Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks
Abstract
Quasi-periodic MicroPulses (QMP) are quasi-periodic microstructural features manifested in individual pulsar radio pulses, the study of which is crucial for understanding pulsar radiation mechanisms. Manual identification of QMP in large-scale pulsar single-pulse datasets remains highly inefficient. To address this, we propose a Dual-Stage Residual Network (DSR) that achieves automated QMP detection in FAST observational data through joint analysis of single-pulse profiles and their Amplitude Distribution Profiles (ADP), defined as the power spectra of the autocorrelation function derivatives of the microstructure residuals. The model was trained on PSR B1933+16 data from 2019 (10,486 single pulses) and evaluated on manually annotated PSR B1933+16 data from 2020 (9,657 single pulses). DSR achieved 96.10\% recall and 95.85\% precision on the test set. This approach provides an automated pipeline for large-scale, reproducible QMP identification and establishes the foundation for in-depth investigation of their physical mechanisms.
Cite
@article{arxiv.2512.19235,
title = {Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks},
author = {Shidong Wang and Hui Liu and Ru-Shuang Zhao and Baoqiang Lao and Yong-Kun Zhang and Y. F. Xiao and Pei Wang and Di Li and R. W. Tian and Z. F. Tu and Q. Zhou and Z. J. Zhang and Qijun Zhi and Shijun Dang and Kun Yang},
journal= {arXiv preprint arXiv:2512.19235},
year = {2025}
}