DRAFTS: A Deep Learning-Based Radio Fast Transient Search Pipeline
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916422929612800 |
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| author | Zhang, Yong-Kun Li, Di Feng, Yi Tsai, Chao-Wei Wang, Pei Niu, Chen-Hui Chen, Hua-Xi Zhu, Yu-Hao |
| author_facet | Zhang, Yong-Kun Li, Di Feng, Yi Tsai, Chao-Wei Wang, Pei Niu, Chen-Hui Chen, Hua-Xi Zhu, Yu-Hao |
| contents | The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio frequency interference (RFI) and signal dispersion in the interstellar medium. Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positives. In this paper, we present DRAFTS, a deep learning-based radio fast transient search pipeline. DRAFTS integrates object detection and binary classification techniques to accurately identify FRBs in radio data. We developed a large, real-world dataset of FRBs for training deep learning models. The search test on FAST real observation data demonstrates that DRAFTS performs exceptionally in terms of accuracy, completeness, and search speed. In the re-search of FRB 20190520B observation data, DRAFTS detected more than three times the number of bursts compared to Heimdall, highlighting the potential for future FRB detection and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03200 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DRAFTS: A Deep Learning-Based Radio Fast Transient Search Pipeline Zhang, Yong-Kun Li, Di Feng, Yi Tsai, Chao-Wei Wang, Pei Niu, Chen-Hui Chen, Hua-Xi Zhu, Yu-Hao Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio frequency interference (RFI) and signal dispersion in the interstellar medium. Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positives. In this paper, we present DRAFTS, a deep learning-based radio fast transient search pipeline. DRAFTS integrates object detection and binary classification techniques to accurately identify FRBs in radio data. We developed a large, real-world dataset of FRBs for training deep learning models. The search test on FAST real observation data demonstrates that DRAFTS performs exceptionally in terms of accuracy, completeness, and search speed. In the re-search of FRB 20190520B observation data, DRAFTS detected more than three times the number of bursts compared to Heimdall, highlighting the potential for future FRB detection and analysis. |
| title | DRAFTS: A Deep Learning-Based Radio Fast Transient Search Pipeline |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2410.03200 |