AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised
  Morphological Classification of Galaxies and Synthetic Image Generation
  We present AstroVaDEr, a variational autoencoder designed to perform
unsupervised clustering and synthetic image generation using astronomical
imaging catalogues. The model is a convolutional neural network that learns to
embed images into a low dimensional latent space, and simultaneously optimises
a Gaussian Mixture Model (GMM) on the embedded vectors to cluster the training
data. By utilising variational inference, we are able to use the learned GMM as
a statistical prior on the latent space to facilitate random sampling and
generation of synthetic images. We demonstrate AstroVaDEr's capabilities by
training it on gray-scaled \textit{gri} images from the Sloan Digital Sky
Survey, using a sample of galaxies that are classified by Galaxy Zoo 2. An
unsupervised clustering model is found which separates galaxies based on
learned morphological features such as axis ratio, surface brightness profile,
orientation and the presence of companions. We use the learned mixture model to
generate synthetic images of galaxies based on the morphological profiles of
the Gaussian components. AstroVaDEr succeeds in producing a morphological
classification scheme from unlabelled data, but unexpectedly places high
importance on the presence of companion objects---demonstrating the importance
of human interpretation. The network is scalable and flexible, allowing for
larger datasets to be classified, or different kinds of imaging data. We also
demonstrate the generative properties of the model, which allow for realistic
synthetic images of galaxies to be sampled from the learned classification
scheme. These can be used to create synthetic image catalogs or to perform
image processing tasks such as deblending.
