Expected and observed $95\%$ CL limits on the absolute value of the up-type quark coupling parameter $|\zeta_\mathrm{u}|$ for masses of the $A$ boson ranging from $20$ to $90\,\mathrm{GeV}$.

Observed $p_0$ values for the fits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses from $20$ to $90\,\mathrm{GeV}$. For the fit on the up-type quark coupling parameter the $p_0$ values agree up to a precision of $O(10^{-5})$.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH3 at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 and ISR starting scale SpaceShower:pT0Ref = 1 GeV at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH3 and ISR starting scale SudakovCommon:pTmin=0.7 GeV at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-proton center-of-mass energy 38.8 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-proton center-of-mass energy 8000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-nucleon center-of-mass energy 8160 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-nucleon center-of-mass energy 13000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 38.8 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 8000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-nucleon center-of-mass energy 8160 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 13000 GeV versus the dilepton mass range of the process.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Observed profile likelihood projection on mH, for the 2e2mu final state, using the N-2D′ VXBS approach. Both statistical and systematic uncertainties have been considered.

Observed profile likelihood projection on mH, for the 2mu2e final state, using the N-2D′ VXBS approach. Both statistical and systematic uncertainties have been considered.

Result of the Higgs boson width measurement using the on-shell production only, shown as 1 − CL curve extracted using the Feldman–Cousins approach (black dots). The error bars represent the spread of the simulated experiments for each point.

Observed profile likelihood projections from the Higgs boson width fit using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Expected profile likelihood projections from the Higgs boson width fit using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Observed profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel.

Expected (dashed) profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel.

Observed 2D profile likelihood projection of the off-shell signal strength parameters (mu off-shell, mu off-shell) from the fit to the combined off-shell H → ZZ → 4l and 2l2nu channels. [Nature Phys. 18 (2022) 1329] The best fit value is shown by the black cross and the SM prediction by the red x. The 68% and 95%CL contours are given by the dashed and solid curves, respectively. The color scale to the right of the plot relates the quantitative values.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 pre-VPF, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 pre-VPF, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 pre-VPF, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 pre-VPF, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2016 pre-VPF.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 post-VPF, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 post-VPF, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 post-VPF, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 post-VPF, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2016 post-VPF.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2017, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2017, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2017, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2017, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2017.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2018, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2018, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2018, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2018, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2018.

The observed likelihood scan as a function of mH using the N-2D'_VXBS$ approach.

The expected likelihood scan as a function of mH using the N-2D'_VXBS$ approach. Expected likelihood has been obtained with the hypothesis mH = 125.38 GeV

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2016 pre-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2016 pre-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2016 post-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2016 post-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2017.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2017.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2018.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2018.

Observed profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel with kappa Q constrained to 0.

Expected profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel with kappa Q constrained to 0.

Observed profile likelihood projection on the Higgs boson width using on- and off-shell production. The analysis of H → ZZ → 4l, considering an unconstrained coupling strength κQ is presented.

Expected profile likelihood projection on the Higgs boson width using on- and off-shell production. The analysis of H → ZZ → 4l, considering an unconstrained coupling strength κQ is presented.

Expected profile likelihood projection on muF off-shell using on- and off-shell production for H → ZZ → 4l.

Observed profile likelihood projection on muF off-shell using on- and off-shell production for H → ZZ → 4l.

Expected profile likelihood projection on muF off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Observed profile likelihood projection on muF off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Expected profile likelihood projection on muV off-shell using on- and off-shell production for H → ZZ → 4l.

Observed profile likelihood projection on muV off-shell using on- and off-shell production for H → ZZ → 4l.

Expected profile likelihood projection on muV off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu.

Observed profile likelihood projection on muV off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Expected profile likelihood projection on mu off-shell using on- and off-shell production for H → ZZ → 4l.

Observed profile likelihood projection on mu off-shell using on- and off-shell production for H → ZZ → 4l.

Expected profile likelihood projection on mu off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Observed profile likelihood projection on mu off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Covariance for full matrix measurement all bins from $m(t\bar{t})$ fit

Results for full matrix measurement inclusive from $p_{T}(t)$

Covariance for full matrix measurement inclusive from $p_{T}(t)$

Results for full matrix measurement all bins from $p_{T}(t)$ fit

Covariance for full matrix measurement all bins from $p_{T}(t)$ fit

Results for full matrix measurement differential in $m(t\bar{t})$

Covariance for full matrix measurement differential in $m(t\bar{t})$

Results for full matrix measurement differential in $m(t\bar{t})$ for $|cos(\theta)| < 0.4$

Covariance for full matrix measurement differential in $m(t\bar{t})$ for $|cos(\theta)| < 0.4$

Results for full matrix measurement differential in $p_{T}(t)$

Covariance for full matrix measurement differential in $p_{T}(t)$

Results for $D$ measurement inclusive from $m(t\bar{t})$

Covariance for $D$ measurement inclusive from $m(t\bar{t})$

Results for $D$ measurement inclusive from $p_{T}(t)$

Covariance for $D$ measurement inclusive from $p_{T}(t)$

Results for $\tilde{D}$ measurement inclusive in $m(t\bar{t})$

Covariance for $\tilde{D}$ measurement inclusive in $m(t\bar{t})$

Results for $\tilde{D}$ measurement inclusive in $p_{T}(t)$

Covariance for $\tilde{D}$ measurement inclusive in $p_{T}(t)$

Results for $D$ measurement all bins from from $m(t\bar{t})$ fit

Covariance for $D$ measurement all bins from from $m(t\bar{t})$ fit

Results for $D$ measurement all bins from from $p_{T}(t)$ fit

Covariance for $D$ measurement all bins from from $p_{T}(t)$ fit

Results for $\tilde{D}$ measurement all bins from from $m(t\bar{t})$ fit

Covariance for $\tilde{D}$ measurement all bins from from $m(t\bar{t})$ fit

Results for $\tilde{D}$ measurement all bins from from $p_{T}(t)$ fit

Covariance for $\tilde{D}$ measurement all bins from from $p_{T}(t)$ fit

Results for $D$ measurement differential in $m(t\bar{t})$

Covariance for $D$ measurement differential in $m(t\bar{t})$

Results for $\tilde{D}$ measurement differential in $m(t\bar{t})$

Covariance for $\tilde{D}$ measurement differential in $m(t\bar{t})$

Results for $\tilde{D}$ measurement differential in $m(t\bar{t})$ for $|cos(\theta)| < 0.4$

Covariance for $\tilde{D}$ measurement differential in $m(t\bar{t})$ for $|cos(\theta)| < 0.4$

Results for $D$ measurement differential in $p_{T}(t)$

Covariance for $D$ measurement differential in $p_{T}(t)$

Results for $\tilde{D}$ measurement differential in $p_{T}(t)$

Covariance for $\tilde{D}$ measurement differential in $p_{T}(t)$

The model-independent 95\% \CL expected and observed upper limits set on ${\sigma(\PP\to 2\Pa+\PX)\mathcal{B}^2(\Pa\to 2\PGm)\alphaGen}$ over the range $0.21 < \MPa < 9\GeV$ for the combined 2016, 2017, and 2018 analyses. Mass ranges that overlap with \JPsi and \PgU resonances are excluded from the search

The 95\% \CL observed upper limits on the effective coupling $\CahLambda$ of the ALP to the SM Higgs assuming the branching fraction of the ALP to muons is 1 (blue) and 0.1 (orange), for both the expected (dashed) and observed (solid) limits

The 95\% \CL observed upper limits on the effective coupling $\cll/\Lambda$ of the ALP to the SM leptons. The orange shaded region represents the parameter space excluded by this search under three choices of $\CahLambda$ $1\TeV^{-2}$ (solid), $0.1\TeV^{-2}$ (dashed), and $0.01\TeV^{-2}$ (dotted)

The 95\% \CL observed upper limits on $\varepsilon^{2} \mathcal{B}(\ZDark \to \sDark \overline{\sDark}) \mathcal{B}^{2}(\sDark \rightarrow 2\PGm)$ for the vector portal model as a function of the dark scalar mass $\MsDark$ and dark vector boson mass $\MZDark$. The yellow and green bands indicate one and two standard deviation values of the limit, respectively. Because the model-independent limits are calculated only up to a dimuon mass of 60\GeV, the \sDark mass considered for these limits is below 60\GeV

The 95\% \CL observed upper limits for $\sigma(\PP\to\PhOneTwo\to 2\PaOne) \mathcal{B}^2(\PaOne\to 2\PGm)$ for the NMSSM as a function of $\MPaOne$ for $\MPhOneTwo$ = 90~GEV. The data presented here reflect the results of the 2017 and 2018 datasets combined with the previously published results of the 2016 dataset in \cite{CMS:2018jid}

The 95\% \CL observed upper limits for $\sigma(\PP\to\PhOneTwo\to 2\PaOne) \mathcal{B}^2(\PaOne\to 2\PGm)$ for the NMSSM as a function of $\MPaOne$ for $\MPhOneTwo$ = 125~GEV. The data presented here reflect the results of the 2017 and 2018 datasets combined with the previously published results of the 2016 dataset in \cite{CMS:2018jid}

The 95\% \CL observed upper limits for $\sigma(\PP\to\PhOneTwo\to 2\PaOne) \mathcal{B}^2(\PaOne\to 2\PGm)$ for the NMSSM as a function of $\MPaOne$ for $\MPhOneTwo$ = 150~GEV. The data presented here reflect the results of the 2017 and 2018 datasets combined with the previously published results of the 2016 dataset in \cite{CMS:2018jid}

The 95\% \CL observed upper limits (black solid curves) from this search as interpreted in the dark SUSY scenario for the process $\PP \to \Ph \to 2\nOne \to 2\gammaDark + 2\nDark \to 4\PGm + \PX$, with \mbox{$\MnOne = 60\GeV$} and $\MnDark = 1\GeV$. The limits are presented in the plane of $\varepsilon$ and $\MgammaDark$. The color gradient represents different branching fraction assumptions for \mbox{$\mathcal{B}(\Ph \to 2\nOne \to 2\gammaDark + 2\nDark)$}, ranging from dark orange (0.05\%) to light orange (10\%). The degradation of the limit around 1\GeV is attributed to the drop in the dimuon branching fraction $\mathcal{B}(\gammaDark \to 2\mu)$ due to the dimuon resonance of the $\phi$ meson~\cite{Batell:2009yf}

$\chi_{c0}(4500) \to J/\psi(\to \mu^+ \mu^-)\phi(\to K^+ K^-)$ diffractive production cross-section, multiplied by $\mathcal{B}(J/\psi \to \mu^+ \mu^-)$ and $\mathcal{B}(\phi \to K^+ K^-)$ branching ratios.

$\chi_{c1}(4685) \ \mathrm{or} \ \chi_{c0}(4700) \to J/\psi(\to \mu^+ \mu^-)\phi(\to K^+ K^-)$ diffractive production cross-section, multiplied by $\mathcal{B}(J/\psi \to \mu^+ \mu^-)$ and $\mathcal{B}(\phi \to K^+ K^-)$ branching ratios.

Nonresonant $J/\psi(\to \mu^+ \mu^-)\phi(\to K^+ K^-)$ diffractive production cross-section, multiplied by $\mathcal{B}(J/\psi \to \mu^+ \mu^-)$ and $\mathcal{B}(\phi \to K^+ K^-)$ branching ratios.

Slices of 2D distributions of observed events and the post-fit templates in the TT pass region, projected onto the plane of leading jet mass mJ1, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the TT pass region, projected onto the plane of leading jet mass mJ1, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the TT pass region, projected onto the plane of leading jet mass mJ1, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the LL pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the LL pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the LL pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the TT pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the TT pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the TT pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the semi-resolved pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the semi-resolved pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the semi-resolved pass region, projected onto the plane of corrected HH mass (mHH) mHH, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the semi-resolved pass region, projected onto the plane of leading jet mass mJ1, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the semi-resolved pass region, projected onto the plane of leading jet mass mJ1, including expected radion signal at 1.5 TeV.

Slices of 2D distributions of observed events and the post-fit templates in the semi-resolved pass region, projected onto the plane of leading jet mass mJ1, including expected radion signal at 1.5 TeV.

The observed (solid black line) and expected (dashed black line) upper limits at 95% CL on $\sigma$(pp->X)B(X->HH->4b) for the narrow spin-0 radion model. The green (yellow) bands represent one (two) standard deviations from the expected limit. The predicted theoretical cross sections are also shown.

The observed (solid black line) and expected (dashed black line) upper limits at 95% CL on $\sigma$(pp->X)B(X->HH->4b) for the narrow width spin-2 bulk graviton model. The green (yellow) bands represent one (two) standard deviations from the expected limit. The predicted theoretical cross sections are also shown.

The observed (solid black line) and expected (dashed black line) upper limits at 95% CL on $\sigma$(pp->X)B(X->HH->4b) for the 10%-width spin-0 radion model. The green (yellow) bands represent one (two) standard deviations from the expected limit. The predicted theoretical cross sections are also shown.

The observed (solid black line) and expected (dashed black line) upper limits at 95% CL on $\sigma$(pp->X)B(X->HH->4b) for the 10%-width spin-2 bulk graviton model. The green (yellow) bands represent one (two) standard deviations from the expected limit. The predicted theoretical cross sections are also shown.