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

摘要: As a unique probe, precision measurement of textit{pp} solar neutrinos is important for studying the Sun's energy mechanism, monitoring thermodynamic equilibrium, and studying neutrino oscillation in the vacuum-dominated region. For a large-scale liquid scintillator detector, one bottleneck for textit{pp} solar neutrino detection comes from pile-up events of intrinsic $^{14}$C decays. This paper presents a few approaches to discriminate textit{pp} solar neutrinos and $^{14}$C pile-up events by considering the difference in their time and spatial distributions. In this work, a Geant4-based Monte Carlo simulation is constructed. Then multivariate analysis and deep learning technology were adopted respectively to investigate the capability of $^{14}$C pile-up reduction. As a result, the BDTG model and VGG network showed good performance in discriminating textit{pp} solar neutrinos and $^{14}$C double pile-up events. hl{At the $^{14}$C concentration assumption $5 times10^{-18} g/g$,} their signal significance can achieve 10.3 and 15.6 using only one day of statistics. In this case, the signal efficiency is 51.1 % for discrimination using the BDTG model when rejecting 99.18 % $^{14}$C double pile-up events, and the signal efficiency is 42.7 % for the case using the VGG network when rejecting 99.81 % $^{14}$C double pile-up events.