@misc{10481/71794,
year = {2021},
month = {8},
url = {http://hdl.handle.net/10481/71794},
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 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.},
organization = {U.S. Department of Energy,
Office of Science, Office of High Energy Physics},
organization = {U.S.
National Science Foundation},
organization = {Science and Technology
Facilities Council (STFC)},
organization = {The Royal Society of the United Kingdom},
organization = {Swiss National Science Foundation},
organization = {Spanish Ministerio de
Ciencia e Innovación (PID2019-104676GB-C32)},
organization = {Junta de Andalucía (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds},
organization = {São Paulo Research Foundation (FAPESP)},
organization = {National Council of Scientific and Technological Development
(CNPq) of Brazil},
organization = {Los Alamos National
Laboratory for LDRD},
organization = {Argonne Leadership Computing Facility},
organization = {Fermi National
Accelerator Laboratory (Fermilab)},
organization = {Fermi Research Alliance, LLC (FRA) DE-AC02-07CH11359},
publisher = {Frontiers Research Foundation},
keywords = {Deep learning},
keywords = {Neutrino physics},
keywords = {SBN program},
keywords = {SBND},
keywords = {UNet},
keywords = {Liquid Ar detectors},
title = {Cosmic Ray Background Removal With Deep Neural Networks in SBND},
doi = {10.3389/frai.2021.649917},
author = {Acciarri, R. and García Gámez, Diego and Nicolás Arnaldos, Francisco Javier and Zamorano García, Bruno},
}