The unfolded differential charge asymmetry as a function of the transverse momentum of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.

The unfolded differential charge asymmetry as a function of the longitudinal boost of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.

The unfolded inclusive leptonic asymmetry. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the invariant mass of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the transverse momentum of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the longitudinal boost of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ inclusive measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.

Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ < 500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [500,750] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [750,1000] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [1000,1500] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ > 1500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ < 30 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ $\in$ [30,120] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ > 120 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ < 200 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [200,300] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [300,400] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ > 400 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ < 20 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ $\in$ [20, 70] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ > 70 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Covariance matrix for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. The total (stat. + syst.) uncertainties are considered.

Covariance matrix for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement. The total (stat. + syst.) uncertainties are considered.

Covariance matrix for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement. The total (stat. + syst.) uncertainties are considered.

Covariance matrix for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement. The total (stat. + syst.) uncertainties are considered.

Covariance matrix for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement. The total (stat. + syst.) uncertainties are considered.

Covariance matrix for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement. The total (stat. + syst.) uncertainties are considered.

Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 38 of the auxilliary material of the publication.

Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 39 of the auxilliary material of the publication.

Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 40 of the auxilliary material of the publication.

Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 31 of the auxilliary material of the publication.

Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 32 of the auxilliary material of the publication.

Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 33 of the auxilliary material of the publication.

Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 34 of the auxilliary material of the publication.

Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 35 of the auxilliary material of the publication.

Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 36 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$), via vector boson fusion ($qq\phi$) or in association with b quarks ($bb\phi$). In this case, $bb\phi$ production rate is profiled, whereas the scan is performed in the $gg\phi$ and $qq\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 64 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 65 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 66 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 67 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 68 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 69 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 70 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 71 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 72 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 73 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 74 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 75 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 76 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 77 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 78 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 79 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 80 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 81 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 82 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 83 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 84 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 85 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 86 of the auxilliary material of the publication.

Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 1 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12a of the publication.

Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 2 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12b of the publication.

Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 3 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 92 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication, but evaluated on Asimov pseudodata.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 99 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 100 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 101 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 102 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 103 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 104 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 105 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 106 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 107 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 108 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 109 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 110 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 111 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 112 of the auxilliary material of the publication.

Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 113 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13a of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13a of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13a of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13a of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13a of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13a of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13b of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13b of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13b of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13b of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13b of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13b of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 114 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 114 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 114 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 114 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 114 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 114 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 115 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 115 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 115 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 115 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 115 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 115 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 116 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 116 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 116 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 116 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 116 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 116 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 117 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 117 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 117 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 117 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 117 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 117 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 118 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 118 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 118 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 118 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 118 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 118 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 119 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 119 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 119 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 119 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 119 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 119 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario. Numerical values provided in this table correspond to the observed contour of Figure 120 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 120 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 120 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 120 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 120 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 120 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 122 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 122 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 122 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 122 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 122 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 122 of the auxilliary material of the publication.

Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 123 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 123 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 123 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 123 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 123 of the auxilliary material of the publication.

Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 123 of the auxilliary material of the publication.

Fractions of the cross-section $\sigma(gg\phi)$ as expected from SM for the loop contributions with only top quarks, only bottom quarks and from the top-bottom interference. These values are used to scale the corresponding signal components for a given mass $m_\phi$.

Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.

Covariance matrix for the total uncertainty (i.e. fit including stat., not extrapolation) for the measurement of the absolute differential cross section as a function of the rho observable at parton level.

Covariance matrix for the statistical uncertainty for the measurement of the absolute differential cross section as a function of the rho observable at parton level.

Covariance matrix for the statistical uncertainty for the measurement of the absolute differential cross section as a function of the rho observable at parton level.

Covariance matrix for the extrapolation uncertainty for the measurement of the absolute differential cross section as a function of the rho observable at parton level.

Covariance matrix for the extrapolation uncertainty for the measurement of the absolute differential cross section as a function of the rho observable at parton level.

Normalized differential cross section as a function of the rho observable at parton level.

Normalized differential cross section as a function of the rho observable at parton level.

Covariance matrix for the total uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.

Covariance matrix for the total uncertainty (i.e. fit including stat., not extrapolation) for the measurement of the normalized differential cross section as a function of the rho observable at parton level.

Covariance matrix for the statistical uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.

Covariance matrix for the statistical uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.

Covariance matrix for the extrapolation uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.

Covariance matrix for the extrapolation uncertainty for the measurement of the normalized differential cross section as a function of the rho observable at parton level.

Correlation matrix for all nuisance parameters and parameters of interest of the Likelihood fit.

Correlation matrix for all nuisance parameters and parameters of interest of the Likelihood fit.

This table is a numerical representation of Fig. 8 for all nuisance parameters.

This table is a numerical representation of Fig. 8 for all nuisance parameters.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.17$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.19$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.21$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.23$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.25$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.27$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.29$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.31$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.33$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for resonant production of paired dijet resonances decaying to a quark-gluon pair, with $M(X)/M(Y) = 0.42$. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to predictions for a scalar diquark with couplings to pairs of up quarks, $y_{uu}$ = 0.4, and to pairs of vector-like quarks, $y_{χ}$ = 0.6.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for the non-resonant production of top squark pairs in the RPV SUSY decay scenario. The corresponding expected limits and their variations at the 1 and 2 standard deviation levels are also shown. Limits are compared to the top squark model cross section.

Observed differential four-jet mass spectrum for 0.22 < $\\a$ < 0.24. The cross-section is calculated by dividing the event yield by the bin width and luminosity.

Observed differential four-jet mass spectrum for 0.24 < $\\a$ < 0.26. The cross-section is calculated by dividing the event yield by the bin width and luminosity.

Observed differential four-jet mass spectrum for 0.26 < $\\a$ < 0.28. The cross-section is calculated by dividing the event yield by the bin width and luminosity.

Observed differential four-jet mass spectrum for all $\\a$ bins together. The cross-section is calculated by dividing the event yield by the bin width and luminosity.

Observed differential average dijet mass spectrum within three $\\a$ bins of the non-resonant search. The cross-section is calculated by dividing the event yield by the bin width and luminosity.

Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2018 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 28$ GeV.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true $L_{xy}$, in all simulated $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and $p_T > 10$ GeV matched to generated muons from $X \rightarrow \mu \mu$ decays.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true $L_{xy}$, in all simulated $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and $p_T > 10$ GeV matched to generated muons from $X \rightarrow \mu \mu$ decays.

Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Background estimation and observed number of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.

Background estimation and observed number of events in the TMS-TMS dimuon category in 2016 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only

Background estimation and observed number of events in the TMS-TMS dimuon category in 2018 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only

Correspondence between the mass intervals in the TMS-TMS category and the parameters of the simulated signal samples.

Correspondence between the probed LLP masses and the chosen mass intervals in the TMS-TMS category.

Background estimation and observed number of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.

Number of events passing consecutive sets of selection criteria for 2018 collision data and the signal process $\Phi(125) \rightarrow XX(20\ GeV, c\tau = 13\ cm) \rightarrow \mu\mu$. Each row introduces a new criterion that is applied in addition to the selection of the previous row. In addition to the total number of events, N(events), the event yields of the individual dimuon vertex categories, STA-STA, TMS-TMS, and STA-TMS, are shown in separate columns for each data set. In these columns, events containing selected dimuons of different categories are independently counted for each category.

Number of events passing consecutive sets of selection criteria, in 2018 data and in a sample of simulated $\Phi \rightarrow XX \rightarrow \mu\mu$ signal events with $m(H) = 125\ GeV$, $m(X) = 20\ GeV$, and $c\tau = 13\ cm$. Each row introduces a new criterion that is applied in addition to the selection of the previous row. In addition to the total number of events $N(\text{total})$, the event yields in the individual dimuon categories, STA-STA, TMS-TMS, and STA-TMS, are shown in separate columns for each data set. In these columns, events containing selected dimuons of different categories are counted independently for each category.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.

The observed and expected (in parentheses) 95% CL lower mass limits on RPV SUSY, Z′ ($\mathcal{B}=0.1$) , and QBH signals for the e$\mu$, e$\tau$, and $\mu\tau$ channels.

Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.

Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.

Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\mu$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the $\mu\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.

Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\mu$ channel. The regions to the left of and above the curves are excluded.

Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\tau$ channel. The regions to the left of and above the curves are excluded.

Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the $\mu\tau$ channel. The regions to the left of and above the curves are excluded.

Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.

Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.

Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.

Results for the covariance matrix where the parameters a, b, c, and d are taken from a linear fit to the different CP-violating samples.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the dijet-1 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the dijet-2 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the dijet-3 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the untagged 1 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the untagged 2 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the untagged 3 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Fits to the $m_{\ell^+\ell^-\gamma}$ data distribution in the untagged 4 categories. In the upper panel, the red solid line shows the result of a signal-plus-background fit to the given category. The red dashed line shows the background component of the fit. The green and yellow bands represent the 68 and 95% CL uncertainties in the fit. Also plotted is the expected SM signal, scaled by a factor of 10. In the lower panel, the data minus the background component of the fit is shown.

Observed signal strength ($\mu$) for a SM Higgs boson at 125.38 GeV. The labels untagged combined,dijet combined and combined represent the results obtained from simultaneous fits of the untagged categories, dijet categories, and full set of categories, respectively. The black solid line shows $\mu=1$, and the red dashed line shows the best fit value $\hat{\mu}=2.4$ of all categories combined.

Upper limit (95% CL) on the signal strength ($\mu$) relative to the SM prediction, as a function of the assumed value of the Higgs boson mass used in the fit.