Deep machine learning for the PANDA software trigger

Peiyong Jiang; Klaus Götzen; Ralf Kliemt; Frank Nerling; Klaus Peters

doi:10.1140/epjc/s10052-023-11494-y

2023 Impact factor 4.2

Particles and Fields

Open Access

Eur. Phys. J. C (2023) 83: 337
https://doi.org/10.1140/epjc/s10052-023-11494-y

Regular Article - Experimental Physics

Deep machine learning for the PANDA software trigger

Peiyong Jiang¹^,2^,3^a, Klaus Götzen¹, Ralf Kliemt¹^,4, Frank Nerling¹^,3^,5, Klaus Peters¹^,3^,5 and the P.A.N.D.A. Collaboration

¹ GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany
² Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
³ Helmholtz Forschungsakademie Hessen für FAIR (HFHF), GSI Helmholtzzentrum für Schwerionenforschung, Campus Frankfurt, Frankfurt am Main, Germany
⁴ Ruhr-Universität Bochum, Bochum, Germany
⁵ Goethe-Universität Frankfurt, Frankfurt am Main, Germany

^a p.jiang@gsi.de, jiangpeiyong@impcas.ac.cn

Received: 29 November 2022
Accepted: 11 April 2023
Published online: 27 April 2023

Abstract

Deep machine learning methods have been studied for the software trigger of the future PANDA experiment at FAIR, using Monte Carlo simulated data from the GEANT-based detector simulation framework PandaRoot. Ten physics channels that cover the main physics topics, including electromagnetic, exotic, charmonium, open charm, and baryonic reaction channels, have been investigated at four different anti-proton beam momenta. Different classification concepts and network architectures have been studied. Finally a residual convolutional neural network with four residual blocks in a binary classification scheme has been chosen due to its extendability, performance and stability. The presented study represents a feasibility study of a completely software-based trigger system. Compared to a conventional selection method, the deep machine learning approach achieved a significant efficiency gain of up to 200%, while keeping the background reduction factor at the required level of 1/1000. Furthermore, it is shown that the use of additional input variables can improve the data quality for subsequent analysis. This study shows that the PANDA software trigger can benefit greatly from the deep machine learning methods.

© The Author(s) 2023

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