X-ray spectra correction based on deep learning CNN-LSTM model. (August 2022) - Ma, Xing-Ke; Huang, Hong-Quan; Huang, Bo-Rui; Shen, Zhi-Wen; Wang, Qing-Tai; Xiao, Yu-Yu; Zhong, Cheng-Lin; Xin, Hao; Sun, Peng; Jiang, Kai-Ming; Tang, Lin; Ding, Wei-Cheng; et al.
Highlights: This paper provides a set of classification and correction methods for the pile-up pulses in radiation measurement. Because the amount of pulse data in the measurement process is too large, this paper uses the currently popular deep learning method to identify the accumulated pulse. Measurement results show that the method can effectively recognize the pile-up pulses, improve the spectrum's counting rate, and obtain more accurate X-ray spectra. Abstract: The two most important indicators in the measurement process for the X-ray spectra are energy resolution and counting rate. However, in the actual detection process, when the interval time between adjacent pulses is less than the pulse shaping time, the pulses pile up. If the pile-up pulse is not separated and recognized, then it greatly affects the X-ray spectrum's accuracy. A method of X-ray spectrum correction is proposed on the basis of a deep learning model, which realizes the separation of the pile-up pulse by recognizing its parameters, and then realizes the correction of the X-ray spectrum. Standard sources 55 Fe and 238 Pu are used as the measurement objects, and the spectra correction method is used to recognize the pile-up pulses. Measurement results show that the method can effectively recognize the pile-up pulses, improve the spectrum's counting rate, and obtain more accurate X-ray spectra.