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 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.
Highlights
An overarching issue of Large Hadron Collider (LHC) experiments is the necessity of massive numbers of simulated collision events to estimate the rates of expected processes in very restricted regions of phase space
We propose an approach based on Graph Neural Networks (GNN) [8, 9]
We propose a different approach to estimate jet based on a neural network built using a GNN
Summary
An overarching issue of Large Hadron Collider (LHC) experiments is the necessity of massive numbers of simulated collision events to estimate the rates of expected processes in very restricted regions of phase space To mitigate this difficulty, a commonly used approach is the event weighting technique which replaces selection cuts with event weights. -far weights have been defined from binned efficiency maps The difficulty in these methods is the range of applicability of efficiency maps that are limited in the number of dimensions (typically two), and subsequently, fail to capture more subtle effects that appear in specific regions of phase space. To account for these dependencies, a multidimensional mapping is required. This implies large statistical fluctuations in the map itself that defies the original purpose of the method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
