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



Collaboration, DUNE, Abud, A Abed, Abi, B, Acciarri, R, Acero, MA, Adames, MR, Adamov, G, Adamowski, M, Adams, D, Adinolfi, M
et al (show 1219 more authors) (2022) Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. [Preprint]

[img] Text
2203.17053v1.pdf - Accepted Version

Download (1MB) | Preview

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 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 data and simulation.

Item Type: Preprint
Additional Information: 31 pages, 15 figures
Uncontrolled Keywords: physics.ins-det, physics.ins-det, hep-ex
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 18 May 2022 13:59
Last Modified: 10 Jul 2022 07:10
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3154991