Cosmic Background Removal with Deep Neural Networks in SBND Article Swipe
SBND Collaboration
,
R. Acciarri
,
C. Adams
,
C. Andreopoulos
,
J. Asaadi
,
M. Babicz
,
C. Backhouse
,
W. Badgett
,
L. Bagby
,
D. Barker
,
V. Basque
,
M. C. Q. Bazetto
,
M. Betancourt
,
A. Bhanderi
,
A. Bhat
,
C. Bonifazi
,
D. Brailsford
,
A. Brandt
,
T. Brooks
,
M. F. Carneiro
,
Y. Chen
,
H. Chen
,
G. Chisnall
,
J. I. Crespo-Anadón
,
E. Cristaldo
,
C. Cuesta
,
I. L. De Icaza Astiz
,
A. De Roeck
,
G. Pereira
,
M. Del Tutto
,
Valerio Benedetto
,
A. Ereditato
,
J. J. Evans
,
A. C. Ezeribe
,
R. S. Fitzpatrick
,
B. T. Fleming
,
W. Foreman
,
D. Franco
,
I. Furic
,
A. P. Furmanski
,
S. Gao
,
D. García-Gámez
,
H. Frandini
,
G. Ge
,
I. Gil‐Botella
,
S. Gollapinni
,
O. Goodwin
,
P. Green
,
W. C. Griffith
,
R. Guénette
,
P. Guzowski
,
Tae Jun Ham
,
J. Henzerling
,
A. Holin
,
B. Howard
,
R. Jones
,
D. Kalra
,
G. Karagiorgi
,
L. Kashur
,
W. Ketchum
,
M. Kim
,
V. A. Kudryavtsev
,
Jeff Larkin
,
H. Lay
,
I. Lepetic
,
B. R. Littlejohn
,
W. C. Louis
,
A.A. Machado
,
M. Malek
,
D. Mardsen
,
C. Mariani
,
F. Marinho
,
A. Mastbaum
,
K. Mavrokoridis
,
N. McConkey
,
V. Meddage
,
D. P. Méndez
,
T. Mettler
,
K. Mistry
,
A. Mogan
,
Jorge Molina
,
M. Mooney
,
L. de Mora
,
C. A. Moura
,
J. Mousseau
,
A. Navrer-Agasson
,
F. Nicolas-Arnaldos
,
J. Nowak
,
O. Palamara
,
V. Pandey
,
J. R. Pater
,
L. Paulucci
,
V. L. Pimentel
,
F. Psihas
,
G. Putnam
,
X. Qian
,
E. Raguzin
,
H. Ray
,
M. Reggiani-Guzzo
,
D. Rivera
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.01301
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.01301
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2012.01301
- https://arxiv.org/pdf/2012.01301
- OA Status
- green
- Cited By
- 7
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3110599556
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- OpenAlex ID
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https://openalex.org/W3110599556Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2012.01301Digital Object Identifier
- Title
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Cosmic Background Removal with Deep Neural Networks in SBNDWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-02Full publication date if available
- Authors
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SBND Collaboration, R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, M. C. Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, A. Brandt, T. Brooks, M. F. Carneiro, Y. Chen, H. Chen, G. Chisnall, J. I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I. L. De Icaza Astiz, A. De Roeck, G. Pereira, M. Del Tutto, Valerio Benedetto, A. Ereditato, J. J. Evans, A. C. Ezeribe, R. S. Fitzpatrick, B. T. Fleming, W. Foreman, D. Franco, I. Furic, A. P. Furmanski, S. Gao, D. García-Gámez, H. Frandini, G. Ge, I. Gil‐Botella, S. Gollapinni, O. Goodwin, P. Green, W. C. Griffith, R. Guénette, P. Guzowski, Tae Jun Ham, J. Henzerling, A. Holin, B. Howard, R. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M. Kim, V. A. Kudryavtsev, Jeff Larkin, H. Lay, I. Lepetic, B. R. Littlejohn, W. C. Louis, A.A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, D. P. Méndez, T. Mettler, K. Mistry, A. Mogan, Jorge Molina, M. Mooney, L. de Mora, C. A. Moura, J. Mousseau, A. Navrer-Agasson, F. Nicolas-Arnaldos, J. Nowak, O. Palamara, V. Pandey, J. R. Pater, L. Paulucci, V. L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. RiveraList of authors in order
- Landing page
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https://arxiv.org/abs/2012.01301Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2012.01301Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2012.01301Direct OA link when available
- Concepts
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Neutrino, Cosmic ray, Physics, Detector, Event (particle physics), Neutrino detector, Fermilab, Muon, COSMIC cancer database, Nuclear physics, Projection (relational algebra), Time projection chamber, Particle physics, Neutrino oscillation, Astrophysics, Optics, Computer science, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_topic.subfield.id | https://openalex.org/subfields/3106 |
| primary_topic.subfield.display_name | Nuclear and High Energy Physics |
| primary_topic.display_name | Neutrino Physics Research |
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| counts_by_year[1].year | 2022 |
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| best_oa_location.source.host_organization_name | Cornell University |
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| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
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| primary_location.version | submittedVersion |
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| primary_location.is_published | False |
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| publication_date | 2020-12-02 |
| publication_year | 2020 |
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| abstract_inverted_index.single | 30, 119 |
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| abstract_inverted_index.events. | 73 |
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| cited_by_percentile_year | |
| countries_distinct_count | 8 |
| institutions_distinct_count | 131 |
| citation_normalized_percentile |