论文标题
神经网络的效率参数化
Efficiency Parameterization with Neural Networks
论文作者
论文摘要
多维效率图通常用于高能量物理实验中，以减轻大型模拟事件样品产生的局限性。但是，bined多维效率图受到统计的强烈限制。我们提出了一种神经网络方法，以学习局部密度的比率，以最佳的时尚效率估算一组参数的函数。图形神经网络技术用于说明事件中不同物理对象之间的高维相关性。我们在特定的玩具模型中显示了该方法如何适用于在HEP实验中为重风味标记分类器生成准确的多维效率图，包括未经训练的过程。
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.
