Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks

TL;DR

This paper introduces a deep learning method using a Dual-Stage Residual Network to automatically detect quasi-periodic microstructures in pulsar radio pulses, significantly improving efficiency and accuracy in large datasets.

Contribution

We developed a novel neural network model that jointly analyzes pulse profiles and their amplitude spectra for automated QMP detection in pulsar data.

Findings

Achieved 96.10% recall and 95.85% precision in QMP detection

Enabled large-scale, reproducible analysis of pulsar microstructures

Provided a foundation for studying physical mechanisms of QMPs

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.