Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909973475229696 |
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| author | Wang, Shidong Liu, Hui Zhao, Ru-Shuang Lao, Baoqiang Zhang, Yong-Kun Xiao, Y. F. Wang, Pei Li, Di Tian, R. W. Tu, Z. F. Zhou, Q. Zhang, Z. J. Zhi, Qijun Dang, Shijun Yang, Kun |
| author_facet | Wang, Shidong Liu, Hui Zhao, Ru-Shuang Lao, Baoqiang Zhang, Yong-Kun Xiao, Y. F. Wang, Pei Li, Di Tian, R. W. Tu, Z. F. Zhou, Q. Zhang, Z. J. Zhi, Qijun Dang, Shijun Yang, Kun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks Wang, Shidong Liu, Hui Zhao, Ru-Shuang Lao, Baoqiang Zhang, Yong-Kun Xiao, Y. F. Wang, Pei Li, Di Tian, R. W. Tu, Z. F. Zhou, Q. Zhang, Z. J. Zhi, Qijun Dang, Shijun Yang, Kun High Energy Astrophysical Phenomena 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. |
| title | Identifying Quasi-Periodic Micropulses in Pulsars with FAST Using Convolutional Neural Networks |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2512.19235 |