Discrimination of neutrons and &gamma;-rays in liquid scintillator based on Elman neural network

Cai-Xun Zhang; Shin-Ted Lin; Jian-Ling Zhao; Xun-Zhen Yu; Li Wang; Jing-Jun Zhu; Hao-Yang Xing

doi:10.1088/1674-1137/40/8/086204

Chinese Physics C> 2016, Vol. 40> Issue(8) : 086204 DOI: 10.1088/1674-1137/40/8/086204

Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network

1.
Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology,Sichuan University, Chengdu 610065, China
2.
School of Physical Science and Technology, Sichuan University, Chengdu 610065, China
3.
Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics,Tsinghua University, Beijing 100084, China

Abstract
HTML
Reference
Related

PDF

Abstract：
In this work, a new neutron and γ(n/γ) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a ²⁴¹Am-⁹Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network (BPNN) as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/γ discrimination. The FOM increases from 0.907 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN. The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.
- liquid scintillator ,
- n/ discrimination ,
- Elman neural network ,
- BP neural network

References

[1]	H. Y. Xing, L. Wang, J. J. Zhu et al, Chin. Phys. C, 37: 026003 (2013)
[2]	V. Chazal, R. Brissot, J. F. Cavaignac et al, Astropart. Phys., 9: 163 (1998)
[3]	H. Wulandari, J. Jochum, W. Rau et al, Astropart. Phys., 22: 313 (2004)
[4]	D. Jordan., J. L. Tain., A. Algora. et al, Astropart. Phys., 42: 1 (2013)
[5]	D. Z. Ding, C. Y. Ye, Z. X. Zhao et al, Neutron Physics (Beijing, Atomic Energy Press, 2001), p. 132 (in Chinese)
[6]	F. D. Brooks, Nucl. Instrum. Methods, 4: 151 (1959).
[7]	J. M. Adams and G.White, Nucl. Instrum. Methods, 156: 459 (1978)
[8]	T. K. Alexander and F. S. Goulding, Nucl. Instrum. Methods, 13: 244 (1961)
[9]	M. L. Roush, M. A. Wilson and W. F. Hornyak, Nucl. Instrum. Methods, 31: 112 (1964)
[10]	D. Savran, B. Loher, M. Miklacec et al, Nucl. Instrum. Methods. A, 624: 675 (2010)
[11]	S. Yousefi and L. Lucchese, M. D. Aspinall, Nucl. Instrum. Methods. A, 598: 551 (2009)
[12]	X. L. Luo, Y. K. Wang, J. Yang et al, Nucl. Instrum. Methods. A, 717: 44 (2013)
[13]	X. Z. Yu, J. J. Zhu, S. T. Lin et al, Nucl. Instrum. Methods. A, 777: 80 (2015)
[14]	C. Zhong, L. F. Miller, and M. Buckner, Nucl. Instrum. Methods. A, 416: 438 (1998)
[15]	B. Esposito, L. Fortuna, and A. Rizzo, Neural Neutron/Gamma Discrimination in Organic Scintillators for Fusion Application, In Proceeding of the 2004 IEEE International joint Conference on Neural networks, edited by IEEE (USA: IEEE, 2004), p. 4: 2931
[16]	G. Liu, M. D. Aspinall, X. Ma et al, Nucl. Instrum. Methods. A, 607: 620 (2009)
[17]	J. L. Elman, Cogn. Sci., 14: 179 (1990)
[18]	B. G. Kermani, S. S. Schiffman, H. T. Nagle et al. Sens. Actuators, B, 110: 13 (2005)
[19]	S. Marrone, D. Cano-Ott, N. Colonna et al, Nucl. Instrum. Methods. A, 490: 299 (2002)
[20]	I. Antcheva, M. Ballintijn, B. Bellenot et al, Comput. Phys. Commun, 182: 1384 (2011)
[21]	R. A. Winyard, J. E. Lukin, and G. W. Mcbeth, Nucl. Instrum. Methods, 95: 141 (1971)
[22]	G. Liu, M. J. Joyce, X. Ma et al, IEEE Trans. Nucl. Sci., 57: 1682 (2010)

Access

Get Citation

Cai-Xun Zhang, Shin-Ted Lin, Jian-Ling Zhao, Xun-Zhen Yu, Li Wang, Jing-Jun Zhu and Hao-Yang Xing. Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network[J]. Chinese Physics C, 2016, 40(8): 086204. doi: 10.1088/1674-1137/40/8/086204

RIS(for EndNote,Reference Manager,ProCite)

BibTex

Txt

Milestone

Received: 2015-09-23
Revised: 2016-03-30

Fund

Supported by National Natural Science Foundation of China (11275134,11475117)

Article Metric

Article Views(2882)
PDF Downloads(195)
Cited by(0)

Policy on re-use

To reuse of subscription content published by CPC, the users need to request permission from CPC, unless the content was published under an Open Access license which automatically permits that type of reuse.

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network

Corresponding author: Jing-Jun Zhu,

Corresponding author: Hao-Yang Xing,

1. Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology,Sichuan University, Chengdu 610065, China
2. School of Physical Science and Technology, Sichuan University, Chengdu 610065, China
3. Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics,Tsinghua University, Beijing 100084, China

Received Date: 2015-09-23
Accepted Date: 2016-03-30
Available Online: 2016-08-05

Fund Project: Supported by National Natural Science Foundation of China (11275134,11475117)

Abstract: In this work, a new neutron and γ(n/γ) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a ²⁴¹Am-⁹Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network (BPNN) as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/γ discrimination. The FOM increases from 0.907 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN. The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.