Wing Sheung Chan
Distributions and modelling of the neural network input variables 133 0 10000 20000 30000 40000 50000 60000 70000 80000 Events / 10 GeV Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τ e → Z -1 = 13 TeV, 139 fb s 1P τ e SR, 0 20 40 60 80 100 120 140 160 180 ) [GeV] had-vis τ ( ∧ E 0.5 0.75 1 1.25 1.5 Data / pred. 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Events / 10 GeV Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τ e → Z -1 = 13 TeV, 139 fb s 3P τ e SR, 0 20 40 60 80 100 120 140 160 180 ) [GeV] had-vis τ ( ∧ E 0.5 0.75 1 1.25 1.5 Data / pred. 0 20 40 60 80 100 3 10 × Events / 10 GeV Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τ e → Z -1 = 13 TeV, 139 fb s 1P τ e SR, 40 − 20 − 0 20 40 60 ) [GeV] had-vis τ ( x ∧ p 0.5 0.75 1 1.25 1.5 Data / pred. 0 5000 10000 15000 20000 25000 Events / 10 GeV Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τ e → Z -1 = 13 TeV, 139 fb s 3P τ e SR, 40 − 20 − 0 20 40 60 ) [GeV] had-vis τ ( x ∧ p 0.5 0.75 1 1.25 1.5 Data / pred. 0 10000 20000 30000 40000 50000 Events / 10 GeV Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τ e → Z -1 = 13 TeV, 139 fb s 1P τ e SR, 0 20 40 60 80 100 120 140 160 180 ) [GeV] had-vis τ ( z ∧ p 0.5 0.75 1 1.25 1.5 Data / pred. 0 2000 4000 6000 8000 10000 12000 14000 Events / 10 GeV Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τ e → Z -1 = 13 TeV, 139 fb s 3P τ e SR, 0 20 40 60 80 100 120 140 160 180 ) [GeV] had-vis τ ( z ∧ p 0.5 0.75 1 1.25 1.5 Data / pred. Figure C.2.: Expected and observed distributions of ˆ E ( τ had - vis ) , ˆ p x ( τ had - vis ) and ˆ p z ( τ had - vis ) in the SR of the eτ channel. In the lower panel of each plot, the ratios of the observed yields to the predicted background yields are shown. The hatched error bands represent the combined statistical and systematic uncertainties. The last bin in each plot includes overflow events.
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