Cosmic ray background removal with deep neural networks in SBND

Autor: Bazetto, Maria Cecilia Queiroga, 1989, Gatti, Heriques Frandini, 1986, Machado, Ana Amélia Bergamini, 1975, Pimentel, Vinicius do Lago, 1973, Segreto, Ettore, 1973, Stenico, Gabriela Vitti, 1991
Přispěvatelé: UNIVERSIDADE ESTADUAL DE CAMPINAS
Rok vydání: 2021
Předmět:
Zdroj: Repositório da Produção Científica e Intelectual da Unicamp
Universidade Estadual de Campinas (UNICAMP)
instacron:UNICAMP
Popis: Agradecimentos: The SBND Collaboration acknowledges the generous support of the following organizations: the U.S. Department of Energy, Office of Science, Office of High Energy Physics; the U.S. National Science Foundation; the Science and Technology Facilities Council (STFC), part of United Kingdom Research and Innovation, and The Royal Society of the United Kingdom; the Swiss National Science Foundation; the Spanish Ministerio de Ciencia e Innovación (PID2019-104676GB-C32) and Junta de Andalucía (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds; and the São Paulo Research Foundation (FAPESP) and the National Council of Scientific and Technological Development (CNPq) of Brazil. We acknowledge Los Alamos National Laboratory for LDRD funding. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. SBND is an experiment at the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359 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 FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ Aberto
Databáze: OpenAIRE
Externí odkaz: