Cosmic Ray Background Removal With Deep Neural Networks in SBND

Acciarri, R, Adams, C, Andreopoulos, C ORCID: 0000-0003-2020-8215, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D, Basque, V
et al (show 120 more authors) (2021) Cosmic Ray Background Removal With Deep Neural Networks in SBND. FRONTIERS IN ARTIFICIAL INTELLIGENCE, 4. 649917-.

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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.

Item Type: Article
Uncontrolled Keywords: deep learning, neutrino physics, SBN program, SBND, UNet, liquid Ar detectors
Depositing User: Symplectic Admin
Date Deposited: 13 Jan 2021 09:58
Last Modified: 18 Jan 2023 23:03
DOI: 10.3389/frai.2021.649917
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