In:
Frontiers in Artificial Intelligence, Frontiers Media SA, Vol. 4 ( 2021-8-24)
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.
Type of Medium:
Online Resource
ISSN:
2624-8212
DOI:
10.3389/frai.2021.649917
DOI:
10.3389/frai.2021.649917.s001
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2021
detail.hit.zdb_id:
2957496-1