Discrimination of pp solar neutrinos and 14C double pile-up events in a large-scale LS detector
Abstract
As a unique probe, precision measurement of 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 pp solar neutrino detection comes from pile-up events of intrinsic 14C decays. This paper presents a few approaches to discriminate pp solar neutrinos and 14C 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 14C pile-up reduction. As a result, the BDTG model and VGG network showed good performance in discriminating pp solar neutrinos and 14C double pile-up events. 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\% 14C double pile-up events, and the signal efficiency is 42.7\% for the case using the VGG network when rejecting 99.81\% 14C double pile-up events.
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