000001333 001__ 1333
000001333 005__ 20190311154429.0
000001333 037__ $$aBELLE2-TALK-CONF-2019-015
000001333 041__ $$aeng
000001333 100__ $$aJames Kahn
000001333 245__ $$aSelective background Monte Carlo simulation at Belle II
000001333 260__ $$a19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019)$$c2019-03-11
000001333 300__ $$amult. p
000001333 520__ $$aThe Belle II experiment, beginning data taking with the full detector in early 2019, is expected to produce a volume of data fifty times that of its predecessor. With this dramatic increase in data comes the opportunity for studies of rare previously inaccessible processes. The investigation of such rare processes in a high data volume environment requires 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 presents a significant challenge in terms of computing resource requirements and calls for more intelligent methods of simulation, in particular 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.
000001333 700__ $$aThomas Kuhr
000001333 700__ $$aMartin Ritter
000001333 8560_ $$fjames.kahn@desy.de
000001333 8564_ $$uhttps://docs.belle2.org/record/1333/files/BELLE2-TALK-CONF-2019-015.pdf