000003222 001__ 3222
000003222 005__ 20220916064254.0
000003222 037__ $$aBELLE2-MTHESIS-2022-017
000003222 041__ $$aeng
000003222 100__ $$aBoyang Yu
000003222 245__ $$aImproved Selective Background Monte Carlo Simulation at Belle II with Graph Attention Networks and Weighted Events
000003222 260__ $$aMunich, Germany$$bLMU Munich$$c2021
000003222 300__ $$amult. p
000003222 500__ $$aPresented on 14 09 2021
000003222 502__ $$aMSc$$bMunich, Germany, LMU Munich$$c2021
000003222 520__ $$aWhen measuring rare processes such as B → K(∗)νν¯ or B → lνγ, a huge luminosity is required, which means a large number of simulations are necessary to determine signal efficiencies and background contributions. However, this process demands high computation costs while most of the simulated data, in particular in case of background, are discarded by the event selection. Thus filters using neural networks are introduced after the Monte Carlo event generation to speed up the following processes of detector simulation and reconstruction. Merely filtering out events will however inevitably introduce bias. Therefore statistical methods are invested to deal with this side effect.
000003222 700__ $$aThomas Kuhr$$edir.
000003222 700__ $$aNikolai Hartmann$$edir.
000003222 8560_ $$fboyang.yu@physik.uni-muenchen.de
000003222 8564_ $$uhttps://docs.belle2.org/record/3222/files/BELLE2-MTHESIS-2022-017.pdf
000003222 980__ $$aTHESIS