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Fig. 3.


Toy example of discrimination efficiencies computed using the FDR formalism. Each (Gaussian) distribution represents the histogram measured for some observable given the model. The amount of overlapping area between any two distributions indicates how distinguishable the two models are according to this observable: the more the curves overlap, the more difficult it is to distinguish the corresponding models, and the higher the discrimination efficiency parameter is. In the example shown, considering model 1 as the hypothesis against which the others are tested, models 2, 3, and 4 have computed discrimination efficiencies of 77%, 37%, and 100%.

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