Selective background Monte Carlo simulation at Belle II

Sumitted to PubDB: 2020-03-05

Category: Talk, Visibility: Public

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Authors	James Kahn, Thomas Kuhr, Kilian Lieret, Andi Lindner
Non-Belle II authors	Emilio Dorigatti
Date	Nov. 5, 2019
Belle II Number	BELLE2-TALK-CONF-2020-019
Abstract	The large volume of data expected to be produced by the Belle II experiment presents the opportunity for for studies of rare, previously inaccessible processes. To investigate such rare processes in a high data volume environment necessitates a correspondingly high volume of Monte Carlo simulations to prepare analyses and gain a deep understanding of the contributing physics processes to each individual study. This resulting challenge, in terms of computing resource requirements, calls for more intelligent methods of simulation, in particular for background processes with very high rejection rates. This work presents a method of predicting in the early stages of the simulation process the likelihood of relevancy of an individual event to the target study using convolutional neural networks. The results show a robust training that is integrated natively into the existing Belle II analysis software framework, with steps taken to mitigate systematic biases induced by the early selection procedure.
Conference	24th International Conference on Computing in High-Energy and Nuclear Physics (CHEP 2019)