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| A Search Technique Based on Deep Learning for Fast Radio Bursts and Initial Results for FRB 20201124A with the NSRT | |
Liu, Yan-Ling1,2,3,4 ; Li, Jian1,3,4; Liu, Zhi-Yong1,3; Chen, Mao-Zheng1,3,4; Yuan, Jian-Ping1,3; Wang, Na1,3; Yuen, Rai1; Yan, Hao1,3,4
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| 2022-10-01 | |
| Source Publication | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
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| ISSN | 1674-4527 |
| Volume | 22Issue:10Pages:105007 |
| Contribution Rank | 1 |
| Abstract | The origin and phenomenology of Fast Radio Bursts (FRBs) remain unknown. Fast and efficient search technology for FRBs is critical for triggering immediate multi-wavelength follow-up and voltage data dump. This paper proposes a dispersed dynamic spectra search (DDSS) pipeline for FRB searching based on deep learning, which performs the search directly from observational raw data, rather than relying on generated FRB candidates from single-pulse search algorithms that are based on de-dispersion. We train our deep learning network model using simulated FRBs as positive and negative samples extracted from the observational data of the Nanshan 26 m radio telescope (NSRT) at Xinjiang Astronomical Observatory. The observational data of PSR J1935+1616 are fed into the pipeline to verify the validity and performance of the pipeline. Results of the experiment show that our pipeline can efficiently search single-pulse events with a precision above 99.6%, which satisfies the desired precision for selective voltage data dump. In March 2022, we successfully detected the FRBs emanating from the repeating case of FRB 20201124A with the DDSS pipeline in L-band observations using the NSRT. The DDSS pipeline shows excellent sensitivity in identifying weak single pulses, and its high precision greatly reduces the need for manual review. |
| Keyword | radio continuum: general methods: data analysis methods: observational |
| DOI | 10.1088/1674-4527/ac833a |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | TRANSIENT SEARCHES ; PULSAR ; CLASSIFIER |
| Funding Project | National Natural Science Foundation of China[11903071] ; Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments |
| WOS Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:000867432900001 |
| Publisher | NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES |
| Funding Organization | National Natural Science Foundation of China ; Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/4909 |
| Collection | 射电天文研究室_天线技术实验室 科研仪器设备产出_利用南山26米射电望远镜（NSRT）观测数据的文章 |
| Corresponding Author | Liu, Yan-Ling |
| Affiliation | 1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Radio Astron, Nanjing 210033, Peoples R China 4.Xinjiang Key Lab Microwave Technol, Urumqi 830011, Peoples R China |
| First Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Corresponding Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Liu, Yan-Ling,Li, Jian,Liu, Zhi-Yong,et al. A Search Technique Based on Deep Learning for Fast Radio Bursts and Initial Results for FRB 20201124A with the NSRT[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2022,22(10):105007. |
| APA | Liu, Yan-Ling.,Li, Jian.,Liu, Zhi-Yong.,Chen, Mao-Zheng.,Yuan, Jian-Ping.,...&Yan, Hao.(2022).A Search Technique Based on Deep Learning for Fast Radio Bursts and Initial Results for FRB 20201124A with the NSRT.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,22(10),105007. |
| MLA | Liu, Yan-Ling,et al."A Search Technique Based on Deep Learning for Fast Radio Bursts and Initial Results for FRB 20201124A with the NSRT".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 22.10(2022):105007. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Liu-2022-A Search Te（1237KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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