Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

Understand

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.