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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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.

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
URI:	http://sro.sussex.ac.uk/id/eprint/103825

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