Wing Sheung Chan

138 Distributions and modelling of the neural network input variables 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 ( τµ→ Z -1 = 13 TeV, 139 fb s 1P τ µ SR, 60 80 100 120 140 160 180 200 ) [GeV] τ , µ ( vis m 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 ( τµ→ Z -1 = 13 TeV, 139 fb s 1P τ µ SR, 60 80 100 120 140 160 180 200 ) [GeV] τ , µ ( coll m 0.5 0.75 1 1.25 1.5 Data / pred. 0 10000 20000 30000 40000 50000 60000 70000 Events / 0.5 Data fakes had-vis τ→ jet ττ→ Z ll → Z Others Total uncertainty ) 3 − = 10 Β ( τµ→ Z -1 = 13 TeV, 139 fb s 1P τ µ SR, 2 − 1 − 0 1 2 3 4 5 ) τ , µ ( α ∆ 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 ( τµ→ Z -1 = 13 TeV, 139 fb s 1P τ µ SR, 0 20 40 60 80 100 120 140 track) [GeV] τ , µ ( m 0.5 0.75 1 1.25 1.5 Data / pred. Figure C.7.: Expected and observed distributions of m vis ( µ, τ ) , m coll ( µ, τ ) , ∆ α ( µ, τ ) and m ( µ, τ track ) in the 1P SR of the µτ 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 uncertain- ties. The last bin in each plot includes overflow events.