2023MNRAS.519.1823Z

Fast radio bursts (FRBs) are one of the most mysterious astronomical transients. Observationally, they can be classified into repeaters and apparent non-repeaters. However, due to the lack of continuous observations, some apparent 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 Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst catalogue 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 analyse 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.