Mainz 2022 – wissenschaftliches Programm

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HK: Fachverband Physik der Hadronen und Kerne

HK 42: Hadron Structure and Spectroscopy VII

HK 42.5: Vortrag

Mittwoch, 30. März 2022, 15:15–15:30, HK-H8

Performance Improvement of Deep Machine Learning for the PANDA Software Trigger — •Peiyong Jiang^1,2, Klaus Goetzen¹, Ralf Kliemt¹, Klaus Peters¹, and Frank Nerling¹ for the PANDA collaboration — ¹GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Darmstadt, Germany — ²Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China

Deep machine learning methods have been studied for the PANDA software trigger with data sets from full Monte Carlo simulation using PandaRoot. Following the first comparison of multiclass and binary classification, the binary classification has been selected because of higher signal efficiencies. In total seven neural network types have been compared and the residual convolutional neural network with 4 residual blocks has been chosen. The results of optimized neural networks and those of the conventional method have been compared, showing an efficiency gain of up to 140% for the deep machine learning method. The flatness quality parameters on Dalitz plots and theta-phi projections have been obtained.