A Search Technique Based on Deep Learning for Fast Radio Bursts and Initial Results for FRB 20201124A with the NSRT
文献类型：期刊论文
| 作者 | Liu, Yan-Ling1,2,3,4 ; Li, Jian1,2,4; Liu, Zhi-Yong2,4; Chen, Mao-Zheng1,2,4; Yuan, Jian-Ping2,4; Wang, Na2,4; Yuen, Rai4; Yan, Hao1,2,4
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| 刊名 | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
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| 出版日期 | 2022-10-01 |
| 卷号 | 22期号:10页码:9 |
| ISSN号 | 1674-4527 |
| 关键词 | radio continuum: general methods: data analysis methods: observational |
| DOI | 10.1088/1674-4527/ac833a |
| 通讯作者 | Liu, Yan-Ling(liuyanling@xao.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | TRANSIENT SEARCHES ; PULSAR ; CLASSIFIER |
| 资助项目 | National Natural Science Foundation of China[11903071] ; Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| 出版者 | NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES |
| WOS记录号 | WOS:000867432900001 |
| 资助机构 | National Natural Science Foundation of China ; Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/4908]  |
| 专题 | 射电天文研究室_天线技术实验室 射电天文研究室_利用南山26米射电望远镜观测数据的文章 |
| 通讯作者 | Liu, Yan-Ling |
| 作者单位 | 1.Xinjiang Key Lab Microwave Technol, Urumqi 830011, Peoples R China 2.Chinese Acad Sci, Key Lab Radio Astron, Nanjing 210033, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China |
| 推荐引用方式 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):9. |
| 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),9. |
| 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):9. |
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来源：新疆天文台
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