Uncloaking hidden repeating fast radio bursts with unsupervised machine learning

The origins of fast radio bursts (FRBs), astronomical transients with millisecond time-scales, remain unknown. One of the difficulties stems from the possibility that observed FRBs could be heterogeneous in origin; as some of them have been observed to repeat, and others have not. Due to limited observing periods and telescope sensitivities, some bursts may be misclassified as non-repeaters. Therefore, it is important to clearly distinguish FRBs into repeaters and non-repeaters, to better understand their origins. In this work, we classify repeaters and non-repeaters using unsupervised machine learning, without relying on expensive monitoring observations. We present a repeating FRB recognition method based on the Uniform Manifold Approximation and Projection (UMAP). The main goals of this work are to: (i) show that the unsupervised UMAP can classify repeating FRB population without any prior knowledge about their repetition, (ii) evaluate the assumption that non-repeating FRBs are contaminated by repeating FRBs, and (iii) recognize the FRB repeater candidates without monitoring observations and release a corresponding catalogue. We apply our method to the Canadian hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) data base. We found that the unsupervised UMAP classification provides a repeating FRB completeness of 95 per cent and identifies 188 FRB repeater source candidates from 474 non-repeater sources. This work paves the way to a new classification of repeaters and non-repeaters based on a single epoch observation of FRBs.

Automated summary

This work utilizes unsupervised machine learning to classify repeating and non-repeating fast radio bursts (FRBs) and demonstrates the efficiency of the UMAP classification method in identifying repeating FRBs. Application of this method on the CHIME/FRB database reveals successful identification of 188 FRB repeater source candidates.