Efficiency Parameterization with Neural Networks

Multidimensional efficiency maps are commonly used in high-energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned efficiency maps are however strongly limited by statistics. We propose a neural network approach to learn ratios of local densities to estimate in an optimal fashion efficiencies as a function of a set of parameters. Graph neural network techniques are used to account for the high dimensional correlations between different physics objects in the event. We show in a specific toy model how this method is applicable to produce accurate multidimensional efficiency maps for heavy-flavor tagging classifiers in HEP experiments, including for processes on which it was not trained.