Cosmic Ray Background Removal With Deep Neural Networks in SBND

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. G. 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. de Sá Pereira; M. Del Tutto; V. Di 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. Garcia-Gamez; H. Frandini; G. Ge; I. Gil-Botella; S. Gollapinni; O. Goodwin; P. Green; W. C. Griffith; R. Guenette; P. Guzowski; T. Ham; J. Henzerling; A. Holin; B. Howard; R. S. Jones; D. Kalra; G. Karagiorgi; L. Kashur; W. Ketchum; M. J. Kim; V. A. Kudryavtsev; J. 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; J. Molina; M. Mooney; L. Mora; C. A. Moura; J. Mousseau; A. Navrer-Agasson; F. J. Nicolas-Arnaldos; J. A. Nowak; O. Palamara; V. Pandey; J. Pater; L. Paulucci; V. L. Pimentel; F. Psihas; G. Putnam; X. Qian; E. Raguzin; H. Ray; M. Reggiani-Guzzo; D. Rivera; M. Roda; M. Ross-Lonergan; G. Scanavini; A. Scarff; D. W. Schmitz; A. Schukraft; E. Segreto; M. Soares Nunes; M. Soderberg; S. Söldner-Rembold; J. Spitz; N. J.C. Spooner; M. Stancari; G. V. Stenico; A. Szelc; W. Tang; J. Tena Vidal; D. Torretta; M. Toups; C. Touramanis; M. Tripathi; S. Tufanli; E. Tyley; G. A. Valdiviesso; E. Worcester; M. Worcester; G. Yarbrough; J. Yu; B. Zamorano; J. Zennamo; A. Zglam

doi:10.3389/frai.2021.649917

Cosmic Ray Background Removal With Deep Neural Networks in SBND

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. G. Brandt, T. Brooks, M. F. Carneiro, Y. ChenH. Chen, G. Chisnall, J. I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I. L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di 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. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, W. C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R. S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M. J. Kim, V. A. Kudryavtsev, J. 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, J. Molina, M. Mooney, L. Mora, C. A. Moura, J. Mousseau, A. Navrer-Agasson, F. J. Nicolas-Arnaldos, J. A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V. L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D. W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N. J.C. Spooner, M. Stancari, G. V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G. A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam

Department of Physics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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 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.

Original language	English (US)
Article number	649917
Journal	Frontiers in Artificial Intelligence
Volume	4
DOIs	https://doi.org/10.3389/frai.2021.649917
State	Published - Aug 24 2021

Keywords

SBN program
SBND
UNet
deep learning
liquid Ar detectors
neutrino physics

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.3389/frai.2021.649917

Cite this

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., ... Zglam, A. (2021). Cosmic Ray Background Removal With Deep Neural Networks in SBND. Frontiers in Artificial Intelligence, 4, [649917]. https://doi.org/10.3389/frai.2021.649917

Acciarri, R, Adams, C, Andreopoulos, C, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D, Basque, V, Bazetto, MCQ, Betancourt, M, Bhanderi, A, Bhat, A, Bonifazi, C, Brailsford, D, Brandt, AG, Brooks, T, Carneiro, MF, Chen, Y, Chen, H, Chisnall, G, Crespo-Anadón, JI, Cristaldo, E, Cuesta, C, de Icaza Astiz, IL, De Roeck, A, de Sá Pereira, G, Del Tutto, M, Di Benedetto, V, Ereditato, A, Evans, JJ, Ezeribe, AC, Fitzpatrick, RS, Fleming, BT, Foreman, W, Franco, D, Furic, I, Furmanski, AP, Gao, S, Garcia-Gamez, D, Frandini, H, Ge, G, Gil-Botella, I, Gollapinni, S, Goodwin, O, Green, P, Griffith, WC, Guenette, R, Guzowski, P, Ham, T, Henzerling, J, Holin, A, Howard, B, Jones, RS, Kalra, D, Karagiorgi, G, Kashur, L, Ketchum, W, Kim, MJ, Kudryavtsev, VA, Larkin, J, Lay, H, Lepetic, I, Littlejohn, BR, Louis, WC, Machado, AA, Malek, M, Mardsen, D, Mariani, C, Marinho, F, Mastbaum, A, Mavrokoridis, K, McConkey, N, Meddage, V, Méndez, DP, Mettler, T, Mistry, K, Mogan, A, Molina, J, Mooney, M, Mora, L, Moura, CA, Mousseau, J, Navrer-Agasson, A, Nicolas-Arnaldos, FJ, Nowak, JA, Palamara, O, Pandey, V, Pater, J, Paulucci, L, Pimentel, VL, 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, DW, Schukraft, A, Segreto, E, Soares Nunes, M, Soderberg, M, Söldner-Rembold, S, Spitz, J, Spooner, NJC, Stancari, M, Stenico, GV, Szelc, A, Tang, W, Tena Vidal, J, Torretta, D, Toups, M, Touramanis, C, Tripathi, M, Tufanli, S, Tyley, E, Valdiviesso, GA, Worcester, E, Worcester, M, Yarbrough, G, Yu, J, Zamorano, B, Zennamo, J & Zglam, A 2021, 'Cosmic Ray Background Removal With Deep Neural Networks in SBND', Frontiers in Artificial Intelligence, vol. 4, 649917. https://doi.org/10.3389/frai.2021.649917

@article{7e02b846c7a04bf9aec188b8c120b6d9,

title = "Cosmic Ray Background Removal With Deep Neural Networks in SBND",

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 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.",

keywords = "SBN program, SBND, UNet, deep learning, liquid Ar detectors, neutrino physics",

author = "R. Acciarri and C. Adams and C. Andreopoulos and J. Asaadi and M. Babicz and C. Backhouse and W. Badgett and L. Bagby and D. Barker and V. Basque and Bazetto, {M. C.Q.} and M. Betancourt and A. Bhanderi and A. Bhat and C. Bonifazi and D. Brailsford and Brandt, {A. G.} and T. Brooks and Carneiro, {M. F.} and Y. Chen and H. Chen and G. Chisnall and Crespo-Anad{\'o}n, {J. I.} and E. Cristaldo and C. Cuesta and {de Icaza Astiz}, {I. L.} and {De Roeck}, A. and {de S{\'a} Pereira}, G. and {Del Tutto}, M. and {Di Benedetto}, V. and A. Ereditato and Evans, {J. J.} and Ezeribe, {A. C.} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and W. Foreman and D. Franco and I. Furic and Furmanski, {A. P.} and S. Gao and D. Garcia-Gamez and H. Frandini and G. Ge and I. Gil-Botella and S. Gollapinni and O. Goodwin and P. Green and Griffith, {W. C.} and R. Guenette and P. Guzowski and T. Ham and J. Henzerling and A. Holin and B. Howard and Jones, {R. S.} and D. Kalra and G. Karagiorgi and L. Kashur and W. Ketchum and Kim, {M. J.} and Kudryavtsev, {V. A.} and J. Larkin and H. Lay and I. Lepetic and Littlejohn, {B. R.} and Louis, {W. C.} and Machado, {A. A.} and M. Malek and D. Mardsen and C. Mariani and F. Marinho and A. Mastbaum and K. Mavrokoridis and N. McConkey and V. Meddage and M{\'e}ndez, {D. P.} and T. Mettler and K. Mistry and A. Mogan and J. Molina and M. Mooney and L. Mora and Moura, {C. A.} and J. Mousseau and A. Navrer-Agasson and Nicolas-Arnaldos, {F. J.} and Nowak, {J. A.} and O. Palamara and V. Pandey and J. Pater and L. Paulucci and Pimentel, {V. L.} and F. Psihas and G. Putnam and X. Qian and E. Raguzin and H. Ray and M. Reggiani-Guzzo and D. Rivera and M. Roda and M. Ross-Lonergan and G. Scanavini and A. Scarff and Schmitz, {D. W.} and A. Schukraft and E. Segreto and {Soares Nunes}, M. and M. Soderberg and S. S{\"o}ldner-Rembold and J. Spitz and Spooner, {N. J.C.} and M. Stancari and Stenico, {G. V.} and A. Szelc and W. Tang and {Tena Vidal}, J. and D. Torretta and M. Toups and C. Touramanis and M. Tripathi and S. Tufanli and E. Tyley and Valdiviesso, {G. A.} and E. Worcester and M. Worcester and G. Yarbrough and J. Yu and B. Zamorano and J. Zennamo and A. Zglam",

note = "Funding Information: 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 Innovaci{\'o}n (PID2019-104676GB-C32) and Junta de Andaluc{\'i}a (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds; and the S{\~a}o 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. Funding Information: 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 Innovaci{\'o}n (PID2019-104676GB-C32) and Junta de Andaluc{\'i}a (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds; and the S{\~a}o 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, Publisher Copyright: {\textcopyright} 2021 Acciarri, Adams, Andreopoulos, Asaadi.",

year = "2021",

month = aug,

day = "24",

doi = "10.3389/frai.2021.649917",

language = "English (US)",

volume = "4",

journal = "Frontiers in Artificial Intelligence",

issn = "2624-8212",

publisher = "Frontiers Media S. A.",

}

TY - JOUR

T1 - Cosmic Ray Background Removal With Deep Neural Networks in SBND

AU - Acciarri, R.

AU - Adams, C.

AU - Andreopoulos, C.

AU - Asaadi, J.

AU - Babicz, M.

AU - Backhouse, C.

AU - Badgett, W.

AU - Bagby, L.

AU - Barker, D.

AU - Basque, V.

AU - Bazetto, M. C.Q.

AU - Betancourt, M.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Bonifazi, C.

AU - Brailsford, D.

AU - Brandt, A. G.

AU - Brooks, T.

AU - Carneiro, M. F.

AU - Chen, Y.

AU - Chen, H.

AU - Chisnall, G.

AU - Crespo-Anadón, J. I.

AU - Cristaldo, E.

AU - Cuesta, C.

AU - de Icaza Astiz, I. L.

AU - De Roeck, A.

AU - de Sá Pereira, G.

AU - Del Tutto, M.

AU - Di Benedetto, V.

AU - Ereditato, A.

AU - Evans, J. J.

AU - Ezeribe, A. C.

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Foreman, W.

AU - Franco, D.

AU - Furic, I.

AU - Furmanski, A. P.

AU - Gao, S.

AU - Garcia-Gamez, D.

AU - Frandini, H.

AU - Ge, G.

AU - Gil-Botella, I.

AU - Gollapinni, S.

AU - Goodwin, O.

AU - Green, P.

AU - Griffith, W. C.

AU - Guenette, R.

AU - Guzowski, P.

AU - Ham, T.

AU - Henzerling, J.

AU - Holin, A.

AU - Howard, B.

AU - Jones, R. S.

AU - Kalra, D.

AU - Karagiorgi, G.

AU - Kashur, L.

AU - Ketchum, W.

AU - Kim, M. J.

AU - Kudryavtsev, V. A.

AU - Larkin, J.

AU - Lay, H.

AU - Lepetic, I.

AU - Littlejohn, B. R.

AU - Louis, W. C.

AU - Machado, A. A.

AU - Malek, M.

AU - Mardsen, D.

AU - Mariani, C.

AU - Marinho, F.

AU - Mastbaum, A.

AU - Mavrokoridis, K.

AU - McConkey, N.

AU - Meddage, V.

AU - Méndez, D. P.

AU - Mettler, T.

AU - Mistry, K.

AU - Mogan, A.

AU - Molina, J.

AU - Mooney, M.

AU - Mora, L.

AU - Moura, C. A.

AU - Mousseau, J.

AU - Navrer-Agasson, A.

AU - Nicolas-Arnaldos, F. J.

AU - Nowak, J. A.

AU - Palamara, O.

AU - Pandey, V.

AU - Pater, J.

AU - Paulucci, L.

AU - Pimentel, V. L.

AU - Psihas, F.

AU - Putnam, G.

AU - Qian, X.

AU - Raguzin, E.

AU - Ray, H.

AU - Reggiani-Guzzo, M.

AU - Rivera, D.

AU - Roda, M.

AU - Ross-Lonergan, M.

AU - Scanavini, G.

AU - Scarff, A.

AU - Schmitz, D. W.

AU - Schukraft, A.

AU - Segreto, E.

AU - Soares Nunes, M.

AU - Soderberg, M.

AU - Söldner-Rembold, S.

AU - Spitz, J.

AU - Spooner, N. J.C.

AU - Stancari, M.

AU - Stenico, G. V.

AU - Szelc, A.

AU - Tang, W.

AU - Tena Vidal, J.

AU - Torretta, D.

AU - Toups, M.

AU - Touramanis, C.

AU - Tripathi, M.

AU - Tufanli, S.

AU - Tyley, E.

AU - Valdiviesso, G. A.

AU - Worcester, E.

AU - Worcester, M.

AU - Yarbrough, G.

AU - Yu, J.

AU - Zamorano, B.

AU - Zennamo, J.

AU - Zglam, A.

N1 - Funding Information: 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. Funding Information: 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, Publisher Copyright: © 2021 Acciarri, Adams, Andreopoulos, Asaadi.

PY - 2021/8/24

Y1 - 2021/8/24

N2 - 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.

AB - 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.

KW - SBN program

KW - SBND

KW - UNet

KW - deep learning

KW - liquid Ar detectors

KW - neutrino physics

UR - http://www.scopus.com/inward/record.url?scp=85118213576&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85118213576&partnerID=8YFLogxK

U2 - 10.3389/frai.2021.649917

DO - 10.3389/frai.2021.649917

M3 - Article

AN - SCOPUS:85118213576

SN - 2624-8212

VL - 4

JO - Frontiers in Artificial Intelligence

JF - Frontiers in Artificial Intelligence

M1 - 649917

ER -

Cosmic Ray Background Removal With Deep Neural Networks in SBND

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this