Detecting FRB by DANCE: a method based on DEnsity ANalysis and Cluster Extraction

TL;DR

DANCE is a novel density-based clustering method that improves detection of weak, narrow-band FRBs in radio data, surpassing traditional techniques by effectively isolating genuine signals with high precision.

Contribution

The paper introduces DANCE, a new cluster analysis tool that enhances FRB detection, especially for weak and narrow-band signals, using density clustering on cleaned spectral data.

Findings

Successfully detects all signals with SNR > 5

Achieves over 93% detection precision

Identifies previously undetectable weak bursts

Abstract

Fast radio bursts (FRBs) are transient signals exhibiting diverse strengths and emission bandwidths. Traditional single-pulse search techniques are widely employed for FRB detection; yet weak, narrow-band bursts often remain undetectable due to low signal-to-noise ratios (SNR) in integrated profiles. We developed DANCE, a detection tool based on cluster analysis of the original spectrum. It is specifically designed to detect and isolate weak, narrow-band FRBs, providing direct visual identification of their emission properties. This method performs density clustering on reconstructed, RFI-cleaned observational data, enabling the extraction of targeted clusters in time-frequency domain that correspond to the genuine FRB emission range. Our simulations show that DANCE successfully extracts all true signals with SNR~>5 and achieves a detection precision exceeding 93%. Furthermore, through the practical detection of FRB 20201124A, DANCE has demonstrated a significant advantage in finding previously undetectable weak bursts, particularly those with distinct narrow-band features or occurring in proximity to stronger bursts.