000003287 001__ 3287
000003287 005__ 20221014075417.0
000003287 037__ $$aBELLE2-POSTER-CONF-2022-003
000003287 041__ $$aeng
000003287 100__ $$aBoyang Yu
000003287 245__ $$aImproved Selective Background Monte Carlo Simulation at Belle II with Graph Attention Networks and Weighted Events
000003287 260__ $$aACAT 2022$$c2022-10-24
000003287 300__ $$a1
000003287 520__ $$aWhen 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 invested statistical methods including sampling and reweighting to deal with biases introduced by the filtering.
000003287 8560_ $$fboyang.yu@physik.uni-muenchen.de
000003287 8564_ $$uhttps://docs.belle2.org/record/3287/files/BELLE2-POSTER-CONF-2022-003.pdf