Signal-background discrimination with convolutional neural networks in the PandaX-Ⅲ experiment using MC simulation

The Panda X-Ⅲ experiment will search for neutrinoless double beta decay of136 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 by214 Bi and208 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 ?_s/(?_b)～1/2 is achieved in comparison with the baseline in the Panda X-Ⅲ conceptual design report.

The PandaX-Ⅲ experiment will search for neutrinoless double beta decay of 136Xe 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 214Bi and 2^208 T1 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 Еs/√Еb is achieved in comparison with the baseline in the PandaX-Ⅲ conceptual design report.