Classification of the CHIME Fast Radio Bursts with Semisupervised Method
Year: 2026
Type: article
Abstract: Fast radio bursts (FRBs) are millisecond-duration radio transients whose physical origin and population structure remain unresolved. A commonly invoked observational distinction—whether a source repeats—has been used to constrain progenitor models, yet this classification is inherently incomplete because the absence of detected repetition does not imply a truly nonrepeating source. Here we cast FRB classification as a positive-unlabeled learning problem and apply a semisupervised machine learning framework to the CHIME/FRB catalog, without presuming that unlabeled sources are genuine nonrepeaters. The resulting classifier successfully recovers most confirmed repeaters and identifies a substantial subset of apparently nonrepeating sources with properties consistent with repetition. Feature-based analyses further reveal that the spectral quality factor and the rest-frame temporal width dominate the population separation. These findings demonstrate that explicitly accounting for label uncertainty is crucial for robust FRB population studies and provides a physically interpretable route toward constraining FRB emission mechanisms.
Source: The Astrophysical Journal
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W7140148103