Cosmic ray background removal with deep neural networks in SBND

Acciarri, R, Adams, C, Andreopoulos, C, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D, Basque, V, Bazetto, M C Q, Betancourt, M, Chisnall, G, de Icaza Astiz, I L, Griffith, W C and others, (2021) Cosmic ray background removal with deep neural networks in SBND. Frontiers in Artificial Intelligence, 4. a6499171-14. ISSN 2624-8212

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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
Keywords: SBN program, SBND, UNet, deep learning, liquid Ar detectors, neutrino physics
Schools and Departments: School of Mathematical and Physical Sciences > Physics and Astronomy
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 14 Jan 2022 12:18
Last Modified: 14 Jan 2022 12:30

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