metadata
license: gpl-2.0
tags:
- Radio Astronomy
- Pulsar
- RRAT
- FRB
- Signal Processing
library_name: pytorch
pipeline_tag: image-segmentation
datasets:
- CRAFTS-FRT
metrics:
- recall
- FPR
FRTSearch: Fast Radio Transient Search
FRTSearch is an end-to-end deep learning framework for detecting and characterizing Fast Radio Transients (FRTs), including: Pulsars, Rotating Radio Transients (RRATs) and Fast Radio Bursts (FRBs).
Model Info
| Item | Value |
|---|---|
| Backbone | HRNet-W32 |
| Input | 256 × 8192 (freq × time) |
| Size | 400 MB |
| Formats | .fits (PSRFITS), .fil (Filterbank) |
| Bit Depth | 1/2/4/8/32-bit |
QUICK START
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
repo_id="waterfall109/FRTSearch",
filename="models/hrnet_epoch_36.pth"
)
Or download directly from Files and versions.
TEST SAMPLES
This repository includes 5 test samples from 2 different telescopes to demonstrate cross-facility performance:
| Telescope | FRB | DM (pc cm⁻³) |
|---|---|---|
| FAST | 20121102, 20180301, 20201124 | 565, 420, 525 |
| ASKAP | 20180119, 20180212 | 400, 168 |
CITATION
@article{zhang2026frtsearch,
title={FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation},
author={Zhang, Bin and Wang, Yabiao and Xie, Xiaoyao et al.},
year={2026}
}
Test Sample References
When using the test samples, please also cite the original observations:
- FAST samples: Guo et al. (2025)
- SKA samples: Shannon et al. (2018)
License & Acknowledgments
GPL-2.0 | Based on MMDetection & PRESTO
Exploring the dynamic universe with AI 🌌📡 | Issues