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2020

Efficiency Parameterization with Neural Networks

Badiali, C., Di Bello, F. A., Frattari, G. et al.

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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 multidimensional 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.

Efficiency Parameterization with Neural Networks · Around

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