Wing Sheung Chan

Distributions and modelling of the neural network input variables 139 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 ( τµ→ Z -1 = 13 TeV, 139 fb s 3P 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 2000 4000 6000 8000 10000 12000 14000 16000 18000 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 3P 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 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 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 3P 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. Figure C.8.: Expected and observed distributions of m vis ( µ, τ ) , m coll ( µ, τ ) , ∆ α ( µ, τ ) and m ( µ, τ track ) in the 3P 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.