Breakdown of syst. uncertainties (fully bin-by-bin correlated) for differential cross section for $e^+e^-\rightarrow\pi^+\pi^-\gamma$, with $50^o<\theta_\gamma<130^o$

Bare cross section for $e^+e^-\rightarrow\pi^+\pi^-$

Statistical covariance matrix for bare cross section for $e^+e^-\rightarrow\pi^+\pi^-$

Inverse statistical covariance matrix for bare cross section for $e^+e^-\rightarrow\pi^+\pi$

Breakdown of syst. uncertainties (fully bin-by-bin correlated) for the cross section for $e^+e^-\rightarrow\pi^+\pi^-$

Pion form factor $|F_\pi|^2$

Statistical covariance matrix for pion form factor $|F_\pi|^2$

Inverse statistical covariance matrix $|F_\pi|^2$

Breakdown of syst. uncertainties (fully bin-by-bin correlated) for the pion form factor $|F_\pi|^2$

Vacuum polarisation correction $\delta_{VP}(s)=(\frac{\alpha_{em}(s)}{\alpha_{em}(0)})^2$, with $\sigma^{bare} = \sigma^{dressed}/\delta$ - calculated using the routine from F.Jegerlehner: https://web.archive.org/web/20170828074416/http://www-com.physik.hu-berlin.de/~fjeger/alphaQEDn.uu (2003, archived on August 28, 2017)

Final State Radiation correction $(1.+\eta_{FSR}(s))$, evaluated using the formula in Eur. Phys. J. C 24, 51-69 (2002)

The differential radiator function cross section $H(M_{\pi\pi}^2,s)\cdot \frac{\pi \alpha^2 \beta_\pi^3}{3 M_{\pi\pi}^2 s}$, inclusive in $\theta_\gamma$, in bins of 0.01 GeV$^2$ in $M_{\pi\pi}^2$. The value used for $s$ in the Monte Carlo production is $s = 999.85\; GeV^2$, corresponding to the mean value of DA$\Phi$NE energy for data collected in 2006. Obtained with the PHOKHARA5 MC generator, see Eur. Phys. J. C 27, 563 (2003) and https://looptreeduality.csic.es/phokhara/phokhara5.0.tar.gz

$\gamma p \rightarrow J/\psi p$ differential cross sections 10.36–11.44 GeV beam energy range, average $t$ and beam energy in bins of $t$. The first cross section uncertainties are statistical, and the second are systematic. The overall average beam energy is 10.82 GeV. There is an additional fully correlated systematic uncertainty of 19.5% on the total cross section, not included here.

Jet multiplicity measured for a leading-pT jet ($p_{T1}$) with 400 < $p_{T1}$ < 800 GeV and for an azimuthal separation between the two leading jets of $0 < \Delta\Phi_{1,2} < 150^{\circ}$. The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Jet multiplicity measured for a leading-pT jet ($p_{T1}$) with 400 < $p_{T1}$ < 800 GeV and for an azimuthal separation between the two leading jets of $150 < \Delta\Phi_{1,2} < 170^{\circ}$. The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Jet multiplicity measured for a leading-pT jet ($p_{T1}$) with 400 < $p_{T1}$ < 800 GeV and for an azimuthal separation between the two leading jets of $170 < \Delta\Phi_{1,2} < 180^{\circ}$. The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Jet multiplicity measured for a leading-pT jet ($p_{T1}$) with $p_{T1}$ > 800 and for an azimuthal separation between the two leading jets of $0 < \Delta\Phi_{1,2} < 150^{\circ}$. The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Jet multiplicity measured for a leading-pT jet ($p_{T1}$) with $p_{T1}$ > 800 and for an azimuthal separation between the two leading jets of $150 < \Delta\Phi_{1,2} < 170^{\circ}$. The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Jet multiplicity measured for a leading-pT jet ($p_{T1}$) with $p_{T1}$ > 800 and for an azimuthal separation between the two leading jets of $170 < \Delta\Phi_{1,2} < 180^{\circ}$. The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Measured transverse momentum of the leading $p_{T}$ jet ($p_{T1}$). The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Measured transverse momentum of the subleading $p_{T}$ jet ($p_{T2}$). The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Measured transverse momentum of the third leading $p_{T}$ jet ($p_{T3}$). The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Measured transverse momentum of the fourth leading $p_{T}$ jet ($p_{T4}$). The full breakdown of the uncertainties is displayed, with PU corresponding to Pileup, PREF to Trigger Prefering, PTHAT to the hard-scale (renormalization and factorization scales), MISS and FAKE to the inefficienties and background, LUMI to integrated luminosity. With JES, JER and stat. unc. following the notation in the paper.

Jet multiplicity distribution in TUnfold binning ($N_{jets},\Delta\phi_{1,2},p_{T1}$) as indicated in the XML file provided as additional resource. The uncertainties follow the notation of Table 1.

Correlation matrix at particle level for the measured jet multiplicity in TUnfold binning ($N_{jets},\Delta\phi_{1,2},p_{T1}$) as indicated in the XML file provided as additional resource.

Jet $p_{T}$ distributions in TUnfold binning ($p_{T1},p_{T2},p_{T3},p_{T4}$) as indicated in the XML file provided as additional resource. The uncertainties follow the notation of Table 1.

Correlation matrix at particle level for the measured jet $p_{T}$ distributions in TUnfold binning ($p_{T1},p_{T2},p_{T3},p_{T4}$) as indicated in the XML file provided as additional resource.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.400\pm0.056~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.597\pm0.057~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.793\pm0.057~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.992\pm0.058~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.189\pm0.058~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.435\pm0.086~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.761\pm0.111~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

Observed yields in the boost_0_ge2J signal region category of the hh channel.

Observed yields in the boost_1_1J signal region category of the hh channel.

Observed yields in the boost_1_1J signal region category of the hh channel.

Observed yields in the boost_1_ge2J signal region category of the hh channel.

Observed yields in the boost_1_ge2J signal region category of the hh channel.

Observed yields in the boost_2 signal region category of the hh channel.

Observed yields in the boost_2 signal region category of the hh channel.

Observed yields in the boost_3 signal region category of the hh channel.

Observed yields in the boost_3 signal region category of the hh channel.

Observed yields in the ttH_0 signal region category of the hh channel.

Observed yields in the ttH_0 signal region category of the hh channel.

Observed yields in the ttH_1 signal region category of the hh channel.

Observed yields in the ttH_1 signal region category of the hh channel.

Observed yields in the VH_0 signal region category of the hh channel.

Observed yields in the VH_0 signal region category of the hh channel.

Observed yields in the VH_1 signal region category of the hh channel.

Observed yields in the VH_1 signal region category of the hh channel.

Observed yields in the VBF_0 signal region category of the hh channel.

Observed yields in the VBF_0 signal region category of the hh channel.

Observed yields in the VBF_1 signal region category of the hh channel.

Observed yields in the VBF_1 signal region category of the hh channel.

Observed yields in the boost_0_1J signal region category of the lh channel.

Observed yields in the boost_0_1J signal region category of the lh channel.

Observed yields in the boost_0_ge2J signal region category of the lh channel.

Observed yields in the boost_0_ge2J signal region category of the lh channel.

Observed yields in the boost_1_1J signal region category of the lh channel.

Observed yields in the boost_1_1J signal region category of the lh channel.

Observed yields in the boost_1_ge2J signal region category of the lh channel.

Observed yields in the boost_1_ge2J signal region category of the lh channel.

Observed yields in the boost_2 signal region category of the lh channel.

Observed yields in the boost_2 signal region category of the lh channel.

Observed yields in the boost_3 signal region category of the lh channel.

Observed yields in the boost_3 signal region category of the lh channel.

Observed yields in the VH_0 signal region category of the lh channel.

Observed yields in the VH_0 signal region category of the lh channel.

Observed yields in the VH_1 signal region category of the lh channel.

Observed yields in the VH_1 signal region category of the lh channel.

Observed yields in the VBF_0 signal region category of the lh channel.

Observed yields in the VBF_0 signal region category of the lh channel.

Observed yields in the VBF_1 signal region category of the lh channel.

Observed yields in the VBF_1 signal region category of the lh channel.

Observed yields in the boost_0_1J signal region category of the ll channel.

Observed yields in the boost_0_1J signal region category of the ll channel.

Observed yields in the boost_0_ge2J signal region category of the ll channel.

Observed yields in the boost_0_ge2J signal region category of the ll channel.

Observed yields in the boost_1_1J signal region category of the ll channel.

Observed yields in the boost_1_1J signal region category of the ll channel.

Observed yields in the boost_1_ge2J signal region category of the ll channel.

Observed yields in the boost_1_ge2J signal region category of the ll channel.

Observed yields in the boost_2 signal region category of the ll channel.

Observed yields in the boost_2 signal region category of the ll channel.

Observed yields in the boost_3 signal region category of the ll channel.

Observed yields in the boost_3 signal region category of the ll channel.

Observed yields in the VH_0 signal region category of the ll channel.

Observed yields in the VH_0 signal region category of the ll channel.

Observed yields in the VH_1 signal region category of the ll channel.

Observed yields in the VH_1 signal region category of the ll channel.

Observed yields in the VBF_0 signal region category of the ll channel.

Observed yields in the VBF_0 signal region category of the ll channel.

Observed yields in the VBF_1 signal region category of the ll channel.

Observed yields in the VBF_1 signal region category of the ll channel.

Best-fit values and uncertainties for the pp $\rightarrow H\rightarrow\tau\tau$ cross-section measurement and the measurement in the four dominant production modes. All measurements include the branching ratio of $H\rightarrow\tau\tau$ and are performed with true Higgs boson rapidity $|y_{H}|<2.5$. The SM predictions for each region, computed using the inclusive cross-section calculations and the simulated event samples are also shown. The contributions to the total uncertainty in the measurements from statistical (Stat. unc.) or systematic uncertainties (Syst. unc.) are given separately.

Best-fit values and uncertainties for the pp $\rightarrow H\rightarrow\tau\tau$ cross-section measurement and the measurement in the four dominant production modes. All measurements include the branching ratio of $H\rightarrow\tau\tau$ and are performed with true Higgs boson rapidity $|y_{H}|<2.5$. The SM predictions for each region, computed using the inclusive cross-section calculations and the simulated event samples are also shown. The contributions to the total uncertainty in the measurements from statistical (Stat. unc.) or systematic uncertainties (Syst. unc.) are given separately.

The measured correlations between each parameter of interest in the measurement of the cross-sections per production mode

The measured correlations between each parameter of interest in the measurement of the cross-sections per production mode

Best-fit values and uncertainties for the $H\rightarrow\tau\tau$ cross-sections, in the reduced stage 1.2 STXS scheme. The EW production mode includes vector-boson fusion and qq$\rightarrow$V($\rightarrow$qq)H processes. All measurements include the branching ratio of $H\rightarrow\tau\tau$ and are performed with true Higgs boson rapidity $|y_{H}|<2.5$. The SM predictions for each region, computed using the inclusive cross-section calculations and the simulated event samples are also shown. The contributions to the total uncertainty in the measurements from statistical (Stat. unc.) or systematic uncertainties (Syst. unc.) are given separately. The asterisk symbol (*) indicates that the criteria for m$_{jj}$ only apply to events with at least two reconstructed jets.

Best-fit values and uncertainties for the $H\rightarrow\tau\tau$ cross-sections, in the reduced stage 1.2 STXS scheme. The EW production mode includes vector-boson fusion and qq$\rightarrow$V($\rightarrow$qq)H processes. All measurements include the branching ratio of $H\rightarrow\tau\tau$ and are performed with true Higgs boson rapidity $|y_{H}|<2.5$. The SM predictions for each region, computed using the inclusive cross-section calculations and the simulated event samples are also shown. The contributions to the total uncertainty in the measurements from statistical (Stat. unc.) or systematic uncertainties (Syst. unc.) are given separately. The asterisk symbol (*) indicates that the criteria for m$_{jj}$ only apply to events with at least two reconstructed jets.

The measured correlations between each pair of parameters of interest in the STXS measurement. The asterisk symbol (*) indicates that the criteria for mjj only apply to events with at least two reconstructed jets.

The measured correlations between each pair of parameters of interest in the STXS measurement. The asterisk symbol (*) indicates that the criteria for mjj only apply to events with at least two reconstructed jets.