frai-04-649917.pdf (3.43 MB)
Cosmic ray background removal with deep neural networks in SBND
journal contribution
posted on 2023-06-10, 02:17 authored by R Acciarri, C Adams, C Andreopoulos, J Asaadi, M Babicz, C Backhouse, W Badgett, L Bagby, D Barker, V Basque, M C Q Bazetto, M Betancourt, Georgia Chisnall, Iker De Icaza Astiz, Clark GriffithClark Griffith, othersIn 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.
History
Publication status
- Published
File Version
- Published version
Journal
Frontiers in Artificial IntelligenceISSN
2624-8212Publisher
Frontiers MediaExternal DOI
Volume
4Article number
a6499171-14Event location
SwitzerlandDepartment affiliated with
- Physics and Astronomy Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-01-14First Open Access (FOA) Date
2022-01-14First Compliant Deposit (FCD) Date
2022-01-14Usage metrics
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