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arXiv · 2511.04966

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

Mao Yuan·Jiarui Niu·Yi Feng·Xu-ning Lv·Chenchen Miao·Lingqi Meng·Bo Peng·Li Deng·Jingye Yan·Weiwei Zhu
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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.

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Mao Yuan, Jiarui Niu, Yi Feng, Xu-ning Lv, Chenchen Miao, Lingqi Meng, Bo Peng, Li Deng, Jingye Yan, Weiwei Zhu. 2025-11-07. Detecting FRB by DANCE: a method based on DEnsity ANalysis and Cluster Extraction. https://doi.org/10.1093/mnras%2Fstaf1910

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