Piled-up neutron-gamma discrimination system for CLLB using convolutional neural network
文献类型：期刊论文
| 作者 | Peng, S. ; Hua, Z. H.; Wu, Q.; Han, J. F.; Qian, S.; Wang, Z. G.; Wei, Q. H.; Qin, L. S.; Ma, L. S.; Yan, M.
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| 刊名 | JOURNAL OF INSTRUMENTATION
 |
| 出版日期 | 2022 |
| 卷号 | 17期号:8页码:T08001 |
| 关键词 | Analysis and statistical methods Neutron detectors (cold, thermal, fast neutrons) |
| DOI | 10.1088/1748-0221/17/08/T08001 |
| 文献子类 | Article |
| 英文摘要 | 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 Cs2LiLaBr6:Ce (CLLB) detector and an Am-Be neutron source are used to train and test the model in the n-gamma 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 (gamma, n, gamma + gamma, gamma + n, n + gamma 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. |
| 电子版国际标准刊号 | 1748-0221 |
| 语种 | 英语 |
| WOS记录号 | WOS:000858793300014 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/299696]  |
| 专题 | 高能物理研究所_实验物理中心 高能物理研究所_管理与技术支持 |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | Peng, S.,Hua, Z. H.,Wu, Q.,et al. Piled-up neutron-gamma discrimination system for CLLB using convolutional neural network[J]. JOURNAL OF INSTRUMENTATION,2022,17(8):T08001. |
| APA | Peng, S..,Hua, Z. H..,Wu, Q..,Han, J. F..,Qian, S..,...&Song, R. Q..(2022).Piled-up neutron-gamma discrimination system for CLLB using convolutional neural network.JOURNAL OF INSTRUMENTATION,17(8),T08001. |
| MLA | Peng, S.,et al."Piled-up neutron-gamma discrimination system for CLLB using convolutional neural network".JOURNAL OF INSTRUMENTATION 17.8(2022):T08001. |
入库方式： OAI收割
来源：高能物理研究所
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