FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation

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. 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 2392 instances across diverse source classes. This dataset enables the training of the Mask R-CNN model for precise trajectory segmentation. Coupled with our physics-driven iterative mask-based parameter inference and calibration algorithm, the framework maps the geometric coordinates of segmented trajectories to directly infer the dispersion measure and time of arrival. Benchmarking on the FAST Dataset for Fast Radio Bursts Exploration 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×. Furthermore, the framework demonstrates robust cross-facility generalization, detecting all 19 tested FRBs from the Australian Square Kilometre Array Pathfinder 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.