Deep Learning Reveals Structural Patterns in Fast Radio Burst Data

While the nature of fast radio bursts (FRBs) remains unknown, population-level analyses can elucidate underlying structure in these signals. In this study, we employ deep learning methods to both classify FRBs and analyze structural patterns in the latent space learned from the first CHIME catalog. We adopt a Supervised Variational Autoencoder (sVAE) architecture which combines the representational learning capabilities of Variational Autoencoders (VAEs) with a supervised classification task, thereby improving both classification performance and the interpretability of the latent space. We construct a learned latent space in which we perform further dimensionality reduction to find underlying structure in the data. Our results demonstrate that the sVAE model achieves high classification accuracy for FRB repeaters and reveals separation between repeater and non-repeater populations. Upon further analysis of the latent space, we observe that dispersion measure excess, spectral index, and spectral running are the dominant features distinguishing repeaters from non-repeaters. We also identify five non-repeating FRBs as repeater candidates, two of which have been independently flagged in previous studies.