Uncloaking hidden repeating fast radio bursts with unsupervised machine learning
Oct 18, 202110 pages
Published in:
- Mon.Not.Roy.Astron.Soc. 509 (2021) 1, 1227-1236
- Published: Nov 13, 2021
e-Print:
- 2110.09440 [astro-ph.HE]
View in:
Citations per year
Abstract: (Oxford University Press)
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.Note:
- 10 pages, 9 figures, accepted for publication in MNRAS. For summary video, please see https://youtu.be/fWfvfFPDhcQ
- methods: data analysis
References(16)
Figures(13)