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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.12344 |
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| _version_ | 1866917406299914240 |
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| author | Zhang, Bin Wang, Yabiao Xie, Xiaoyao You, Shanping Yu, Xuhong Li, Qiuhua Li, Hongwei Du, Shaowen Miao, Chenchen Zhou, Dengke Fang, Jianhua Wu, Jiafu Wang, Pei Li, Di |
| author_facet | Zhang, Bin Wang, Yabiao Xie, Xiaoyao You, Shanping Yu, Xuhong Li, Qiuhua Li, Hongwei Du, Shaowen Miao, Chenchen Zhou, Dengke Fang, Jianhua Wu, Jiafu Wang, Pei Li, Di |
| contents | The exponential growth of data from modern radio telescopes presents a significant challenge to traditional single-pulse search algorithms, which are computationally intensive and prone to high false-positive rates due to Radio Frequency Interference (RFI). In this work, we introduce FRTSearch, an end-to-end framework unifying the detection and physical characterization of Fast Radio Transients (FRTs). Leveraging the morphological universality of dispersive trajectories in time-frequency dynamic spectra, we reframe FRT detection as a pattern recognition problem governed by the cold plasma dispersion relation. To facilitate this, we constructed CRAFTS-FRT, a pixel-level annotated dataset derived from the Commensal Radio Astronomy FAST Survey (CRAFTS), comprising 2{,}392 instances across diverse source classes. This dataset enables the training of a Mask R-CNN model for precise trajectory segmentation. Coupled with our physics-driven IMPIC algorithm, the framework maps the geometric coordinates of segmented trajectories to directly infer the Dispersion Measure (DM) and Time of Arrival (ToA). Benchmarking on the FAST-FREX dataset shows that FRTSearch achieves a 98.0\% recall, competitive with exhaustive search methods, while reducing false positives by over 99.9\% compared to PRESTO and delivering a processing speedup of up to $13.9\times$. Furthermore, the framework demonstrates robust cross-facility generalization, detecting all 19 tested FRBs from the ASKAP survey without retraining. By shifting the paradigm from ``search-then-identify'' to ``detect-and-infer,'' FRTSearch provides a scalable, high-precision solution for real-time discovery in the era of petabyte-scale radio astronomy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12344 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation Zhang, Bin Wang, Yabiao Xie, Xiaoyao You, Shanping Yu, Xuhong Li, Qiuhua Li, Hongwei Du, Shaowen Miao, Chenchen Zhou, Dengke Fang, Jianhua Wu, Jiafu Wang, Pei Li, Di Instrumentation and Methods for Astrophysics Artificial Intelligence The exponential growth of data from modern radio telescopes presents a significant challenge to traditional single-pulse search algorithms, which are computationally intensive and prone to high false-positive rates due to Radio Frequency Interference (RFI). In this work, we introduce FRTSearch, an end-to-end framework unifying the detection and physical characterization of Fast Radio Transients (FRTs). Leveraging the morphological universality of dispersive trajectories in time-frequency dynamic spectra, we reframe FRT detection as a pattern recognition problem governed by the cold plasma dispersion relation. To facilitate this, we constructed CRAFTS-FRT, a pixel-level annotated dataset derived from the Commensal Radio Astronomy FAST Survey (CRAFTS), comprising 2{,}392 instances across diverse source classes. This dataset enables the training of a Mask R-CNN model for precise trajectory segmentation. Coupled with our physics-driven IMPIC algorithm, the framework maps the geometric coordinates of segmented trajectories to directly infer the Dispersion Measure (DM) and Time of Arrival (ToA). Benchmarking on the FAST-FREX dataset shows that FRTSearch achieves a 98.0\% recall, competitive with exhaustive search methods, while reducing false positives by over 99.9\% compared to PRESTO and delivering a processing speedup of up to $13.9\times$. Furthermore, the framework demonstrates robust cross-facility generalization, detecting all 19 tested FRBs from the ASKAP survey without retraining. By shifting the paradigm from ``search-then-identify'' to ``detect-and-infer,'' FRTSearch provides a scalable, high-precision solution for real-time discovery in the era of petabyte-scale radio astronomy. |
| title | FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation |
| topic | Instrumentation and Methods for Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2604.12344 |