AUTHOR=Acciarri R. , Adams C. , Andreopoulos C. , Asaadi J. , Babicz M. , Backhouse C. , Badgett W. , Bagby L. , Barker D. , Basque V. , Bazetto M. C. Q. , Betancourt M. , Bhanderi A. , Bhat A. , Bonifazi C. , Brailsford D. , Brandt A. G. , Brooks T. , Carneiro M. F. , Chen Y. , Chen H. , Chisnall G. , Crespo-Anadón J. I. , Cristaldo E. , Cuesta C. , de Icaza Astiz I. L. , De Roeck A. , de Sá Pereira G. , Del Tutto M. , Di Benedetto V. , Ereditato A. , Evans J. J. , Ezeribe A. C. , Fitzpatrick R. S. , Fleming B. T. , Foreman W. , Franco D. , Furic I. , Furmanski A. P. , Gao S. , Garcia-Gamez D. , Frandini H. , Ge G. , Gil-Botella I. , Gollapinni S. , Goodwin O. , Green P. , Griffith W. C. , Guenette R. , Guzowski P. , Ham T. , Henzerling J. , Holin A. , Howard B. , Jones R. S. , Kalra D. , Karagiorgi G. , Kashur L. , Ketchum W. , Kim M. J. , Kudryavtsev V. A. , Larkin J. , Lay H. , Lepetic I. , Littlejohn B. R. , Louis W. C. , Machado A. A. , Malek M. , Mardsen D. , Mariani C. , Marinho F. , Mastbaum A. , Mavrokoridis K. , McConkey N. , Meddage V. , Méndez D. P. , Mettler T. , Mistry K. , Mogan A. , Molina J. , Mooney M. , Mora L. , Moura C. A. , Mousseau J. , Navrer-Agasson A. , Nicolas-Arnaldos F. J. , Nowak J. A. , Palamara O. , Pandey V. , Pater J. , Paulucci L. , Pimentel V. L. , Psihas F. , Putnam G. , Qian X. , Raguzin E. , Ray H. , Reggiani-Guzzo M. , Rivera D. , Roda M. , Ross-Lonergan M. , Scanavini G. , Scarff A. , Schmitz D. W. , Schukraft A. , Segreto E. , Soares Nunes M. , Soderberg M. , Söldner-Rembold S. , Spitz J. , Spooner N. J. C. , Stancari M. , Stenico G. V. , Szelc A. , Tang W. , Tena Vidal J. , Torretta D. , Toups M. , Touramanis C. , Tripathi M. , Tufanli S. , Tyley E. , Valdiviesso G. A. , Worcester E. , Worcester M. , Yarbrough G. , Yu J. , Zamorano B. , Zennamo J. , Zglam A. 

TITLE=Cosmic Ray Background Removal With Deep Neural Networks in SBND

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=4

YEAR=2021

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.649917

DOI=10.3389/frai.2021.649917

ISSN=2624-8212

ABSTRACT=<p>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.</p>