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Cosmic ray background removal with deep neural networks in SBND

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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, others
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Frontiers in Artificial Intelligence

ISSN

2624-8212

Publisher

Frontiers Media

Volume

4

Article number

a6499171-14

Event location

Switzerland

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-01-14

First Open Access (FOA) Date

2022-01-14

First Compliant Deposit (FCD) Date

2022-01-14

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