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Cosmic Ray Background Removal With Deep Neural Networks in SBND / SBND Collaboration
In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. [...]
arXiv:2012.01301; FERMILAB-PUB-20-641-ND.- 2021-08-24 - 17 p. - Published in : Front. Artif. Intell. 4 (2021) 649917 Fulltext: frai-04-649917 - PDF; 2012.01301 - PDF; fermilab-pub-20-641-nd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server (fulltext available)

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The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector / MicroBooNE Collaboration
The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. [...]
AIDA-2020-PUB-2017-002; arXiv:1708.03135; FERMILAB-PUB-17-306-ND.- Geneva : CERN, 2018 - 25 p. - Published in : Eur. Phys. J. C 78 (2018) 82 Fulltext: PDF;

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