DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio-frequency interference and signal dispersion in the interstellar medium. Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positi...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/ad8f31 |
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| Summary: | The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio-frequency interference 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 data set of FRBs for training deep-learning models. The search test on Five-hundred-meter Aperture Spherical radio Telescope 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 3 times the number of bursts compared to Heimdall , highlighting the potential for future FRB detection and analysis. |
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| ISSN: | 0067-0049 |