Efficiency Parameterization with Neural Networks

TL;DR

This paper introduces a neural network method, utilizing graph neural networks, to accurately estimate multidimensional efficiency maps in high energy physics, overcoming statistical limitations of traditional binned approaches.

Contribution

It presents a novel neural network approach with graph techniques to learn efficiency ratios, improving accuracy and generalization in high energy physics event analysis.

Findings

Effective in producing accurate efficiency maps in toy models

Handles high-dimensional correlations between physics objects

Generalizes to processes not seen during training

Abstract

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. 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.