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

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.