Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events
Abstract
Abstract When measuring rare processes at Belle II, a huge luminosity is required, which means a large number of simulations are necessary to determine signal efficiencies and background contributions. However, this process demands high computation costs while most of the simulated data, in particular in case of background, are discarded by the event selection. Thus, filters using graph neural networks are introduced at an early stage to save the resources for the detector simulation and reconstruction of events discarded at analysis level. In our work, we improved the performance of the filters using graph attention and investigated statistical methods including sampling and reweighting to deal with the biases introduced by the filtering.
Fields of Study & Subjects
Atmospheric and Environmental Gas Dynamics
Medical Imaging Techniques and Applications
Air Quality Monitoring and Forecasting
Publication details
- Journal / source
- Journal of Physics Conference Series
- Publisher
- IOP Publishing
- Volume
- 3206
- Issue
- 1
- Pages
- 012089–012089
- Publication date
- 1 April 2026
- Language
- EN