Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder
Sambatra Andrianomena,
Hongming Tang
Abstract:We propose to learn latent space representations of radio galaxies, and train a very deep
variational autoencoder (VDVAE) on RGZ DR1, an unlabeled dataset, to this end. We show
that the encoded features can be leveraged for downstream tasks such as classifying galaxies in
labeled datasets, and similarity search. Results show that the model is able to reconstruct its
given inputs, capturing the salient features of the latter. We use the latent codes of galaxy
images, from MiraBest Confident and FR-DEE… Show more
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