Cosmic Background Removal with Deep Neural Networks in SBND



Collaboration, SBND, Acciarri, R, Adams, C, Andreopoulos, C ORCID: 0000-0003-2020-8215, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D
et al (show 121 more authors) Cosmic Background Removal with Deep Neural Networks in SBND.

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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 semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Item Type: Article
Uncontrolled Keywords: physics.data-an, physics.data-an
Depositing User: Symplectic Admin
Date Deposited: 13 Jan 2021 09:58
Last Modified: 22 Apr 2021 06:10
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3113381