Heidelberg 2022 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 53: Data Analysis, Information Technology and Artificial Intelligence 3

T 53.2: Vortrag

Dienstag, 22. März 2022, 16:30–16:45, T-H38

Analysis Specific Filters for Selective Background Monte Carlo Simulations at Belle II — •Luca Schinnerl, Boyang Yu, Nikolai Hartmann, and Thomas Kuhr — Ludwig Maximilians University Munich, Munich, Germany

The Belle II experiment is expected to accumulate a data sample of 50 ab-1 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 with a retention rate of 4.25%. Here a maximum speedup of 2.1 was reached. In this work we iteratively introduce analysis specific filters to the training of the neural networks, which can greatly increase efficiencies. For the rare process B -> K*vv this methodology has been successful in significantly improving simulation speed.