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

作　　者：Hao Qiao ChunYu Lu Xun Chen Ke Han XiangDong Ji SiGuang Wang 

机构地区：[1]School of Physics and State Key Laboratory of Nuclear Physics and Technology and Center for High Energy Physics, Peking University, Beijing 100871, China [2]Institute of Particle and Nuclear Physics and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai 200240, China [3]Tsung-Dao Lee Institute, Shanghai 200240, China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2018年第10期51-59,共9页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the Ministry of Science and Technology of China (Grant No. 2016YFA0400302);the National Natural Science Foundation of China (Grant Nos. 11505122, and 11775142);supported in part by the Chinese Academy of Sciences Center for Excellence in Particle Physics (CCEPP)

摘　　要：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.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.

关 键 词：NEUTRINO double beta decay convolutional neural networks background suppression 

分 类 号：O572.2[理学—粒子物理与原子核物理] TP183[理学—物理]