Electron $p_{T}$ distribution using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$p_{T}^{miss}$ distribution in the electron channel using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

Muon $p_T$ distribution using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$p_T^{miss}$ distribution in the muon channel using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$M_T$ distribution for the E+INVISIBLE system using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$M_T$ distribution for the MU+INVISIBLE system using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

Theoretical prediction for the production and decay cross-section of SSM WPRIME bosons (W --> LEPTON + NU) as a function of the WPRIME mass, for the range 200-6400 GeV and inclusive in phase-space (no cuts). The uncertainty shown covers PDF and ALPHAS uncertainties. These theoretical predictions are obtained with PYTHIA (LO) and corrected to NNLO in QCD using FEWZ program.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the product of WPRIME cross-section production and BF(WPRIME --> LEPTON + NU) for an SSM WPRIME boson, as a function of the boson mass, for the electron decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits. The theoretical predictions for the SSM at NNLO precision in QCD are shown with narrow gray bands corresponding to the PDF and ALPHAS uncertainties.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the product of WPRIME cross-section production and BF(WPRIME --> LEPTON + NU) for an SSM WPRIME boson, as a function of the boson mass, for the muon decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits. The theoretical predictions for the SSM at NNLO precision in QCD are shown with narrow gray bands corresponding to the PDF and ALPHAS uncertainties.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the product of WPRIME cross-section production and BF(WPRIME --> LEPTON + NU) for an SSM WPRIME boson, as a function of the boson mass, for the combined electron and muon decay channels. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits. The theoretical predictions for the SSM at NNLO precision in QCD are shown with narrow gray bands corresponding to the PDF and ALPHAS uncertainties.

The 95% CL observed (solid line) and expected (dashed line) model independent cross section upper limits as function of the $MT_{min}$ threshold, for the electron decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits.

The 95% CL observed (solid line) and expected (dashed line) model independent cross section upper limits as function of the $MT_{min}$ threshold, for the muon decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits.

The 95% CL observed (solid line) and expected (dashed line) model independent cross section upper limits as function of the $MT_{min}$ threshold, for the combined electron and muon decay channels. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the coupling strength ratio, gWprime/gW as a function of the mass of the WPRIME boson, for the electron channel. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. The dotted line represents the case of SSM coupling, gWprime, being equal to gW, the SM coupling.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the coupling strength ratio, gWprime/gW, as a function of the mass of the WPRIME boson, for the muon channel. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. The dotted line represents the case of SSM coupling, gWprime , being equal to gW, the SM coupling.

Exclusion limits in the 2D plane (1/R, $\mu$) for the split-UED interpretation for the n = 2 case, for the electron channel for the 2016-2018 data sets. R is the radius of the additional spatial dimension and $\mu$ the bulk mass for the fermion field in five dimensions. The expected limit is depicted as a black dashed line. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The experimentally excluded region is the entire area to the left of the solid black line.

Exclusion limits in the 2D plane (1/R, $\mu$) for the split-UED interpretation for the n = 2 case, for the muon channel for the 2016-2018 data sets. R is the radius of the additional spatial dimension and $\mu$ the bulk mass for the fermion field in five dimensions. The expected limit is depicted as a black dashed line. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The experimentally excluded region is the entire area to the left of the solid black line.

Exclusion limits in the 2D plane (1/R, $\mu$) for the split-UED interpretation for the n = 2 case, for the combined electron and muon channels for the 2016-2018 data sets. R is the radius of the additional spatial dimension and $\mu$ the bulk mass for the fermion field in five dimensions. The expected limit is depicted as a black dashed line. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The experimentally excluded region is the entire area to the left of the solid black line.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the ${\lambda}_{231}$ coupling in the RPV SUSY model with a STAU mediator, as a function of the mass of STAU, for the electron channel and for the production coupling, ${\lambda}^{'}_{3ij}$=0.5 . The couplings ${\lambda}^{'}_{3ij}$, ${\lambda}_{231}$, and ${\lambda}_{132}$ are defined in Fig. 1. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. Limits for other values of ${\lambda}^{'}_{3ij}$ are obtained multiplying the ones shown by the ratio ${\lambda}^{'}_{3ij}$/0.5

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the ${\lambda}_{132}$ coupling in the RPV SUSY model with a stau mediator, as a function of the mass of the stau, for the muon channel and for the production coupling, ${\lambda}^{'}_{3ij}$ = 0.5 . The couplings ${\lambda}^{'}_{3ij}$, ${\lambda}_{231}$, and ${\lambda}_{132}$ are defined in Fig. 1. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. Limits for other values of ${\lambda}^{'}_{3ij}$ are obtained multiplying these ones by the ratio ${\lambda}^{'}_{3ij}$/0.5

Region in the oblique (W,Y) parameter phase space allowed by the current analysis at 95% CL. The combination of the electron and muon channel distributions from the 2017 and 2018 data sets at sqrt(s) = 13 TeV is used, having 101 fb-1 of luminosity.

Negative Log-Likelihood for the determination of the oblique W parameter, for the combination of the electron and muon channel and for 2017 and 2018 data sets (L=101 fb-1) at sqrt(s) = 13 TeV.

Total background estimate and observed data in all bins used in the limit calculation for the SSM WPRIME limits. The total event sample is split into the three years of data taking and the two lepton flavors.The distributions being shown are $M_T$ ones. The associated integrated luminosities for each year are of 35.9, 41.5, and 59.4 fb$̣^{-1}. The mass bins are numbered in ascending order.

Negative log-likelihood difference in $\text{c}_{\varphi\text{Q}}^{3}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\varphi\text{t}}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\varphi\text{tb}}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\text{tW}}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\text{bW}}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\text{tZ}}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\text{t}\varphi}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\varphi\text{Q}}^{-}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\varphi\text{Q}}^{3}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\varphi\text{t}}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\varphi\text{tb}}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\text{tW}}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\text{bW}}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\text{tZ}}$ where the other Wilson coefficients are simultaneously fit.

Negative log-likelihood difference in $\text{c}_{\varphi\text{t}}, \text{c}_{\varphi\text{Q}}^{-}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\varphi\text{Q}}^{3}, \text{c}_{\varphi\text{Q}}^{-}$ where the other Wilson coefficients are fixed to 0.

Negative log-likelihood difference in $\text{c}_{\text{tW}}, \text{c}_{\text{tZ}}$ where the other Wilson coefficients are fixed to 0.

Normalized dijet angular distribution for events with 4.2 < dijet mass < 4.8 TeV.

Normalized dijet angular distribution for events with 3.6 < dijet mass < 4.2 TeV.

Normalized dijet angular distribution for events with 3.6 < dijet mass < 4.2 TeV.

Normalized dijet angular distribution for events with 3.0 < dijet mass < 3.6 TeV.

Normalized dijet angular distribution for events with 3.0 < dijet mass < 3.6 TeV.

Normalized dijet angular distribution for events with 2.4 < dijet mass < 3.0 TeV.

Normalized dijet angular distribution for events with 2.4 < dijet mass < 3.0 TeV.

Normalized dijet angular distribution for events with 1.9 < dijet mass < 2.4 TeV.

Normalized dijet angular distribution for events with 1.9 < dijet mass < 2.4 TeV.

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.

The data and fitted signal and background distributions for the $D_{b\overline{b}}$-subleading jet regressed mass for the ggF BDT event category 2. The SM $HH$ ($\kappa_{2V}=\kappa_{V}=\kappa_{\lambda}=1$) signal is scaled to the best fit signal strength $\mu = 3.5$.

The data and fitted signal and background distributions for the $D_{b\overline{b}}$-subleading jet regressed mass for the ggF BDT event category 3. The SM $HH$ ($\kappa_{2V}=\kappa_{V}=\kappa_{\lambda}=1$) signal is scaled to the best fit signal strength $\mu = 3.5$.

The data and fitted signal and background distributions for the $D_{b\overline{b}}$-subleading jet regressed mass for the ggF QCD multijet background control region. The SM $HH$ ($\kappa_{2V}=\kappa_{V}=\kappa_{\lambda}=1$) signal is scaled to the best fit signal strength $\mu = 3.5$.

The data in the ggF BDT event category 1 after subtracting all background processes except the QCD multijet one compared to the QCD multijet background prediction derived from the dedicated control region.

The data in the ggF BDT event category 2 after subtracting all background processes except the QCD multijet one compared to the QCD multijet background prediction derived from the dedicated control region.

The data in the ggF BDT event category 3 after subtracting all background processes except the QCD multijet one compared to the QCD multijet background prediction derived from the dedicated control region.

The data and expected background distributions in the $t\overline{t}$ control region of the $D_{b\overline{b}}$-leading jet $m_{ ext{SD}}$.

The data and expected background distributions in the $t\overline{t}$ control region of the $D_{b\overline{b}}$-leading jet $D_{b\overline{b}}$.

The data and expected background distributions in the $t\overline{t}$ control region of $m_{HH}$.

The data and expected background distributions in the $t\overline{t}$ control region of $p_{\text{T} j_{1}}/m_{HH}$.

Expected and observed 95% CL upper limits on $HH$ production with respect to the SM expectation ($\kappa_{\lambda}=\kappa_{t}=\kappa_{V}=\kappa_{2V}=1$) in the individual ggF and VBF search categories and their combination.

Expected and observed 95% CL upper limits on $HH$ production with respect to the expectation for $\kappa_{\lambda}=\kappa_{t}=\kappa_{V}=1$, $\kappa_{2V}=0$ in the individual ggF and VBF search categories and their combination.

Observed and expected 95% CL exclusion limit on the product of the inclusive $HH$ production cross section and the branching fraction into $b\overline{b}b\overline{b}$ as a function of $\kappa_{\lambda}$ with other couplings fixed to the SM values.

Observed and expected 95% CL exclusion limit on the product of the inclusive $HH$ production cross section and the branching fraction into $b\overline{b}b\overline{b}$ as a function of $\kappa_{2V}$ with other couplings fixed to the SM values.

Observed and expected 95% CL exclusion limit on the product of the inclusive $HH$ production cross section and the branching fraction into $b\overline{b}b\overline{b}$ as a function of $\kappa_{V}$ with other couplings fixed to the SM values.

Observed and expected 95% CL exclusion limit on the product of the VBF $HH$ production cross section and the branching fraction into $b\overline{b}b\overline{b}$ as a function of $\kappa_{2V}$ with other couplings fixed to the SM values.

Observed and expected 95% CL exclusion limit on the product of the VBF $HH$ production cross section and the branching fraction into $b\overline{b}b\overline{b}$ as a function of $\kappa_{V}$ with other couplings fixed to the SM values.

Two-parameter profile likelihood test statistic ($-2\Delta\ln\mathcal{L}$) scan in data as a function of $\kappa_{2V}$ and $\kappa_{V}$.

Major sources of uncertainty in the measurement of the signal strength modifier $\mu$, and their observed impact ($\Delta\mu$) from a fit to the combined data set.

Kinematic distributions of single l + > 5-jet events in data (points), compared to MC simulation normalized to the number of events observed in data. Shown are pT spectra for muons (a) and electrons (b), and jet spectra for the channels $\mu$+jets (c) and e+jets (d). The reconstructed mtg distribution is shown for the $\mu$+jets channel in (e) and for e+jets in (f).

Kinematic distributions of single l + > 5-jet events in data (points), compared to MC simulation normalized to the number of events observed in data. Shown are pT spectra for muons (a) and electrons (b), and jet spectra for the channels $\mu$+jets (c) and e+jets (d). The reconstructed mtg distribution is shown for the $\mu$+jets channel in (e) and for e+jets in (f).

Kinematic distributions of single l + > 5-jet events in data (points), compared to MC simulation normalized to the number of events observed in data. Shown are pT spectra for muons (a) and electrons (b), and jet spectra for the channels $\mu$+jets (c) and e+jets (d). The reconstructed mtg distribution is shown for the $\mu$+jets channel in (e) and for e+jets in (f).

Reconstructed mass spectrum for the $tg$ system in data (points), along with a fit of the background $f(m)$ to the data in the $\mu$+jets channel (a) and $e$+jets channel (b). The reconstructed masses correspond to the results of kinematic fits for the jet-quark assignments that provide the best match to the $t^{*}\bar{t}^{*}$ hypothesis. Also shown are the expectations of $t^*$ signals for $m_{t^*}=750$ and 850 GeV normalized to the integrated luminosity of the data. Information about the fit: F = 501885/(1+exp((x+251.828)/112.311)) where x = $M_{tg}$.

Reconstructed mass spectrum for the $tg$ system in data (points), along with a fit of the background $f(m)$ to the data in the $\mu$+jets channel (a) and $e$+jets channel (b). The reconstructed masses correspond to the results of kinematic fits for the jet-quark assignments that provide the best match to the $t^{*}\bar{t}^{*}$ hypothesis. Also shown are the expectations of $t^*$ signals for $m_{t^*}=750$ and 850 GeV normalized to the integrated luminosity of the data. Information about the fit: F = 62101.5/(1+exp((x+6.58811)/107.886)) where x = $M_{tg}$.

The observed (solid line) and expected (dashed line) 95%CL upper limits for the product of the inclusive $t^{*}\bar{t}^{*}$ production cross section and the branching fraction $B(t^{*}{\rightarrow}tg)$, as a function of the $t^{*}$ mass, for the combined lepton data. The ranges for ${\pm}1$ and ${\pm}2$ standard deviations for the expected limits are shown by the bands.

Comparison of data and background pT distribution at the preselection level for the second leading jet.

Comparison of data and background BDT discriminant distributions at the preselection level for LQ mass hypotheses of 1500 GeV.

Comparison of data and background BDT discriminant distributions at the preselection level for LQ mass hypotheses of 1800 GeV.

Comparison of data and background BDT discriminant distributions at the preselection level for LQ mass hypotheses of 2000 GeV.

Total signal selection efficiency, defined as the number of events passing the selection divided by the number of generated events. Enhanced preselection is defined as preselection with the additional requirements m_uu>250 and m_uujj>m_lq. The discrete nature of the final selection for each LQ candidate mass produces the observed variation in the efficiency. Relative uncertainties are less than one percent in all cases.

Event yields in the combined 2016--2018 data at the final selection level.

The expected and observed upper limits at 95% CL on the product of the scalar LQ pair production cross section and the branching fractions β^2 as a function of mLQ. The solid lines represent the observed limits, the dashed lines represent the median expected limits, and the inner dark-green and outer light-yellow bands represent the 68% and 95% CL intervals. The σtheory curves and their blue bands represent the theoretical scalar LQ pair production cross sections and the uncertainties on the cross sections due to the PDF prediction and renormalization and factorization scales, respectively.

The expected and observed exclusion limits at 95% CL as a function of the leptoquark mass and the branching fraction β. The solid line represents the observed limits, the dashed line represents the median expected limits, and the inner dark-green and outer light-yellow bands represent the 68% and 95% CL intervals. The area below the observed limit is excluded.

Selection efficiencies in % for the signal in the top squark search, estimated from the simulation.

The final $S_{\text{T}}$ distribution for the leptoquark search with the $\text{e}\tau_{\text{h}}$ and $\mu\tau_{\text{h}}$ channels combined. The last bin contains the overflow events.

The final $S_{\text{T}}$ distribution for the top squark search with the $\text{e}\tau_{\text{h}}$ and $\mu\tau_{\text{h}}$ channels combined. The last bin contains the overflow events.

The expected and observed combined upper limits on the third-generation LQ pair production cross section $\sigma$ times the square of the branching fraction, $\mathcal{B}^2$, at 95% CL, as a function of the LQ mass. These limits also apply to top squarks decaying directly via the coupling $\lambda^{\prime}_{333}$.

The expected and observed combined upper limits on the top squark pair production cross section $\sigma$ times the square of the branching fraction, $\mathcal{B}^2$, at 95% CL, as a function of the top squark mass. These limits apply to top squarks with a chargino-mediated decay through the coupling $\lambda^{\prime}_{3jk}$.

The expected and observed 95% CL upper limits on the branching fraction for the leptoquark decay to a tau lepton and a bottom quark, as a function of the leptoquark mass. A search for top squark pair production [EPJC 73(2013)2677] has the same kinematic signature as the leptoquark decay to a tau neutrino and a top quark. This search is reinterpreted to provide the expected and observed 95% CL upper limits for low values of $\mathcal{B}$, assuming the leptoquark only decays to third-generation fermions.