the p2T distributions for the photon-photon process from two models [33,34] within the STAR acceptance in 60%–80% Au + Au collisions. The dashed lines illustrate the corresponding p2T distributions for e+e− pairs from the model [33] traversing 1 fm in a constant magnetic field of 10^14 T perpendicular to the beam line.

The p2T distributions of excess yields within the STAR acceptance in the mass regions of 1.2–2.6 GeV/c2 in 60%–80% Au + Au and U + U collisions. The systematic uncertainties are shown as gray boxes. The solid and dotted lines are exponential fits to the data in Au + Au and U + U collisions, respectively. The dot-dashed and dot-dot- dashed lines represent the p2T distributions for the photon-photon process from two models [33,34] within the STAR acceptance in 60%–80% Au + Au collisions. The dashed lines illustrate the corresponding p2T distributions for e+e− pairs from the model [33] traversing 1 fm in a constant magnetic field of 10^14 T perpendicular to the beam line.

The corresponding sqrt(p2T) of excess yields. The vertical bars on data points are the combined statistical and systematic uncertainties.

The corresponding sqrt(p2T) of excess yields. The vertical bars on data points are the combined statistical and systematic uncertainties.

The observed upper limits on the model cross-section in units of pb for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production. Three points at $M({\tilde{\chi}}^{0}_{1})=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.

The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.

Distributions in SR-low of the data and post-fit background prediction for H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for m_eff^3&#8467;/H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for m_eff^3&#8467;/H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for p_T^soft/(p_T^soft + m_eff^3&#8467;). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for p_T^soft/(p_T^soft + m_eff^3&#8467;). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for m_T. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for m_T. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for R(E_T^miss,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for R(E_T^miss,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^soft. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^soft. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^jets. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^jets. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.

Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.

Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.

Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.

Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.

Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.

Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.

Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

The normalized particle-level $\mathrm{t}\overline{\mathrm{t}}$ differential cross section in the fiducial region as a function of the XCone-jet mass.

The normalized particle-level $\mathrm{t}\overline{\mathrm{t}}$ differential cross section in the fiducial region as a function of the XCone-jet mass.

The covariance matrix containing the statistical uncertainties of Figure 2a is shown.

The covariance matrix containing the statistical uncertainties of Figure 2a is shown.

The covariance matrix containing the experimental uncertainties of Figure 2a is shown.

The covariance matrix containing the experimental uncertainties of Figure 2a is shown.

The covariance matrix containing the model uncertainties of Figure 2a is shown.

The covariance matrix containing the model uncertainties of Figure 2a is shown.

The covariance matrix containing the statistical uncertainties of Figure 2b is shown.

The covariance matrix containing the statistical uncertainties of Figure 2b is shown.

The covariance matrix containing the experimental uncertainties of Figure 2b is shown.

The covariance matrix containing the experimental uncertainties of Figure 2b is shown.

The covariance matrix containing the model uncertainties of Figure 2b is shown.

The covariance matrix containing the model uncertainties of Figure 2b is shown.

Transverse momentum spectra for charged pions at midrapidity.

Transverse momentum spectra for protons at midrapidity.

Transverse momentum spectra for antiprotons at midrapidity.

Transverse momentum spectra for kaons at midrapidity.

dN/dy of pi+ and proton, normalised by N_part/2.

dN/dy of pi+ and proton, normalised by N_part/2.

dN/dy of pi+ and proton, normalised by N_part/2.

dN/dy of K+ and K-, normalised by N_part/2.

dN/dy of K+ and K-, normalised by N_part/2.

dN/dy of K+ and K-, normalised by N_part/2.

Transverse momentum of pi+, K+ and proton.

Transverse momentum of pi+, K+ and proton.

Transverse momentum of pi+, K+ and proton.

K-/K+, K-/pi-, p/pi+ and K+/pi+ vs. N_part.

K-/K+, K-/pi-, p/pi+ and K+/pi+ vs. N_part.

K-/K+, K-/pi-, p/pi+ and K+/pi+ vs. N_part.

dN/d_eta, normalised by N_part/2, as a function of sqrt(S_NN).

dN/dy, normalised by N_part/2; and m_t - m of pi+ and pi- as functions of sqrt(S_NN).

dN/dy, normalised by N_part/2; and m_t - m of K+ and K- as functions of sqrt(S_NN).

pi-/pi+, pbar/p, K-/K+, K+/pi+ and K-/pi- as functions of sqrt(S_NN).

v1 as a function of eta and eta/y-beam from different experiments and methods.

v2 as a function of p_T for different particle species and experiments.

v2 as a function of s_NN from different experiments.

T-mu_B plot for heavy-ion collisions at different s_NN.

$v_{2}$ of $\Upsilon(\mathrm{1S})$ as a function of $p_{\mathrm{T}}$ in three centrality ranges, 10-30%, 30-50% and 50-90%.

$v_{2}$ of $\Upsilon(\mathrm{1S})$ as a function of $p_{\mathrm{T}}$ in 5-60% centrality range.