The pNN input variable visible transverse momentum $p_{\mathrm{T}}^{\mathrm{vis}}(\mu\tau_{\mathrm{had}})$ is shown in the SR with no cut on the pNN discriminant. The signal shape is normalized to the same integral as the total background prediction. Overflow events are included in the last bins.

The pNN input variable transverse mass $m_{\mathrm{T}}(\mu,E_{\mathrm{T}}^{\mathrm{miss}})$ is shown in the SR with no cut on the pNN discriminant. The signal shape is normalized to the same integral as the total background prediction. Overflow events are included in the last bins.

The pNN input variable MMC mass $m^{\mathrm{MMC}}(bb\tau\tau)$ is shown in the SR with no cut on the pNN discriminant. The signal shape is normalized to the same integral as the total background prediction. Overflow events are included in the last bins.

Mass distribution of the $\tau^+\tau^-$ system for each of the generated MC signal mass points for the $\mu\tau_{\mathrm{h}}$ category. All distributions are normalized assuming $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=10\%$.

Mass distribution of the $\tau^+\tau^-$ system for each of the generated MC signal mass points for the $e\mu$ category. All distributions are normalized assuming $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=10\%$.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($e\mu$, $1b$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($\mu\tau_{\mathrm{had}}$,$2b$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($e\mu$,$1B$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($e\tau_{\mathrm{had}}$,$2b$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The observed (solid) 95% C.L. upper limits on $(\sigma(H)/\sigma_{\mathrm{SM}}(H)) \mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)$ as a function of $m_a$ and the expected (dashed) limits under the background-only hypothesis when combining all categories. The inner green and outer yellow shaded bands show the $\pm 1\sigma$ and $\pm 2\sigma$ uncertainties of the expected limits.

The observed (solid) 95% C.L. upper limits on $(\sigma(H)/\sigma_{\mathrm{SM}}(H)) \mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)$ as a function of $m_a$ and the expected (dashed) limits under the background-only hypothesis when considering different categories based on the heavy-flavor objects separately. When the category is not used for a mass hypothesis, the limit is listed as zero.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for Multilepton.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for combination.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}b\bar{b}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\tau\tau$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for Multilepton.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for combination.

Observed and expected 95% CL upper limits on the signal strength for the inclusive ggF HH production from the bb̄τ⁺τ^-, bb̄γγ, bb̄bb̄, multilepton and bb̄ℓℓ+E_T^miss decay channels, and their statistical combination. When deriving the limit on μ_ggF^HH (μ_VBF^HH), the VBF (ggF) HH production cross-section is fixed to the SM predicted value for m_H=125 GeV. The expected limit, along with the ±1σ and ±2σ bands, is calculated under the assumption of no HH process and with all NPs profiled to the observed data.

Observed and expected 95% CL upper limits on the signal strength for the VBF HH production from the bb̄τ⁺τ^-, bb̄γγ, bb̄bb̄, multilepton and bb̄ℓℓ+E_T^miss decay channels, and their statistical combination. When deriving the limit on μ_ggF^HH (μ_VBF^HH), the VBF (ggF) HH production cross-section is fixed to the SM predicted value for m_H=125 GeV. The expected limit, along with the ±1σ and ±2σ bands, is calculated under the assumption of no HH process and with all NPs profiled to the observed data.

Observed and expected upper limits at 95% CL on the inclusive ggF and VBF HH production cross-section from the bb̄τ⁺τ^-, bb̄γγ, bb̄bb̄, multilepton and bb̄ℓℓ+E_T^miss decay channels, and their statistical combination. The Higgs boson mass is set to 125 GeV when deriving the predicted SM cross-section (red band). The expected limits, along with the ±1σ and ±2σ bands, are calculated under the assumption of no Higgs boson pair production and with all NPs profiled to the observed data.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for Multilepton.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for combination.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for Multilepton.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for combination.

Observed (solid lines) and expected (dashed lines) 95% CL exclusion limits on the HH production cross-sections of (a) the inclusive ggF and VBF processes as a function of κ_λ, for the bb̄γγ (purple), bb̄τ⁺τ^- (green), multilepton (cyan), bb̄bb̄ (blue) and bb̄ℓℓ+E_T^miss (brown) decay channels and their combination (black). The expected limits assume no HH production. The red line shows the theory prediction for the ggF and VBF HH production cross-section as a function of κ_λ. The bands surrounding the red cross-section lines indicate the theoretical uncertainties on the predicted cross-section.

Observed (solid lines) and expected (dashed lines) 95% CL exclusion limits on the HH production cross-sections of the VBF process as a function of κ_2V, for the bb̄γγ (purple), bb̄τ⁺τ^- (green), multilepton (cyan), bb̄bb̄ (blue) and bb̄ℓℓ+E_T^miss (brown) decay channels and their combination (black). The expected limits assume no VBF HH production. The ggF HH production cross-section is assumed to be as predicted by the SM. The ggF HH production cross-section is assumed to be as predicted by the SM. The red line shows the predicted VBF HH cross-section as a function of κ_2V. The bands surrounding the red cross-section lines indicate the theoretical uncertainties on the predicted cross-section.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for combination.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}b\bar{b}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\tau\tau$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for combination.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for combination.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}b\bar{b}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\tau\tau$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for combination.

Observed and expected 95% CL combined upper limits on the cross-section for the SM and seven BSM HEFT benchmarks (arXiv:2304.01968 [hep-ph]) in the ggF process, describing representative signal kinematics and m_HH shape features obtained by varying multiple HEFT coefficients. The expected limits from the bb̄τ⁺τ^-, bb̄γγ and bb̄bb̄ decay channels are presented as well. Theoretical predictions, estimated using specific sets of coefficient values defined in the benchmarks, are shown as red cross dots.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side $\Delta_{AA}$ for ${\pi/2<\Delta\phi<\pi}$

Differential away-side $\Delta_{AA}$ for ${\pi/2<\Delta\phi<\pi}$

Differential away-side $\Delta_{AA}$ for ${\pi/2<\Delta\phi<\pi}$

R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, pT,jet

R32 for delta y

R32 for delta y max

R32 for $m_{jj}$

R32 for $m_{jj, max}$

R43 for $H_{T2}$, 60 GeV < $p_{T,3}$

R43 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R43 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R43 for $H_{T2}$, pT,jet

R43 for $Delta y_{jj}$

R43 for $Delta y_{jj, max}$

R43 for $m_{jj}$

R43 for $m_{jj, max}$

R54 for $H_{T2}$, 60 GeV < $p_{T,3}$

R54 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R54 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R54 for $Delta y_{jj}$

R54 for $Delta y_{jj, max}$

R54 for $m_{jj}$

R54 for $m_{jj, max}$

R42 for $H_{T2}$, 60 GeV < $p_{T,3}$

R42 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R42 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R42, pT,jet

R42 for $Delta y_{jj}$

R42 for $Delta y_{jj, max}$

R42 for $m_{jj}$

R42 for $m_{jj}$

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 2

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=3

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=4

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 5

pTnincl

pTnincl

pTnincl

mjj max, $N_{jet}$ >= 2

mjj max, $N_{jet}$ >= 3

mjj max, $N_{jet}$ >= 4

mjj max, $N_{jet}$ >= 5

$m_{jj}$ $N_{jet}$ >= 2

$m_{jj}$ $N_{jet}$ >= 3

$m_{jj}$ $N_{jet}$ >= 4

$m_{jj}$ $N_{jet}$ >= 5

$Delta y_{jj}$ $N_{jet}$ >= 2

$Delta y_{jj}$ $N_{jet}$ >= 3

$Delta y_{jj}$ $N_{jet}$ >= 4

$Delta y_{jj}$ $N_{jet}$ >= 5

$Delta y_{jj, max}$, $N_{jet}$ >= 2

$Delta y_{jj, max}$, $N_{jet}$ >= 3

$Delta y_{jj, max}$, $N_{jet}$ >= 4

$Delta y_{jj, max}$, $N_{jet}$ >= 5

R32 for $H_{T2}$, 60 GeV < $p_{T,3}$

R32 for $H_{T2}$, 0.05 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

Differential cross-section in bins of subleading muon $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading muon $\eta$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading muon $\eta$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading muon $\phi$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading muon $\phi$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet azimuth. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet azimuth. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet mass. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet mass. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet constituent multiplicity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet constituent multiplicity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_1$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $\tau_1$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_2$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $\tau_2$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_3$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $\tau_3$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_{21}$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of $\Delta R$ between the leading charged particle jet and the dilepton system. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Statistical covariance between bins of Table 2

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 3

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 4

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 5

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 6

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 7

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 8

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 9

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 10

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 11

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 12

Mean-$p_{T}$ of $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 13

Mean-$p_{T}$ of $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 14

Mean-$p_{T}$ of $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 15

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 16

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 17

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 18

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 19

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 20

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 21

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 22

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 23

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 24

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 25

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 26

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 27

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 28

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 29

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 30

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 31

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 32

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 33

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 34

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 35

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 36

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 37

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 38

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 39

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 40

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 41

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 42

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 43

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 44

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 45

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 46

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 47

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 48

Mean-$p_{T}$ of $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 49

Mean-$p_{T}$ of $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 50

Mean-$p_{T}$ of $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 51

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 52

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 53

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 54

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 55

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 56

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 57

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 58

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 59

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 60

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 61

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 62

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 63

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 64

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 65

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 66

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 67

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 68

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 69

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 70

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 71

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 72

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the combined fit, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the combined fit, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the combined fit, when all other coupling modifiers are fixed to their SM predictions.

Likelihood observed contours at 68% CL (solid line) and 95% CL (dashed line) in the ($\kappa_{\lambda}$, $\kappa_{2V}$) parameter space, when all other coupling modifiers are fixed to their SM predictions. The best-fit value, denoted by a cross in the plot, corresponds to the very first entry in the table.

Likelihood expected contours at 68% CL (teal shaded region) and 95% CL (yellow shaded region) in the ($\kappa_{\lambda}$, $\kappa_{2V}$) parameter space, when all other coupling modifiers are fixed to their SM predictions. In the plot, the SM prediction is indicated by the star.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{gghh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross. The best-fit value corresponds to the very first entry in the table.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{gghh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{tthh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross. The best-fit value corresponds to the very first entry in the table.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{tthh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{H}$, $c_{H◻}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross. The best-fit value corresponds to the very first entry in the table.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{H}$, $c_{H◻}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross.

The acceptance times efficiency [in %] for the signal ggF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel. The other coupling modifiers affecting ggF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal ggF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ SLT channel. The other coupling modifiers affecting ggF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal ggF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ LTT channel. The other coupling modifiers affecting ggF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ SLT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ LTT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{2V}$ in each analysis category for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{2V}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ SLT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{2V}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ LTT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Summary of the expected (blue) and observed (orange) one-dimensional constraints at 68% (solid error bars) and 95% (dashed error bars) CL on the coefficients of selected HEFT (top) and SMEFT (bottom) operators describing Higgs boson interactions affecting $HH$ production through gluon-gluon fusion. The results are obtained from one dimensional scans of the profile log-likelihood assuming that all other Wilson coefficients are fixed to their SM values. The contribution from VBF $HH$ production is neglected.

Pre-fit background composition of the SR$3\ell$ Prod. The table shows the event yields as opposed to just the percentages of the relevant background processes.

Post-fit plot of $H_\text{T}(\text{jets})$ in the SR$2\ell$ Dec from a signal-blinded background-only fit.

Post-fit plot of $m(t_\text{SM}, b\text{-jet}_0)$ in the SR$2\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Dec from a signal-blinded background-only fit.

Post-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Prod from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Prod from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$e$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$\mu$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ $t\bar{t}V$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$e$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$\mu$ from the full fit to data.

Post-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}W$ from the full fit to data.

Post-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}Z$ from the full fit to data.

Observed and expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hu)$ for different analyses and their statistical combination.

Observed and expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hc)$ for different analyses and their statistical combination.

Post-fit normalisation factors of free-floating background processes and the signal normalisation.

Post-fit predicted and observed yields in all $2\ell$SS signal and control regions. Pre-fit signal contributions for a signal cross section equivalent to $\mathcal{B}(t\to Hq)=0.1\,\%$ are given as well.

Post-fit predicted and observed yields in all $3\ell$ signal and control regions. Pre-fit signal contributions for a signal cross section equivalent to $\mathcal{B}(t\to Hq)=0.1\,\%$ are given as well.

Expected upper limits on $\mathcal{B}(t\to Hq)$ for the nominal fit and alternative fit configurations. One contains the full phase space but only considers statistical uncertainties. Two other configurations consider the full set of systematic uncertainties, but only encompass one final state.

Expected and observed upper limits on $\mathcal{B}(t\to Hq)$ and $|C_{u\phi}^{qt,tq}|$ for the full fit containing all systematic uncertainties.

Pre-fit plot of $H_\text{T}(\text{jets})$ in the SR$2\ell$ Dec from a signal-blinded background-only fit.

Pre-fit plot of $m(t_\text{SM}, b\text{-jet}_0)$ in the SR$2\ell$ Prod from a signal-blinded background-only fit.

Pre-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Dec from a signal-blinded background-only fit.

Pre-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Prod from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$e$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$\mu$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ $t\bar{t}V$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$e$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$\mu$ from the full fit to data.

Pre-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}W$ from the full fit to data.

Pre-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}Z$ from the full fit to data.

Ranking of fit nuisance parameters according to their impact on the post-fit $tHu$ signal normalisation when fixed to $\pm1\sigma$

Ranking of fit nuisance parameters according to their impact on the post-fit $tHc$ signal normalisation when fixed to $\pm1\sigma$

Expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hu)$ for each individual final state and the full analysis.

Expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hc)$ for each individual final state and the full analysis.

Figure 8(left) of the article. Differential fiducial cross-section of the Z boson $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 8(right) of the article. Differential fiducial cross-section of the leading $b$-jet $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 9 of the article. Differential fiducial cross-section of the $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 10(left) of the article. Differential fiducial cross-section of the $\Delta \phi$ between the leading and sub-leading $b$-jets in events with $Z \left( \rightarrow \ell\ell \right) + 2 b$-jets. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 10(right) of the article. Differential fiducial cross-section of the invariant mass of the leading and sub-leading $b$-jets in events with $Z \left( \rightarrow \ell\ell \right) + 2 b$-jets. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 11(left) of the article. Differential fiducial cross-section of the Z boson $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 11(right) of the article. Differential fiducial cross-section of the leading $c$-jet $p_T$ in events with $Z \left( \rightarrow \ell\ell ight) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 12 of the article. Differential fiducial cross-section of the leading $c$-jet $x_F$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 18 & 24) for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 4.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 19 & 25) for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 9.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 20 & 26) for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 5.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 21 & 27) for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 10.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 22 & 28) for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 11.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper of the paper to hadron level with R 0.3 cone labelling (see Tables 23 & 29) for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 6.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level R 0.3 cone labelling for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 12.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 13.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 14.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 15.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 16.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 17.

Correction factors from parton level to hadron level for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors statistical uncertainty of Pythia8 simulations. See Table 12.

Correction factors from parton level to hadron level for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 13.

Correction factors from parton level to hadron level for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 14.

Correction factors from parton level to hadron level for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 15.

Correction factors from parton level to hadron level for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 16.

Correction factors from parton level to hadron level for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 17.

Dijet invariant mass distributions data compared to the fitted background estimates for the $j b b$ channel. The distributions are shown here with the $m_{jj}$ resolution binning.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.