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
† huangyb@gxu.edu.cn
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Discrimination of
Guo-Ming Chen, Xin Zhang, Ze-Yuan Yu, et al. Discrimination of
Discrimination of
Guo-Ming Chen, Xin Zhang, Ze-Yuan Yu, et al. Discrimination of
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 intrinsic ,14,C 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,-18, g/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%.
Liquid scintillator detectorpp solar neutrinos14C pile-upMultivariate analysisDeep learning
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