Post-fit $m_\mathrm{MMC}$ distribution in the high-mass SR for the $m_A = 90\,\mathrm{GeV}$ signal mass hypothesis. $m_\mathrm{MMC}$ is the mass reconstructed by the Missing Mass Calculator. Processes contributing to the background Others are $Z/\gamma^* \rightarrow ee/\mu\mu$ and SM Higgs. The subscript on the $A\to\tau\tau$ process indicates the mass of the $A$ boson. otal includes all backgrounds and the signal process. The high-mass Signal Region is defined as: - 1 electron and 1 muon with opposite charge - $p_\mathrm{T}$ requirements of the leptons are a combination of the following: - $p_\mathrm{T}^e > 18\,\mathrm{GeV}$ and $p_\mathrm{T}^\mu > 15\,\mathrm{GeV}$ or - $p_\mathrm{T}^e > 10\,\mathrm{GeV}$ and $p_\mathrm{T}^\mu > 25\,\mathrm{GeV}$ or - $p_\mathrm{T}^e > 27\,\mathrm{GeV}$ and $p_\mathrm{T}^\mu > 10\,\mathrm{GeV}$ - $\vert \eta_e \vert < 2.47$, excluding $1.37 < \vert \eta_e \vert < 1.52$ - $\vert \eta_\mu \vert < 2.7$ - no jets with $b$-quarks - $\Delta R_{\ell\ell} < 1.0$ - $E_\mathrm{T}^\mathrm{miss} > 30\,\mathrm{GeV}$ - $m_\mathrm{T}^\mathrm{tot} = \sqrt{\left(p_\mathrm{T}^e+p_\mathrm{T}^\mu+E_\mathrm{T}^\mathrm{miss}\right)^2-\left(\vec{p}_\mathrm{T}^{\,e}+\vec{p}_\mathrm{T}^{\,\mu}+\vec{E}_\mathrm{T}^{\,\mathrm{miss}}\right)^2} < 65\,\mathrm{GeV}$ - $35\,\mathrm{GeV} < m_\mathrm{MMC} < 130\,\mathrm{GeV}$

Expected and observed $95\%$ CL limits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses ranging from $20$ to $90\,\mathrm{GeV}$.

Expected and observed $95\%$ CL limits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses ranging from $20$ to $90\,\mathrm{GeV}$.

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 $90\,\mathrm{GeV}$.

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 $O(10^{-5})$.

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 $O(10^{-5})$.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the compressed SUSY simplified model with a bino-like LSP and wino-like NLSPs being considered. These are shown with $\pm1\sigma_\text{exp}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm1\sigma^{\text{SUSY}}_{\text{theory}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the ATLAS searches using the soft lepton signature is illustrated by the blue region while the limit imposed by the LEP experiments is shown in grey.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the compressed SUSY simplified model with a bino-like LSP and wino-like NLSPs being considered. These are shown with $\pm1\sigma_ ext{exp}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm1\sigma^{ ext{SUSY}}_{ ext{theory}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the ATLAS searches using the soft lepton signature is illustrated by the blue region while the limit imposed by the LEP experiments is shown in grey.

The expected upper limits on the cross-sections at 95% CL for the simplified SUSY model featuring the production of mass-degenerate pairs of wino-like $\widetilde{\chi}_{2}^{0}$ and $\widetilde{\chi}_{1}^{\pm}$ particles in association with at least two jets. The production modes considered are $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$.

The observed upper limits on the cross-sections at 95% CL for the simplified SUSY model featuring the production of mass-degenerate pairs of wino-like $\widetilde{\chi}_{2}^{0}$ and $\widetilde{\chi}_{1}^{\pm}$ particles in association with at least two jets. The production modes considered are $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$.

Truth-level signal acceptances in $\text{SR}_\text{2j}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation. The generator-level selections are the same as the ones at reconstruction level.

Truth-level signal acceptances in $\text{SR}_{\geq\text{3j}}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation. The generator-level selections are the same as the ones at reconstruction level.

Signal efficiencies in $\text{SR}_\text{2j}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. The efficiency is defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level. The generator-level selections are the same as the ones at reconstruction level.

Signal efficiencies in $\text{SR}_{\geq\text{3j}}$ with BDT score $\in [0.88, 1.0]$. All of the considered production modes ($\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{1}^{\pm}$, $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\mp}$, $\widetilde{\chi}_{2}^{0}\widetilde{\chi}_{2}^{0}$, and $\widetilde{\chi}_{1}^{\pm}\widetilde{\chi}_{1}^{\pm}$) are included. The efficiency is defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level. The generator-level selections are the same as the ones at reconstruction level.

Event selection cutflows for signal samples with $m(\tilde\chi^0_2) =100~\text{GeV}$ and $\Delta m(\tilde\chi^0_2 / \tilde\chi^\pm_1,\tilde\chi^0_1) = 0.2~\text{GeV}, 1.0~\text{GeV},$ and $5.0~\text{GeV}$. The first number in the column for each mass point corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut. All of the production modes are included. The total cross-section used to obtain the initial number of events ($\sigma$) includes the cuts applied at parton level as described in the main text.

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.

Distributions of the $\mathrm{NN^{SvsB}}$ output for data and the expected background after the likelihood fit in the $SR_{cQu --}$ signal region. The post-fit background expectations are shown as filled histograms, the combined pre-fit background expectations are shown as dashed lines. The signal distribution using the Wilson coefficient values $c_{tu}^{(1)}=0.04$, $c_{Qu}^{(1)}=0.1$, $c_{Qu}^{(8)}=0.1$ is shown with a dotted line, normalized to the same number of events as the background.

Summary of the observed and predicted number of events in the five 2$\ell$ control regions. The background prediction is shown after the combined likelihood fit to data under the signal-plus-background hypothesis across all control regions and signal regions. The uncertainties in the total yields are smaller than the sum in quadrature of the uncertainties in the individual contributions due to anti-correlations resulting from the likelihood fit. Int Conv refers to events with photon conversions in the inner detector, while Mat Conv refers to events with photon conversions in the detector material. QMisID refers to events with charge-misidentified lepton.

Summary of the observed and predicted number of events in the four 3$\ell$ control regions. The background prediction is shown after the combined likelihood fit to data under the signal-plus-background hypothesis across all control regions and signal regions. The uncertainties in the total yields are smaller than the sum in quadrature of the uncertainties in the individual contributions due to anti-correlations resulting from the likelihood fit. Int Conv refers to events with photon conversions in the inner detector, while Mat Conv refers to events with photon conversions in the detector material. QMisID refers to events with charge-misidentified lepton.

Comparison of the observed limits on the three WCs to the limit obtained by the ATLAS Run~1 analysis of events with $b$-tagged jets and a same-sign lepton pair [JHEP10(2015)150]. The cross-section limits from that analysis are converted to the limits on the WCs by using the fitted EFT parametrization. The bounds on the WCs are shown at the 68$\%$ CL (solid) and/or 95$\%$ CL (dashed) levels. For the ATLAS Run~1 analysis only the bounds at 95$\%$ CL are available. The vertical bar represents the SM prediction. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$.

Observed lower limits at 95$\%$ confidence level on the scale of new physics $\Lambda$ for Wilson coefficient values of 0.01, 1 and $4\pi^2$. The limits are compared with the limits obtained by the ATLAS Run~1 analysis of events with $b$-tagged jets and a same-sign lepton pair.

Observed and expected limits at 95$\%$ confidence level on the cross-section of $\sigma(pp \rightarrow tt)$.

2D likelihood scans of the Wilson coefficients $c_{tu}^{(1)}$ versus $c_{Qu}^{(1)}$. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient pair.

2D likelihood scans of the Wilson coefficients $c_{tu}^{(1)}$ versus $c_{Qu}^{(8)}$. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient pair.

2D likelihood scans of the Wilson coefficients $c_{Qu}^{(1)}$ versus $c_{Qu}^{(8)}$. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient pair.

1D-likelihood scan for $c_{tu}^{(1)}$. The observed and expected limits at 68\% and 95\% confidence level (CL) can be read off by finding the intersection of the likelihood scan curve with the 1$\sigma$ and 2$\sigma$ lines. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient value.

1D-likelihood scan for $c_{Qu}^{(1)}$. The observed and expected limits at 68\% and 95\% confidence level (CL) can be read off by finding the intersection of the likelihood scan curve with the 1$\sigma$ and 2$\sigma$ lines. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient value.

1D-likelihood scan for $c_{Qu}^{(8)}$. The observed and expected limits at 68\% and 95\% confidence level (CL) can be read off by finding the intersection of the likelihood scan curve with the 1$\sigma$ and 2$\sigma$ lines. The new-physics scale is set to $\Lambda = 1 \, \text{TeV}$. In the table the $2\Delta$NLL values are shown which correspond to the minimized negative log likelihood for a given Wilson coefficient value.

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].

HF Fraction at $1.2 < |\eta| < 2.0$ Top Muon Rich Data

HF Fraction at $1.2 < |\eta| < 2.0$ Bottom Ratio

Charged Hadron $v_2$; $1.2 < |\eta| < 2.0$

Charged Hadron; $1.2 < |\eta| < 2.0$

HF -> $\mu$; $1.2 < |\eta| < 2.0$

HF -> $e$; $|\eta| < 0.35$

JPSI v2 in Au+Au collisions as a function of pT (GeV/c) for 10%--40% centrality with pT binned by [0, 2], [2, 5], and [5, 10] GeV/c.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{loose}}^{2\ell3j}$ signal region, which uses the invariant mass of the c-tagged jet and the geometrically closest b-tagged jet, $m_{\mathit{cb}}^{\mathrm{min}\Delta R}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{loose}}^{1\ell6ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{tight}}^{1\ell6ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{loose}}^{2\ell4ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Post-fit agreement between data and MC prediction for the $SR_{\mathrm{tight}}^{2\ell4ji}$ signal region, which uses the jet multiplicity, $N_{\mathrm{jets}}$, as an observable. The hatched uncertainty bands include all uncertainties and their correlations. The last bins contain overflow events. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes.

Jet multiplicity and tagging criteria for the SRs and CRs in the single-lepton and dilepton channels. All single-lepton regions are defined in the 5-jet-exclusive and 6-jet-inclusive jet selections, all dilepton regions in the 3-jet-exclusive and 4-jet-inclusive jet selections. By construction, $\mathrm{SR}^{2\ell}_{\mathrm{tight}}$ only exists for the 4-jet-inclusive selection. Identical requirements on inclusive and exclusive working points, e.g., one c@22% and one c@11% in the $\mathrm{CR}_1^{1\ell}$, indicate a veto of additional tags at the inclusive working point.

Breakdown of the fiducial $t\bar{t}+{\geq}2c$ and $t\bar{t}+1c$ cross-section uncertainties. Systematic uncertainties are grouped into signal and background modeling, instrumental, and MC statistics categories. The table lists the fractional uncertainty in the measured cross-sections in percent and is estimated from the covariance matrix of the profile likelihood fit, following EPJC 84 (2024) 593. Individual groups of uncertainties can be added in quadrature to obtain the total uncertainty. JES and JER denote the jet energy scale and resolution uncertainties, respectively. The fractional uncertainties in the fitted $t\bar{t}+{\geq}1c$ and $t\bar{t}+\text{light}$ normalization factors are included in the data statistics category. For presentation purposes, all uncertainties are symmetrized.

Measured fiducial cross-section values in comparison with various NLO+PS predictions. The uncertainties in the predictions include independent and simultaneous variations of the $\mu_R$ and $\mu_F$ scales and uncertainties in the choice of the PDF set, but no uncertainties in the parton shower or hadronization. The uncertainties in the measured values include all statistical and systematic uncertainties. For presentation purposes, the quoted measurement and prediction uncertainties are symmetrized.

Measured and predicted values for the $t\bar{t}+{\geq}1b$, $t\bar{t}+{\geq}2c$, and $t\bar{t}+1c$ cross-sections and for the cross-section ratios of these processes to total $t\bar{t}+\text{jets}$ production. The quoted uncertainties in the measurements include statistical and systematic uncertainties. The predictions are taken from the nominal setup using inclusive $t\bar{t}$ Powheg+Pythia8 predictions for the $t\bar{t}+{\geq}2c$, $t\bar{t}+1c$, and $t\bar{t}+\text{light}$ processes, and $t\bar{t}+b\bar{b}$ Powheg+Pythia8 predictions for the $t\bar{t}+{\geq}1b$ process. The first use the 5FS, the latter the 4FS, where the $b$-quarks are treated as massive particles and are included in the matrix element calculation. The uncertainties in the predictions include independent and simultaneous variations of the $\mu_R$ and $\mu_F$ scales and uncertainties in the choice of the PDF set.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+{\geq}2c$ cross-section measurement. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+1c$ cross-section measurement. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+{\geq}2c$ ratio to total $t\bar{t}+\text{jets}$ production. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Nuisance parameters with a large impact on the fiducial measurement: ranking of the ten most impactful nuisance parameters for the $t\bar{t}+1c$ ratio to total $t\bar{t}+\text{jets}$ production. The colored boxes indicate the impact of the nuisance parameters, $\Delta\mu$ (top axis), on the corresponding parameter of interest, $\mu$. They are calculated from the covariance matrix using the post-fit up (down) uncertainties of the parameter of interest and the nuisance parameter, $\sigma_{\mathrm{POI}}^{\mathrm{up}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{up}}$ ($\sigma_{\mathrm{POI}}^{\mathrm{down}}$ and $\sigma_{\mathrm{NP}}^{\mathrm{down}}$), and their linear correlation coefficient, $\rho$, following EPJC 84 (2024) 593. The data points and error bars indicate the post-fit values and uncertainties of the nuisance parameters relative to their pre-fit values, $\theta_0$, in units of their pre-fit uncertainties, $\Delta \theta$ (bottom axis). A hollow circle indicates that the nuisance parameter has a Poisson constraint.

Efficiencies and rejection rates for the $b$- and $c$-jet tagging working points of the $b/c$-tagger, as well as the selection cuts on the discriminants, $\mathcal{D}_b'$ and $\mathcal{D}_c'$ that define the tagging bins. The primes indicate that a standard logistic function is applied to the discriminants. The b@70% and c@22% working points are inclusive of their respective tighter working points. The values were estimated from single-lepton and dilepton $t\bar{t}$ events simulated with Powheg + Pythia 8.

Efficiencies and rejection rates for the $b$- and $c$-jet tagging working points of the $b/c$-tagger, as well as the selection cuts on the discriminants, $\mathcal{D}_b'$ and $\mathcal{D}_c'$ that define the tagging bins. The primes indicate that a standard logistic function is applied to the discriminants. The b@70% and c@22% working points are inclusive of their respective tighter working points. The values were estimated from single-lepton and dilepton $t\bar{t}$ events simulated with Powheg + Pythia 8.

Pre-fit event yields in the twelve single-lepton and dilepton control regions (CRs) of the analysis. The quoted uncertainties include both statistical and systematic components, but all normalization factors are fixed to their pre-fit values with no uncertainties. All uncertainties are assumed to be uncorrelated. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Pre-fit event yields in the seven signal regions (SRs) of the analysis. The quoted uncertainties include both statistical and systematic components, but all normalization factors are fixed to their pre-fit values with no uncertainties. All uncertainties are assumed to be uncorrelated. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Post-fit event yields in the twelve single-lepton and dilepton control regions (CRs) of the analysis. The quoted uncertainties include both statistical and systematic components, and post-fit correlations between uncertainties are taken into account. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

Post-fit event yields in the seven signal regions (SRs) of the analysis. The quoted uncertainties include both statistical and systematic components, and post-fit correlations between uncertainties are taken into account. "Other Top" includes single-top-quark production and associated production of $t\bar{t}$ and single top quarks with bosons. "Non-Top" includes W+jets, Z+jets, and diboson processes. The "Fakes" category includes events from misidentified leptons and non-prompt leptons, estimated through the data-driven matrix method in the single-lepton channel and through MC predictions in the dilepton channel.

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 $p_{T}(t)$