Background rejection in NEXT using deep neural networks

We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal...

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Detalles Bibliográficos
Autores principales: Renner, J., Farbin, A., Vidal, J. Muñoz, Benlloch-Rodríguez, J.M., Botas, A., Ferrario, P., Gómez-Cadenas, J.J., Álvarez, V., Azevedo, C.D.R., Borges, F.I.G., Cárcel, S., Carrión, J.V., Cebrián, S., Cervera, A., Conde, C.A.N., Díaz, J., Diesburg, M., Esteve, R., Fernandes, L.M.P., Ferreira, A.L., Freitas, E.D.C., Goldschmidt, A., González-Díaz, D., Gutiérrez, R.M., Hauptman, J., Henriques, C.A.O., Hernando Morata, J. A., Herrero, V., Jones, B., Labarga, L., Laing, A., Lebrun, P., Liubarsky, I., López-March, N., Lorca, D., Losada, M., Martín-Albo, J., Martínez-Lema, G., Martínez, A., Monrabal, F., Monteiro, C.M.B., Mora, F.J., Moutinho, L.M., Nebot-Guinot, M., Novella, P., Nygren, D., Palmeiro, B., Para, A., Pérez, J., Querol, M., Ripoll, L., Rodríguez, J., Santos, F.P., dos Santos, J.M.F., Serra, L., Shuman, D., Simón, A., Sofka, C., Sorel, M., Toledo, J.F., Torrent, J., Tsamalaidze, Z., Veloso, J.F.C.A., White, J., Webb, R., Yahlali, N., Yepes-Ramírez, H.
Lenguaje:eng
Publicado: 2016
Materias:
hep-ex
Particle Physics - Experiment
physics.ins-det
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1088/1748-0221/12/01/T01004
http://cds.cern.ch/record/2217394
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author Renner, J.
Farbin, A.
Vidal, J. Muñoz
Benlloch-Rodríguez, J.M.
Botas, A.
Ferrario, P.
Gómez-Cadenas, J.J.
Álvarez, V.
Azevedo, C.D.R.
Borges, F.I.G.
Cárcel, S.
Carrión, J.V.
Cebrián, S.
Cervera, A.
Conde, C.A.N.
Díaz, J.
Diesburg, M.
Esteve, R.
Fernandes, L.M.P.
Ferreira, A.L.
Freitas, E.D.C.
Goldschmidt, A.
González-Díaz, D.
Gutiérrez, R.M.
Hauptman, J.
Henriques, C.A.O.
Hernando Morata, J. A.
Herrero, V.
Jones, B.
Labarga, L.
Laing, A.
Lebrun, P.
Liubarsky, I.
López-March, N.
Lorca, D.
Losada, M.
Martín-Albo, J.
Martínez-Lema, G.
Martínez, A.
Monrabal, F.
Monteiro, C.M.B.
Mora, F.J.
Moutinho, L.M.
Nebot-Guinot, M.
Novella, P.
Nygren, D.
Palmeiro, B.
Para, A.
Pérez, J.
Querol, M.
Ripoll, L.
Rodríguez, J.
Santos, F.P.
dos Santos, J.M.F.
Serra, L.
Shuman, D.
Simón, A.
Sofka, C.
Sorel, M.
Toledo, J.F.
Torrent, J.
Tsamalaidze, Z.
Veloso, J.F.C.A.
White, J.
Webb, R.
Yahlali, N.
Yepes-Ramírez, H.
author_facet Renner, J.
Farbin, A.
Vidal, J. Muñoz
Benlloch-Rodríguez, J.M.
Botas, A.
Ferrario, P.
Gómez-Cadenas, J.J.
Álvarez, V.
Azevedo, C.D.R.
Borges, F.I.G.
Cárcel, S.
Carrión, J.V.
Cebrián, S.
Cervera, A.
Conde, C.A.N.
Díaz, J.
Diesburg, M.
Esteve, R.
Fernandes, L.M.P.
Ferreira, A.L.
Freitas, E.D.C.
Goldschmidt, A.
González-Díaz, D.
Gutiérrez, R.M.
Hauptman, J.
Henriques, C.A.O.
Hernando Morata, J. A.
Herrero, V.
Jones, B.
Labarga, L.
Laing, A.
Lebrun, P.
Liubarsky, I.
López-March, N.
Lorca, D.
Losada, M.
Martín-Albo, J.
Martínez-Lema, G.
Martínez, A.
Monrabal, F.
Monteiro, C.M.B.
Mora, F.J.
Moutinho, L.M.
Nebot-Guinot, M.
Novella, P.
Nygren, D.
Palmeiro, B.
Para, A.
Pérez, J.
Querol, M.
Ripoll, L.
Rodríguez, J.
Santos, F.P.
dos Santos, J.M.F.
Serra, L.
Shuman, D.
Simón, A.
Sofka, C.
Sorel, M.
Toledo, J.F.
Torrent, J.
Tsamalaidze, Z.
Veloso, J.F.C.A.
White, J.
Webb, R.
Yahlali, N.
Yepes-Ramírez, H.
author_sort Renner, J.
collection CERN
description We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
id cern-2217394
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
record_format invenio
spelling cern-22173942022-08-10T12:34:42Zdoi:10.1088/1748-0221/12/01/T01004http://cds.cern.ch/record/2217394engRenner, J.Farbin, A.Vidal, J. MuñozBenlloch-Rodríguez, J.M.Botas, A.Ferrario, P.Gómez-Cadenas, J.J.Álvarez, V.Azevedo, C.D.R.Borges, F.I.G.Cárcel, S.Carrión, J.V.Cebrián, S.Cervera, A.Conde, C.A.N.Díaz, J.Diesburg, M.Esteve, R.Fernandes, L.M.P.Ferreira, A.L.Freitas, E.D.C.Goldschmidt, A.González-Díaz, D.Gutiérrez, R.M.Hauptman, J.Henriques, C.A.O.Hernando Morata, J. A.Herrero, V.Jones, B.Labarga, L.Laing, A.Lebrun, P.Liubarsky, I.López-March, N.Lorca, D.Losada, M.Martín-Albo, J.Martínez-Lema, G.Martínez, A.Monrabal, F.Monteiro, C.M.B.Mora, F.J.Moutinho, L.M.Nebot-Guinot, M.Novella, P.Nygren, D.Palmeiro, B.Para, A.Pérez, J.Querol, M.Ripoll, L.Rodríguez, J.Santos, F.P.dos Santos, J.M.F.Serra, L.Shuman, D.Simón, A.Sofka, C.Sorel, M.Toledo, J.F.Torrent, J.Tsamalaidze, Z.Veloso, J.F.C.A.White, J.Webb, R.Yahlali, N.Yepes-Ramírez, H.Background rejection in NEXT using deep neural networkshep-exParticle Physics - Experimentphysics.ins-detDetectors and Experimental TechniquesWe investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.arXiv:1609.06202FERMILAB-PUB-16-422-CDoai:cds.cern.ch:22173942016-09-20
spellingShingle hep-ex
Particle Physics - Experiment
physics.ins-det
Detectors and Experimental Techniques
Renner, J.
Farbin, A.
Vidal, J. Muñoz
Benlloch-Rodríguez, J.M.
Botas, A.
Ferrario, P.
Gómez-Cadenas, J.J.
Álvarez, V.
Azevedo, C.D.R.
Borges, F.I.G.
Cárcel, S.
Carrión, J.V.
Cebrián, S.
Cervera, A.
Conde, C.A.N.
Díaz, J.
Diesburg, M.
Esteve, R.
Fernandes, L.M.P.
Ferreira, A.L.
Freitas, E.D.C.
Goldschmidt, A.
González-Díaz, D.
Gutiérrez, R.M.
Hauptman, J.
Henriques, C.A.O.
Hernando Morata, J. A.
Herrero, V.
Jones, B.
Labarga, L.
Laing, A.
Lebrun, P.
Liubarsky, I.
López-March, N.
Lorca, D.
Losada, M.
Martín-Albo, J.
Martínez-Lema, G.
Martínez, A.
Monrabal, F.
Monteiro, C.M.B.
Mora, F.J.
Moutinho, L.M.
Nebot-Guinot, M.
Novella, P.
Nygren, D.
Palmeiro, B.
Para, A.
Pérez, J.
Querol, M.
Ripoll, L.
Rodríguez, J.
Santos, F.P.
dos Santos, J.M.F.
Serra, L.
Shuman, D.
Simón, A.
Sofka, C.
Sorel, M.
Toledo, J.F.
Torrent, J.
Tsamalaidze, Z.
Veloso, J.F.C.A.
White, J.
Webb, R.
Yahlali, N.
Yepes-Ramírez, H.
Background rejection in NEXT using deep neural networks
title Background rejection in NEXT using deep neural networks
title_full Background rejection in NEXT using deep neural networks
title_fullStr Background rejection in NEXT using deep neural networks
title_full_unstemmed Background rejection in NEXT using deep neural networks
title_short Background rejection in NEXT using deep neural networks
title_sort background rejection in next using deep neural networks
topic hep-ex
Particle Physics - Experiment
physics.ins-det
Detectors and Experimental Techniques
url https://dx.doi.org/10.1088/1748-0221/12/01/T01004
http://cds.cern.ch/record/2217394
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