Improved Selective Background Monte Carlo Simulation at Belle II with Graph Attention Networks and Weighted Events

Sumitted to PubDB: 2022-10-14

Category: Poster, Visibility: Public

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Authors	Boyang Yu
Date	Oct. 24, 2022
Belle II Number	BELLE2-POSTER-CONF-2022-003
Abstract	When 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.
Conference	ACAT 2022