Machine learning classification of CHIME fast radio bursts: II. Unsupervised Methods

TL;DR

This paper applies multiple unsupervised machine learning algorithms to classify CHIME FRBs into clusters, revealing differences between repeaters and non-repeaters and identifying credible repeater candidates for future observations.

Contribution

It introduces the use of various unsupervised machine learning methods to classify FRBs without prior assumptions, enhancing the understanding of their features and aiding in candidate selection.

Findings

Clusters correspond to physical differences between repeaters and non-repeaters.

Several algorithms successfully identified credible repeater candidates.

Comparison with supervised methods improves candidate prioritization.

Abstract

Fast radio bursts (FRBs) are one of the most mysterious astronomical transients. Observationally, they can be classified into repeaters and apparently non-repeaters. However, due to the lack of continuous observations, some apparently repeaters may have been incorrectly recognized as non-repeaters. In a series of two papers, we intend to solve such problem with machine learning. In this second paper of the series, we focus on an array of unsupervised machine learning methods. We apply multiple unsupervised machine learning algorithms to the first CHIME/FRB catalog to learn their features and classify FRBs into different clusters without any premise about the FRBs being repeaters or non-repeaters. These clusters reveal the differences between repeaters and non-repeaters. Then, by comparing with the identities of the FRBs in the observed classes, we evaluate the performance of various algorithms and analyze the physical meaning behind the results. Finally, we recommend a list of most credible repeater candidates as targets for future observing campaigns to search for repeated bursts in combination of the results presented in Paper I using supervised machine learning methods.