Enhancing Fast Radio Transient Detection with Mask R-CNN Image Segmentation

TL;DR

This paper introduces a deep learning approach using Mask R-CNN for direct image analysis of DM-time domain data to improve fast radio transient detection, reducing false candidates and enabling real-time processing.

Contribution

The authors develop a novel deep learning pipeline that bypasses traditional thresholding, utilizing Mask R-CNN for direct detection of astrophysical signals in DM-time images, trained on simulated and real data.

Findings

Successfully detected all bright bursts in test data

Detected two fainter bursts missed by traditional methods

Reduced false candidate rate significantly

Abstract

Traditionally, fast radio transient searches are conducted on dedispersed time series using thresholding techniques based on the statistical properties of the data. However, peaks in dedispersed time series do not directly provide information on the nature of the source. In the DM-time domain, the S/N variation of real, dispersed astrophysical signals forms a characteristic bow tie shape, whereas radio frequency interference (RFI) can take multiple different forms. We have developed a method that bypasses the thresholding step of traditional single-pulse searches in favour of a direct DM-time domain image analysis. The backbone of our pipeline is a Mask R-CNN, a deep learning model designed for object detection, enabling it to identify the bow tie signature and distinguish real sources from RFI. Previous deep learning models often include a snippet of the DM-time domain in their input. We have trained the model on simulated bursts injected on top of real MeerKAT noise observations. We tested the model on MeerKAT follow-up observations of the repeater FRB20240114A and we were able to recover all bursts with a signal-to-noise above the traditional threshold, while detecting two bursts that were fainter. Our new approach considerably reduces the number of candidates above a nominal threshold while being capable of running in real time for typical surveys. We also propose a modified version of the traditional dedispersion plan optimised for this method.