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

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Issue Date
2022-10-12
Embargo End Date
Authors
Abed Abud, Adam
Abi, B.
Meyer, Holger
Muether, Mathew
Roy, P.
Solomey, Nickolas
Advisor
Citation

Abed Abud, A., Abi, B., Acciarri, R. et al. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. Eur. Phys. J. C 82, 903 (2022). https://doi.org/10.1140/epjc/s10052-022-10791-2

Abstract

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. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.

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