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T: Fachverband Teilchenphysik
T 56: Experimental methods III
T 56.4: Vortrag
Mittwoch, 1. April 2020, 17:15–17:30, H-ÜR 1
Selective background simulation using graph neural networks at Belle II — •James Kahn1, Andreas Lindner2, Emilio Dorigatti2, and Thomas Kuhr2 for the Belle II collaboration — 1Karlsruher Institut für Technologie — 2Ludwig-Maximilians-Universität München
The large volume of data expected to be produced by the Belle II experiment presents the opportunity for studies of rare, previously inaccessible processes. Investigating 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 graph neural networks. The results show a robust training that is integrated natively into the existing Belle II analysis software framework.