Uncloaking hidden repeating fast radio bursts with unsupervised machine learning. Issue 1 (22nd October 2021)

• Record Type: Journal Article
• Authors: Chen, Bo Han; Hashimoto, Tetsuya; Goto, Tomotsugu; Kim, Seong Jin; Santos, Daryl Joe D; On, Alvina Y L; Lu, Ting-Yi; Hsiao, Tiger Y-Y
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 509:Issue 1(2022)
• Page Start: 1227
• Page End: 1236
• Publication Date: 2021-10-22
• Subjects: methods: data analysis; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab2994
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: 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.