000003643 001__ 3643
000003643 005__ 20230614084756.0
000003643 037__ $$aBELLE2-MTHESIS-2023-031
000003643 041__ $$aeng
000003643 100__ $$aLuca Schinnerl
000003643 245__ $$aAnalysis Specific Filters for Smart Background Simulations at Belle II
000003643 260__ $$aMunich$$bLMU$$c2022
000003643 300__ $$a85
000003643 500__ $$aPresented on 08 08 2022
000003643 502__ $$aMSc$$bMunich, LMU$$c2022
000003643 520__ $$aThe Belle II experiment is expected to accumulate a data sample of 50/ab in its lifetime. For rare processes, strong background suppression is needed to precisely measure these types of events. Because of this, an extremely large number of simulated background events is necessary for an effective analysis. However, a significant portion of the simulated data is discarded trivially in the first stage of analysis, demanding a better method of simulation to keep up with the amount of data. For this purpose a neural network is implemented to select the relevant data after the Monte Carlo event generation and then only run the costly detector simulation and reconstruction for selected events. Existing methods have shown good success with graph neural networks. However, the total speedup of simulations is limited when considering generic selections. Here a maximum speedup of the simulation with the smart background filter over the brute force method of 2.1 was reached. In this work I iteratively introduce analysis specific filters to the training of the neural networks, which can greatly increase efficiencies. For an analysis selection in the search for the rare process B -> K* nu nu this methodology has been successful in significantly improving simulation speed.
000003643 700__ $$aThomas Kuhr$$edir.
000003643 700__ $$aNikolai Hartmann$$edir.
000003643 8560_ $$fthomas.kuhr@lmu.de
000003643 8564_ $$uhttps://docs.belle2.org/record/3643/files/BELLE2-MTHESIS-2023-031.pdf
000003643 980__ $$aTHESIS