TY - JOUR
T1 - Discrimination between neutrino events and backgrounds using pulse shape information in reactor neutrino experiments
AU - Xu, Ye
AU - Wu, Bin
AU - Zhang, Ying Jie
AU - Liu, Ze Long
AU - Sun, Hao Kai
AU - Di, Dan Ning
N1 - Funding Information:
This work is supported in part by the National Natural Science Foundation of China (NSFC) under the Contract no. 10605014 and the national undergraduate innovative plan of China under the Contract no. 081005517.
PY - 2009/11/1
Y1 - 2009/11/1
N2 - The application of Bayesian neural networks (BNN) to discriminate neutrino events from backgrounds in reactor neutrino experiments has been described in Xu et al. (2008) [1] and Xu et al. (2009) [2]. In the present paper, the pulse shape information for a fast signal of a neutrino event or a background event is used as a part of inputs to BNN to discriminate neutrino events from backgrounds. The numbers of photoelectrons received by PMTs and the delay time for a delayed signal are used as the other part of inputs to BNN (Xu et al., 2009) [2]. As a result, compared to Xu et al. (2009) [2], the identification efficiency of fast neutron background events is significantly improved using the BNN in the present paper. The other identification efficiencies are consistent with those in Xu et al. (2009) [2].
AB - The application of Bayesian neural networks (BNN) to discriminate neutrino events from backgrounds in reactor neutrino experiments has been described in Xu et al. (2008) [1] and Xu et al. (2009) [2]. In the present paper, the pulse shape information for a fast signal of a neutrino event or a background event is used as a part of inputs to BNN to discriminate neutrino events from backgrounds. The numbers of photoelectrons received by PMTs and the delay time for a delayed signal are used as the other part of inputs to BNN (Xu et al., 2009) [2]. As a result, compared to Xu et al. (2009) [2], the identification efficiency of fast neutron background events is significantly improved using the BNN in the present paper. The other identification efficiencies are consistent with those in Xu et al. (2009) [2].
KW - Bayesian neural networks
KW - Neutrino oscillation
KW - Pulse shape discrimination
UR - http://www.scopus.com/inward/record.url?scp=71749117974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71749117974&partnerID=8YFLogxK
U2 - 10.1016/j.nima.2009.08.071
DO - 10.1016/j.nima.2009.08.071
M3 - Article
AN - SCOPUS:71749117974
SN - 0168-9002
VL - 610
SP - 590
EP - 596
JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
IS - 2
ER -