A simulation-trained convolutional autoencoder identifies three distinct FRB morphological classes and classifies repeatability with 86% recall.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
astro-ph.HE 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Semi-supervised morphological classification of fast radio bursts from the second CHIME/FRB catalogue
A simulation-trained convolutional autoencoder identifies three distinct FRB morphological classes and classifies repeatability with 86% recall.