Analysis Specific Filters for Smart Background Simulations at Belle II

Sumitted to PubDB: 2023-06-14

Category: Master Thesis, Visibility: Public

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Authors	Nikolai Krug, Thomas Kuhr, Luca Schinnerl
Date	Jan. 1, 2022
Belle II Number	BELLE2-MTHESIS-2023-031
Abstract	The 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.
Conference	Munich