DRAFTS: A Deep Learning-Based Radio Fast Transient Search Pipeline
Abstract
The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio frequency interference (RFI) 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 dataset of FRBs for training deep learning models. The search test on FAST 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 three times the number of bursts compared to Heimdall, highlighting the potential for future FRB detection and analysis.
Cite
@article{arxiv.2410.03200,
title = {DRAFTS: A Deep Learning-Based Radio Fast Transient Search Pipeline},
author = {Yong-Kun Zhang and Di Li and Yi Feng and Chao-Wei Tsai and Pei Wang and Chen-Hui Niu and Hua-Xi Chen and Yu-Hao Zhu},
journal= {arXiv preprint arXiv:2410.03200},
year = {2024}
}
Comments
20 pages, 10 figures, submitted