Piled-up neutron-gamma discrimination system for CLLB using convolutional neural network
Sancheng Peng, Z.H. Hua, Qing Wu, Jifeng Han, Sharon Qian, Zhihao Wang, Qianjun Wei, Lutong Qin, Li Ma, Min Yan, Ruizhen Song
Abstract
Abstract A piled-up neutron-gamma discrimination system is designed to discriminate single and piled-up events under high counting rate. The data acquired by a Cs 2 LiLaBr 6 :Ce (CLLB) detector and an Am-Be neutron source are used to train and test the model in the n- γ discrimination system. The charge comparison method is applied to discriminate the non-piled-up events in the experimental data and label the dataset of single events. As a result of the method, the figure-of-merit (FOM) value is 1.10, which indicates that the wrong labeling ratio is about 0.248%. A dataset of piled-up events is created by adding up waveforms and labels of the events in the single-pulse dataset. The discrimination system consists of three convolutional models, called Model_PulseNum, Model_OnePulse and Model_TwoPulses. All the models are trained and tested by the created dataset. Model_PulseNum is created and trained to define the number of pulses in the waveform of the event, with an accuracy of 99.94%. The other two models (Model_OnePulse and Model_TwoPulses) are created and trained to discriminate the particle types for non-piled-up and two-fold piled-up events with the accuracy of 99.5% and 98.6%, respectively. For the whole discrimination system, the accurcy for the particle identification is over 97% for each class ( γ , n, γ + γ , γ + n, n + γ and n + n). These results indicate that CNN model can improve the performance of particle detection systems by effectively discriminate neutron and gamma for both piled-up and non-piled-up events under high counting rates.