Upper 95% CL limits on HH signal cross section scanned over the $\kappa_{\lambda}$ parameter while fixing the $\kappa_{2V}$ and $\kappa_{V}$ to their SM-predicted values.

Upper 95% CL limits on VHH signal cross section scanned over the $\kappa_{2V}$ parameter while fixing the $\kappa_{\lambda}$ and $\kappa_{V}$ to their SM-predicted values.

Upper 95% CL limits on HH signal cross section scanned over the $\kappa_{2V}$ parameter while fixing the $\kappa_{\lambda}$ and $\kappa_{V}$ to their SM-predicted values.

Upper 95% CL limits on VHH signal cross section scanned over the $\kappa_{V}$ parameter while fixing the $\kappa_{2V}$ and $\kappa_{\lambda}$ to their SM-predicted values.

Upper 95% CL limits on HH signal cross section scanned over the $\kappa_{V}$ parameter while fixing the $\kappa_{2V}$ and $\kappa_{\lambda}$ to their SM-predicted values.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

The fitted slopes of the E3C/E2C data distributions as a function of jet pt are used to illustrate the dependency of alphas on jet pt.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

The chi2 scan result using eq.3 for different alphas.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The energy weight of E2C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

The energy weight of E3C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

Differential cross sections normalized to the fiducial cross section as a function of the $p_T$ of the second-highest-$p_T$ jet

Differential cross sections normalized to the fiducial cross section as a function of the $|\eta|$ of the highest-$p_T$ jet

Differential cross sections normalized to the fiducial cross section as a function of the $|\eta|$ of the second-highest-$p_T$ jet

Differential cross sections normalized to the fiducial cross section as a function of the pseudorapidity difference between the two highest-$p_T$ jets in the event

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the two highest-$p_T$ jets in the event

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system, in the full $m_{4l}$ range

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with 0 jet, in the on-shell ZZ region

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with 1 jet, in the on-shell ZZ region

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with 2 jets, in the on-shell ZZ region

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with at least 3 jets, in the on-shell ZZ region

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with 0 jet, in the full $m_{4l}$ range

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with 1 jet, in the full $m_{4l}$ range

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with 2 jets, in the full $m_{4l}$ range

Differential cross sections normalized to the fiducial cross section as a function of the invariant mass of the four-lepton system in events with at least 3 jets, in the full $m_{4l}$ range

Expected and observed 95% CL upper limits on $|V_\mathrm{N}|^2$ as a function of $m_\mathrm{N}$ for the mixing scenario ($r_e$, $r_\mu$, $r_\tau$) = (0.33, 0.33, 0.33) and in the Majorana scenario.

Expected and observed 95% CL upper limits on $|V_\mathrm{N}|^2$ as a function of $m_\mathrm{N}$ for the mixing scenario ($r_e$, $r_\mu$, $r_\tau$) = (0.0, 1.0, 0.0) and in the Dirac-like scenario.

Expected and observed 95% CL upper limits on $|V_\mathrm{N}|^2$ as a function of $m_\mathrm{N}$ for the mixing scenario ($r_e$, $r_\mu$, $r_\tau$) = (0.0, 0.5, 0.5) and in the Dirac-like scenario.

Expected and observed 95% CL upper limits on $|V_\mathrm{N}|^2$ as a function of $m_\mathrm{N}$ for the mixing scenario ($r_e$, $r_\mu$, $r_\tau$) = (0.5, 0.5, 0.0) and in the Dirac-like scenario.

Expected and observed 95% CL upper limits on $|V_\mathrm{N}|^2$ as a function of $m_\mathrm{N}$ for the mixing scenario ($r_e$, $r_\mu$, $r_\tau$) = (0.33, 0.33, 0.33) and in the Dirac-like scenario.

Observed 95% CL lower limits on $c\tau_\mathrm{N}$ as functions of the mixing ratios ($r_e$, $r_\mu$, $r_\tau$) for a fixed N mass of 1.0 GeV in the Majorana scenario.

Observed 95% CL lower limits on $c\tau_\mathrm{N}$ as functions of the mixing ratios ($r_e$, $r_\mu$, $r_\tau$) for a fixed N mass of 1.5 GeV in the Majorana scenario.

Observed 95% CL lower limits on $c\tau_\mathrm{N}$ as functions of the mixing ratios ($r_e$, $r_\mu$, $r_\tau$) for a fixed N mass of 2.0 GeV in the Majorana scenario.

Observed 95% CL lower limits on $c\tau_\mathrm{N}$ as functions of the mixing ratios ($r_e$, $r_\mu$, $r_\tau$) for a fixed N mass of 1.0 GeV in the Dirac-like scenario.

Observed 95% CL lower limits on $c\tau_\mathrm{N}$ as functions of the mixing ratios ($r_e$, $r_\mu$, $r_\tau$) for a fixed N mass of 1.5 GeV in the Dirac-like scenario.

Observed 95% CL lower limits on $c\tau_\mathrm{N}$ as functions of the mixing ratios ($r_e$, $r_\mu$, $r_\tau$) for a fixed N mass of 2.0 GeV in the Dirac-like scenario.

Observed profiled likelihood on $f_{\Lambda1}$ using Approach 1. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{a3}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{a3}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on $f_{\Lambda1}^{Z\gamma}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{\Lambda1}^{Z\gamma}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on the $\delta c_{Z}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $\delta c_{Z}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on the $c_{Z\Box}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $c_{Z\Box}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on the $c_{ZZ}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $c_{ZZ}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on the $\tilde{c}_{ZZ}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $\tilde{c}_{ZZ}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on $f_{a3}^{ggH}$. The signal strength modifiers and the CP-odd HVV anomalous coupling cross section fraction are treated as free parameters.

Observed profiled likelihood on $f_{a3}^{ggH}$. The signal strength modifiers and the CP-odd HVV anomalous coupling cross section fraction are treated as free parameters.

Distribution of jet girth of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of uGNN output score of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of aGNN output score of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Template fit of the DeepJet discriminator used to determine the b jet fraction of the non-EMJ tagged jets in data events that pass the 1-EMJ selection of the event selection criteria defined in the paper.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers u-tag 1, u-tag 2, and u-tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers uGNN tag 1, uGNN tag 2, and uGNN tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers u-tag 4 and u-tag 5, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The expected and observed 95% CL upper limits on the production cross section for various signal models in the unflavored scenario model-generic and GNN EMJ tagging methods.

The expected and observed 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario model-generic and GNN EMJ tagging methods.

The observed yield of events in data satisfying the validation selection criteria with at least two jets passing the corresponding validation tag, and the estimation based on the misidentification rate calculated using validation events with exactly one jet passing the validation tagger scaled by the factor given in Equation (4) of the paper.

The estimated number of events from the background prediction based on control samples in data and the observed event yields.

Distribution of event $H_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of second leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of third leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of forth leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of Track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $D_{N}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of jet $\overline{\tau}_{2/1}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of $|\Delta\eta|$ between jet and tracks of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of $|\Delta\phi|$ between jet and tracks of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{z}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $p_\mathrm{T}$ fraction of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers a-tag 1, a-tag 2, a-tag 3, and a-tag 4, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers aGNN tag 1, aGNN tag 2, and aGNN tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

Signal acceptance of signal models in the unflavored scenario with using the designated cut sets with the model agnostic and GNN EJ tagging methods.

Signal acceptance of signal models in the flavor-aligned scenario with using the designated cut sets with the model agnostic and GNN EJ tagging methods.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Distribution of jet-associated tracks of jets in SM multijet processes that pass the basic kinematic selection, the uGNN tag 1 tagging criteria, and aGNN tag 1 EMJ tagging criteria as a function of $\Delta R$ between the jet and track of interest and $\mathrm{sign}(d_{xy})\cdot\ln\left( 1+\left\lvert\frac{d_{xy}}{1\mathrm{cm}}\right\rvert\right)$, with $d_{xy}$ being the transverse impact parameter of the track of interest.

Example cut flow used for selecting signals events using the u-set 4 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 5 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 4 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 5 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 2 selection criteria. Both background and MC events has been normalized to unity.

Distributions of $S_{\mathrm{ML}}$ for data, simulated background and signal events with $n_{\mathrm{track}}$ of 4. The distributions are shown for split-SUSY signals with a gluino mass of 2000 GeV and neutralino mass of 1800 GeV. Different gluino proper decay lengths are shown as $c\tau$ in the legend. All distributions are normalized to unity.

Distributions of $S_{\mathrm{ML}}$ for data, simulated background and signal events with $n_{\mathrm{track}}$ of $\geq$ 5. The distributions are shown for split-SUSY signals with a gluino mass of 2000 GeV and neutralino mass of 1900 GeV. Different gluino proper decay lengths are shown as $c\tau$ in the legend. All distributions are normalized to unity.

Distributions of $S_{\mathrm{ML}}$ for data, simulated background and signal events with $n_{\mathrm{track}}$ of $\geq$ 5. The distributions are shown for split-SUSY signals with a gluino mass of 2000 GeV and neutralino mass of 1800 GeV. Different gluino proper decay lengths are shown as $c\tau$ in the legend. All distributions are normalized to unity.

The distribution of $n_{\mathrm{track}}$ in different $S_{\mathrm{ML}}$ regions for simulated background events. Events with 0 $ < S_{\mathrm{ML}} < $ 0.2 (blue), 0.2 $ < S_{\mathrm{ML}} < $ 0.6 (red), and 0.6 $ < S_{\mathrm{ML}} < $ 1.0 (green) are compared. All distributions are normalized to unity. The similar $n_{\mathrm{track}}$ distributions demonstrate that $n_{\mathrm{track}}$ and $S_{\mathrm{ML}}$ are decorrelated.

The distribution of $d_{\mathrm{BV}}$ in $K_{\mathrm{S}}^{\mathrm{0}}$ vertices in data (black) and simulation (purple). The lower panel shows the ratio between data and simulation.

The vertex reconstruction efficiency for artificially displaced vertices in data (black) and simulation (red). In this example, the artificially displaced vertices are corrected to mimic split-SUSY signal events with gluino mass of 2000 GeV and neutralino mass of 1800 GeV.

The ML tagging efficiency for artificially displaced vertices in data (black) and simulation (red). In this example, the artificially displaced vertices are corrected to mimic split-SUSY signal events with gluino mass of 2000 GeV and neutralino mass of 1800 GeV.

Number of predicted and observed events in the control, validation, and search regions. Predictions are calculated using Eqs. (2) and (3) and fitting the data under the background-only hypothesis. Regions are organized by $S_{\mathrm{ML}}$ and $n_{\mathrm{track}}$ values, and region names corresponding with Fig. 7 are given in parentheses. The predicted number of events that pass the $S_{\mathrm{ML}}$ selection and the observed number of events that pass or fail the $S_{\mathrm{ML}}$ selection are shown in seperate rows.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY signal model with a mass splitting of 100 GeV, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY signal model with a mass splitting of 100 GeV, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY model with a $c\tau$ of 10 mm, shown as a function of gluino mass and mass splitting. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the split-SUSY model with a $c\tau$ of 10 mm, shown as a function of gluino mass and mass splitting. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the GMSB SUSY signal model, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

The 95% CL upper limit on the product of the cross section and branching fraction squared for the GMSB SUSY signal model, shown as a function of gluino mass and $c\tau$. The observed (solid black) and expected (dashed red) exclusion curves are overlaid on the limit plot.

Observed upper limits at 95% CL on B($\text{H} \rightarrow \text{a}_{1}\text{a}_{1}$) in percent, obtained from the combination of the $\mu\mu$bb and $\tau\tau$bb channels. The results are obtained as functions of $m_{\text{a}_{1}}$ for 2HDM+S Type I (independent of tan$\beta$), Type II (tan$\beta$=2.0), Type III (tan$\beta$=2.0), and Type IV (tan$\beta$=0.6), respectively.

Correlation matrix for the differential branching fraction extraction between different $q^2$ bins in the simultaneous fit.

The product of acceptance and efficiency ($A\epsilon$) of the $\mathcal{B}(\mathrm{B}^+\to\mathrm{K}^+\mu^+\mu^-)$ channel, as a function of the muon pair $q^2$, as measured in simulated signal events, after all the corrections applied. In the figure, regions corresponding to resonances are displayed with red markers.

Same as Table 5, but for the bins containing the J$\psi$ and $\psi$(2S) resonances.

Integrated branching fraction $\mathcal{B}(\mathrm{B}^\pm \to \mathrm{K}^\pm\mu^+\mu^-)$ in the low-$q^2$ region.

Ratio of branching fractions $\mathcal{B}(\text{B}^\pm \to \text{K}^\pm\mu^+\mu^-)$ to $\mathcal{B}(\text{B}^\pm \to \text{K}^\pm\text{e}^+\text{e}^-)$, determined from the measured double ratio $R$(K) of these decays to the respective branching fractions of the decay chains $\text{B}^\pm\to\text{J/}\psi\text{K}^\pm$ with $\text{J/}\psi\to\mu^+\mu^-$ and $\text{e}^+\text{e}^-$.

Negative log likelihood function from the fit profiled as a function of the inverse ratio $R(\mathrm{K})^{-1}$.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{1})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}^{\mathrm{add.}}_{2})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{2})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{add.}})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\Delta\mathrm{R}_{\mathrm{b}\mathrm{b}}^{\mathrm{avg}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}_{3})|$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}_{3})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}_{3})$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}_{3})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}_{4})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}_{4})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{b}}_{\mathrm{T}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{b}}_{\mathrm{T}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $|\eta(\mathrm{b}^{\mathrm{extra}}_{1})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{1})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}^{\mathrm{extra}}_{2})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{2})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{extra}})|$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $H^{\mathrm{j}}_{\mathrm{T}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{j}}_{\mathrm{T}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $\mathrm{m}_{\mathrm{b}\mathrm{b}}^{\mathrm{max}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $H^{\mathrm{light}}_{\mathrm{T}}$ in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $H^{\mathrm{light}}_{\mathrm{T}}$ in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space.

Normalized differential cross section of $N_{\mathrm{jets}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Normalized differential cross section of $N_{\mathrm{jets}}$ in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space.

Normalized differential cross section of $N_{\mathrm{b}}$ in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space.

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{add.}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\Delta\mathrm{R}_{\mathrm{b}\mathrm{b}}^{\mathrm{avg}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}_{4})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}_{4})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{extra}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $\mathrm{m}_{\mathrm{b}\mathrm{b}}^{\mathrm{max}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Correlation of parameters of interest in fit of $N_{\mathrm{jets}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $N_{\mathrm{jets}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Correlation of parameters of interest in fit of $N_{\mathrm{b}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{add.}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{add.}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{add.}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{add.}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\Delta\mathrm{R}_{\mathrm{b}\mathrm{b}}^{\mathrm{avg}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}_{3})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}_{3})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}_{4})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}_{4})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{b}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $|\Delta\phi(\mathrm{lj}^{\mathrm{extra}}_{1},\mathrm{b}_{\mathrm{soft}})|$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{1})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}^{\mathrm{extra}}_{2})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}^{\mathrm{extra}}_{2})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $|\eta(\mathrm{b}\mathrm{b}^{\mathrm{extra}})|$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\Delta\mathrm{R}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\mathrm{m}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{b}\mathrm{b}^{\mathrm{extra}})$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $p_{\mathrm{T}}(\mathrm{lj}^{\mathrm{extra}}_{1})$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{j}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $\mathrm{m}_{\mathrm{b}\mathrm{b}}^{\mathrm{max}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 6$ jets: $\geq 3 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $H^{\mathrm{light}}_{\mathrm{T}}$ observable in $\geq 7$ jets: $\geq 4 \mathrm{b}$, $\geq 3$ light phase space

Covariances of all nuisance parameters and POIs in fit of $N_{\mathrm{jets}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $N_{\mathrm{jets}}$ observable in $\geq 6$ jets: $\geq 4 \mathrm{b}$ phase space

Covariances of all nuisance parameters and POIs in fit of $N_{\mathrm{b}}$ observable in $\geq 5$ jets: $\geq 3 \mathrm{b}$ phase space

Fiducial cross sections from the measurements of all observables, compared to predictions from different ttbb simulation approaches. For each of the normalized differential measurements the fiducial cross section in the respective phase space is also determined. In the paper only one representative observable is quoted for each fiducial phase space, while here the measured cross section with the uncertainties from the fit to the respective observable is summarized.

Correlation matrix associated to the statistical covariance matrix of the data and MC for the primary Lund jet plane density for AK8 jets in a one-dimensional representation with bin indices. The mapping between the bin indices and the physical binning can be imported from the XML file attached to this HepData record using the TUnfoldBinningXML class of ROOT (qualitatively, it corresponds to slicing the Lund plane horizontally from low kT to high kT).

Postfit score of the $\text{t}\bar{\text{t}}\text{Z}$ output node from the multiclass classifier in $\text{SR}_\text{3l,3j}$ for events with at least 2 b jets

Postfit score of the tWZ output node from the binary classifier in $\text{SR}_\text{3l,2j}$

$\text{p}_\text{T}$ of the leading jet in the $\text{SR}_\text{3l,2j}$

$\text{p}_\text{T}$ of the leading lepton in the $\text{SR}_\text{3l,2j}$

Max $\text{p}_\text{T}$ of the lepton-jet in the $\text{SR}_\text{3l,3j}$

Invariant mass of the system of leptons, jets, and $\vec{\text{p}}^\text{miss}_\text{T}$ in the $\text{SR}_\text{3l,3j}$

Two-dimensional likelihood scan of the signal strenghts of tWZ ($\mu_\text{tWZ}$) vs. $\text{t}\bar{\text{t}}\text{Z}$ ($\mu_{\text{t}\bar{\text{t}}\text{Z}}$). The blue lines show the 68%, 95%, and 99% confidence level contours. The black cross represents the best-fit value, while the red diamond the SM expectation

Two-dimensional likelihood scan of the signal strenghts of tWZ ($\mu_\text{tWZ}$) vs. $\text{t}\bar{\text{t}}\text{Z}$ ($\mu_{\text{t}\bar{\text{t}}\text{Z}}$). The blue lines show the 68%, 95%, and 99% confidence level contours. The black cross represents the best-fit value, while the red diamond the SM expectation

Observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance. The acceptance $\mathcal{A}$ is defined as $\mathcal{A} = N$(events with $m_{X}^{GEN} > 85\% m_{X}^{input}$) / $N$(events generated in the full phase space defined by the CMS default generator settings). Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Mass exclusion ranges of the benchmark signal scenarios are depicted with hatched areas inside the black contours.

Observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance. The acceptance $\mathcal{A}$ is defined as $\mathcal{A} = N$(events with $m_{X}^{GEN} > 85\% m_{X}^{input}$) / $N$(events generated in the full phase space defined by the CMS default generator settings). Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Mass exclusion ranges of the benchmark signal scenarios are depicted with hatched areas inside the black contours.

Efficiencies of the selection requirements on the benchmark signal processes: $Z_{R} \to ggg$ with nominal width ($\Gamma_{X}/m_{X}\sim 3\%$). A value of -1 means that the corresponding efficiency is not calculated for this year.

Efficiencies of the selection requirements on the benchmark signal processes: $Z_{R} \to ggg$ with narrow width ($\Gamma_{X}/m_{X}\sim 0.01\%$). A value of -1 means that the corresponding efficiency is not calculated for this year.

Efficiencies of the selection requirements on the benchmark signal processes: $G_{KK} \to {\varphi}(gg)g$, where $\varphi$ is the radion. A value of -1 means that the corresponding efficiency is not calculated for this year.

Efficiencies of the selection requirements on the benchmark signal processes: $q^{*} \to V(qq)q$, where $V$ is a beyond-the-SM vector boson. A value of -1 means that the corresponding efficiency is not calculated for this year.

Acceptance of the signal selection requirement $m_{X}^{\text{GEN}}/m_{X}^{\text{input}} > 85\%$ on the benchmark signal process $Z_{R} \to ggg$. The acceptance is defined as $\mathcal{A} = N$(events with $m_{X}^{\text{GEN}}/m_{X}^{\text{input}} > 85\%$)/$N$(events generated in the full phase space defined by the CMS default generator settings).

Acceptance of the signal selection requirement $m_{X}^{\text{GEN}}/m_{X}^{\text{input}} > 85\%$ on the benchmark signal process $G_{KK} \to {\varphi}(gg)g$.

Acceptance of the signal selection requirement $m_{X}^{\text{GEN}}/m_{X}^{\text{input}} > 85\%$ on the benchmark signal process $q^{*} \to V(qq)q$.

Observed local significance for a 3-body decay $ggg$ resonance, shown for resonances with nominal width (blue solid line) and narrow width (red dashed line).

Observed local significance for a cascade decay $ggg$ resonance.

Observed local significance for a cascade decay $qqq$ resonance.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.2$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.3$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.4$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.5$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.6$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.7$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(gg)g) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.8$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance $m_{Y} / m_{X} = 0.2$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.3$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.4$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.5$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.6$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.7$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal.

Expected and observed limits at 95% CL on $\sigma \mathcal{B} (X \to Y(qq)q) \mathcal{A}$ for a cascade decay trijet resonance with $m_{Y} / m_{X} = 0.8$. Only 2016 data are used to derive limits below 2.0 TeV because of higher trigger thresholds in 2017 and 2018. Theoretical predictions of the benchmark are depicted with the red curve. A value of -1 in the table means that the corresponding theoretical prediction is not calculated for this signal

Cut flow table of the selection requirments for the $Z_{R}$ model scenarios. Values shown are absolute efficiencies, e.g., values shown in the column of 'Efficiency ($\Delta_{R}^{max} < 3.0$)' represent the cumulative efficiencies achieved through all selection requirements applied up to and including the current selection criterion.

Cut flow table of the selection requirments for the $G_{KK}$ model scenarios. Values shown are absolute efficiencies, e.g., values shown in the column of 'Efficiency ($\Delta_{R}^{max} < 3.0$)' represent the cumulative efficiencies achieved through all selection requirements applied up to and including the current selection criterion.

Cut flow table of the selection requirments for the $q^{*}$ model scenarios. Values shown are absolute efficiencies, e.g., values shown in the column of 'Efficiency ($\Delta_{R}^{max} < 3.0$)' represent the cumulative efficiencies achieved through all selection requirements applied up to and including the current selection criterion.

Predicted production cross section of the benchmark signal process $pp \to Z_{R} \to ggg$.

Predicted production cross section of the benchmark signal process $pp \to G_{KK} \to \varphi(gg)g$.

Predicted production cross section of the benchmark signal process $pp \to q^{*} \to V(qq)q$.

STEREO Detector Response Matrix for Phase-II and III. The matrix is given as a 22x22 matrix, with 22 rows corresponding to the 22 bins of the prompt spectrum (1.625-7.125MeV) and 22 columns corresponding to 22 true antineutrino energy bins described with the efficiency vectors. Before use, the matrix should be normalized so that $\sum_j R_{ji} = \text{efficiency}_i$.

Filter matrix associated with the Tikhonov-regularized unfolding. The filter matrix $A$ encodes all biases coming from the unfolding process itself. The matrix is given as a 22x22 matrix, bins 2-21 correspond to the 20 bins of the unfolded spectrum (2.375-7.875 MeV), bin 1 integrates from 1.875 MeV to 2.375 MeV, bin 22 integrates from 7.875 MeV to 8.125 MeV. It brings any model from true $E_\nu$ space to the filtered $E_\nu$ space where it can be compared to the unfolded spectrum $\Phi$, for instance with $\chi2 = (A \cdot \text{model} - \Phi)^T V_\Phi^{-1} (A\cdot \text{model} - \Phi)$. The unfolding process introduce very little biases so $A$ is close to diagonal.

Exclusion limits at 95% CL for direct stop pair production, where the stops decay to either a top and the lightest neutralino, or a bottom and the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 5$ to 1000 cm while the stop mass is fixed to 1100 GeV and the neutralino's mass is fixed to 1000 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.