DRAFTS: A Deep Learning-Based Radio Fast Transient Search Pipeline

TL;DR

DRAFTS is a deep learning pipeline that improves the detection of fast radio bursts by increasing accuracy, speed, and reducing false positives in radio astronomy data analysis.

Contribution

The paper introduces DRAFTS, a novel deep learning-based pipeline that enhances FRB detection efficiency and accuracy over traditional methods.

Findings

DRAFTS outperforms Heimdall in detecting FRBs.

DRAFTS detects over three times more bursts in real data.

The pipeline demonstrates high accuracy and speed in FRB searches.

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.