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.

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.

The standard deviation (DISPERSION) of the subject multiplicity for gluon jets and quark jets for different values of the subject resolution parameter Y0.

The ratio of the multiplicities and their standard deviations for the subject in quark and gluon jets as a function of the subject resolution parameter Y0.

The measured fragmentation function for charged particles within quark and gluon jets.

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

The CP-averaged observable S3 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The CP-averaged observable S4 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The CP-averaged observable S5 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The CP-averaged observable Afb versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The CP-averaged observable S7 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The CP-averaged observable S8 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The CP-averaged observable S9 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable Fl versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P1 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P2 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P3 versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P4' versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P5' versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P6' versus q2. The first (second) error bars represent the statistical (total) uncertainties.

The optimised observable P8' versus q2. The first (second) error bars represent the statistical (total) uncertainties.

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 0.10 < q2 < 0.98 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 1.10 < q2 < 2.50 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 2.50 < q2 < 4.00 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 4.00 < q2 < 6.00 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 6.00 < q2 < 8.00 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 11.00 < q2 < 12.50 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 15.00 < q2 < 17.00 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 17.00 < q2 < 19.00 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 1.10 < q2 < 6.00 GeV2/c4

Correlation matrix for the CP-averaged observables FL, AFB and S3–S9 from the maximum-likelihood fit in the interval 15.00 < q2 < 19.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 0.10 < q2 < 0.98 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 1.10 < q2 < 2.50 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 2.50 < q2 < 4.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 4.00 < q2 < 6.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 6.00 < q2 < 8.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 11.00 < q2 < 12.50 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 15.00 < q2 < 17.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 17.00 < q2 < 19.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 1.10 < q2 < 6.00 GeV2/c4

Correlation matrix for the optimised observables FL and P1–P'8 from the maximum-likelihood fit in the interval 15.00 < q2 < 19.00 GeV2/c4

Optimised angular observables evaluated by the unbinned maximum likelihood fit. The first uncertainties are statistical and the second systematic.

CP-averaged angular observables evaluated using the method of moments. The first uncertainties are statistical and the second systematic.

CP-asymmetries evaluated using the method of moments. The first uncertainties are statistical and the second systematic.

Optimised observables evaluated using the method of moments. The first uncertainties are statistical and the second systematic.

Zero-crossing points determined with an amplitude fit.

Likelihood correlation matrix $0.1 < q^2 < 0.98~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $1.1 < q^2 < 2.5~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $2.5 < q^2 < 4.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $4.0 <q^2< 6.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $6.0 < q^2 < 8.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $11.0 <q^2< 12.5 ~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $15.0 < q^2 < 17.0 ~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $17.0 <q^2< 19.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $1.1 <q^2< 6.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $15.0 <q^2< 19.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $0.1 < q^2 < 0.98~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $1.1 < q^2 < 2.5~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $2.5 < q^2 < 4.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $4.0 <q^2< 6.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $6.0 < q^2 < 8.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $11.0 <q^2< 12.5 ~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $15.0 <q^2< 17.0 ~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $17.0 <q^2< 19.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $1.1 <q^2< 6.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $15.0 <q^2< 19.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $0.1 < q^2 < 0.98~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $1.1 < q^2 < 2.5~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $2.5 < q^2 < 4.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $4.0 <q^2< 6.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $6.0 < q^2 < 8.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $11.0 <q^2< 12.5 ~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $15.0 <q^2< 17.0 ~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $17.0 <q^2< 19.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $1.1 <q^2< 6.0~{\rm GeV}^2/c^4$.

Likelihood correlation matrix $15.0 <q^2< 19.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $0.10 < q^2 < 0.98~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $1.1 < q^2 < 2.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $2.0 < q^2 < 3.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $3.0 < q^2 < 4.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $4.0 < q^2 < 5.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $5.0 < q^2 < 6.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $6.0 < q^2 < 7.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $7.0 < q^2 < 8.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $11.00 <q^2 < 11.75~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $11.75 <q^2 < 12.50~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $15.0 <q^2 < 16.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $16.0 <q^2 < 17.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $17.0 <q^2 < 18.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $18.0 <q^2 < 19.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $15.0 <q^2 < 19.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $0.10 < q^2 < 0.98~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $1.1 < q^2 < 2.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $2.0 < q^2 < 3.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $3.0 < q^2 < 4.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $4.0 < q^2 < 5.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $5.0 < q^2 < 6.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $6.0 < q^2 < 7.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $7.0 < q^2 < 8.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $11.00 <q^2 < 11.75~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $11.75 <q^2 < 12.50~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $15.0 <q^2 < 16.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $16.0 <q^2 < 17.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $17.0 <q^2 < 18.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $18.0 <q^2 < 19.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $15.0 <q^2 < 19.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $0.1 <q^2 < 0.98~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $1.1 <q^2 < 2.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $2.0 <q^2 < 3.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $3.0 <q^2 < 4.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $4.0 <q^2 < 5.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $5.0 <q^2 < 6.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $6.0 <q^2 < 7.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $7.0 <q^2 < 8.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $11.0 <q^2 < 11.75~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $11.75 <q^2 < 12.5~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $15.0 <q^2 < 16.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $16.0 <q^2 < 17.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $17.0 < q^2 < 18.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $18.0 < q^2 < 19.0~{\rm GeV}^2/c^4$.

Bootstrap correlation matrix $15.0 < q^2 < 19.0~{\rm GeV}^2/c^4$.

FIG. 2: a) left side: The correlation data for the ratio of the histograms of same-event-pairs to mixed-event-pairs for unlike-sign charged pairs, shown in a two-dimensional (2-D) perspective plot $\Delta\phi$ - $\Delta\eta$. The plot was normalized to a mean of 1. b) right side: The similar correlation data for like-sign charge pairs.

FIG. 3: a) left side: Folded correlation data for unlike-sign charge pairs on $\Delta\phi$ and $\Delta\eta$ based on demonstrated symmetries in the data. This increases the statistics in a typical bin by a factor of four. Henceforth we will be dealing with folded data only. b) right side: Similar folded data for like-sign charge pairs.

FIG. 3: a) left side: Folded correlation data for unlike-sign charge pairs on $\Delta\phi$ and $\Delta\eta$ based on demonstrated symmetries in the data. This increases the statistics in a typical bin by a factor of four. Henceforth we will be dealing with folded data only. b) right side: Similar folded data for like-sign charge pairs.

FIG. 4: “(Color online)”a) left side: The 2 dimensional (2-D) approximately gaussian signal shape equation (4) from the fit to the normalized and folded unlike-sign charge pairs signal data. b) right side: The unlike-sign charge pairs signal data corresponding to the adjoining signal function on the left side.

FIG. 4: “(Color online)”a) left side: The 2 dimensional (2-D) approximately gaussian signal shape equation (4) from the fit to the normalized and folded unlike-sign charge pairs signal data. b) right side: The unlike-sign charge pairs signal data corresponding to the adjoining signal function on the left side.

FIG. 5 :“(Color online)”a) left side: A perspective plot of the fit to the 2-D like-sign charge pairs signal shape of equation (5). b) right side: Like-sign charge pairs signal data corresponding to the adjacent signal fit on the left side.

FIG. 5: “(Color online)”a) left side: A perspective plot of the fit to the 2-D like-sign charge pairs signal shape of equation (5). b) right side: Like-sign charge pairs signal data corresponding to the adjacent signal fit on the left side.

FIG. 6: “(Color online)”a) left side: The charge dependent (CD) signal shape is a 2 dimensional (2-D) approximate gaussian which is symmetrical. The average gaussian width is 27.5$^{\circ}$ $\pm$ 3.2$^{\circ}$ (0.48 $\pm$ 0.056 rad) both in $\Delta\phi$ and $\Delta\eta$. Thus we are observing a greater probability for unlike-sign charge pairs of particles than for like-sign charge pairs emitted randomly on 2-D $\eta\phi$ directions. b) right side: The CD signal data corresponding to the adjacent signal fit on the left.

FIG. 6: “(Color online)”a) left side: The charge dependent (CD) signal shape is a 2 dimensional (2-D) approximate gaussian which is symmetrical. The average gaussian width is 27.5$^{\circ}$ $\pm$ 3.2$^{\circ}$ (0.48 $\pm$ 0.056 rad) both in $\Delta\phi$ and $\Delta\eta$. Thus we are observing a greater probability for unlike-sign charge pairs of particles than for like-sign charge pairs emitted randomly on 2-D $\eta\phi$ directions. b) right side: The CD signal data corresponding to the adjacent signal fit on the left.

FIG. 7: “(Color online)”a) left side: The fit to the charge independent (CI) signal shape which is the sum of the fits of like-sign plus unlike-sign charge pairs signals. The 2-D gaussian equivalent RMS signal has a $\Delta\phi$ width of about 32$^{\circ}$ (0.52 rad), and $\Delta\eta$ width of about 66$^{\circ}$ (1.15 rad) which is about double the $\Delta\phi$ width. b) right side: The CI signal data corresponding to the adjacent fit on the left.

FIG. 7: “(Color online)”a) left side: The fit to the charge independent (CI) signal shape which is the sum of the fits of like-sign plus unlike-sign charge pairs signals. The 2-D gaussian equivalent RMS signal has a $\Delta\phi$ width of about 32$^{\circ}$ (0.52 rad), and $\Delta\eta$ width of about 66$^{\circ}$ (1.15 rad) which is about double the $\Delta\phi$ width. b) right side: The CI signal data corresponding to the adjacent fit on the left.

FIG. 8: a) left side: The charge independent (CI) signal data with the lower range of elliptic flow amplitude and the best $\chi^2$ (the same as Figure 7b) plotted as a 2-D perspective plot on $\Delta\phi$ vs. $\Delta\eta$. b) right side: The charge independent (CI) signal data with our maximum elliptic flow amplitude used in our analysis ($\chi^2$ was worse by 1$\sigma$).

FIG. 8: a) left side: The charge independent (CI) signal data with the lower range of elliptic flow amplitude and the best $\chi^2$ (the same as Figure 7b) plotted as a 2-D perspective plot on $\Delta\phi$ vs. $\Delta\eta$. b) right side: The charge independent (CI) signal data with our maximum elliptic flow amplitude used in our analysis ($\chi^2$ was worse by 1$\sigma$).

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>