Uncloaking hidden repeating fast radio bursts with unsupervised machine
  learning
  The origins of fast radio bursts (FRBs), astronomical transients with
millisecond timescales, 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) recognise 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) database. 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.
