Trapezoidal pile-up nuclear pulse parameter identification method based on deep learning transformer model

• Published in: Applied radiation and isotopes Vol. 190; p. 110515
• Main Authors: Wang, Qingtai, Huang, Hongquan, Ma, Xingke, Shen, Zhiwen, Zhong, Chenglin, Ding, Weicheng, Zhou, Wei, Zhou, Jianbin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2022
• Subjects: Attention mechanism; Nuclear pulse; Transformer; Attention mechanism; Nuclear pulse; Transformer
• ISSN: 0969-8043; 1872-9800
• DOI: 10.1016/j.apradiso.2022.110515

Pile-up between adjacent nuclear pulses is unavoidable in the actual detection process. Some scholars have tried to apply deep learning techniques to identify pile-up nuclear pulse parameters. However, traditional deep learning recurrent neural networks (RNNs) suffer from inefficient pulse recognition and poor recognition of pile-up nuclear pulses with short intervals between adjacent pulses. In this paper, a Transformer model with an attention mechanism as the core to recognize pile-up nuclear pulses is innovatively applied, aiming to provide a more accurate and efficient method for pile-up nuclear pulse recognition. Thus, it gives a better help for the spectrum correction with a high count rate. •Using the more advanced Transformer model in the field of deep learning.•Short intervals of pile-up pulses can be identified.•The recognition accuracy has been improved compared to previous studies.