Cosmic Ray Background Removal With Deep Neural Networks in SBND

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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 pr...

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Detalles Bibliográficos
Autores principales: 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 Sa 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.
Lenguaje:eng
Publicado: 2020
Materias:
physics.data-an
Other Fields of Physics
Acceso en línea:https://dx.doi.org/10.3389/frai.2021.649917
http://cds.cern.ch/record/2746483
_version_ 1780968820912947200
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 Sa 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.
author_facet 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 Sa 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.
author_sort Acciarri, R.
collection CERN
description 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.
id cern-2746483
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
record_format invenio
spelling cern-27464832023-07-27T03:44:39Zdoi:10.3389/frai.2021.649917http://cds.cern.ch/record/2746483engAcciarri, 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 Sa 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.Cosmic Ray Background Removal With Deep Neural Networks in SBNDphysics.data-anOther Fields of PhysicsIn 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.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.arXiv:2012.01301FERMILAB-PUB-20-641-NDoai:cds.cern.ch:27464832020-12-02
spellingShingle physics.data-an
Other Fields of Physics
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 Sa 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.
Cosmic Ray Background Removal With Deep Neural Networks in SBND
title Cosmic Ray Background Removal With Deep Neural Networks in SBND
title_full Cosmic Ray Background Removal With Deep Neural Networks in SBND
title_fullStr Cosmic Ray Background Removal With Deep Neural Networks in SBND
title_full_unstemmed Cosmic Ray Background Removal With Deep Neural Networks in SBND
title_short Cosmic Ray Background Removal With Deep Neural Networks in SBND
title_sort cosmic ray background removal with deep neural networks in sbnd
topic physics.data-an
Other Fields of Physics
url https://dx.doi.org/10.3389/frai.2021.649917
http://cds.cern.ch/record/2746483
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AT spitzj cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT spoonernjc cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT stancarim cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT stenicogv cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT szelca cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT tangw cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT tenavidalj cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT torrettad cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT toupsm cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT touramanisc cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT tripathim cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT tufanlis cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT tyleye cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT valdiviessoga cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT worcestere cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT worcesterm cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT yarbroughg cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT yuj cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT zamoranob cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT zennamoj cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd
AT zglama cosmicraybackgroundremovalwithdeepneuralnetworksinsbnd

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