Anomaly detection in Hyper Suprime-Cam galaxy images with generative
  adversarial networks
  The problem of anomaly detection in astronomical surveys is becoming
increasingly important as data sets grow in size. We present the results of an
unsupervised anomaly detection method using a Wasserstein generative
adversarial network (WGAN) on nearly one million optical galaxy images in the
Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like
galaxies that follow the distribution of the data set; anomalous images are
defined based on a poor reconstruction by the generator and outlying features
learned by the discriminator. We find that the discriminator is more attuned to
potentially interesting anomalies compared to the generator, and compared to a
simpler autoencoder-based anomaly detection approach, so we use the
discriminator-selected images to construct a high-anomaly sample of
$\sim$13,000 objects. We propose a new approach to further characterize these
anomalous images: we use a convolutional autoencoder to reduce the
dimensionality of the residual differences between the real and
WGAN-reconstructed images and perform UMAP clustering on these. We report
detected anomalies of interest including galaxy mergers, tidal features, and
extreme star-forming galaxies. A follow-up spectroscopic analysis of one of
these anomalies is detailed in the Appendix; we find that it is an unusual
system most likely to be a metal-poor dwarf galaxy with an extremely blue,
higher-metallicity HII region. We have released a catalog with the WGAN anomaly
scores; the code and catalog are available at
https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive
visualization tool for exploring the clustered data is at
https://weirdgalaxi.es.
