Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks
Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol - Ide, C.V. ELSEVIER, 2017, a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics, Amsterdam |
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| Übergeordnetes Werk: |
volume:947 ; year:2019 ; day:11 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.nima.2019.162604 |
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| 520 | |a Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. | ||
| 520 | |a Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. | ||
| 650 | 7 | |a Deep learning |2 Elsevier | |
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10.1016/j.nima.2019.162604 doi GBV00000000000777.pica (DE-627)ELV048153206 (ELSEVIER)S0168-9002(19)31110-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Li, A. verfasserin aut Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Deep learning Elsevier Background rejection Elsevier Neutrino detector Elsevier Spallation Elsevier Neural network Elsevier Liquid scintillator Elsevier Elagin, A. oth Fraker, S. oth Grant, C. oth Winslow, L. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:947 year:2019 day:11 month:12 pages:0 https://doi.org/10.1016/j.nima.2019.162604 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 947 2019 11 1211 0 |
| spelling |
10.1016/j.nima.2019.162604 doi GBV00000000000777.pica (DE-627)ELV048153206 (ELSEVIER)S0168-9002(19)31110-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Li, A. verfasserin aut Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Deep learning Elsevier Background rejection Elsevier Neutrino detector Elsevier Spallation Elsevier Neural network Elsevier Liquid scintillator Elsevier Elagin, A. oth Fraker, S. oth Grant, C. oth Winslow, L. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:947 year:2019 day:11 month:12 pages:0 https://doi.org/10.1016/j.nima.2019.162604 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 947 2019 11 1211 0 |
| allfields_unstemmed |
10.1016/j.nima.2019.162604 doi GBV00000000000777.pica (DE-627)ELV048153206 (ELSEVIER)S0168-9002(19)31110-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Li, A. verfasserin aut Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Deep learning Elsevier Background rejection Elsevier Neutrino detector Elsevier Spallation Elsevier Neural network Elsevier Liquid scintillator Elsevier Elagin, A. oth Fraker, S. oth Grant, C. oth Winslow, L. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:947 year:2019 day:11 month:12 pages:0 https://doi.org/10.1016/j.nima.2019.162604 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 947 2019 11 1211 0 |
| allfieldsGer |
10.1016/j.nima.2019.162604 doi GBV00000000000777.pica (DE-627)ELV048153206 (ELSEVIER)S0168-9002(19)31110-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Li, A. verfasserin aut Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Deep learning Elsevier Background rejection Elsevier Neutrino detector Elsevier Spallation Elsevier Neural network Elsevier Liquid scintillator Elsevier Elagin, A. oth Fraker, S. oth Grant, C. oth Winslow, L. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:947 year:2019 day:11 month:12 pages:0 https://doi.org/10.1016/j.nima.2019.162604 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 947 2019 11 1211 0 |
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10.1016/j.nima.2019.162604 doi GBV00000000000777.pica (DE-627)ELV048153206 (ELSEVIER)S0168-9002(19)31110-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Li, A. verfasserin aut Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. Deep learning Elsevier Background rejection Elsevier Neutrino detector Elsevier Spallation Elsevier Neural network Elsevier Liquid scintillator Elsevier Elagin, A. oth Fraker, S. oth Grant, C. oth Winslow, L. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:947 year:2019 day:11 month:12 pages:0 https://doi.org/10.1016/j.nima.2019.162604 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 947 2019 11 1211 0 |
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suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks |
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Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks |
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Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. |
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Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. |
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Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product 10C is an important background in the region of interest between 2–3MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network (CNN) that uses the temporal and spatial correlations in light emissions to identify 10C background events. Using a simple Monte Carlo simulation of a monolithic liquid scintillator detector like KamLAND, we find that the algorithm is capable of identifying 61.6% of the 10C at 90% signal acceptance, with a total uncertainty of 2.7%. A detector with perfect light collection can identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to other frequently-used methods and can be expanded to other background sources. This work forms the foundation for more in depth studies of detector-dependent effects and more advanced CNN-based algorithms. |
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Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks |
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