Research on the Application of Lightweight Neural Network Models for Pulse Parameter Prediction

In nuclear radiation measurement, pulse distortion is inevitable due to the interference of the measurement system itself and the measurement environment. If the parameters of such pulses cannot be accurately estimated, the resolution performance of the energy spectrum will be reduced. [Purpose]: In order to accurately estimate the height of distorted pulses, this article proposes using six lightweight neural network models for parameter prediction of distorted pulses, including pulse amplitude parameters and distortion time parameters. [Methods]: Based on the distorted pulses generated by predefined mathematical models, the dataset required for model training is obtained through digital triangulation. [Results]: When evaluating the parameter prediction performance of six neural network models, the UNet model achieved the lowest relative error on the test set, with a relative error of approximately 0.57% for amplitude parameters and 3.51% for time parameters. In the signal-to-noise ratio experiment, noise was superimposed on the test set to obtain noise test sets with different signal-to-noise ratios. [Conclusions]: The results show that the proposed models can achieve accurate estimation of the parameters of distorted pulses.