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

Bin Zhang; Yabiao Wang; Xiaoyao Xie; Shanping You; Xuhong Yu; Qiuhua Li; Hongwei Li; Shaowen Du

astro-ph.IMApr 14, 2026

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

Authors: Bin Zhang, Yabiao Wang, Xiaoyao Xie, Shanping You, Xuhong Yu, Qiuhua Li, Hongwei Li, Shaowen Du,

Organizations: School of Mathematical Sciences, Guizhou Normal University, Guiyang 550001, China · Guizhou Provincial Key Laboratory of Information and Computing Science , Guizhou Normal University, Guiyang 550001, China · School of Cyber Science and Technology, Guizhou Normal University, Guiyang 550001, China · Tencent Youtu Lab, Shanghai 201103, China · College of Physics and Electronic Engineering, Qilu Normal University, Jinan 250200, China · Zhejiang Lab, Hangzhou, Zhejiang 311121, China · National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China · Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing, 102206, China · State Key Laboratory of Radio Astronomy and Technology, Chinese Academy of Sciences, A20 Datun Road, Chaoyang District, Beijing, 100101, China · New Cornerstone Science Laboratory, Department of Astronomy, Tsinghua University, Beijing 100084, China

Abstract

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.

Figures & tables

Figure 1: Waterfall plots for three typical source classes detected by the CRAFTS survey with the FAST telescope: PSR B1911 $-$ 04 (left), RRAT J2325 $-$ 0530 (middle), and FRB 20121102 (right). Each plot displays a time window beginning at the source’s ToA. Inset Panels: The corresponding de-dispersed pulse profiles, obtained by aligning the signal in time using the source’s DM.

Figure 2: Visualization of key stages in the FRTSearch pipeline using seven representative examples (resized to $256\times 256$ ). Columns (a)–(d): Four distinct FRT sources exhibiting varied dispersive features. Columns (e)–(g): Pulsar B0820 $+$ 02 observed simultaneously by three adjacent FAST receiver beams, demonstrating signal variation. Row 1: Preprocessed time-frequency dynamic spectra (model input). Row 2: Manually annotated ground-truth masks for training. Row 3: Mask R-CNN predictions displaying segmentation masks (pink contours), bounding boxes (pink rectangles), and confidence scores. Row 4: De-dispersed dynamic spectra with IMPIC-inferred DM values.

Figure 3: Distribution of observational parameters (DM, S/N, and pulse width) across the full dataset of 2,392 FRT instances.

No.	Source Name	R.A. (hh:mm)	Decl. (dd:mm)	DM (pc cm -3 )	Files	FRT Instances	Set
1	PSR B0559-05	06:01	$-$ 05:27	80.5	17	278	Training
2	PSR B0621-04	06:24	$-$ 04:24	70.8	12	88	Training
3	PSR B1841-05	18:44	$-$ 05:38	411.7	10	299	Training
4	PSR B1846-06	18:49	$-$ 06:37	148.2	6	15	Training
5	PSR J0458-0505	04:58	$-$ 05:05	47.8	14	28	Training
6	PSR J0459-0210	04:59	$-$ 02:10	21.0	14	77	Training

Table 1: Detailed composition and train-validation splitting of the CRAFTS-FRT dataset.

Figure 4: The end-to-end FRTSearch pipeline automatically processes radio observation files, generating diagnostic plots and a list of associated parameters for FRT candidates.

Figure 5: The architecture of the Mask R-CNN model designed for FRT detection.

Backbone	Optimizer	Base LR	Weight Decay	Specific Settings
ResNet/ResNeXt/HRNet	SGD	$0.02$	$10^{-4}$	Momentum = $0.9$
Swin-T	AdamW	$10^{-4}$	$0.05$	$\beta=(0.9,0.999)$ , zero decay for pos_embed/norm/bias
ConvNeXt	AdamW	$10^{-4}$	$0.05$	$\beta=(0.9,0.999)$ , layer-wise decay rate = $0.95$

Table 2: Optimization configurations for different backbone architectures.

Backbone	TP	# Candidates	Segmentation Metrics		GT-level Metrics		File-level Metrics
Backbone	TP	# Candidates	bbox AP (%)	segm AP (%)	Recall (%)	FPPI	Recall (%)	FPPI
ResNet-18	1237	2143	75.8	52.7	97.4	1.5	100.0	9.0
ResNet-50	1001	1839	72.9	49.6	92.7	1.4	96.0	8.3
ResNet-101	1058	3046	72.9	52.3	95.9	3.3	97.0	19.8
ResNeXt-101	990	2395	74.7	51.0	95.2	2.3	97.0	14.0
HRNet $\star$	887	1357	77.9	57.3	95.1	0.8	100.0	4.7

Table 3: Backbone architecture comparison on the validation set (100 files, 587 GT instances). The selected model is marked with

\star

Figure 6: Evaluation of IMPIC hyperparameters. The plot compares the mean

R^{2}

score (left axis, solid lines) and processing time (right axis, dashed line) across different sample sizes (

N_{\mathrm{sample}}

) and iterations (

N_{\mathrm{iter}}

). The red star highlights the adopted configuration (

N_{\mathrm{sample}}=100

N_{\mathrm{iter}}=15

) used in this work.

Source	Ref DM (pc cm -3 )	TP	Mean DM (pc cm -3 )	Std (pc cm -3 )	$\mathbf{R^{2}}$	$\mathbf{1\sigma}$ (%)	$\mathbf{2\sigma}$ (%)
RRAT J2325-0530	14.9	3	19.7	2.1	0.969	66.7	100.0
PSR B0820+02	23.7	325	30.0	14.9	0.965	92.6	95.1
PSR B1911-04	89.4	103	88.3	3.9	0.991	90.3	97.1
PSR J1820-0509	102.4	109	101.5	4.0	0.995	89.9	96.3
PSR J0652-0142	116.3	42	113.8	6.2	0.992	73.8	95.2
PSR J1948+2333	198.2	72	200.1	14.4	0.995	90.3	95.8

Table 4: DM Inference Statistics for Validation Sources. Columns: reference DM; number of true positive detections (TP); mean and standard deviation of inferred DMs; mean coefficient of determination (

R^{2}

) of the trajectory fit; and fractional accuracy within

\pm 1\sigma

and

\pm 2\sigma

tolerance.

Figure 7: Error correlation with observational parameters. Scatter plots show absolute ToA residuals (top row: a–c) and DM residuals (bottom row: d–f) versus S/N (left column), pulse width (middle column), and trajectory completeness (right column). Points are color-coded by detection confidence scores.

Figure 8: Diagnostic plots for three representative candidates. Each column displays: (top) de-dispersed pulse profile with on-pulse region (vertical dotted lines) and metadata; (middle) de-dispersed dynamic spectrum with frequency-averaged bandpass (right); (bottom) dispersed dynamic spectrum showing frequency-time trajectory. (a) Candidate using IMPIC-inferred parameters. (b–c) FRB 20121102 (missed by FRTSearch ) using parameters provided by FAST-FREX [ 25 ] .

Method	Threshold	TP	#Candidates	Recall (%)	FPPI (per file)	Time (s/file)
PRESTO	S/N $\geq$ 5	479	2,990,577	79.8	4,983.5	221.8
Heimdall	S/N $\geq$ 5	505	29,901	84.2	49.0	16.0
TransientX	S/N $\geq$ 5	600	49,405	100.0	81.3	60.3
FRTSearch	Confidence $\geq$ 0.1	588	3,050	98.0	4.1	8.7 / 16.0

Table 5: Benchmark comparison on the FAST-FREX dataset [ 25 ] (600 FRB bursts). Best results are highlighted in bold . For FRTSearch , execution time is reported as parameter-only / with diagnostic plots .

Parameter	CRAFTS	ASKAP
File Format	PSRFITS	Filterbank
Sampling Time ( $\mu$ s)	98.3	1266.5
Channel Width (MHz)	0.122	1.0
Bit Depth	2	8
Integration Time (s)	12.88	3345.33
Frequency Range (MHz)	1000–1500	1130–1465

Table 6: Comparison of instrumental parameters between CRAFTS-FRT (training data) and ASKAP (test data).

Figure 9: FRTSearch detection results on 19 ASKAP FRBs [ 62 ] . The panels show time-frequency spectra ( $336\times 336$ pixels) with overlaid model predictions: segmentation masks (pink contours), bounding boxes (pink rectangles), and confidence scores (white text).

Figure 10: Detection efficiency of FRTSearch on the FAST-FREX benchmark. The recall rate is evaluated as a function of S/N, revealing a progressive decrease in detection reliability for

\mathrm{S/N}<10

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

FRB Name	R.A.	Decl.	S/N	Published DM	Inferred DM	Inferred ToA	Confidence
	(hh:mm)	(dd:mm)		(pc cm -3 )	(pc cm -3 )	(s)
FRB20170416	22:13	$-$ 55:02	8.3	523.2	494.0	3260.7	0.58
FRB20170428	21:47	$-$ 41:51	8.2	991.7	1070.3	1893.5	0.36
FRB20170707	02:59	$-$ 57:16	10.5	235.2	239.5	570.1	0.82
FRB20170712	22:36	$-$ 60:57	13.8	312.8	310.6	2638.1	0.97
FRB20170906	21:59	$-$ 19:57	18.6	390.3	385.1	2016.1	0.84

Appendix

Table 7: Detection performance of FRTSearch on 19 ASKAP FRBs. Published parameters from [ 62 ] ; inferred DM, ToA, and confidence scores from IMPIC and Mask R-CNN.

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Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning models. These detectors achieve exceptional performance, but they require large task-specific training datasets and cannot be redefined without retraining. In this work, we evaluate whether small, open-weight, locally run generalist Vision-Language Models (VLMs) can detect FRBs in dynamic spectra under a zero-shot, prompt-only regime, with no fine-tuning and no labeled examples, returning structured decisions with a natural-language justification. From a controlled set of 3000 simulated L-band dynamic spectra containing FRBs, structured Radio Frequency Interference (RFI), and noise, we draw a balanced binary benchmark of 2000 samples and compare two such VLMs (Gemma 4 2B and 4B), sample by sample, against the state-of-the-art specialized detector SwinYNet. At the default threshold, Gemma 4 2B reaches an accuracy of 93.65%, with no statistically significant difference from SwinYNet (92.90%), while showing a significantly lower false-positive rate on structured RFI (6.4% vs. 25.0%) and no false positives on pure noise. SwinYNet retains a perfect probabilistic ranking on this benchmark (ROC-AUC of 1.0000 vs. 0.9482), a ceiling that the zero-shot VLM approaches from general-purpose pretraining alone. Rewriting the prompt alone reconfigures the same models for three-class FRB/RFI/noise classification on the full set of 3000 spectra, where they reach up to 86% accuracy without a single false FRB.

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