Article ; Online: Cosmic Ray Background Removal With Deep Neural Networks in SBND.
Frontiers in artificial intelligence
2021 Volume 4, Page(s) 649917
Abstract: 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 ... ...
Abstract | 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. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions. |
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Language | English |
Publishing date | 2021-08-24 |
Publishing country | Switzerland |
Document type | Journal Article |
ISSN | 2624-8212 |
ISSN (online) | 2624-8212 |
DOI | 10.3389/frai.2021.649917 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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