Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events

Conference · 01 January 2023 · J.Phys.Conf.Ser.

DOI:https://doi.org/10.1088/1742-6596/2438/1/012125· OSTI ID:2000988

Abed Abud, Adam ^[1]

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1. Liverpool U.; CERN

Deep Learning (DL) methods and Computer Vision are becoming important tools for event reconstruction in particle physics detectors. In this work, we report on the use of submanifold sparse convolutional neural networks (SparseNets) for the classification of track and shower hits from a DUNE prototype liquid-argon detector at CERN (ProtoDUNE-SP). By taking advantage of the three-dimensional nature of the problem we use a set of nine input features to classify sparse and locally dense hits associated to track or shower particles. The SparseNet has been trained on a test sample and shows promising results: efficiencies and purities greater than 90%. This has also been achieved with a considerable speedup and substantially less resource utilization with respect to other DL networks such as graph neural networks. This method offers great scalability advantages for future large neutrino detectors such as the planned DUNE experiment.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• Contributing Organization: DUNE
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 2000988
• Report Number(s): FERMILAB-CONF-23-250-V; oai:inspirehep.net:2633518
• Conference Information: Journal Name: J.Phys.Conf.Ser. Journal Issue: 1 Journal Volume: 2438
• Country of Publication: United States
• Language: English

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS