A Practical Framework for Estimating the Repetition Likelihood of Fast Radio Bursts from Spectral Morphology

Abstract The repeating behavior of fast radio bursts (FRBs) is regarded as a key clue to understanding their physical origin, yet reliably distinguishing repeaters from apparent nonrepeaters with current observations remains challenging. Here we propose a physically interpretable and practically quantifiable classification framework based on spectral morphology. Using dimensionality reduction, clustering, and feature-importance analysis, we identify the spectral running r and spectral index γ as the most critical parameters for distinguishing repeaters from apparent nonrepeaters in the CHIME/FRB sample. In the γ– r space, repeaters preferentially occupy regions with steeper, narrower-band spectra, whereas nonrepeaters cluster in flatter, broader-band regions, resulting in a clear density separation. We further construct a probability map in the γ – r space based on Gaussian mixture model posterior analysis, revealing a clear gradient of repetition likelihood from ∼67% in the high-repetition region to ∼7% in the low-repetition region. This model also identifies several apparent nonrepeaters with high inferred repetition probability, highlighting them as priority targets for future monitoring. This framework provides a simple and generalizable tool for assessing repeatability in the CHIME/FRB sample and highlights the diagnostic power of spectral morphology in unveiling FRB origins.