Values of A2 and X*G2 from proton and deuterium target data at mean electron scattering angle of 2.75 degrees and incident energy 32.3 GeV. Errors shown are statistical only.

Values of A2 and X*G2 from proton and deuterium target data at mean electron scattering angle of 5.5 degrees and incident energy 32.3 GeV. Errors shown are statistical only.

Values of A2 and X*G2 from proton and deuterium target data at mean electron scattering angle of 10.5 degrees and incident energy 32.3 GeV. Errors shown are statistical only.

The values of A2 and X*G2 from proton and deuterium target data for all the individual spectrometer data combined. Errors are statistical only.

Results for the $P_3$ angular observable. The first uncertainties are statistical and the second systematic.

Results for the $P_4^\prime$ angular observable. The first uncertainties are statistical and the second systematic.

Results for the $P_5^\prime$ angular observable. The first uncertainties are statistical and the second systematic.

Results for the $P_6^\prime$ angular observable. The first uncertainties are statistical and the second systematic.

Results for the $P_8^\prime$ angular observable. The first uncertainties are statistical and the second systematic.

Results for the CP averaged observables $F_L$ and $P_1$–$P_8^\prime$. The first uncertainties are statistical and the second systematic.

Correlation matrix of angular observables Fl and P1–P8', considering only the statistical uncertainties from the maximum-likelihood fit in the interval 1.1 < $q^2$ < 2 GeV$^2$

Correlation matrix of angular observables Fl and P1–P8', considering only the statistical uncertainties from the maximum-likelihood fit in the interval 2 < $q^2$ < 4.3 GeV$^2$

Correlation matrix of angular observables Fl and P1–P8', considering only the statistical uncertainties from the maximum-likelihood fit in the interval 4.3 < $q^2$ < 6 GeV$^2$

Correlation matrix of angular observables Fl and P1–P8', considering only the statistical uncertainties from the maximum-likelihood fit in the interval 6 < $q^2$ < 8.68 GeV$^2$

Correlation matrix of angular observables Fl and P1–P8', considering only the statistical uncertainties from the maximum-likelihood fit in the interval 10.09 < $q^2$ < 12.86 GeV$^2$

Correlation matrix of angular observables Fl and P1–P8', considering only the statistical uncertainties from the maximum-likelihood fit in the interval 14.18 < $q^2$ < 16 GeV$^2$

IRing2 for HT2>=500 GeV, NJets>=5

IRing2 for HT2>=1000 GeV, NJets>=2

IRing2 for HT2>=1000 GeV, NJets>=3

IRing2 for HT2>=1000 GeV, NJets>=4

IRing2 for HT2>=1000 GeV, NJets>=5

IRing2 for HT2>=1500 GeV, NJets>=2

IRing2 for HT2>=1500 GeV, NJets>=3

IRing2 for HT2>=1500 GeV, NJets>=4

IRing2 for HT2>=1500 GeV, NJets>=5

IRing128 for HT2>=500 GeV, NJets>=2

IRing128 for HT2>=500 GeV, NJets>=3

IRing128 for HT2>=500 GeV, NJets>=4

IRing128 for HT2>=500 GeV, NJets>=5

IRing128 for HT2>=1000 GeV, NJets>=2

IRing128 for HT2>=1000 GeV, NJets>=3

IRing128 for HT2>=1000 GeV, NJets>=4

IRing128 for HT2>=1000 GeV, NJets>=5

IRing128 for HT2>=1500 GeV, NJets>=2

IRing128 for HT2>=1500 GeV, NJets>=3

IRing128 for HT2>=1500 GeV, NJets>=4

IRing128 for HT2>=1500 GeV, NJets>=5

ICyl16 for HT2>=500 GeV, NJets>=2

ICyl16 for HT2>=500 GeV, NJets>=3

ICyl16 for HT2>=500 GeV, NJets>=4

ICyl16 for HT2>=500 GeV, NJets>=5

ICyl16 for HT2>=1000 GeV, NJets>=2

ICyl16 for HT2>=1000 GeV, NJets>=3

ICyl16 for HT2>=1000 GeV, NJets>=4

ICyl16 for HT2>=1000 GeV, NJets>=5

ICyl16 for HT2>=1500 GeV, NJets>=2

ICyl16 for HT2>=1500 GeV, NJets>=3

ICyl16 for HT2>=1500 GeV, NJets>=4

ICyl16 for HT2>=1500 GeV, NJets>=5

IRing2 covariance for HT2>=500 GeV, NJets>=2 (Table 1)

IRing2 covariance for HT2>=500 GeV, NJets>=3 (Table 2)

IRing2 covariance for HT2>=500 GeV, NJets>=4 (Table 3)

IRing2 covariance for HT2>=500 GeV, NJets>=5 (Table 4)

IRing2 covariance for HT2>=1000 GeV, NJets>=2 (Table 5)

IRing2 covariance for HT2>=1000 GeV, NJets>=3 (Table 6)

IRing2 covariance for HT2>=1000 GeV, NJets>=4 (Table 7)

IRing2 covariance for HT2>=1000 GeV, NJets>=5 (Table 8)

IRing2 covariance for HT2>=1500 GeV, NJets>=2 (Table 9)

IRing2 covariance for HT2>=1500 GeV, NJets>=3 (Table 10)

IRing2 covariance for HT2>=1500 GeV, NJets>=4 (Table 11)

IRing2 covariance for HT2>=1500 GeV, NJets>=5 (Table 12)

IRing128 covariance for HT2>=500 GeV, NJets>=2 (Table 13)

IRing128 covariance for HT2>=500 GeV, NJets>=3 (Table 14)

IRing128 covariance for HT2>=500 GeV, NJets>=4 (Table 15)

IRing128 covariance for HT2>=500 GeV, NJets>=5 (Table 16)

IRing128 covariance for HT2>=1000 GeV, NJets>=2 (Table 17)

IRing128 covariance for HT2>=1000 GeV, NJets>=3 (Table 18)

IRing128 covariance for HT2>=1000 GeV, NJets>=4 (Table 19)

IRing128 covariance for HT2>=1000 GeV, NJets>=5 (Table 20)

IRing128 covariance for HT2>=1500 GeV, NJets>=2 (Table 21)

IRing128 covariance for HT2>=1500 GeV, NJets>=3 (Table 22)

IRing128 covariance for HT2>=1500 GeV, NJets>=4 (Table 23)

IRing128 covariance for HT2>=1500 GeV, NJets>=5 (Table 24)

ICyl16 covariance for HT2>=500 GeV, NJets>=2 (Table 25)

ICyl16 covariance for HT2>=500 GeV, NJets>=3 (Table 26)

ICyl16 covariance for HT2>=500 GeV, NJets>=4 (Table 27)

ICyl16 covariance for HT2>=500 GeV, NJets>=5 (Table 28)

ICyl16 covariance for HT2>=1000 GeV, NJets>=2 (Table 29)

ICyl16 covariance for HT2>=1000 GeV, NJets>=3 (Table 30)

ICyl16 covariance for HT2>=1000 GeV, NJets>=4 (Table 31)

ICyl16 covariance for HT2>=1000 GeV, NJets>=5 (Table 32)

ICyl16 covariance for HT2>=1500 GeV, NJets>=2 (Table 33)

ICyl16 covariance for HT2>=1500 GeV, NJets>=3 (Table 34)

ICyl16 covariance for HT2>=1500 GeV, NJets>=4 (Table 35)

ICyl16 covariance for HT2>=1500 GeV, NJets>=5 (Table 36)

IRing2 covariance, complete

1-IRing128 covariance, complete

1-ICyl16 covariance, complete

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

The fitted slopes of the E3C/E2C data distributions as a function of jet pt are used to illustrate the dependency of alphas on jet pt.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

The chi2 scan result using eq.3 for different alphas.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The energy weight of E2C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

The energy weight of E3C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

Results for ALPHAS from fits of the ln R-matching predictions for the fractional 2-jet rate observable (D2), and the mean jet multiplicities (N) for the Durham and Cambridge schemes. The errors shown are total errors and contain all the statistics and systematics.

Results for ALPHAS at the Z0 mass from fits of the O(alphas**2) predicitonsfor the energy evolution of the mean 2-jet cross section <Y23> for the DURHAM a nd JADE schemes. The errors shown are total errors and contain all the statistics and systematics.

Results for ALPHAS at the Z0 mass from fits of the O(alphas**2) predicitonsfor the 3-jet fractions (R3) for the JADE, DURHAM and CAMBRIDGE schemes. The errors shown are total errors and contain all the statistics and systematics.

N-Jet rates from the JADE collaboration at c.m. energy 35 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the JADE collaboration at c.m. energy 44 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 91 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 133 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 161 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 172 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 183 GeV. Jets define using the JADE/E0 alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 189 GeV. Jets define using the JADE/E0 alogrithm.

Mean value of the observable Ynm (the value of YCUT at the boundary betweenn and (n+1=m) jets) as a function of the c.m. energy. Data from JADE and OPAL collaborations. Jets defined using the JADE/E0 alogrithm.

N-Jet rates from the JADE collaboration at c.m. energy 35 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the JADE collaboration at c.m. energy 44 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 91 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 133 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 161 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 172 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 183 GeV. Jets defined using the DURHAM alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 189 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the JADE collaboration at c.m. energy 35 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the JADE collaboration at c.m. energy 44 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the OPAL collaboration at c.m. energy 91 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the OPAL collaboration at c.m. energy 133 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the OPAL collaboration at c.m. energy 161 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the OPAL collaboration at c.m. energy 172 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the OPAL collaboration at c.m. energy 183 GeV. Jets defined using the DURHAM alogrithm.

Differential distributions in Ynm (the minimum YCUT for the separation inton and m(=n+1) jets). ) from the OPAL collaboration at c.m. energy 189 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the JADE collaboration at c.m. energy 35 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the JADE collaboration at c.m. energy 44 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 91 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 133 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 161 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 172 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 183 GeV. Jets defined using the DURHAM alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 189 GeV. Jets defined using the DURHAM alogrithm.

Mean value of the observable Ynm (the value of YCUT at the boundary betweenn and (n+1=m) jets) as a function of the c.m. energy. Data from JADE and OPAL collaborations. Jets defined using the DURHAM alogrithm.

N-Jet rates from the JADE collaboration at c.m. energy 35 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the JADE collaboration at c.m. energy 44 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 91 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 133 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 161 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 172 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 183 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from the OPAL collaboration at c.m. energy 189 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the JADE collaboration at c.m. energy 35 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the JADE collaboration at c.m. energy 44 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the OPAL collaboration at c.m. energy 91 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the OPAL collaboration at c.m. energy 133 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the OPAL collaboration at c.m. energy 161 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the OPAL collaboration at c.m. energy 172 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the OPAL collaboration at c.m. energy 183 GeV. Jets defined using the CAMBRIDGE alogrithm.

Differential N-Jet rates from the OPAL collaboration at c.m. energy 189 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the JADE collaboration at c.m. energy 35 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the JADE collaboration at c.m. energy 44 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 91 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 133 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 161 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 172 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 183 GeV. Jets defined using the CAMBRIDGE alogrithm.

Mean jet multiplicity as a function of YCUT from the OPAL collaboration at c.m. energy 189 GeV. Jets defined using the CAMBRIDGE alogrithm.

N-Jet rates from JADE collaboration at c.m. energy 35 GeV. Jets define using the CONE alogrithm.

N-Jet rates from JADE collaboration at c.m. energy 35 GeV. Jets define using the CONE alogrithm.

N-Jet rates from JADE collaboration at c.m. energy 44 GeV. Jets define using the CONE alogrithm.

N-Jet rates from JADE collaboration at c.m. energy 44 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 91 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 91 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 133 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 133 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 161 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 161 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 172 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 172 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 183 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 183 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 189 GeV. Jets define using the CONE alogrithm.

N-Jet rates from OPAL collaboration at c.m. energy 189 GeV. Jets define using the CONE alogrithm.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Differential fiducial cross sections for the leading jet pT

Data corrected for finite acceptance and resolution of the detector and for intial state photon radiation. No corrections for hadronic effects are applied.. Errors include statistical and systematic uncertainties, added in quadrature.

Data corrected for finite acceptance and resolution of the detector and for intial state photon radiation. No corrections for hadronic effects are applied.. Errors include statistical and systematic uncertainties, added in quadrature.

Data corrected for finite acceptance and resolution of the detector and for intial state photon radiation. No corrections for hadronic effects are applied.. Errors include statistical and systematic uncertainties, added in quadrature.. YCUT is the cut off value used to define the jets in this case using the 'Durham' scheme.

Data corrected for finite acceptance and resolution of the detector and for intial state photon radiation. No corrections for hadronic effects are applied.. Errors include statistical and systematic uncertainties, added in quadrature.. YCUT is the cut off value used to define the jets in this case using the 'Durham' scheme.. D2 is the differential jet rate.

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