Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation
Science China Physics, Mechanics & Astronomy, Vol.61, 101007(2018)
The PandaX-III experiment will search for neutrinoless double beta decay of ${}^{136}Xe$ with high pressure gaseous time projection chambers at the China Jin-Ping underground Laboratory. The tracking feature of gaseous detectors helps suppress the background level, resulting in the improvement of the detection sensitivity. We study a method based on the convolutional neural networks to discriminate double beta decay signals against the background from high energy gammas generated by ${}^{214}Bi$ and ${}^{208}Tl$ decays based on detailed Monte Carlo simulation. Using the 2-dimensional projections of recorded tracks on two planes, the method successfully suppresses the background level by a factor larger than 100 with a high signal efficiency. An improvement of 62% on the efficiency ratio of $\epsilon_{s}/\sqrt{\epsilon_{b}}$ is achieved in comparison with the baseline in the PandaX-III conceptual design report.