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

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.