Identifying Quasiperiodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks

Abstract Quasiperiodic micropulses (QMPs) are quasiperiodic microstructural features manifested in individual pulsar radio pulses, the study of which is crucial for understanding pulsar radiation mechanisms. Manual identification of QMPs 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 Five-hundred-meter Aperture Spherical radio Telescope observational data through joint analysis of single-pulse profiles and their amplitude distribution profiles, 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 (9657 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.