Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
The NEXT collaboration [verfasserIn] M. Kekic [verfasserIn] C. Adams [verfasserIn] K. Woodruff [verfasserIn] J. Renner [verfasserIn] E. Church [verfasserIn] M. Del Tutto [verfasserIn] J. A. Hernando Morata [verfasserIn] J. J. Gómez-Cadenas [verfasserIn] V. Álvarez [verfasserIn] L. Arazi [verfasserIn] I. J. Arnquist [verfasserIn] C. D. R. Azevedo [verfasserIn] K. Bailey [verfasserIn] F. Ballester [verfasserIn] J. M. Benlloch-Rodríguez [verfasserIn] F. I. G. M. Borges [verfasserIn] N. Byrnes [verfasserIn] S. Cárcel [verfasserIn] J. V. Carrión [verfasserIn] S. Cebrián [verfasserIn] C. A. N. Conde [verfasserIn] T. Contreras [verfasserIn] G. Díaz [verfasserIn] J. Díaz [verfasserIn] M. Diesburg [verfasserIn] J. Escada [verfasserIn] R. Esteve [verfasserIn] R. Felkai [verfasserIn] A. F. M. Fernandes [verfasserIn] L. M. P. Fernandes [verfasserIn] P. Ferrario [verfasserIn] A. L. Ferreira [verfasserIn] E. D. C. Freitas [verfasserIn] J. Generowicz [verfasserIn] S. Ghosh [verfasserIn] A. Goldschmidt [verfasserIn] D. González-Díaz [verfasserIn] R. Guenette [verfasserIn] R. M. Gutiérrez [verfasserIn] J. Haefner [verfasserIn] K. Hafidi [verfasserIn] J. Hauptman [verfasserIn] C. A. O. Henriques [verfasserIn] P. Herrero [verfasserIn] V. Herrero [verfasserIn] Y. Ifergan [verfasserIn] B. J. P. Jones [verfasserIn] L. Labarga [verfasserIn] A. Laing [verfasserIn] P. Lebrun [verfasserIn] N. López-March [verfasserIn] M. Losada [verfasserIn] R. D. P. Mano [verfasserIn] J. Martín-Albo [verfasserIn] A. Martínez [verfasserIn] G. Martínez-Lema [verfasserIn] M. Martínez-Vara [verfasserIn] A. D. McDonald [verfasserIn] Z.-E. Meziani [verfasserIn] F. Monrabal [verfasserIn] C. M. B. Monteiro [verfasserIn] F. J. Mora [verfasserIn] J. Muñoz Vidal [verfasserIn] P. Novella [verfasserIn] D. R. Nygren [verfasserIn] B. Palmeiro [verfasserIn] A. Para [verfasserIn] J. Pérez [verfasserIn] M. Querol [verfasserIn] A. B. Redwine [verfasserIn] L. Ripoll [verfasserIn] Y. Rodríguez García [verfasserIn] J. Rodríguez [verfasserIn] L. Rogers [verfasserIn] B. Romeo [verfasserIn] C. Romo-Luque [verfasserIn] F. P. Santos [verfasserIn] J. M. F. dos Santos [verfasserIn] A. Simón [verfasserIn] C. Sofka [verfasserIn] M. Sorel [verfasserIn] T. Stiegler [verfasserIn] J. F. Toledo [verfasserIn] J. Torrent [verfasserIn] A. Usón [verfasserIn] J. F. C. A. Veloso [verfasserIn] R. Webb [verfasserIn] R. Weiss-Babai [verfasserIn] J. T. White [verfasserIn] N. Yahlali [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Journal of High Energy Physics - SpringerOpen, 2016, (2021), 1, Seite 22 |
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| Übergeordnetes Werk: |
year:2021 ; number:1 ; pages:22 |
| Links: |
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| DOI / URN: |
10.1007/JHEP01(2021)189 |
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| Katalog-ID: |
DOAJ00885064X |
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| 245 | 1 | 0 | |a Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment |
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| 520 | |a Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. | ||
| 650 | 4 | |a Dark Matter and Double Beta Decay (experiments) | |
| 653 | 0 | |a Nuclear and particle physics. Atomic energy. Radioactivity | |
| 700 | 0 | |a M. Kekic |e verfasserin |4 aut | |
| 700 | 0 | |a C. Adams |e verfasserin |4 aut | |
| 700 | 0 | |a K. Woodruff |e verfasserin |4 aut | |
| 700 | 0 | |a J. Renner |e verfasserin |4 aut | |
| 700 | 0 | |a E. Church |e verfasserin |4 aut | |
| 700 | 0 | |a M. Del Tutto |e verfasserin |4 aut | |
| 700 | 0 | |a J. A. Hernando Morata |e verfasserin |4 aut | |
| 700 | 0 | |a J. J. Gómez-Cadenas |e verfasserin |4 aut | |
| 700 | 0 | |a V. Álvarez |e verfasserin |4 aut | |
| 700 | 0 | |a L. Arazi |e verfasserin |4 aut | |
| 700 | 0 | |a I. J. Arnquist |e verfasserin |4 aut | |
| 700 | 0 | |a C. D. R. Azevedo |e verfasserin |4 aut | |
| 700 | 0 | |a K. Bailey |e verfasserin |4 aut | |
| 700 | 0 | |a F. Ballester |e verfasserin |4 aut | |
| 700 | 0 | |a J. M. Benlloch-Rodríguez |e verfasserin |4 aut | |
| 700 | 0 | |a F. I. G. M. Borges |e verfasserin |4 aut | |
| 700 | 0 | |a N. Byrnes |e verfasserin |4 aut | |
| 700 | 0 | |a S. Cárcel |e verfasserin |4 aut | |
| 700 | 0 | |a J. V. Carrión |e verfasserin |4 aut | |
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| 700 | 0 | |a D. González-Díaz |e verfasserin |4 aut | |
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| 700 | 0 | |a R. M. Gutiérrez |e verfasserin |4 aut | |
| 700 | 0 | |a J. Haefner |e verfasserin |4 aut | |
| 700 | 0 | |a K. Hafidi |e verfasserin |4 aut | |
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| 700 | 0 | |a C. A. O. Henriques |e verfasserin |4 aut | |
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| 700 | 0 | |a P. Lebrun |e verfasserin |4 aut | |
| 700 | 0 | |a N. López-March |e verfasserin |4 aut | |
| 700 | 0 | |a M. Losada |e verfasserin |4 aut | |
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10.1007/JHEP01(2021)189 doi (DE-627)DOAJ00885064X (DE-599)DOAJ4a26af73701c4985a3782da446c27452 DE-627 ger DE-627 rakwb eng QC770-798 The NEXT collaboration verfasserin aut Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. Dark Matter and Double Beta Decay (experiments) Nuclear and particle physics. Atomic energy. Radioactivity M. Kekic verfasserin aut C. Adams verfasserin aut K. Woodruff verfasserin aut J. Renner verfasserin aut E. Church verfasserin aut M. Del Tutto verfasserin aut J. A. Hernando Morata verfasserin aut J. J. Gómez-Cadenas verfasserin aut V. Álvarez verfasserin aut L. Arazi verfasserin aut I. J. Arnquist verfasserin aut C. D. R. Azevedo verfasserin aut K. Bailey verfasserin aut F. Ballester verfasserin aut J. M. Benlloch-Rodríguez verfasserin aut F. I. G. M. Borges verfasserin aut N. Byrnes verfasserin aut S. Cárcel verfasserin aut J. V. Carrión verfasserin aut S. Cebrián verfasserin aut C. A. N. Conde verfasserin aut T. Contreras verfasserin aut G. Díaz verfasserin aut J. Díaz verfasserin aut M. Diesburg verfasserin aut J. Escada verfasserin aut R. Esteve verfasserin aut R. Felkai verfasserin aut A. F. M. Fernandes verfasserin aut L. M. P. Fernandes verfasserin aut P. Ferrario verfasserin aut A. L. Ferreira verfasserin aut E. D. C. Freitas verfasserin aut J. Generowicz verfasserin aut S. Ghosh verfasserin aut A. Goldschmidt verfasserin aut D. González-Díaz verfasserin aut R. Guenette verfasserin aut R. M. Gutiérrez verfasserin aut J. Haefner verfasserin aut K. Hafidi verfasserin aut J. Hauptman verfasserin aut C. A. O. Henriques verfasserin aut P. Herrero verfasserin aut V. Herrero verfasserin aut Y. Ifergan verfasserin aut B. J. P. Jones verfasserin aut L. Labarga verfasserin aut A. Laing verfasserin aut P. Lebrun verfasserin aut N. López-March verfasserin aut M. Losada verfasserin aut R. D. P. Mano verfasserin aut J. Martín-Albo verfasserin aut A. Martínez verfasserin aut G. Martínez-Lema verfasserin aut M. Martínez-Vara verfasserin aut A. D. McDonald verfasserin aut Z.-E. Meziani verfasserin aut F. Monrabal verfasserin aut C. M. B. Monteiro verfasserin aut F. J. Mora verfasserin aut J. Muñoz Vidal verfasserin aut P. Novella verfasserin aut D. R. Nygren verfasserin aut B. Palmeiro verfasserin aut A. Para verfasserin aut J. Pérez verfasserin aut M. Querol verfasserin aut A. B. Redwine verfasserin aut L. Ripoll verfasserin aut Y. Rodríguez García verfasserin aut J. Rodríguez verfasserin aut L. Rogers verfasserin aut B. Romeo verfasserin aut C. Romo-Luque verfasserin aut F. P. Santos verfasserin aut J. M. F. dos Santos verfasserin aut A. Simón verfasserin aut C. Sofka verfasserin aut M. Sorel verfasserin aut T. Stiegler verfasserin aut J. F. 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10.1007/JHEP01(2021)189 doi (DE-627)DOAJ00885064X (DE-599)DOAJ4a26af73701c4985a3782da446c27452 DE-627 ger DE-627 rakwb eng QC770-798 The NEXT collaboration verfasserin aut Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. Dark Matter and Double Beta Decay (experiments) Nuclear and particle physics. Atomic energy. Radioactivity M. Kekic verfasserin aut C. Adams verfasserin aut K. Woodruff verfasserin aut J. Renner verfasserin aut E. Church verfasserin aut M. Del Tutto verfasserin aut J. A. Hernando Morata verfasserin aut J. J. Gómez-Cadenas verfasserin aut V. Álvarez verfasserin aut L. Arazi verfasserin aut I. J. Arnquist verfasserin aut C. D. R. Azevedo verfasserin aut K. Bailey verfasserin aut F. Ballester verfasserin aut J. M. Benlloch-Rodríguez verfasserin aut F. I. G. M. Borges verfasserin aut N. Byrnes verfasserin aut S. Cárcel verfasserin aut J. V. Carrión verfasserin aut S. Cebrián verfasserin aut C. A. N. Conde verfasserin aut T. Contreras verfasserin aut G. Díaz verfasserin aut J. Díaz verfasserin aut M. Diesburg verfasserin aut J. Escada verfasserin aut R. Esteve verfasserin aut R. Felkai verfasserin aut A. F. M. Fernandes verfasserin aut L. M. P. Fernandes verfasserin aut P. Ferrario verfasserin aut A. L. Ferreira verfasserin aut E. D. C. Freitas verfasserin aut J. Generowicz verfasserin aut S. Ghosh verfasserin aut A. Goldschmidt verfasserin aut D. González-Díaz verfasserin aut R. Guenette verfasserin aut R. M. Gutiérrez verfasserin aut J. Haefner verfasserin aut K. Hafidi verfasserin aut J. Hauptman verfasserin aut C. A. O. Henriques verfasserin aut P. Herrero verfasserin aut V. Herrero verfasserin aut Y. Ifergan verfasserin aut B. J. P. Jones verfasserin aut L. Labarga verfasserin aut A. Laing verfasserin aut P. Lebrun verfasserin aut N. López-March verfasserin aut M. Losada verfasserin aut R. D. P. Mano verfasserin aut J. Martín-Albo verfasserin aut A. Martínez verfasserin aut G. Martínez-Lema verfasserin aut M. Martínez-Vara verfasserin aut A. D. McDonald verfasserin aut Z.-E. Meziani verfasserin aut F. Monrabal verfasserin aut C. M. B. Monteiro verfasserin aut F. J. Mora verfasserin aut J. Muñoz Vidal verfasserin aut P. Novella verfasserin aut D. R. Nygren verfasserin aut B. Palmeiro verfasserin aut A. Para verfasserin aut J. Pérez verfasserin aut M. Querol verfasserin aut A. B. Redwine verfasserin aut L. Ripoll verfasserin aut Y. Rodríguez García verfasserin aut J. Rodríguez verfasserin aut L. Rogers verfasserin aut B. Romeo verfasserin aut C. Romo-Luque verfasserin aut F. P. Santos verfasserin aut J. M. F. dos Santos verfasserin aut A. Simón verfasserin aut C. Sofka verfasserin aut M. Sorel verfasserin aut T. Stiegler verfasserin aut J. F. 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Yahlali verfasserin aut In Journal of High Energy Physics SpringerOpen, 2016 (2021), 1, Seite 22 (DE-627)320910571 (DE-600)2027350-2 10298479 nnns year:2021 number:1 pages:22 https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/article/4a26af73701c4985a3782da446c27452 kostenfrei https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/toc/1029-8479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021 1 22 |
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10.1007/JHEP01(2021)189 doi (DE-627)DOAJ00885064X (DE-599)DOAJ4a26af73701c4985a3782da446c27452 DE-627 ger DE-627 rakwb eng QC770-798 The NEXT collaboration verfasserin aut Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. Dark Matter and Double Beta Decay (experiments) Nuclear and particle physics. Atomic energy. Radioactivity M. Kekic verfasserin aut C. Adams verfasserin aut K. Woodruff verfasserin aut J. Renner verfasserin aut E. Church verfasserin aut M. Del Tutto verfasserin aut J. A. Hernando Morata verfasserin aut J. J. Gómez-Cadenas verfasserin aut V. Álvarez verfasserin aut L. Arazi verfasserin aut I. J. Arnquist verfasserin aut C. D. R. Azevedo verfasserin aut K. Bailey verfasserin aut F. Ballester verfasserin aut J. M. Benlloch-Rodríguez verfasserin aut F. I. G. M. Borges verfasserin aut N. Byrnes verfasserin aut S. Cárcel verfasserin aut J. V. Carrión verfasserin aut S. Cebrián verfasserin aut C. A. N. Conde verfasserin aut T. Contreras verfasserin aut G. Díaz verfasserin aut J. Díaz verfasserin aut M. Diesburg verfasserin aut J. Escada verfasserin aut R. Esteve verfasserin aut R. Felkai verfasserin aut A. F. M. Fernandes verfasserin aut L. M. P. Fernandes verfasserin aut P. Ferrario verfasserin aut A. L. Ferreira verfasserin aut E. D. C. Freitas verfasserin aut J. Generowicz verfasserin aut S. Ghosh verfasserin aut A. Goldschmidt verfasserin aut D. González-Díaz verfasserin aut R. Guenette verfasserin aut R. M. Gutiérrez verfasserin aut J. Haefner verfasserin aut K. Hafidi verfasserin aut J. Hauptman verfasserin aut C. A. O. Henriques verfasserin aut P. Herrero verfasserin aut V. Herrero verfasserin aut Y. Ifergan verfasserin aut B. J. P. Jones verfasserin aut L. Labarga verfasserin aut A. Laing verfasserin aut P. Lebrun verfasserin aut N. López-March verfasserin aut M. Losada verfasserin aut R. D. P. Mano verfasserin aut J. Martín-Albo verfasserin aut A. Martínez verfasserin aut G. Martínez-Lema verfasserin aut M. Martínez-Vara verfasserin aut A. D. McDonald verfasserin aut Z.-E. Meziani verfasserin aut F. Monrabal verfasserin aut C. M. B. Monteiro verfasserin aut F. J. Mora verfasserin aut J. Muñoz Vidal verfasserin aut P. Novella verfasserin aut D. R. Nygren verfasserin aut B. Palmeiro verfasserin aut A. Para verfasserin aut J. Pérez verfasserin aut M. Querol verfasserin aut A. B. Redwine verfasserin aut L. Ripoll verfasserin aut Y. Rodríguez García verfasserin aut J. Rodríguez verfasserin aut L. Rogers verfasserin aut B. Romeo verfasserin aut C. Romo-Luque verfasserin aut F. P. Santos verfasserin aut J. M. F. dos Santos verfasserin aut A. Simón verfasserin aut C. Sofka verfasserin aut M. Sorel verfasserin aut T. Stiegler verfasserin aut J. F. 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Yahlali verfasserin aut In Journal of High Energy Physics SpringerOpen, 2016 (2021), 1, Seite 22 (DE-627)320910571 (DE-600)2027350-2 10298479 nnns year:2021 number:1 pages:22 https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/article/4a26af73701c4985a3782da446c27452 kostenfrei https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/toc/1029-8479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021 1 22 |
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10.1007/JHEP01(2021)189 doi (DE-627)DOAJ00885064X (DE-599)DOAJ4a26af73701c4985a3782da446c27452 DE-627 ger DE-627 rakwb eng QC770-798 The NEXT collaboration verfasserin aut Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. Dark Matter and Double Beta Decay (experiments) Nuclear and particle physics. Atomic energy. Radioactivity M. Kekic verfasserin aut C. Adams verfasserin aut K. Woodruff verfasserin aut J. Renner verfasserin aut E. Church verfasserin aut M. Del Tutto verfasserin aut J. A. Hernando Morata verfasserin aut J. J. Gómez-Cadenas verfasserin aut V. Álvarez verfasserin aut L. Arazi verfasserin aut I. J. Arnquist verfasserin aut C. D. R. Azevedo verfasserin aut K. Bailey verfasserin aut F. Ballester verfasserin aut J. M. Benlloch-Rodríguez verfasserin aut F. I. G. M. Borges verfasserin aut N. Byrnes verfasserin aut S. Cárcel verfasserin aut J. V. Carrión verfasserin aut S. Cebrián verfasserin aut C. A. N. Conde verfasserin aut T. Contreras verfasserin aut G. Díaz verfasserin aut J. Díaz verfasserin aut M. Diesburg verfasserin aut J. Escada verfasserin aut R. Esteve verfasserin aut R. Felkai verfasserin aut A. F. M. Fernandes verfasserin aut L. M. P. Fernandes verfasserin aut P. Ferrario verfasserin aut A. L. Ferreira verfasserin aut E. D. C. Freitas verfasserin aut J. Generowicz verfasserin aut S. Ghosh verfasserin aut A. Goldschmidt verfasserin aut D. González-Díaz verfasserin aut R. Guenette verfasserin aut R. M. Gutiérrez verfasserin aut J. Haefner verfasserin aut K. Hafidi verfasserin aut J. Hauptman verfasserin aut C. A. O. Henriques verfasserin aut P. Herrero verfasserin aut V. Herrero verfasserin aut Y. Ifergan verfasserin aut B. J. P. Jones verfasserin aut L. Labarga verfasserin aut A. Laing verfasserin aut P. Lebrun verfasserin aut N. López-March verfasserin aut M. Losada verfasserin aut R. D. P. Mano verfasserin aut J. Martín-Albo verfasserin aut A. Martínez verfasserin aut G. Martínez-Lema verfasserin aut M. Martínez-Vara verfasserin aut A. D. McDonald verfasserin aut Z.-E. Meziani verfasserin aut F. Monrabal verfasserin aut C. M. B. Monteiro verfasserin aut F. J. Mora verfasserin aut J. Muñoz Vidal verfasserin aut P. Novella verfasserin aut D. R. Nygren verfasserin aut B. Palmeiro verfasserin aut A. Para verfasserin aut J. Pérez verfasserin aut M. Querol verfasserin aut A. B. Redwine verfasserin aut L. Ripoll verfasserin aut Y. Rodríguez García verfasserin aut J. Rodríguez verfasserin aut L. Rogers verfasserin aut B. Romeo verfasserin aut C. Romo-Luque verfasserin aut F. P. Santos verfasserin aut J. M. F. dos Santos verfasserin aut A. Simón verfasserin aut C. Sofka verfasserin aut M. Sorel verfasserin aut T. Stiegler verfasserin aut J. F. Toledo verfasserin aut J. Torrent verfasserin aut A. Usón verfasserin aut J. F. C. A. Veloso verfasserin aut R. Webb verfasserin aut R. Weiss-Babai verfasserin aut J. T. White verfasserin aut N. Yahlali verfasserin aut In Journal of High Energy Physics SpringerOpen, 2016 (2021), 1, Seite 22 (DE-627)320910571 (DE-600)2027350-2 10298479 nnns year:2021 number:1 pages:22 https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/article/4a26af73701c4985a3782da446c27452 kostenfrei https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/toc/1029-8479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021 1 22 |
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10.1007/JHEP01(2021)189 doi (DE-627)DOAJ00885064X (DE-599)DOAJ4a26af73701c4985a3782da446c27452 DE-627 ger DE-627 rakwb eng QC770-798 The NEXT collaboration verfasserin aut Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. Dark Matter and Double Beta Decay (experiments) Nuclear and particle physics. Atomic energy. Radioactivity M. Kekic verfasserin aut C. Adams verfasserin aut K. Woodruff verfasserin aut J. Renner verfasserin aut E. Church verfasserin aut M. Del Tutto verfasserin aut J. A. Hernando Morata verfasserin aut J. J. Gómez-Cadenas verfasserin aut V. Álvarez verfasserin aut L. Arazi verfasserin aut I. J. Arnquist verfasserin aut C. D. R. Azevedo verfasserin aut K. Bailey verfasserin aut F. Ballester verfasserin aut J. M. Benlloch-Rodríguez verfasserin aut F. I. G. M. Borges verfasserin aut N. Byrnes verfasserin aut S. Cárcel verfasserin aut J. V. Carrión verfasserin aut S. Cebrián verfasserin aut C. A. N. Conde verfasserin aut T. Contreras verfasserin aut G. Díaz verfasserin aut J. Díaz verfasserin aut M. Diesburg verfasserin aut J. Escada verfasserin aut R. Esteve verfasserin aut R. Felkai verfasserin aut A. F. M. Fernandes verfasserin aut L. M. P. Fernandes verfasserin aut P. Ferrario verfasserin aut A. L. Ferreira verfasserin aut E. D. C. Freitas verfasserin aut J. Generowicz verfasserin aut S. Ghosh verfasserin aut A. Goldschmidt verfasserin aut D. González-Díaz verfasserin aut R. Guenette verfasserin aut R. M. Gutiérrez verfasserin aut J. Haefner verfasserin aut K. Hafidi verfasserin aut J. Hauptman verfasserin aut C. A. O. Henriques verfasserin aut P. Herrero verfasserin aut V. Herrero verfasserin aut Y. Ifergan verfasserin aut B. J. P. Jones verfasserin aut L. Labarga verfasserin aut A. Laing verfasserin aut P. Lebrun verfasserin aut N. López-March verfasserin aut M. Losada verfasserin aut R. D. P. Mano verfasserin aut J. Martín-Albo verfasserin aut A. Martínez verfasserin aut G. Martínez-Lema verfasserin aut M. Martínez-Vara verfasserin aut A. D. McDonald verfasserin aut Z.-E. Meziani verfasserin aut F. Monrabal verfasserin aut C. M. B. Monteiro verfasserin aut F. J. Mora verfasserin aut J. Muñoz Vidal verfasserin aut P. Novella verfasserin aut D. R. Nygren verfasserin aut B. Palmeiro verfasserin aut A. Para verfasserin aut J. Pérez verfasserin aut M. Querol verfasserin aut A. B. Redwine verfasserin aut L. Ripoll verfasserin aut Y. Rodríguez García verfasserin aut J. Rodríguez verfasserin aut L. Rogers verfasserin aut B. Romeo verfasserin aut C. Romo-Luque verfasserin aut F. P. Santos verfasserin aut J. M. F. dos Santos verfasserin aut A. Simón verfasserin aut C. Sofka verfasserin aut M. Sorel verfasserin aut T. Stiegler verfasserin aut J. F. Toledo verfasserin aut J. Torrent verfasserin aut A. Usón verfasserin aut J. F. C. A. Veloso verfasserin aut R. Webb verfasserin aut R. Weiss-Babai verfasserin aut J. T. White verfasserin aut N. Yahlali verfasserin aut In Journal of High Energy Physics SpringerOpen, 2016 (2021), 1, Seite 22 (DE-627)320910571 (DE-600)2027350-2 10298479 nnns year:2021 number:1 pages:22 https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/article/4a26af73701c4985a3782da446c27452 kostenfrei https://doi.org/10.1007/JHEP01(2021)189 kostenfrei https://doaj.org/toc/1029-8479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021 1 22 |
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Journal of High Energy Physics |
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The NEXT collaboration @@aut@@ M. Kekic @@aut@@ C. Adams @@aut@@ K. Woodruff @@aut@@ J. Renner @@aut@@ E. Church @@aut@@ M. Del Tutto @@aut@@ J. A. Hernando Morata @@aut@@ J. J. Gómez-Cadenas @@aut@@ V. Álvarez @@aut@@ L. Arazi @@aut@@ I. J. Arnquist @@aut@@ C. D. R. Azevedo @@aut@@ K. Bailey @@aut@@ F. Ballester @@aut@@ J. M. Benlloch-Rodríguez @@aut@@ F. I. G. M. Borges @@aut@@ N. Byrnes @@aut@@ S. Cárcel @@aut@@ J. V. Carrión @@aut@@ S. Cebrián @@aut@@ C. A. N. Conde @@aut@@ T. Contreras @@aut@@ G. Díaz @@aut@@ J. Díaz @@aut@@ M. Diesburg @@aut@@ J. Escada @@aut@@ R. Esteve @@aut@@ R. Felkai @@aut@@ A. F. M. Fernandes @@aut@@ L. M. P. Fernandes @@aut@@ P. Ferrario @@aut@@ A. L. Ferreira @@aut@@ E. D. C. Freitas @@aut@@ J. Generowicz @@aut@@ S. Ghosh @@aut@@ A. Goldschmidt @@aut@@ D. González-Díaz @@aut@@ R. Guenette @@aut@@ R. M. Gutiérrez @@aut@@ J. Haefner @@aut@@ K. Hafidi @@aut@@ J. Hauptman @@aut@@ C. A. O. Henriques @@aut@@ P. Herrero @@aut@@ V. Herrero @@aut@@ Y. Ifergan @@aut@@ B. J. P. Jones @@aut@@ L. Labarga @@aut@@ A. Laing @@aut@@ P. Lebrun @@aut@@ N. López-March @@aut@@ M. Losada @@aut@@ R. D. P. Mano @@aut@@ J. Martín-Albo @@aut@@ A. Martínez @@aut@@ G. Martínez-Lema @@aut@@ M. Martínez-Vara @@aut@@ A. D. McDonald @@aut@@ Z.-E. Meziani @@aut@@ F. Monrabal @@aut@@ C. M. B. Monteiro @@aut@@ F. J. Mora @@aut@@ J. Muñoz Vidal @@aut@@ P. Novella @@aut@@ D. R. Nygren @@aut@@ B. Palmeiro @@aut@@ A. Para @@aut@@ J. Pérez @@aut@@ M. Querol @@aut@@ A. B. Redwine @@aut@@ L. Ripoll @@aut@@ Y. Rodríguez García @@aut@@ J. Rodríguez @@aut@@ L. Rogers @@aut@@ B. Romeo @@aut@@ C. Romo-Luque @@aut@@ F. P. Santos @@aut@@ J. M. F. dos Santos @@aut@@ A. Simón @@aut@@ C. Sofka @@aut@@ M. Sorel @@aut@@ T. Stiegler @@aut@@ J. F. Toledo @@aut@@ J. Torrent @@aut@@ A. Usón @@aut@@ J. F. C. A. Veloso @@aut@@ R. Webb @@aut@@ R. Weiss-Babai @@aut@@ J. T. White @@aut@@ N. Yahlali @@aut@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
320910571 |
| id |
DOAJ00885064X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ00885064X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310014104.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/JHEP01(2021)189</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ00885064X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4a26af73701c4985a3782da446c27452</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC770-798</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">The NEXT collaboration</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. 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The NEXT collaboration M. Kekic C. Adams K. Woodruff J. Renner E. Church M. Del Tutto J. A. Hernando Morata J. J. Gómez-Cadenas V. Álvarez L. Arazi I. J. Arnquist C. D. R. Azevedo K. Bailey F. Ballester J. M. Benlloch-Rodríguez F. I. G. M. Borges N. Byrnes S. Cárcel J. V. Carrión S. Cebrián C. A. N. Conde T. Contreras G. Díaz J. Díaz M. Diesburg J. Escada R. Esteve R. Felkai A. F. M. Fernandes L. M. P. Fernandes P. Ferrario A. L. Ferreira E. D. C. Freitas J. Generowicz S. Ghosh A. Goldschmidt D. González-Díaz R. Guenette R. M. Gutiérrez J. Haefner K. Hafidi J. Hauptman C. A. O. Henriques P. Herrero V. Herrero Y. Ifergan B. J. P. Jones L. Labarga A. Laing P. Lebrun N. López-March M. Losada R. D. P. Mano J. Martín-Albo A. Martínez G. Martínez-Lema M. Martínez-Vara A. D. McDonald Z.-E. Meziani F. Monrabal C. M. B. Monteiro F. J. Mora J. Muñoz Vidal P. Novella D. R. Nygren B. Palmeiro A. Para J. Pérez M. Querol A. B. Redwine L. Ripoll Y. Rodríguez García J. Rodríguez L. Rogers B. Romeo C. Romo-Luque F. P. Santos J. M. F. dos Santos A. Simón C. Sofka M. Sorel T. Stiegler J. F. Toledo J. Torrent A. Usón J. F. C. A. Veloso R. Webb R. Weiss-Babai J. T. White N. Yahlali |
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Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment |
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Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. |
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Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. |
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Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. |
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