@Article{DUNECollaborationAbud_etal2023,
author="DUNE Collaboration (Abud, A. A. et al
and Amedo, P.
and Antonova, M.
and Barenboim, G.
and Cervera-Villanueva, A.
and De Romeri, V.
and Fernandez Menendez, P.
and Garcia-Peris, M. A.
and Martin-Albo, J.
and Martinez-Mirave, P.
and Mena, O.
and Molina Bueno, L.
and Novella, P.
and Pompa, F.
and Rocabado Rocha, J. L.
and Sorel, M.
and Ternes, C. A.
and Tortola, M.
and Valle, J. W. F.",
title="Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora",
journal="European Physical Journal C",
year="2023",
publisher="Springer",
volume="83",
number="7",
pages="618 - 25pp",
abstract="The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80{\%} for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 +/- 0.6{\%} and 84.1 +/- 0.6{\%}, respectively. The efficiencies measured for test-beam data are shown to be within 5{\%} of those predicted by the simulation.",
optnote="WOS:001061746600005",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=5721), last updated on Mon, 30 Oct 2023 15:56:42 +0000",
issn="1434-6044",
doi="10.1140/epjc/s10052-023-11733-2",
opturl="https://arxiv.org/abs/2206.14521",
opturl="https://doi.org/10.1140/epjc/s10052-023-11733-2",
language="English"
}