Collaboration, SBND, Acciarri, R, Adams, C et al. (128 more authors) (2021) Cosmic ray background removal with deep neural networks in SBND. Frontiers in Artificial Intelligence, 4. 649917.
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. http://creativecommons.org/licenses/by/4.0/ |
Keywords: | deep learning; neutrino physics; SBN program; SBND; UNet; liquid Ar detectors |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield) |
Funding Information: | Funder Grant number Science and Technology Facilities Council ST/N001141/1; ST/N000277/1; ST/P00573X/1; ST/S003398/1 Science and Technology Facilities Council ST/R000042/1; ST/R006709/1; ST/S000747/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Apr 2021 11:39 |
Last Modified: | 13 Sep 2021 08:49 |
Status: | Published |
Publisher: | Frontiers Media SA |
Refereed: | Yes |
Identification Number: | 10.3389/frai.2021.649917 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173307 |