Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537

P. A. Söderström; G. Jaworski; J. J. Valiente Dobón; J. Nyberg; J. Agramunt; G. de Angelis; S. Carturan; J. Egea; M. N. Erduran; S. Ertürk; G. de France; A. Gadea; A. Goasduff; V. González; K. Hadyńska-Klȩk; T. Hüyük; V. Modamio; M. Moszynski; A. Di Nitto; M. Palacz; N. Pietralla; E. Sanchis; D. Testov; A. Triossi; R. Wadsworth

doi:10.1016/j.nima.2018.11.122

Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537

P. A. Söderström^*, G. Jaworski, J. J. Valiente Dobón, J. Nyberg, J. Agramunt, G. de Angelis, S. Carturan, J. Egea, M. N. Erduran, S. Ertürk, G. de France, A. Gadea, A. Goasduff, V. González, K. Hadyńska-Klȩk, T. Hüyük, V. Modamio, M. Moszynski, A. Di Nitto, M. PalaczN. Pietralla, E. Sanchis, D. Testov, A. Triossi, R. Wadsworth

^*Corresponding author for this work

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

Abstract

In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and γ rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher γ-ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of γ rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neutrons could be identified in BC-501A using artificial neural networks down to a recoil proton energy of 800 keV compared to a recoil deuteron energy of 1200 keV for BC-537. We conclude that using artificial neural networks it is possible to obtain the same γ-ray rejection quality from both BC-501A and BC-537 for neutrons above a low-energy threshold. This threshold is, however, lower for BC-501A, which is important for nuclear structure spectroscopy experiments of rare reaction channels where low-energy interactions dominates.

Original language	English
Pages (from-to)	238-245
Number of pages	8
Journal	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume	916
Early online date	30 Nov 2018
DOIs	https://doi.org/10.1016/j.nima.2018.11.122
Publication status	Published - 1 Feb 2019

Bibliographical note

Keywords

BC-501A
BC-537
Digital pulse-shape discrimination
Fast-neutron detection
Liquid scintillator
Neural networks

Access to Document

10.1016/j.nima.2018.11.122

BC501A_BC537_PSA_accepted
© 2018 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.
Accepted author manuscript, 9.47 MBLicence: CC BY-NC-ND

Cite this

Söderström, P. A., Jaworski, G., Valiente Dobón, J. J., Nyberg, J., Agramunt, J., de Angelis, G., Carturan, S., Egea, J., Erduran, M. N., Ertürk, S., de France, G., Gadea, A., Goasduff, A., González, V., Hadyńska-Klȩk, K., Hüyük, T., Modamio, V., Moszynski, M., Di Nitto, A., ... Wadsworth, R. (2019). Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 916, 238-245. https://doi.org/10.1016/j.nima.2018.11.122

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abstract = "In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and γ rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher γ-ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of γ rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neutrons could be identified in BC-501A using artificial neural networks down to a recoil proton energy of 800 keV compared to a recoil deuteron energy of 1200 keV for BC-537. We conclude that using artificial neural networks it is possible to obtain the same γ-ray rejection quality from both BC-501A and BC-537 for neutrons above a low-energy threshold. This threshold is, however, lower for BC-501A, which is important for nuclear structure spectroscopy experiments of rare reaction channels where low-energy interactions dominates.",

keywords = "BC-501A, BC-537, Digital pulse-shape discrimination, Fast-neutron detection, Liquid scintillator, Neural networks",

author = "S{\"o}derstr{\"o}m, {P. A.} and G. Jaworski and {Valiente Dob{\'o}n}, {J. J.} and J. Nyberg and J. Agramunt and {de Angelis}, G. and S. Carturan and J. Egea and Erduran, {M. N.} and S. Ert{\"u}rk and {de France}, G. and A. Gadea and A. Goasduff and V. Gonz{\'a}lez and K. Hady{\'n}ska-Klȩk and T. H{\"u}y{\"u}k and V. Modamio and M. Moszynski and {Di Nitto}, A. and M. Palacz and N. Pietralla and E. Sanchis and D. Testov and A. Triossi and R. Wadsworth",

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Söderström, PA, Jaworski, G, Valiente Dobón, JJ, Nyberg, J, Agramunt, J, de Angelis, G, Carturan, S, Egea, J, Erduran, MN, Ertürk, S, de France, G, Gadea, A, Goasduff, A, González, V, Hadyńska-Klȩk, K, Hüyük, T, Modamio, V, Moszynski, M, Di Nitto, A, Palacz, M, Pietralla, N, Sanchis, E, Testov, D, Triossi, A & Wadsworth, R 2019, 'Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537', Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 916, pp. 238-245. https://doi.org/10.1016/j.nima.2018.11.122

Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537. / Söderström, P. A.; Jaworski, G.; Valiente Dobón, J. J. et al.
In: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 916, 01.02.2019, p. 238-245.

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

TY - JOUR

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AU - Söderström, P. A.

AU - Jaworski, G.

AU - Valiente Dobón, J. J.

AU - Nyberg, J.

AU - Agramunt, J.

AU - de Angelis, G.

AU - Carturan, S.

AU - Egea, J.

AU - Erduran, M. N.

AU - Ertürk, S.

AU - de France, G.

AU - Gadea, A.

AU - Goasduff, A.

AU - González, V.

AU - Hadyńska-Klȩk, K.

AU - Hüyük, T.

AU - Modamio, V.

AU - Moszynski, M.

AU - Di Nitto, A.

AU - Palacz, M.

AU - Pietralla, N.

AU - Sanchis, E.

AU - Testov, D.

AU - Triossi, A.

AU - Wadsworth, R.

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N2 - In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and γ rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher γ-ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of γ rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neutrons could be identified in BC-501A using artificial neural networks down to a recoil proton energy of 800 keV compared to a recoil deuteron energy of 1200 keV for BC-537. We conclude that using artificial neural networks it is possible to obtain the same γ-ray rejection quality from both BC-501A and BC-537 for neutrons above a low-energy threshold. This threshold is, however, lower for BC-501A, which is important for nuclear structure spectroscopy experiments of rare reaction channels where low-energy interactions dominates.

AB - In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and γ rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher γ-ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of γ rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neutrons could be identified in BC-501A using artificial neural networks down to a recoil proton energy of 800 keV compared to a recoil deuteron energy of 1200 keV for BC-537. We conclude that using artificial neural networks it is possible to obtain the same γ-ray rejection quality from both BC-501A and BC-537 for neutrons above a low-energy threshold. This threshold is, however, lower for BC-501A, which is important for nuclear structure spectroscopy experiments of rare reaction channels where low-energy interactions dominates.

KW - BC-501A

KW - BC-537

KW - Digital pulse-shape discrimination

KW - Fast-neutron detection

KW - Liquid scintillator

KW - Neural networks

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JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

ER -

Söderström PA, Jaworski G, Valiente Dobón JJ, Nyberg J, Agramunt J, de Angelis G et al. Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2019 Feb 1;916:238-245. Epub 2018 Nov 30. doi: 10.1016/j.nima.2018.11.122

Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537

Abstract

Bibliographical note

Keywords

Access to Document

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Nuclear Physics Consolidated Grant

Nuclear Physics Consolidated Grant

Cite this

Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Projects

Nuclear Physics Consolidated Grant

Nuclear Physics Consolidated Grant

Cite this