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Distribution of exclusive jet multiplicity.

Breakdown of systematic uncertainties in percent in exclusive jet multiplicity in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in exclusive jet multiplicity in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (leading jet) [GeV] with at least one jet in the event.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (leading jet) [GeV] with exactly one jet in the event.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with exactly one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with exactly one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (leading jet) [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (leading jet) [GeV] with at least three jets in the event.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (leading jet) [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (2nd jet) [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in pT (2nd jet) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (2nd jet) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (3rd jet) [GeV] with at least three jets in the event.

Breakdown of systematic uncertainties in percent in pT (3rd jet) [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (3rd jet) [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (4th jet) [GeV] with at least four jets in the event.

Breakdown of systematic uncertainties in percent in pT (4th jet) [GeV] with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (4th jet) [GeV] with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of pT (5th jet) [GeV] with at least five jets in the event.

Breakdown of systematic uncertainties in percent in pT (5th jet) [GeV] with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in pT (5th jet) [GeV] with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of leading jet rapidity with at least one jet in the event.

Breakdown of systematic uncertainties in percent in leading jet rapidity with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in leading jet rapidity with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of 2nd jet rapidity with at least two jets in the event.

Breakdown of systematic uncertainties in percent in 2nd jet rapidity with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in 2nd jet rapidity with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with at least one jet in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with exactly one jet in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with exactly one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with exactly one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with exactly two jets in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with exactly two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with exactly two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with at least three jets in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with exactly three jets in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with exactly three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with exactly three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with at least four jets in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of HT [GeV] with at least five jets in the event.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in HT [GeV] with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of DPhi(jj) [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in DPhi(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in DPhi(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of Dy(jj) [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in Dy(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in Dy(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of DR(jj) [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in DR(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in DR(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of m(jj) [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in m(jj) [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in m(jj) [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of 3rd jet rapidity with at least three jets in the event.

Breakdown of systematic uncertainties in percent in 3rd jet rapidity with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in 3rd jet rapidity with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of 4th jet rapidity with at least four jets in the event.

Breakdown of systematic uncertainties in percent in 4th jet rapidity with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in 4th jet rapidity with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of 5th jet rapidity with at least five jets in the event.

Breakdown of systematic uncertainties in percent in 5th jet rapidity with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in 5th jet rapidity with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with at least one jet in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least one jet in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least one jet in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with at least two jets in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with exactly two jets in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with exactly two jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with exactly two jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with at least three jets in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with exactly three jets in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with exactly three jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with exactly three jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with at least four jets in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least four jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least four jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Distribution of ST [GeV] with at least five jets in the event.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least five jets in the event in the electron channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Breakdown of systematic uncertainties in percent in ST [GeV] with at least five jets in the event in the muon channel.Uncertainties have been symmetrised and the sign denotes the sign of the original up-variation.

Differential ep cross section for dijet production as a function of the average pseudorapidity the two jets in two transverse energy regions and in the Y region 0.1 to 0.5.

Differential ep cross section for dijet production as a function of the average pseudorapidity the two jets in two transverse energy regions and in the Y region 0.5 to 0.9.

Differential ep cross section for dijet production as a function of the photon momentum fraction X(C=GAMMA) in two regions of proton fractional momentum XP.

Differential ep cross section for dijet production as a function of the proton momentum fraction XP in two regions of photon fractional momentum X(C=GAMMA).

Differential ep cross section for dijet production as a function of the photon momentum fraction X(C=GAMMA) in two regions maximum jet transverseenergy.

Differential ep cross section for dijet production as a function of COS(THETA*) in two regions of fractional photon momentum fraction X(C=GAMMA).

Differential ep cross section for dijet production as a function of COS(THETA*) for dijet masses > 65 GeV in two regions of fractional photon momentum fraction X(C=GAMMA).

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{4})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\text{min}\Delta R(bb)$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at leastfour $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{4})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

The measured normalised differential cross-section as a function of $N_{b-\text{jets}}$ in the $e\mu+\geq2b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{4})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{4})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\text{min}\Delta R(bb)$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least two b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{4})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{4})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\text{min}\Delta R(bb)$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.332 GeV and W = 1.839 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.433 GeV and W = 1.889 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.534 GeV and W = 1.939 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.635 GeV and W = 1.987 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.737 GeV and W = 2.035 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.838 GeV and W = 2.081 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 1.939 GeV and W = 2.126 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.039 GeV and W = 2.170 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.139 GeV and W = 2.212 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.240 GeV and W = 2.255 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.341 GeV and W = 2.296 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.443 GeV and W = 2.338 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.543 GeV and W = 2.377 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.642 GeV and W = 2.416 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ LAMBDA for incident energy = 2.741 GeV and W = 2.454 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.232 GeV and W = 1.787 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.332 GeV and W = 1.839 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.433 GeV and W = 1.889 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.534 GeV and W = 1.939 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.635 GeV and W = 1.987 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.737 GeV and W = 2.035 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.838 GeV and W = 2.081 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 1.939 GeV and W = 2.126 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.039 GeV and W = 2.170 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.139 GeV and W = 2.212 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.240 GeV and W = 2.255 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.341 GeV and W = 2.296 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.443 GeV and W = 2.338 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.543 GeV and W = 2.377 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.642 GeV and W = 2.416 GeV.

Coefficients Cx and Cz for the reaction GAMMA P --> K+ SIGMA0 for incident energy = 2.741 GeV and W = 2.454 GeV.

Normalized distributions of Tranverse Thrust Minor for 5 lower limit values of leading particle PT.

Normalized distributions of Tranverse Sphericity for 4 ranges of leading particle PT.

Normalized distributions of Tranverse Sphericity for 5 lower limit values of leading particle PT.

Mean Values of Thrust, Thrust Minor and Sphericity verses Multiplicity.

Mean Values of Thrust, Thrust Minor and Sphericity verses charged particle PT scalar sum.

Numbers of observed and background events for SR3L low for each bin of the distribution in Meff. The table corresponds to Fig. 4(d). The statistical and systematic uncertainties are combined for the predicted numbers.

Numbers of observed and background events for SR3L high for each bin of the distribution in Meff. The table corresponds to Fig. 4(e). The statistical and systematic uncertainties are combined for the predicted numbers.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The efficiencies are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The efficiencies are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The efficiencies are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into b s and gluinos decay into t stop (see Fig. 5d in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The efficiencies are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The acceptances (in percent, %) are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The acceptances (in percent, %) are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The acceptances (in percent, %) are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The acceptances (in percent, %) are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The limits on observed cross section are calculated for all simplified models. The simplified models are for direct pair production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair-production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop)-20 GeV.

The signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

Experimental uncertainties on the signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

Experimental uncertainties on the signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

Experimental uncertainties on the signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

Statistical uncertainties on the signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

Statistical uncertainties on the signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

Statistical uncertainties on the signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W ^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The confidence levels are calculated for all simplified models. For each model, the observed and expected values are given. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.

The confidence levels are calculated for all simplified models. For each model, the observed and expected values are given. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).

The confidence levels are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluion), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).

The confidence levels are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the expected and observed values are given. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).

The confidence levels are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the expected and observed values are given. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.

The confidence levels are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the expected and observed values are given.

The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).

The confidence levels are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the expected and observed values are given. The model assumes tan(beta)=30, A0=2m0, and mu>0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range < 1.5 and the ET range > 8 GeV using the inclusive KT jet finding algorithm.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range 1.5 TO 2.2 and the ET range > 8 GeV using the inclusive KT jet finding algorithm.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range > 2.2 and the ET range > 8 GeV using the inclusive KT jet finding algorithm.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range < 1.5 and the ET range 5 TO 8 GeV using the cone jet finding algorthm with cone radius = 1.0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range 1.5 TO 2.2 and the ET range 5 TO 8 GeV using the cone jet finding algorthm with cone radius = 1.0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range > 2.2 and the ET range 5 TO 8 GeV using the cone jet finding algorthm with cone radius = 1.0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range < 1.5 and the ET range > 8 GeV using the cone jet finding algorthm with cone radius = 1.0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range 1.5 TO 2.2 and the ET range > 8 GeV using the cone jet finding algorthm with cone radius = 1.0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range > 2.2 and the ET range > 8 GeV using the cone jet finding algorthm with cone radius = 1.0.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range < 1.5 and the ET range 5 TO 8 GeV using the cone jet finding algorthm with cone radius = 0.7.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range 1.5 TO 2.2 and the ET range 5 TO 8 GeV using the cone jet finding algorthm with cone radius = 0.7.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range > 2.2 and the ET range 5 TO 8 GeV using the cone jet finding algorthm with cone radius = 0.7.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range < 1.5 and the ET range > 8 GeV using the cone jet finding algorthm with cone radius = 0.7.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range 1.5 TO 2.2 and the ET range > 8 GeV using the cone jet finding algorthm with cone radius = 0.7.

The dependence of the jet shapes on the transverse jet energy ET in the pseudorapidity range > 2.2 and the ET range > 8 GeV using the cone jet finding algorthm with cone radius = 0.7.

The average number of subjets as a function of the resolution parameter YCUT using the inclusive KT jet finding algorithm in the pseudorapidity range < 1.5 and the ET range 5 TO 8 GeV YCUT using the inclusive KT jet finding algorithm.

The average number of subjets as a function of the resolution parameter YCUT using the inclusive KT jet finding algorithm in the pseudorapidity range 1.5 TO 2.2 and the ET range 5 TO 8 GeV YCUT using the inclusive KT jet finding algorithm.

The average number of subjets as a function of the resolution parameter YCUT using the inclusive KT jet finding algorithm in the pseudorapidity range > 2.2 and the ET range 5 TO 8 GeV YCUT using the inclusive KT jet finding algorithm.

The average number of subjets as a function of the resolution parameter YCUT using the inclusive KT jet finding algorithm in the pseudorapidity range < 1.5 and the ET range > 8 GeV YCUT using the inclusive KT jet finding algorithm.

The average number of subjets as a function of the resolution parameter YCUT using the inclusive KT jet finding algorithm in the pseudorapidity range 1.5 TO 2.2 and the ET range > 8 GeV YCUT using the inclusive KT jet finding algorithm.

The average number of subjets as a function of the resolution parameter YCUT using the inclusive KT jet finding algorithm in the pseudorapidity range > 2.2 and the ET range > 8 GeV YCUT using the inclusive KT jet finding algorithm.

Differential cross section for inclusive D*+- production in the visible kinematic region as a function of Q**2.

Differential cross section for inclusive D*+- production in the visible kinematic region as a function of X.

Differential cross section for inclusive D*+- production in the visible kinematic region as a function of W.

Differential cross section for inclusive D*+- production in the visible kinematic region as a function of PT.

Differential cross section for inclusive D*+- production in the visible kinematic region as a function of ETARAP.

Differential cross section for inclusive D*+- production in the visible kinematic region as a function of Z.

Double differential cross section for inclusive D*+- production in bins of Q**2 and X.

Double differential cross section for inclusive D*+- production in bins of Q**2 and X.

Double differential cross section for inclusive D*+- production in bins of Q**2 and X.

Double differential cross section for inclusive D*+- production in bins of Q**2 and X.

Double differential cross section for inclusive D*+- production in bins of Q**2 and X.

Differential cross section for inclusive D*+- production as a function of Q**2 with the constraint that the transverse momentum of the D* in the virtual photon-proton centre-of-mass frame is > 2 GeV.

Differential cross section for inclusive D*+- production as a function of X with the constraint that the transverse momentum of the D* in the virtual photon-proton centre-of-mass frame is > 2 GeV.

Differential cross section for inclusive D*+- production as a function of PT with the constraint that the transverse momentum of the D* in the virtual photon-proton centre-of-mass frame is > 2 GeV.

Differential cross section for inclusive D*+- production as a function of ETARAP with the constraint that the transverse momentum of the D* in the virtualphoton-proton centre-of-mass frame is > 2 GeV.

Differential cross section for D*+- production with dijets as a function of Q**2.

Differential cross section for D*+- production with dijets as a function of X.

Differential cross section for D*+- production with dijets as a function of ET(C=JET1).

Differential cross section for D*+- production with dijets as a function of M(C=JET2).

Double differential cross section for D*+- production with dijets in bins of Q**2 and PHI(JET1)-PHI(JET2).

Double differential cross section for D*+- production with dijets in bins of Q**2 and PHI(JET1)-PHI(JET2).

Differential cross section for D*+- production with dijets as a function of pseudorapidity of the jet containing D*.

Differential cross section for D*+- production with dijets as a function of pseudorapidity of the other jet.

Differential cross section for D*+- production with dijets as a function of the pseudorapidity difference of the two jets.

Differential cross section for D*+- production with dijets as a function of X(C=GAMMA), the observed fraction of the virtual photon momentum carried by the partons.

Double differential cross section for D*+- production with dijets in bins of Q**2 and X(C=GAMMA), the observed fraction of the virtual photon momentum carried by the partons.

Differential cross section for D*+- production with dijets as a function of X(C=GLUON), the observed fraction of the proton momentum carried by the gluon.

Double differential cross section for D*+- production with dijets in bins of Q**2 and X(C=GLUON), the observed fraction of the proton momentum carried by the gluon.