Cosmic Ray Background Removal With Deep Neural Networks in SBND
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Acciarri, R.; García Gámez, Diego; Nicolás Arnaldos, Francisco Javier; Zamorano García, BrunoEditorial
Frontiers Research Foundation
Materia
Deep learning Neutrino physics SBN program SBND UNet Liquid Ar detectors
Date
2021-08-24Referencia bibliográfica
Acciarri, R... [et al.] (2021). Cosmic Ray Background Removal With Deep Neural Networks in SBND. Frontiers in artificial intelligence, 4. doi: [10.3389/frai.2021.649917]
Sponsorship
U.S. Department of Energy, Office of Science, Office of High Energy Physics; U.S. National Science Foundation; Science and Technology Facilities Council (STFC); The Royal Society of the United Kingdom; Swiss National Science Foundation; Spanish Ministerio de Ciencia e Innovación (PID2019-104676GB-C32); Junta de Andalucía (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds; São Paulo Research Foundation (FAPESP); National Council of Scientific and Technological Development (CNPq) of Brazil; Los Alamos National Laboratory for LDRD; Argonne Leadership Computing Facility; Fermi National Accelerator Laboratory (Fermilab); Fermi Research Alliance, LLC (FRA) DE-AC02-07CH11359Abstract
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 fromsurface 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.