Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

0563954 - FZÚ 2023 RIV DE eng J - Journal Article
Abed Abud, A. - Abi, B. - Acciarri, R. - Filip, Peter - Kvasnička, Jiří - Lokajíček, Miloš - Pěč, Viktor - Zálešák, Jaroslav - Zuklín, Josef … Total 1228 authors
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network.
European Physical Journal C. Roč. 82, č. 10 (2022), č. článku 903. ISSN 1434-6044. E-ISSN 1434-6052
Research Infrastructure: Fermilab-CZ II - 90113
Institutional support: RVO:68378271
Keywords : DUNE * neural network * efficiency * performance
OECD category: Particles and field physics
Impact factor: 4.4, year: 2022
Method of publishing: Open access

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented.
Permanent Link: https://hdl.handle.net/11104/0335738