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

Sumitted to PubDB: 2022-09-16

Category: Master Thesis, Visibility: Public

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Authors	Nikolai Krug, Thomas Kuhr, Boyang Yu
Date	Jan. 1, 2021
Belle II Number	BELLE2-MTHESIS-2022-017
Abstract	When 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.
Conference	Munich, Germany