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Highly-parallelized simulation of a pixelated LArTPC on a GPU / DUNE Collaboration
The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. [...]
arXiv:2212.09807; FERMILAB-PUB-22-926-LBNF.- 2023-04-26 - 26 p. - Published in : JINST 18 (2023) P04034 Fulltext: 2212.09807 - PDF; 81def4c089fd89d474ade1e24d67c3df - PDF; FERMILAB-PUB-22-926-LBNF - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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DUNE Offline Computing Conceptual Design Report / DUNE Collaboration
This document describes the conceptual design for the Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE). [...]
arXiv:2210.15665 ; FERMILAB-DESIGN-2022-01. - 229.
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Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora / DUNE Collaboration
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. [...]
arXiv:2206.14521; FERMILAB-PUB-22-488-AD-ESH-LBNF-ND-SCD; CERN-EP-DRAFT-MISC-2022-007.- 2023-07-14 - 39 p. - Published in : Eur. Phys. J. C 83 (2023) 618 Fulltext: jt - PDF; 2206.14521 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC / DUNE Collaboration
DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. [...]
arXiv:2203.16134; CERN-EP-DRAFT-MISC-2022-003; FERMILAB-PUB-22-242-LBNF; CERN-EP-DRAFT-MISC-2022-003; FERMILAB-PUB-22-242-LBNF.- 2022-07-16 - 31 p. - Published in : Eur. Phys. J. C 82 (2022) 618 Fulltext: 2203.16134 - PDF; Publication - PDF; jt - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network / DUNE Collaboration
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). [...]
arXiv:2203.17053; FERMILAB-PUB-22-240-AD-ESH-LBNF-ND-SCD; CERN-EP-2022-077.- Geneva : CERN, 2022-10-12 - 31 p. - Published in : Eur. Phys. J. C 82 (2022) 903 Fulltext: CERN-EP-DRAFT-MISC-2022-002 - PDF; 2203.17053 - PDF; jt - PDF; Fulltext from Publisher: PDF; Fulltext from publisher: PDF; External link: Fermilab Library Server

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Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept / Uboldi, Lorenzo (CERN) ; Ruth, David (Unlisted, US, IL) ; Andrews, Michael (Carnegie Mellon U.) ; Wang, Michael H.L.S. (Fermilab) ; Wenzel, Hans Joachim (Fermilab) ; Wu, Wanwei (Fermilab) ; Yang, Tingjun (Fermilab)
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. [...]
arXiv:2106.09911; FERMILAB-PUB-21-030-ND-SCD.- 2022-04-01 - 9 p. - Published in : Nucl. Instrum. Methods Phys. Res., A 1028 (2022) 166371 Fulltext: fermilab-pub-21-030-nd-scd - PDF; 2106.09911 - PDF; External link: Fermilab Library Server

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