Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Distribution of ∆φ(pmiss, pT ) for the 2018 data sets.

Comparison of data and background post-fit distributions in the eν + γ CRs for the combination of 2017 and 2018 data sets. The last bin of the distribution includes the overflow events. The ratios of data to the background predictions are shown in the lower panels, with the uncertainty bands including the combination of all systematic uncertainties.

Comparison of data and background post-fit distributions in the μν +γ CRs for the combination of 2017 and 2018 data sets. The last bin of the distribution includes the overflow events. The ratios of data to the background predictions are shown in the lower panels, with the uncertainty bands including the combination of all systematic uncertainties.

Comparison of data and background post-fit distributions in the ee + γ CRs for the combination of 2017 and 2018 data sets. The last bin of the distribution includes the overflow events. The ratios of data to the background predictions are shown in the lower panels, with the uncertainty bands including the combination of all systematic uncertainties.

Comparison of data and background post-fit distributions in the μμ + γ CRs for the combination of 2017 and 2018 data sets. The last bin of the distribution includes the overflow events. The ratios of data to the background predictions are shown in the lower panels, with the uncertainty bands including the combination of all systematic uncertainties.

Comparison of data and background post-fit distributions for the vertical regions for the combination of 2017 and 2018 data sets.

Comparison of data and background post-fit distributions for the horizontal regions for the combination of 2017 and 2018 data sets.

The ratio of 95% CL upper cross section limits to the theoretical cross section (μ95) for simplified DM models with vector mediators, using the full 2016–2018 data set.

The ratio of 95% CL upper cross section limits to the theoretical cross section (μ95) for simplified DM models with vector mediators, using the full 2016–2018 data set.

The ratio of 95% CL upper cross section limits to the theoretical cross section (μ95) for simplified DM models with vector mediators, using the full 2016–2018 data set.

Relic density data for Vector mediator used in Figure 7.

The ratio of 95% CL upper cross section limits to the theoretical cross section (μ95) for simplified DM models with Axial vector mediators, using the full 2016–2018 data set.

The ratio of 95% CL upper cross section limits to the theoretical cross section (μ95) for simplified DM models with Axial vector mediators, using the full 2016–2018 data set.

The ratio of 95% CL upper cross section limits to the theoretical cross section (μ95) for simplified DM models with Axial vector mediators, using the full 2016–2018 data set.

Relic density data for Axial Vector mediator used in Figure 7.

Relic density data for Axial Vector mediator used in Figure 7.

The 90% CL exclusion limits on the $\chi$--nucleon spin-independent (left) scattering cross sections involving vector operators, respectively, are shown as a function of $m_{\rm DM}$, using the 2016--2018 data set. Simplified model DM parameters $g_q = 0.25$ and $g_{\rm DM} = 1$ are assumed.

The 90% CL data from CDMS lite experiment used in Figure 8.

The 90% CL data from LZ experiment used in Figure 8.

The 90% CL data from Panda experiment used in Figure 8.

The 90% CL data from CRESST experiment used in Figure 8.

The 90% CL data from XENONnT 2023 experiment used in Figure 8.

The 90% CL exclusion limits on the $\chi$--nucleon spin-dependent scattering cross sections involving Axial vector operators are shown as a function of $m_{\rm DM}$, using the 2016--2018 data set. Simplified model DM parameters $g_q = 0.25$ and $g_{\rm DM} = 1$ are assumed.

The 90% CL data from PICO experiment used in Figure 8 (right).

The 90% CL data from PICASSO experiment used in Figure 8 (right).

The 90% CL data from Kamiokade experiment used in Figure 8 (right).

The 90% CL data from Icecube experiment used in Figure 8 (right).

Observed and expected 95% CL limits on Suppression Scale as a function of M_{DM}, including ±1σ and ±2σ uncertainty bands.

The 95% \CL\ observed and expected cross-section limits with ±1σ and ±2σ uncertainty bands as a function of $M_D$.

Leading-order theoretical cross-section prediction as a function of $M_D$.

The 95% \CL\ upper limits on the ADD graviton production cross section as a function of \mD, for $n=3$ extra dimensions.

Acceptance × Efficiency for ADD models with varying fundamental Planck scale (M_D) and number of extra dimensions (d). This auxiliary material is not shown in any figure or table but is provided for reproducibility and reinterpretation purposes.

Acceptance × Efficiency for DM-EW models with varying dark matter mass.

Acceptance × Efficiency for DM simplified model for vector mediator.

Acceptance × Efficiency for DM simplified model for Axial vector mediator.

Cut flow for some signal points.

Figure 6. Postfit $m_{j\gamma}$ spectra in the SRZ2. The lower panel shows the pull distributions with respect to the best-fit function. The signals with the largest local significances are shown normalized to the observed cross section upper limits.

Figure 7. The 95% CL upper limits on the product of production cross section and branching fraction $\sigma\mathcal{B}$ for $Z'\rightarrow H\gamma$ 0.01% width. Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits. The red line represents the theory benchmark model used for $Z'$ signal simulation.

Figure 7. The 95% CL upper limits on the product of production cross section and branching fraction $\sigma\mathcal{B}$ for $S\rightarrow Z\gamma$ 0.014% width. Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits

Figure 7. The 95% CL upper limits on the product of production cross section and branching fraction $\sigma\mathcal{B}$ for $S\rightarrow Z\gamma$ 5.6% width. Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits

Figure 7. The 95% CL upper limits on the product of production cross section and branching fraction $\sigma\mathcal{B}$ for $S\rightarrow Z\gamma$ 10% width. Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits

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

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

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

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

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

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

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

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

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

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

Observed (black markers) and expected distributions of number of jets after event selection. The hatched (grey) areas denote the systematic (total) uncertainties in the expected yields. Events from all data-taking periods and all channels are combined. The lower panel of each plot shows the ratio between observed and expected distributions. The last bin includes all events above the plotted range. The entry Background corresponds to the sum of all the SM predictions.

Observed (black markers) and expected distributions of number of b tagged jets after event selection. The hatched (grey) areas denote the systematic (total) uncertainties in the expected yields. Events from all data-taking periods and all channels are combined. The lower panel of each plot shows the ratio between observed and expected distributions. The last bin includes all events above the plotted range. The entry Background corresponds to the sum of all the SM predictions.

Difference between $p_{\mathrm{T,gen.}}^\text{miss}$ and $p_{\mathrm{T,rec.}}^\text{miss}$ as a function of the $p_{\mathrm{T,gen.}}^\text{miss}$ for signal events. The mean difference between the generated and the $p_{\mathrm{T,rec.}}^\text{miss}$ per bin is shown as solid line. The results for $p_{\mathrm{T}}^\text{miss}$ corrected by the DNN regression (light blue), derived with the PUPPI algorithm (red), and the PF algorithm (orange) are shown. A simulated sample for the 2018 data-taking period is used.

Difference between $p_{\mathrm{T,gen.}}^\text{miss}$ and $p_{\mathrm{T,rec.}}^\text{miss}$ as a function of the $p_{\mathrm{T,gen.}}^\text{miss}$ for signal events. The dashed line shows the standard deviation $\sigma$, which corresponds to the resolution of the $p_{\mathrm{T,gen.}}^\text{miss}$ and $p_{\mathrm{T,rec.}}^\text{miss}$ difference. The results for $p_{\mathrm{T}}^\text{miss}$ corrected by the DNN regression (light blue), derived with the PUPPI algorithm (red), and the PF algorithm (orange) are shown. A simulated sample for the 2018 data-taking period is used.

Difference between $p_{\mathrm{T,gen.}}^\text{miss}$ and $p_{\mathrm{T,rec.}}^\text{miss}$ as a function of the number of primary vertices for signal events. The mean difference between the generated and the $p_{\mathrm{T,rec.}}^\text{miss}$ per bin is shown as solid line. The results for $p_{\mathrm{T}}^\text{miss}$ corrected by the DNN regression (light blue), derived with the PUPPI algorithm (red), and the PF algorithm (orange) are shown. A simulated sample for the 2018 data-taking period is used.

Difference between $p_{\mathrm{T,gen.}}^\text{miss}$ and $p_{\mathrm{T,rec.}}^\text{miss}$ as a function of the $p_{\mathrm{T,gen.}}^\text{miss}$ for signal events. The dashed line shows the standard deviation $\sigma$, which corresponds to the resolution of the $p_{\mathrm{T,gen.}}^\text{miss}$ and $p_{\mathrm{T,rec.}}^\text{miss}$ difference. The results for $p_{\mathrm{T}}^\text{miss}$ corrected by the DNN regression (light blue), derived with the PUPPI algorithm (red), and the PF algorithm (orange) are shown. A simulated sample for the 2018 data-taking period is used.

Results of the $\chi^{2}$ tests for the absolute and normalized differential cross section measurements for each of the predictions. The $\chi^{2}$ values including uncertainties on the predictions are given in parentheses in the table in the paper and in a separate column here.

The p-value of the $\chi^{2}$ tests for the absolute and normalized cross section measurements for each of the predictions. The resulting p-values including uncertainties on the predictions are given in parentheses in the table in the paper and in a separate column here.

Breakdown of the relative contribution from experimental uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from theory uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from experimental uncertainties in the differential cross section measurement as a function of $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from theory uncertainties in the differential cross section measurement as a function of $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from experimental uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0<$\phi$<0.56. Each group of four bins corresponds to $p_{\text{T}}^{\nu\nu}$ bins, and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ bin edges are indicated by vertical solid lines. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from experimental uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.56<$\phi$<1.08. Each group of four bins corresponds to $p_{\text{T}}^{\nu\nu}$ bins, and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ bin edges are indicated by vertical solid lines. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from experimental uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 1.08<$\phi$<3.14. Each group of four bins corresponds to $p_{\text{T}}^{\nu\nu}$ bins, and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ bin edges are indicated by vertical solid lines. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from theory uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0<$\phi$<0.56. Each group of four bins corresponds to $p_{\text{T}}^{\nu\nu}$ bins, and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ bin edges are indicated by vertical solid lines. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from theory uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.56<$\phi$<1.08. Each group of four bins corresponds to $p_{\text{T}}^{\nu\nu}$ bins, and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ bin edges are indicated by vertical solid lines. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

Breakdown of the relative contribution from theory uncertainties in the differential cross section measurement as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 1.08<$\phi$<3.14. Each group of four bins corresponds to $p_{\text{T}}^{\nu\nu}$ bins, and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ bin edges are indicated by vertical solid lines. The statistical uncertainty (dark grey) takes the size of the available simulated samples and the recorded data into account. The last bin includes all events above the plotted range.

The observed (black markers) and simulated distributions of $p_{\mathrm{T,DNN}}^\text{miss}$ is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated distributions of $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated two-dimensional distribution of $p_{\mathrm{T,DNN}}^\text{miss}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0<$\phi$<0.28 is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated two-dimensional distribution of $p_{\mathrm{T,DNN}}^\text{miss}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.28<$\phi$<0.56 is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated two-dimensional distribution of $p_{\mathrm{T,DNN}}^\text{miss}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.56<$\phi$<0.82 is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated two-dimensional distribution of $p_{\mathrm{T,DNN}}^\text{miss}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.82<$\phi$<1.08 is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated two-dimensional distribution of $p_{\mathrm{T,DNN}}^\text{miss}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 1.08<$\phi$<2.14 is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

The observed (black markers) and simulated two-dimensional distribution of $p_{\mathrm{T,DNN}}^\text{miss}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T, DNN}}^{\text{miss}},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 2.14<$\phi$<3.2 is shown. Events from all data-taking periods and all channels are combined. The hatched (grey) areas indicate the systematic (total) uncertainty in the simulation. The lower panel of each plot shows the ratio of observed data to the expectation from MC simulation in each bin. The last bin includes all events above the plotted range.

Result of the closure test based on simulation accounting for potential BSM contributions based on a top squark pair production scenario with a stop mass of 525 GeV and a neutralino mass of 350 GeV. The potential BSM contribution is scaled by a factor of ten. The test is performed for the $p_{\text{T}}^{\nu\nu}$ using the nominal (black), the regularized (orange), and the bin-by-bin unfolding (purple), based on all data-taking periods combined. The unfolded distributions are compared to the expected distribution (red) based on the sum of the nominal dileptonic $t\bar{t}$ signal sample and BSM signal with the corresponding $\chi^2/\text{ndf}$ values given in the legend in square brackets. The distribution used for the response matrix, is shown in blue. The error bars correspond to the statistical uncertainty. The lower part shows the ratios between the unfolded and the expected distributions.

Result of the closure test based on simulation. The potential BSM contribution is scaled by a factor of ten. The test is performed for the 2D measurement of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ in the azimuthal angle range 0<$\phi$<0.56 using the nominal (black), the regularized (orange), and the bin-by-bin unfolding (purple), based on all data-taking periods combined. The unfolded distributions are compared to the expected distribution (red) based on the sum of the nominal dileptonic $t\bar{t}$ signal sample and BSM signal with the corresponding $\chi^2/\text{ndf}$ values given in the legend. The nominal distribution, used for the response matrix, is shown in blue. The error bars correspond to the statistical uncertainty. The lower part shows the ratios between the unfolded and the expected distributions.

Result of the closure test based on simulation. The potential BSM contribution is scaled by a factor of ten. The test is performed for the 2D measurement of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ in the azimuthal angle range 0.56<$\phi$<1.08 using the nominal (black), the regularized (orange), and the bin-by-bin unfolding (purple), based on all data-taking periods combined. The unfolded distributions are compared to the expected distribution (red) based on the sum of the nominal dileptonic $t\bar{t}$ signal sample and BSM signal with the corresponding $\chi^2/\text{ndf}$ values given in the legend. The nominal distribution, used for the response matrix, is shown in blue. The error bars correspond to the statistical uncertainty. The lower part shows the ratios between the unfolded and the expected distributions.

Result of the closure test based on simulation. The potential BSM contribution is scaled by a factor of ten. The test is performed for the 2D measurement of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ in the azimuthal angle range 1.08<$\phi$<3.14 using the nominal (black), the regularized (orange), and the bin-by-bin unfolding (purple), based on all data-taking periods combined. The unfolded distributions are compared to the expected distribution (red) based on the sum of the nominal dileptonic $t\bar{t}$ signal sample and BSM signal with the corresponding $\chi^2/\text{ndf}$ values given in the legend. The nominal distribution, used for the response matrix, is shown in blue. The error bars correspond to the statistical uncertainty. The lower part shows the ratios between the unfolded and the expected distributions.

The measured differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The measured differential signal cross section (black markers) as a function of $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The measured two-dimensional differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0<$\phi$<0.56 is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The measured two-dimensional differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.56<$\phi$<1.08 is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The measured two-dimensional differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 1.08<$\phi$<3.14 is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The normalized measured differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The normalized measured differential signal cross section (black markers) as a function of $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The normalized measured two-dimensional differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0<$\phi$<0.56 is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The normalized measured two-dimensional differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 0.56<$\phi$<1.08 is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

The normalized measured two-dimensional differential signal cross section (black markers) as a function of $p_{\text{T}}^{\nu\nu}$ and $\text{min}[\Delta\phi(\vec{p}_{\text{T}}^{\nu\nu},\vec{p}_{\text{T}}^{\ell})]$ for the azimuthal angle range 1.08<$\phi$<3.14 is shown. The theoretical predictions from POWHEG+PYTHIA (dark red), POWHEG+HERWIG (orange), MC@NLO+PYTHIA (purple), and the fixed-order NLO (light blue) and NNLO (brown) calculations are compared to the measurement. The total (statistical) uncertainty in the measurement is shown as an orange (dark grey) band. The lower panel of each plot shows the ratio between theoretical predictions and the measurement.

Signal strength and significance for $\text{t}\overline{\text{t}}\text{Z}(\text{Z}\to\text{b}\overline{\text{b}})$ decays with respect to the standard model expectation of unity.

Signal strength and significance for $\text{t}\overline{\text{t}}\text{Z}(\text{Z}\to\text{c}\overline{\text{c}})$ decays with respect to the standard model expectation of unity.

2D likelihood scans for $\kappa_{b}$ and $\kappa_{c}$ for $\text{t}\overline{\text{t}}\text{H}(\text{H}\to\text{c}\overline{\text{c}})$ and $\text{t}\overline{\text{t}}\text{H}(\text{H}\to\text{b}\overline{\text{b}})$.

The $m_{jj}$ and $m_{J}$ projections for the number of observed events (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the CR. Pass and Fail categories are shown. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as $(\text{observed events}{-}\text{expected events})/\sqrt{\smash[b]{\sigma_\text{obs}^{2} + \sigma_\text{exp}^{2}}}$, where $\sigma_\text{obs}$ and $\sigma_\text{exp}$ are the total uncertainties in the observation and the background estimation, respectively.

The $m_{jj}$ and $m_{J}$ projections for the number of observed events (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass and Fail categories are shown. The expected contribution from the signal benchmark with $M_{X} = 2200\,\mathrm{GeV}$ and $M_{Y} = 250\,\mathrm{GeV}$ is overlaid in the MR Pass, assuming a production cross section of 5 fb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate.

The $m_{jj}$ and $m_{J}$ projections for the number of observed events (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass and Fail categories are shown. The expected contribution from the signal benchmark with $M_{X} = 2200\,\mathrm{GeV}$ and $M_{Y} = 250\,\mathrm{GeV}$ is overlaid in the MR Pass, assuming a production cross section of 5 fb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate.

The $m_{jj}$ and $m_{J}$ projections for the number of observed events (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass and Fail categories are shown.

The $m_{jj}$ and $m_{J}$ projections for the number of observed events (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass and Fail categories are shown.

The expected and observed 95% confidence level upper limits on the cross section $\sigma(\mathrm{pp} \to \mathrm{X} \to (\mathrm{H} \to b\bar{b})(\mathrm{Y} \to WW \to 4q))$ are shown for different values of $M_X$ and $M_Y$. The limits have been evaluated in discrete steps corresponding to the centers of the boxes. The numbers in the boxes are given in fb. In addition to the observed and median expected limits, the expected limits at $\pm1\sigma$ and $\pm2\sigma$ quantiles are also provided. These represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis.

The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $M_{X}$. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis.

The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $M_{X}$. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis.

The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $M_{X}$. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis.

The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $M_{X}$. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis.

Selection efficiency as a function of MX and MY

Selection efficiency as a function of MX for fixed MY

Selection efficiency as a function of MX for fixed MY

Selection efficiency as a function of MX for fixed MY

The d$^{2}\sigma$/dydp$_{\mathrm{T}}$ production cross section of D$^{0}$ for $8 < p_{\mathrm{T}} < 12$ GeV in ultraperipheral PbPb collisions.

Measured fiducial cross section of the VBF production mode.

Correlation matrix for fit configuration 1

Correlation matrix for fit configuration 2

Correlation matrix for fit configuration 3