Revised classification of the CHIME fast radio bursts with machine learning*

Abstract Fast radio bursts (FRBs) are short-duration and energetic radio transients of unknown origin. Observationally, they are commonly categorized into repeaters and non-repeaters. However, this binary classification may be influenced by observational limitations such as sensitivity and time coverage of telescopes. In this study, we employ unsupervised machine learning techniques to re-examine the CHIME/FRB catalog, with the goal of identifying intrinsic groupings in the FRB population without relying on preassigned labels. Using t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for clustering, we find that the FRB sample naturally separates into two major clusters. One cluster contains nearly all known repeaters but is contaminated by some apparently non-repeaters, while the other cluster is dominated by non-repeaters. This suggests that certain FRBs previously labeled as non-repeaters may share intrinsic similarities with repeaters. Mutual information analysis reveals that rest-frame frequency width and peak frequency are the most informative features governing the clustering structure. Even when reducing the input space to just these two features, the classification remains robust.