  We present an anomaly detection method using Wasserstein generative
adversarial networks (WGANs) on optical galaxy images from the wide-field
survey conducted with the Hyper Suprime-Cam (HSC) on the Subaru Telescope in
Hawai'i. The WGAN is trained on the entire sample, and learns to generate
realistic HSC-like images that follow the distribution of the training data. We
identify images which are less well-represented in the generator's latent
space, and which the discriminator flags as less realistic; these are thus
anomalous with respect to the rest of the data. We propose a new approach to
characterize these anomalies based on a convolutional autoencoder (CAE) to
reduce the dimensionality of the residual differences between the real and
WGAN-reconstructed images. We construct a subsample of ~9,000 highly anomalous
images from our nearly million object sample, and further identify interesting
anomalies within these; these include galaxy mergers, tidal features, and
extreme star-forming galaxies. The proposed approach could boost unsupervised
discovery in the era of big data astrophysics.
