Discrimination of pp solar neutrinos and^(14)C double pile-up events in a large-scale LS detector  

作　　者：Guo-Ming Chen Xin Zhang Ze-Yuan Yu Si-Yuan Zhang Yu Xu Wen-Jie Wu Yao-Guang Wang Yong-Bo Huang 

机构地区：[1]School of Physical Science and Technology,Guangxi University,Nanning 530004,China [2]Institute of High Energy Physics,Beijing 100049,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]School of Physics,Sun Yat-Sen University,Guangzhou 510275,China [5]Department of Physics and Astronomy,University of California,Irvine,CA,USA

出　　处：《Nuclear Science and Techniques》2023年第9期69-81,共13页核技术（英文）

基　　金：supported by National Natural Science Foundation of China(No.12005044);the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA10011200);Guangxi Science and Technology Program(No.GuiKeAD21220037).

摘　　要：As a unique probe,the precision measurement of pp solar neutrinos is important for studying the sun’s energy mechanism as it enables monitoring the thermodynamic equilibrium and studying neutrino oscillations in the vacuum-dominated region.For a large-scale liquid scintillator detector,a bottleneck for pp solar neutrino detection is the pile-up events of intrinsic14C decay.This paper presents a few approaches to discriminating between pp solar neutrinos and ^(14)C pile-up events by considering the differences in their time and spatial distributions.In this study,a Geant4-based Monte Carlo simulation is conducted.Multivariate analysis and deep learning technology are adopted to investigate the capability of ^(14)C pile-up reduction.The BDTG (boosted decision trees with gradient boosting) model and VGG network demonstrate good performance in discriminating pp solar neutrinos and ^(14)C double pile-up events.Under the ^(14)C concentration assumption of 5×10-18g/g,the signal significance can achieve 10.3 and 15.6 using the statistics of only one day.In this case,the signal efficiency for discrimination using the BDTG model while rejecting 99.18% ^(14)C double pile-up events is 51.1%,and that for the case where the VGG network is used while rejecting 99.81%of the ^(14)C double pile-up events is 42.7%.

关 键 词：Liquid scintillator detector pp solar neutrinos 14C pile-up Multivariate analysis Deep learning 

分 类 号：TL81[核科学技术—核技术及应用]