0563954 - FZÚ 2023 RIV DE eng J - Článek v odborném periodiku
Abed Abud, A. - Abi, B. - Acciarri, R. - Filip, Peter - Kvasnička, Jiří - Lokajíček, Miloš - Pěč, Viktor - Zálešák, Jaroslav - Zuklín, Josef … celkem 1228 autorů
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
Výzkumná infrastruktura: Fermilab-CZ II - 90113
Institucionální podpora: RVO:68378271
Klíčová slova: DUNE * neural network * efficiency * performance
Obor OECD: Particles and field physics
Impakt faktor: 4.4, rok: 2022 ; AIS: 1.162, rok: 2022
Způsob publikování: Open access
DOI: https://doi.org/10.1140/epjc/s10052-022-10791-2

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.
Trvalý link: https://hdl.handle.net/11104/0335738