Abstract
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-DEEP NVSS datasets, to train various non-neural network
classifiers. It is found that the latter can differentiate FRI from FRII galaxies achieving
accuracy ≥ 76%, roc-auc ≥ 0.86, specificity ≥ 0.73 and
recall ≥ 0.78 on MiraBest Confident dataset, comparable to results obtained in
previous studies. The performance of simple classifiers trained on FR-DEEP NVSS data
representations is on par with that of a deep learning classifier (CNN based) trained on images in
previous work, highlighting how powerful the compressed information is. We successfully exploit
the learned representations to search for galaxies in a dataset that are semantically similar to a
query image belonging to a different dataset. Although generating new galaxy images (e.g. for data
augmentation) is not our primary objective, we find that the VDVAE model is a relatively
good emulator. Finally, as a step toward detecting anomaly/novelty, a density estimator — Masked
Autoregressive Flow (MAF) — is trained on the latent codes, such that the log-likelihood
of data can be estimated. The downstream tasks conducted in this work demonstrate the
meaningfulness of the latent codes.