Cosmic Ray Background Removal With Deep Neural Networks in SBND



Acciarri, R, Adams, C, Andreopoulos, C ORCID: 0000-0003-2020-8215, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D, Basque, V
et al (show 120 more authors) (2021) Cosmic Ray Background Removal With Deep Neural Networks in SBND. FRONTIERS IN ARTIFICIAL INTELLIGENCE, 4. 649917-.

Access the full-text of this item by clicking on the Open Access link.
[thumbnail of 2012.01301v2.pdf] Text
2012.01301v2.pdf - Submitted version

Download (4MB) | Preview

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
Uncontrolled Keywords: deep learning, neutrino physics, SBN program, SBND, UNet, liquid Ar detectors
Depositing User: Symplectic Admin
Date Deposited: 13 Jan 2021 09:58
Last Modified: 18 Jan 2023 23:03
DOI: 10.3389/frai.2021.649917
Open Access URL: https://www.frontiersin.org/articles/10.3389/frai....
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3113381